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The purpose of this study is to develop an optimized system for predicting Knee Adduction Moment (KAM) using wearable Inertial Measurement Unit (IMU) sensors and Long Short-Term Memory (LSTM) RNN. Traditional KAM measurement methods are limited by the need for complex laboratory equipment and significant time and cost investments. This study proposes two systems for predicting Knee Adduction Moment based on wearable IMU sensor data and gait patterns: the Multi-model Gait-based KAM Prediction System and the Single-model KAM Prediction System. The Multi-model system pre-classifies different gait patterns and uses specific prediction models tailored for each pattern, while the Single-model system handles all gait patterns with one unified model. Both systems were evaluated using IMU sensor data and GRF data collected from participants in a controlled laboratory environment. The overall performance of the Multi-model Gait-based KAM Prediction System showed an approximately 20% improvement over the Single-model KAM Prediction System. Specifically, the RMSE for the Multi-model system was 6.84 N · m , which is lower than the 8.82 N · m of the Single-model system, indicating a better predictive accuracy. The Multi-model system also achieved a MAPE of 8.47%, compared with 12.95% for the Single-model system, further demonstrating its superior performance. [ABSTRACT FROM AUTHOR]

Elderly patients often have more than one disease that affects walking behavior. An objective tool to identify which disease is the main cause of functional limitations may aid clinical decision making. Therefore, we investigated whether gait patterns could be used to identify degenerative diseases using machine learning. Data were extracted from a clinical database that included sagittal joint angles and spatiotemporal parameters measured using seven inertial sensors, and anthropometric data of patients with unilateral knee or hip osteoarthritis, lumbar or cervical spinal stenosis, and healthy controls. Various classification models were explored using the MATLAB Classification Learner app, and the optimizable Support Vector Machine was chosen as the best performing model. The accuracy of discrimination between healthy and pathologic gait was 82.3%, indicating that it is possible to distinguish pathological from healthy gait. The accuracy of discrimination between the different degenerative diseases was 51.4%, indicating the similarities in gait patterns between diseases need to be further explored. Overall, the differences between pathologic and healthy gait are distinct enough to classify using a classical machine learning model; however, routinely recorded gait characteristics and anthropometric data are not sufficient for successful discrimination of the degenerative diseases. [ABSTRACT FROM AUTHOR]

Purpose: Anterior cruciate ligament reconstruction (ACLR) in children is indicated to reduce recurrent knee instability and further damage to the joint. Postoperative modified gait pattern was reported in the adult population after ACLR. The aim of this study was to analyse gait abnormalities, and especially knee and ankle adaptations during gait in children after ACLR. Methods: A prospective study was performed between 2018 and 2022 on 50 children, aged nine to 15 years with unilateral ACL deficiency. Changes in gait pattern were evaluated by gait analysis before surgery and at the latest follow-up of 24 months. Kinematic data of ACL-deficient limb were compared to contralateral limb and to those of a matched control group of healthy children. Results: Compared to control group, knee flexion was decreased for both ACL-deficient and contralateral knee before surgery. Decreased knee flexion during gait cycle persisted at latest follow-up. Ankle kinematics showed decreased dorsal flexion for both ACL-deficient and contralateral limb before surgery. At latest follow-up, ankle kinematics were modified for ACL-reconstructed limbs only at initial contact and showed no significant difference for contralateral limb compared to the control group. Conclusion: In children with ACL injury, abnormal gait patterns persist two years after ligament reconstruction, in spite of extensive rehabilitation and no clinical complaints. These findings might guide neuromuscular training to improve clinical outcomes and reduce the rerupture rate. [ABSTRACT FROM AUTHOR]

Enhancing human–robot interaction has been a primary focus in robotic gait assistance, with a thorough understanding of human motion being crucial for personalizing gait assistance. Traditional gait trajectory references from Clinical Gait Analysis (CGA) face limitations due to their inability to account for individual variability. Recent advancements in gait pattern generators, integrating regression models and Artificial Neural Network (ANN) techniques, have aimed at providing more personalized and dynamically adaptable solutions. This article introduces a novel approach that expands regression and ANN applications beyond mere angular estimations to include three-dimensional spatial predictions. Unlike previous methods, our approach provides comprehensive spatial trajectories for hip, knee and ankle tailored to individual kinematics, significantly enhancing end-effector rehabilitation robotic devices. Our models achieve state-of-the-art accuracy: overall RMSE of 13.40 mm and a correlation coefficient of 0.92 for the regression model, and RMSE of 12.57 mm and a correlation of 0.99 for the Long Short-Term Memory (LSTM) model. These advancements underscore the potential of these models to offer more personalized gait trajectory assistance, improving human–robot interactions. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop an optimized system for predicting Knee Adduction Moment (KAM) using wearable Inertial Measurement Unit (IMU) sensors and Long Short-Term Memory (LSTM) RNN. Traditional KAM measurement methods are limited by the need for complex laboratory equipment and significant time and cost investments. This study proposes two systems for predicting Knee Adduction Moment based on wearable IMU sensor data and gait patterns: the Multi-model Gait-based KAM Prediction System and the Single-model KAM Prediction System. The Multi-model system pre-classifies different gait patterns and uses specific prediction models tailored for each pattern, while the Single-model system handles all gait patterns with one unified model. Both systems were evaluated using IMU sensor data and GRF data collected from participants in a controlled laboratory environment. The overall performance of the Multi-model Gait-based KAM Prediction System showed an approximately 20% improvement over the Single-model KAM Prediction System. Specifically, the RMSE for the Multi-model system was 6.84 N·m, which is lower than the 8.82 N·m of the Single-model system, indicating a better predictive accuracy. The Multi-model system also achieved a MAPE of 8.47%, compared with 12.95% for the Single-model system, further demonstrating its superior performance.

Older adults could experience split falls or feet-forward falls following an unexpected slip in gait due to different neuromuscular vulnerabilities, and different intervention strategies would be required for each type of faller. Thus, this study aimed to investigate the key factors affecting the fall types based on regular gait pattern. A total of 105 healthy older adults who experienced a laboratory-induced slip and fall were included. Their natural walking trial immediately prior to the novel slip trial was analyzed. To identify the factors related to fall type, gait characteristics and demographic factors were determined using univariate logistic regression, and then stepwise logistic regression was conducted to assess the slip-induced fall type based on these factors. The best fall-type prediction model involves gait speed and recovery foot angular velocity, which could predict 70.5% of feet-forward falls and 86.9% of split falls. Body mass index was also a crucial fall-type prediction with an overall prediction accuracy of 70.5%. Along with gait parameters, 84.1% of feet-forward falls and 78.7% of split falls could be predicted. The findings in this study revealed the determinators related to fall types, which enhances our knowledge of the mechanism associated to slip-induced fall and would be helpful for the development of tailored interventions for slip-induced fall prevention. [ABSTRACT FROM AUTHOR]

• Design and train 55-layer CNN model on the CIFAR-100 dataset. • Build two-stream network to extract spatial and temporal features from the images. • Feature optimization is performed using a Genetic algorithm. Human Gait Recognition (HGR) is referred to as a biometric tactic that is broadly used for the recognition of an individual by using the pattern of walking. There are some key factors such as angle variation, clothing variation, foot shadows, and carrying conditions that affect the human gait. In this work, a new approach is proposed for the HGR that contains five major steps. In the first step, the video data is converted into image frames. In the second step, RGB to GRAY conversion is carried out. After that, a two-stream network is designed by using a 55-layer CNN model called CNN-55 trained on CIFAR-100. The CNN-55 is designed from scratch and trained on the CIFAR-100 dataset by selecting hyperparameters. This pre-trained CNN-55 is used to build a two-stream network. In Stream-1 the optical flow frames are obtained by Horn and Schunk algorithm. These frames are fed into a CNN-55 to extract temporal features. In Stream-2 the GRAY frames are fed to the CNN-55 model for extraction of spatial features. After that, both vectors are serially fused. In the fourth step, the fused feature vector is fed into the Genetic Algorithm for optimization. Finally, the feature vector is fed into the One-Versus-All SVM classifier for recognition. The system is tested on all CASIA-B angles such as 000, 180, 360, 540, 720, 900, 1080, 1260, 1440, 1620, and 1800 which provides accuracy of 97.10%, 96.80%, 94.60%, 98.0%, 98.30%, 96.80%, 97.60, 96.90%, 99.60%, 96.80%, and 97.60%, respectively. The proposed method produces better outcomes compared to recent techniques. [ABSTRACT FROM AUTHOR]

Background: Autism spectrum disorders are a type of developmental disorder that primarily disrupt social interactions and communications. Autism has no treatment, but early diagnosis of it is crucial to reduce these effects. The incidence of autism is represented in repetitive patterns of children’s motion. When walking, these children tighten their muscles and cannot control and maintain their body position. Autism is not only a mental health disorder but also a movement disorder. Method: This study aims to identify autistic children based on data recorded from their gait patterns using a Kinect sensor. The database used in this study comprises walking information, such as joint positions and angles between joints, of 50 autistic and 50 healthy children. Two groups of features were extracted from the Kinect data in this study. The first one was statistical features of joints’ position and angles between joints. The second group was the features based on medical knowledge about autistic children’s behaviors. Then, extracted features were evaluated through statistical tests, and optimal features were selected. Finally, these selected features were classified by naïve Bayes, support vector machine, k-nearest neighbors, and ensemble classifier. Results: The highest classification accuracy for medical knowledge-based features was 87% with 86% sensitivity and 88% specificity using an ensemble classifier; for statistical features, 84% of accuracy was obtained with 86% sensitivity and 82% specificity using naïve Bayes. Conclusion: The dimension of the resulted feature vector based on autistic children’s medical knowledge was 16, with an accuracy of 87%, showing the superiority of these features compared to 42 statistical features.

In children with therapy refractory epilepsy, the functional disconnection of one hemisphere (hemispherotomy) may be considered as a treatment option. The visual field defects and hand function effects associated with the procedure have been extensively studied. However, the effect of the hemispherotomy on gait pattern has thus far only been analyzed qualitatively, and there is limited quantitative data. At the Children's Hospital, we regularly perform standardized quantitative gait analysis studies and care for children with complex epilepsies. During the standard routine of care for two children with structural therapy refractory epilepsy, gait analysis was performed prior to and after hemispherotomy. Both patients had prenatal ischemic brain lesions, had developed severe epilepsy during the first 3 years of life, and were treated with the hemispherotomy at about 7 years of age. Interestingly, one patient did not show any changes in gait pattern, while for the other patient, differences could be observed by means of three-dimensional gait analysis. However, greater deviations to controls postoperatively may also be related to day-to-day variability. [ABSTRACT FROM AUTHOR]

The Lokomat provides task-oriented therapy for patients with gait disorders. This robotic technology drives the lower limbs in the sagittal plane. However, normative gait also involves motions in the coronal and transverse planes. This study aimed to compare the Lokomat with Treadmill gait through three-dimensional (3D)-joint kinematics and inter-joint coordination. Lower limb kinematics was recorded in 18 healthy participants who walked at 3 km/h on a Treadmill or in a Lokomat with nine combinations of Guidance (30%, 50%, 70%) and bodyweight support (30%, 50%, 70%). Compared to the Treadmill, the Lokomat altered pelvic rotation, decreased pelvis obliquity and hip adduction, and increased ankle rotation. Moreover, the Lokomat resulted in significantly slower velocity at the hip, knee, and ankle flexion compared to the treadmill condition. Moderate to strong correlations were observed between the Treadmill and Lokomat conditions in terms of inter-joint coordination between hip–knee (r = 0.67–0.91), hip–ankle (r = 0.66–0.85), and knee–ankle (r = 0.90–0.95). This study showed that some gait determinants, such as pelvis obliquity, rotation, and hip adduction, are altered when walking with Lokomat in comparison to a Treadmill. Kinematic deviations induced by the Lokomat were most prominent at high levels of bodyweight support. Interestingly, different levels of Guidance did not affect gait kinematics. The present results can help therapists to adequately select settings during Lokomat therapy. [ABSTRACT FROM AUTHOR]

Abstract Background Interventions using split belt treadmills (SBTM) aim to improve gait symmetry (GA) in Parkinson's disease (PD). Comparative effects in conjugated SBTM conditions were not studied systematically despite potentially affecting intervention outcomes. We compared gait adaptation effects instigated by SBTM walking with respect to the type (increased\decreased speed) and the side (more/less affected) of the manipulated belt in PD. Methods Eight individuals with PD performed four trials of SBTM walking, each consisted of baseline tied belt configuration, followed by split belt setting – either WS or BS belt's speed increased or decreased by 50% from baseline, and final tied belt configuration. Based on the disease's motor symptoms, a 'worst' side (WS) and a 'best' side (BS) were defined for each participant. Results SB initial change in GA was significant regardless of condition (p ≤ 0.02). This change was however more pronounced for BS-decrease compared with its matching condition WS-increase (p = 0.016). Similarly, the same was observed for WS-decrease compared to BS-increase (p = 0.013). Upon returning to tied belt condition, both BS-decrease and WS-increased resulted in a significant change in GA (p = 0.04). Upper limb asymmetry followed a similar trend of GA reversal, although non-significant. Conclusions Stronger effects on GA were obtained by decreasing the BS belt’s speed of the best side, rather than increasing the speed of the worst side. Albeit a small sample size, which limits the generalisability of these results, we propose that future clinical studies would benefit from considering such methodological planning of SBTM intervention, for maximising of intervention outcomes. Larger samples may reveal arm swinging asymmetries alterations to match SBTM adaptation patterns. Finally, further research is warranted to study post-adaption effects in order to define optimal adaptation schemes to maximise the therapeutic effect of SBTM based interventions.

Enhancing human–robot interaction has been a primary focus in robotic gait assistance, with a thorough understanding of human motion being crucial for personalizing gait assistance. Traditional gait trajectory references from Clinical Gait Analysis (CGA) face limitations due to their inability to account for individual variability. Recent advancements in gait pattern generators, integrating regression models and Artificial Neural Network (ANN) techniques, have aimed at providing more personalized and dynamically adaptable solutions. This article introduces a novel approach that expands regression and ANN applications beyond mere angular estimations to include three-dimensional spatial predictions. Unlike previous methods, our approach provides comprehensive spatial trajectories for hip, knee and ankle tailored to individual kinematics, significantly enhancing end-effector rehabilitation robotic devices. Our models achieve state-of-the-art accuracy: overall RMSE of 13.40 mm and a correlation coefficient of 0.92 for the regression model, and RMSE of 12.57 mm and a correlation of 0.99 for the Long Short-Term Memory (LSTM) model. These advancements underscore the potential of these models to offer more personalized gait trajectory assistance, improving human–robot interactions.

Latin dance involves fundamental walking steps, integral to the dance process. While resembling daily walking, Latin dance demands higher balance levels, necessitating body adjustments by dancers. These adaptations affect dancers' gait biomechanics, prompting our study on gait differences between Latin dancers (LDs) and non-dancers (NDs). We enlisted 21 female Latin dancers and 21 subjects based on specific criteria. Participants executed walking tasks, with an independent sample t-test for 1-dimensional statistical parameter mapping (SPM 1d) analyzing stance phase variations between LDs and NDs. Notably, significant differences in ankle and hip external rotation were evident during the 16.43–29.47% (p = 0.015) and 86.35–100% (p = 0.014) stance phase. Moreover, pronounced distinctions in rectus Achilles tendon force (ATF) (12.83–13.10%, p = 0.049; 15.89–80.19%, p < 0.001) and Patellofemoral joint contact force (PTF) (15.85–18.31%, p = 0.039; 21.14–24.71%, p = 0.030) during stance were noted between LDs (Latin dancers) and NDs (Non-dancers). The study revealed dancers' enhanced balance attributed to external ankle rotation for dance stability, coupled with augmented Achilles tendon and patellofemoral joint strength from prolonged practice. Moreover, integrating suitable Latin dance into rehabilitation may benefit those with internal rotation gait issues. [ABSTRACT FROM AUTHOR]

Background: Interventions using split belt treadmills (SBTM) aim to improve gait symmetry (GA) in Parkinson's disease (PD). Comparative effects in conjugated SBTM conditions were not studied systematically despite potentially affecting intervention outcomes. We compared gait adaptation effects instigated by SBTM walking with respect to the type (increased\decreased speed) and the side (more/less affected) of the manipulated belt in PD. Methods: Eight individuals with PD performed four trials of SBTM walking, each consisted of baseline tied belt configuration, followed by split belt setting – either WS or BS belt's speed increased or decreased by 50% from baseline, and final tied belt configuration. Based on the disease's motor symptoms, a 'worst' side (WS) and a 'best' side (BS) were defined for each participant. Results: SB initial change in GA was significant regardless of condition (p ≤ 0.02). This change was however more pronounced for BS-decrease compared with its matching condition WS-increase (p = 0.016). Similarly, the same was observed for WS-decrease compared to BS-increase (p = 0.013). Upon returning to tied belt condition, both BS-decrease and WS-increased resulted in a significant change in GA (p = 0.04). Upper limb asymmetry followed a similar trend of GA reversal, although non-significant. Conclusions: Stronger effects on GA were obtained by decreasing the BS belt's speed of the best side, rather than increasing the speed of the worst side. Albeit a small sample size, which limits the generalisability of these results, we propose that future clinical studies would benefit from considering such methodological planning of SBTM intervention, for maximising of intervention outcomes. Larger samples may reveal arm swinging asymmetries alterations to match SBTM adaptation patterns. Finally, further research is warranted to study post-adaption effects in order to define optimal adaptation schemes to maximise the therapeutic effect of SBTM based interventions. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson's disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 healthy subjects (HS) were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors (τ) 1–6. Differences between swPD and HS were calculated at each τ, and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE, RCMSE, and CIs showed to differentiate swPD from HS. MSE in the anteroposterior direction at τ4 and τ5, and MSE in the ML direction at τ4 showed to characterize the gait disorders of swPD with the best trade-off between positive and negative posttest probabilities and correlated with the motor disability, pelvic kinematics, and stance phase. Using a time series of 2000 data points, a scale factor of 4 or 5 in the MSE procedure can yield the best trade-off in terms of post-test probabilities when compared to other scale factors for detecting gait variability and complexity in swPD. [ABSTRACT FROM AUTHOR]

The aim of this study was to verify whether providing older adults with actual corrected glasses for refractive errors would have a positive impact on their gait patterns. This study included 30 subjects with an average age of 78.77 ± 7.27 years. The measurement of gait patterns was performed using the Optogait analysis system, and a comparative analysis was performed to compare the patients before and after they wore correction glasses. Step length, stride length, and width showed significant increases after the patients wore correction glasses, and cadence showed a decrease compared to before they wore correction glasses (p < 0.05). A strong positive correlation was observed between step length and stride length (r = 0.779, p < 0.001), and a decrease in cadence (r = −0.686, p < 0.001) was observed with increasing step length. In addition, a proportional increase in speed with increasing step length was observed after the patients wore correction glasses (r = 0.416, p < 0.05). The correction of residual refractive errors in order to provide optimal visual information can have a positive effect on gait patterns. Therefore, efforts by optometry professionals to provide the accurate correction of refractive errors in elderly patients can contribute to the prevention of falls, and collaboration with other experts is needed. [ABSTRACT FROM AUTHOR]

Posttraumatic osteoarthritis may lead to surgical fusion of the ankle joint if non-surgical therapy fails. The indication for a fusion of the joint is based on the pain and disability of the patient, radiographic imaging, and surgeon experience, with no strict guidelines. We aimed to compare outcomes after tibiotalocalcaneal arthrodesis (TTCA) and tibiotalar arthrodesis (TTA) to highlight the functional importance of the subtalar joint. In total, 432 patients with ankle arthrodesis were retrospectively enrolled. Group A (n = 216) underwent TTCA; group B (n = 216) underwent TTA. Demographics, Olerud & Molander Ankle Score (OMAS), Foot Function Index (FFI-D), and Short Form-12 Questionnaire (SF-12) were recorded at a mean follow-up of 6.2 years. The mean OMAS was 50.7; the mean FFI-D was 68.9; the mean SF-12 physical component summary was 39.1. These scores differed significantly between the groups (p < 0.001). The overall revision rate was 18%, primarily for revision of non-union and infection (p < 0.001). Approximately 16% of group A and 26% of group B were able to return to previous work (p < 0.001). Based on significantly worse clinical scores of TTCA compared to TTA and the prolonged downtime and permanent incapacity, the indication for a generous subtalar joint arthrodesis with planned ankle arthrodesis should always be critically examined. [ABSTRACT FROM AUTHOR]

Background: The identification of patients with gait disturbance associated with idiopathic normal pressure hydrocephalus (iNPH) is challenging. This is due to the multifactorial causes of gait disturbance in elderly people and the single moment examination of laboratory tests. Objective: We aimed to assess whether the use of gait sensors in a patient's home environment could help establish a reliable diagnostic tool to identify patients with iNPH by differentiating them from elderly healthy controls (EHC). Methods: Five wearable inertial measurement units were used in 11 patients with iNPH and 20 matched EHCs. Data were collected in the home environment for 72 h. Fifteen spatio-temporal gait parameters were analyzed. Patients were examined preoperatively and postoperatively. We performed an iNPH sub-group analysis to assess differences between responders vs. non-responders. We aimed to identify parameters that are able to predict a reliable response to VP-shunt placement. Results: Nine gait parameters significantly differ between EHC and patients with iNPH preoperatively. Postoperatively, patients with iNPH showed an improvement in the swing phase (p = 0.042), and compared to the EHC group, there was no significant difference regarding the cadence and traveled arm distance. Patients with a good VP-shunt response (NPH recovery rate of =5) significantly differ from the non-responders regarding cycle time, cycle time deviation, number of steps, gait velocity, straight length, stance phase, and stance to swing ratio. A receiver operating characteristic analysis showed good sensitivity for a preoperative stride length of =0.44m and gait velocity of =0.39 m/s. Conclusion: There was a significant difference in 60% of the analyzed gait parameters between EHC and patients with iNPH, with a clear improvement toward the normalization of the cadence and traveled arm distance postoperatively, and a clear improvement of the swing phase. Patients with iNPH with a good response to VP-shunt significantly differ from the non-responders with an ameliorated gait pattern. [ABSTRACT FROM AUTHOR]

The present study examined the effects of wearable visual cues, provided by a wearable laser device, on the gait pattern and stability in patients with Parkinson’s disease (PD). In total, 18 patients with a clinical diagnosis of idiopathic PD (Hoehn and Yahr stage II-III) and 18 healthy controls were included. The main outcome measures included spatiotemporal parameters, sagittal plane kinematic parameters of joints in lower limbs, and dynamic center of pressure (COP) parameters. Significant intra-group improvement in gait parameters was observed in PD patients. Compared with that at baseline, the gait pattern improved in PD patients under the cued condition, with longer stride length and higher toe clearance, as well as shortening of double stance phase, especially the stride length, double stance phase and toe clearance were not significantly different between cued condition and healthy control groups. In kinematics, the ankle peak dorsiflexion in swing phase and the hip range of motion (ROM) in gait cycle was significantly improved in PD patients with visual cues and close to healthy controls. Decreased anteroposterior (AP) position of COP improved gait stability in patients with PD under the cued condition. Multiple linear regression analysis showed that the AP position has a negative correlation with ankle peak dorsiflexion in swing phase. Pearson’s correlation coefficients showed that the minimum toe clearance (Mini TC) was positively correlated with the ankle peak dorsiflexion in swing phase. The immediate effect of wearable visual cues improved the gait pattern and stability in PD patients, suggesting that it may be effective when applied as an alternative technique in rehabilitation training for PD patients. [ABSTRACT FROM AUTHOR]

Abstract Background The potential of auditory feedback for motor learning in the rehabilitation of various diseases has become apparent in recent years. However, since the volume of auditory feedback has played a minor role so far and its influence has hardly been considered, we investigate the volume effect of auditory feedback on gait pattern and gait direction and its interaction with pitch. Methods Thirty-two healthy young participants were randomly divided into two groups: Group 1 (n = 16) received a high pitch (150-250 Hz) auditory feedback; group 2 (n = 16) received a lower pitch (95-112 Hz) auditory feedback. The feedback consisted of a real-time sonification of the right and left foot ground contact. After an initial condition (no auditory feedback and full vision), both groups realized a 30-minute habituation period followed by a 30-minute asymmetry period. At any condition, the participants were asked to walk blindfolded and with auditory feedback towards a target at 15 m distance and were stopped 5 m before the target. Three different volume conditions were applied in random order during the habituation period: loud, normal, and quiet. In the subsequent asymmetry period, the three volume conditions baseline, right quiet and left quiet were applied in random order. Results In the habituation phase, the step width from the loud to the quiet condition showed a significant interaction of volume*pitch with a decrease at high pitch (group 1) and an increase at lower pitch (group 2) (group 1: loud 1.02 ± 0.310, quiet 0.98 ± 0.301; group 2: loud 0.95 ± 0.229, quiet 1.11 ± 0.298). In the asymmetry period, a significantly increased ground contact time on the side with reduced volume could be found (right quiet: left foot 0.988 ± 0.033, right foot 1.003 ± 0.040, left quiet: left foot 1.004 ± 0.036, right foot 1.002 ± 0.033). Conclusions Our results suggest that modifying the volume of auditory feedback can be an effective way to improve gait symmetry. This could facilitate gait therapy and rehabilitation of hemiparetic and arthroplasty patients, in particular if gait improvement based on verbal corrections and conscious motor control is limited.

BackgroundThe identification of patients with gait disturbance associated with idiopathic normal pressure hydrocephalus (iNPH) is challenging. This is due to the multifactorial causes of gait disturbance in elderly people and the single moment examination of laboratory tests.ObjectiveWe aimed to assess whether the use of gait sensors in a patient's home environment could help establish a reliable diagnostic tool to identify patients with iNPH by differentiating them from elderly healthy controls (EHC).MethodsFive wearable inertial measurement units were used in 11 patients with iNPH and 20 matched EHCs. Data were collected in the home environment for 72 h. Fifteen spatio-temporal gait parameters were analyzed. Patients were examined preoperatively and postoperatively. We performed an iNPH sub-group analysis to assess differences between responders vs. non-responders. We aimed to identify parameters that are able to predict a reliable response to VP-shunt placement.ResultsNine gait parameters significantly differ between EHC and patients with iNPH preoperatively. Postoperatively, patients with iNPH showed an improvement in the swing phase (p = 0.042), and compared to the EHC group, there was no significant difference regarding the cadence and traveled arm distance. Patients with a good VP-shunt response (NPH recovery rate of ≥5) significantly differ from the non-responders regarding cycle time, cycle time deviation, number of steps, gait velocity, straight length, stance phase, and stance to swing ratio. A receiver operating characteristic analysis showed good sensitivity for a preoperative stride length of ≥0.44 m and gait velocity of ≥0.39 m/s.ConclusionThere was a significant difference in 60% of the analyzed gait parameters between EHC and patients with iNPH, with a clear improvement toward the normalization of the cadence and traveled arm distance postoperatively, and a clear improvement of the swing phase. Patients with iNPH with a good response to VP-shunt significantly differ from the non-responders with an ameliorated gait pattern.

This study, conducted on a large sample of older adults at elevated fall risk (1), aimed to verify statistical differences in gait stability ratio (GSR) and body balance (BB) according to sex, (2) to examine and compare GSR and BB performance between older adult fallers and non-fallers, (3) to determine an association between GSR and BB according to the history of falls, and (4) to explore whether GSR and BB mediate the association between sex and falls. We included 619 individuals (69.8 ± 5.6 years) living in the Autonomous Region of Madeira, Portugal. The frequency of falls was obtained by self-report. BB was determined by the Fullerton Advanced Balance scale, while GSR was established by dividing cadence by gait speed and data collected during the 50-foot walk test. Males indicated a lower prevalence of falls in the last 12 months (23.6%), while females had a higher score (48.7%), as well as a lower balance performance (p < 0.001) and higher GSR scores (p < 0.001). Lower BB control (p < 0.001), as well as higher GSR, were more expressive for fallers (p < 0.001). We found a large, negative and significant correlation between GSR and BB for historical falls (r = −0.560; p < 0.001), and between male and female cohorts (r = −0.507; p < 0.001). The total effect of sex on falls mediated by GSR and BB was 16.4%. Consequently, GSR and BB mediated this association by approximately 74.0% and 22.5%, respectively. [ABSTRACT FROM AUTHOR]

The Lokomat provides task-oriented therapy for patients with gait disorders. This robotic technology drives the lower limbs in the sagittal plane. However, normative gait also involves motions in the coronal and transverse planes. This study aimed to compare the Lokomat with Treadmill gait through three-dimensional (3D)-joint kinematics and inter-joint coordination. Lower limb kinematics was recorded in 18 healthy participants who walked at 3 km/h on a Treadmill or in a Lokomat with nine combinations of Guidance (30%, 50%, 70%) and bodyweight support (30%, 50%, 70%). Compared to the Treadmill, the Lokomat altered pelvic rotation, decreased pelvis obliquity and hip adduction, and increased ankle rotation. Moreover, the Lokomat resulted in significantly slower velocity at the hip, knee, and ankle flexion compared to the treadmill condition. Moderate to strong correlations were observed between the Treadmill and Lokomat conditions in terms of inter-joint coordination between hip–knee (r = 0.67–0.91), hip–ankle (r = 0.66–0.85), and knee–ankle (r = 0.90–0.95). This study showed that some gait determinants, such as pelvis obliquity, rotation, and hip adduction, are altered when walking with Lokomat in comparison to a Treadmill. Kinematic deviations induced by the Lokomat were most prominent at high levels of bodyweight support. Interestingly, different levels of Guidance did not affect gait kinematics. The present results can help therapists to adequately select settings during Lokomat therapy.

Latin dance involves fundamental walking steps, integral to the dance process. While resembling daily walking, Latin dance demands higher balance levels, necessitating body adjustments by dancers. These adaptations affect dancers’ gait biomechanics, prompting our study on gait differences between Latin dancers (LDs) and non-dancers (NDs). We enlisted 21 female Latin dancers and 21 subjects based on specific criteria. Participants executed walking tasks, with an independent sample t-test for 1-dimensional statistical parameter mapping (SPM 1d) analyzing stance phase variations between LDs and NDs. Notably, significant differences in ankle and hip external rotation were evident during the 16.43–29.47% (p = 0.015) and 86.35–100% (p = 0.014) stance phase. Moreover, pronounced distinctions in rectus Achilles tendon force (ATF) (12.83–13.10%, p = 0.049; 15.89–80.19%, p < 0.001) and Patellofemoral joint contact force (PTF) (15.85–18.31%, p = 0.039; 21.14–24.71%, p = 0.030) during stance were noted between LDs (Latin dancers) and NDs (Non-dancers). The study revealed dancers’ enhanced balance attributed to external ankle rotation for dance stability, coupled with augmented Achilles tendon and patellofemoral joint strength from prolonged practice. Moreover, integrating suitable Latin dance into rehabilitation may benefit those with internal rotation gait issues.

Self-esteem is a significant kind of psychological resource, and behavioral self-esteem assessments are rare currently. Using ordinary cameras to capture one’s gait pattern to reveal people’s self-esteem meets the requirement for real-time population-based assessment. A total of 152 healthy students who had no walking issues were recruited as participants. The self-esteem scores and gait data were obtained using a standard 2D camera and the Rosenberg Self-Esteem Scale (RSES). After data preprocessing, dynamic gait features were extracted for training machine learning models that predicted self-esteem scores based on the data. For self-esteem prediction, the best results were achieved by Gaussian processes and linear regression, with a correlation of 0.51 (p < 0.001), 0.52 (p < 0.001), 0.46 (p < 0.001) for all participants, males, and females, respectively. Moreover, the highest reliability was 0.92 which was achieved by RBF-support vector regression. Gait acquired by a 2D camera can predict one’s self-esteem quite well. This innovative approach is a good supplement to the existing methods in ecological recognition of self-esteem leveraged by video-based gait. [ABSTRACT FROM AUTHOR]

Stroke survivors are at a relatively higher risk of falling than their healthy counterparts. To identify the key gait characteristics affecting fall risk in this population, this study analyzed the gait kinematics and gait asymmetries for 36 community-dwelling people with chronic stroke (PwCS). According to their fall history in the last 12 months, they were divided into a fall group (n = 21) and non-fall group (n = 15), and then the gait kinematics (step length, stride length, stance time, swing time, trunk angle, and segment angles for lower limbs) and their asymmetries (symmetry ratio and symmetry index) were compared between these two groups. To investigate the relationship between fall types and gait characteristics, these variables were also compared between 11 slip-fallers and non-fallers, as well as between 7 trip-fallers and non-fallers. Our results indicated that the fallers showed smaller trunk and thigh angle, larger shank angle, and higher gait asymmetries (trunk and foot). Such changes in gait pattern could also be found in the trip-fallers, except the trunk angle. Additionally, the trip-fallers also showed a shorter step length, shorter stride length, shorter swing time, larger foot angle on the paretic side, and higher asymmetries in shank angle and step length, while the slip-fallers only showed changes in trunk angle and thigh angle and higher asymmetries in step length and foot angle compared to the non-fall group. Our results indicated that improper or pathological gait patterns (i.e., smaller thigh angle or higher foot asymmetry) increases the risk of falling in PwCS, and different fall types are associated with different gait characteristics. Our findings would be helpful for the development of fall risk assessment methods that are based on kinematic gait measurements. Implementation of objective fall risk assessments in PwCS has the potential to reduce fall-related injuries, leading to a reduction in associated hospital costs. [ABSTRACT FROM AUTHOR]

Abstract Background TheraTogs promotes proprioceptive sense of a child with cerebral palsy and improves abnormal muscle tone, posture alignment, balance, and gait. Therefore, the aim of this study was to investigate the efficacy of TheraTogs orthotic undergarment on gait pattern in children with dyskinetic cerebral palsy. Thirty children with dyskinetic cerebral palsy were selected for this randomized controlled study. They were randomly assigned to (1) an experimental group that received TheraTogs orthotic undergarment (12 h/day, 3 days/week) plus traditional physical therapy for 3 successive months and (2) a control group that received only traditional physical therapy program for the same time period. Gait parameters were measured at baseline and after 3 months of intervention using Pro-Reflex motion analysis. Results Children in both groups showed significant improvements in the gait parameters (P < 0.05), with significantly greater improvements in the experimental group than in the control group. Conclusions The use of TheraTogs may have a positive effect to improve gait pattern in children with dyskinetic cerebral palsy. Trial registration This trial was registered in the ClinicalTrial.gov PRS ( NCT03037697 ).

Abstract Background Previous studies have pointed to the impact of self-perceptions of aging (SPA) on self-reported physical function in later life. However, less is known about associations of SPA with objectively measured physical function, especially gait. Research that examined other psychological variables and objectively measured gait has focused on single gait parameters such as gait speed, which seems to fall short for the complexity of this movement. Some approaches have proposed ways to identify gait patterns in specific patient groups, but not in community samples. Our goal was (a) to identify gait patterns based on a combination of important gait parameters in a community sample, and (b) to investigate differential associations of gain- and loss-related SPA with these gait patterns. Methods The study used an electronic walkway to assess gait parameters of 150 community dwelling adults aged 71–93 years (61.0% women) at their usual and maximum gait speed. SPA were assessed with a questionnaire. We used latent profile analysis (LPA) to identify groups exhibiting distinct gait patterns and binary logistic regression to investigate associations of SPA with these groups, controlling for personality traits, number of illnesses, age, gender, and education. To compare overall function between groups, a t-test for scores in the Short Physical Performance Battery was used. Results LPA revealed two distinct groups in both gait speed conditions. The fit group exhibited a stable, well-coordinated and faster gait pattern, while the functionally limited group’s gait pattern was less stable, less coordinated and slower. The odds of belonging to the functionally limited group were increased by loss-related SPA at usual gait speed, while the odds of belonging to the fit group were increased by gain-related SPA at individual maximum speed. Conclusions The findings (a) suggest LPA as a useful approach to investigate complex gait patterns considering several gait parameters simultaneously, and (b) provide first evidence for differential associations of gain- and loss-related SPA with gait patterns at usual and maximum gait speed. Intervention studies addressing gait in older adults should additionally address gain-related views on aging.

Working status in walking and personal identity are critical to worker safety and management in the workplace, which could be recognized through gait patterns. However, detecting gait data from images could violate workers' privacy and, most importantly, cause confidentiality problems. The present study proposed utilizing gait data from environmental surface detection to classify walking status and personal identification simultaneously. In addition to general walking, twenty participants were instructed to simulate three walking scenarios: wearing a backpack, pulling a suitcase, and watching a video on the cell phone. Two datasets containing ground reaction forces and center of pressure from a force plate (FP) and acceleration and angular velocity data from inertial measurement units (IMU) embedded in a walking surface were utilized as input features for eighty classifications. These datasets were then processed using the matching network of few-shot learning and sliding-window long short-term memory (SW-LSTM) models. The best accuracy of the few-shot learning model was 98% for four walking scenarios and 99% for an 80-class classification. In comparison, the SW-LSTM model was 92% for the FP features and 99% for the IMU features to identify twenty individuals. The present study demonstrated the IMU environmental device can capture gait features to classify walking status and identify individuals. Additionally, the SW-LSTM model showed potential for instant identification of gait cycles, rather than relying on a few-shot learning method until the entire gait cycle is completed. This practical approach provides an alternative for gait pattern recognition and deep learning model selection in the industry. [ABSTRACT FROM AUTHOR]

The aim of this study was to verify whether providing older adults with actual corrected glasses for refractive errors would have a positive impact on their gait patterns. This study included 30 subjects with an average age of 78.77 ± 7.27 years. The measurement of gait patterns was performed using the Optogait analysis system, and a comparative analysis was performed to compare the patients before and after they wore correction glasses. Step length, stride length, and width showed significant increases after the patients wore correction glasses, and cadence showed a decrease compared to before they wore correction glasses (p < 0.05). A strong positive correlation was observed between step length and stride length (r = 0.779, p < 0.001), and a decrease in cadence (r = −0.686, p < 0.001) was observed with increasing step length. In addition, a proportional increase in speed with increasing step length was observed after the patients wore correction glasses (r = 0.416, p < 0.05). The correction of residual refractive errors in order to provide optimal visual information can have a positive effect on gait patterns. Therefore, efforts by optometry professionals to provide the accurate correction of refractive errors in elderly patients can contribute to the prevention of falls, and collaboration with other experts is needed.

Stroke causes disability in the lower-limb symmetry gait pattern in affected patients. The patients would not be able to regain their usual walking ability independently unless they benefit from rehabilitation therapies. Footplate-based gait robo-assisted systems can help patients to conduct effective training/exercising while tracking their progress of recovery and can dramatically reduce the clinical labor costs of physiotherapy. In the sense of simulation and not the design of the mechanical structure, this study aims to perform a combination of dynamic and control simulation of a five degrees-of-freedom footplate-based gait robo-assisted system established according to the Stewart platform structure for use in lower limb rehabilitation of stroke patients. The effectiveness and performance of the proposed mechanism were assessed through a clinical gait pattern of a healthy male individual. The proposed robo-assisted system enables the simulation of the hip and knee flexion/extension as well as the ankle dorsiflexion/plantar flexion of stroke patients to reproduce their typical symmetry gait pattern. The results were interpreted as the dynamic movement characteristics of the right and left thigh, leg, and foot compared to the clinical gait pattern with a mean percentage error of 6.70% to show the effectiveness and accuracy of the developed robo-assisted system for lower limb actuation in the simulation process. [ABSTRACT FROM AUTHOR]

Background: The potential of auditory feedback for motor learning in the rehabilitation of various diseases has become apparent in recent years. However, since the volume of auditory feedback has played a minor role so far and its influence has hardly been considered, we investigate the volume effect of auditory feedback on gait pattern and gait direction and its interaction with pitch.Methods: Thirty-two healthy young participants were randomly divided into two groups: Group 1 (n = 16) received a high pitch (150-250 Hz) auditory feedback; group 2 (n = 16) received a lower pitch (95-112 Hz) auditory feedback. The feedback consisted of a real-time sonification of the right and left foot ground contact. After an initial condition (no auditory feedback and full vision), both groups realized a 30-minute habituation period followed by a 30-minute asymmetry period. At any condition, the participants were asked to walk blindfolded and with auditory feedback towards a target at 15 m distance and were stopped 5 m before the target. Three different volume conditions were applied in random order during the habituation period: loud, normal, and quiet. In the subsequent asymmetry period, the three volume conditions baseline, right quiet and left quiet were applied in random order.Results: In the habituation phase, the step width from the loud to the quiet condition showed a significant interaction of volume*pitch with a decrease at high pitch (group 1) and an increase at lower pitch (group 2) (group 1: loud 1.02 ± 0.310, quiet 0.98 ± 0.301; group 2: loud 0.95 ± 0.229, quiet 1.11 ± 0.298). In the asymmetry period, a significantly increased ground contact time on the side with reduced volume could be found (right quiet: left foot 0.988 ± 0.033, right foot 1.003 ± 0.040, left quiet: left foot 1.004 ± 0.036, right foot 1.002 ± 0.033).Conclusions: Our results suggest that modifying the volume of auditory feedback can be an effective way to improve gait symmetry. This could facilitate gait therapy and rehabilitation of hemiparetic and arthroplasty patients, in particular if gait improvement based on verbal corrections and conscious motor control is limited. [ABSTRACT FROM AUTHOR]

Prolonging ambulation is an important treatment goal in children with Duchenne muscular dystrophy (DMD). Three-dimensional gait analysis (3DGA) could provide sensitive parameters to study the efficacy of clinical trials aiming to preserve ambulation. However, quantitative descriptions of the natural history of gait features in DMD are first required. The overall goal was to provide a full delineation of the progressive gait pathology in children with DMD, covering the entire period of ambulation, by performing a so-called mixed cross-sectional longitudinal study. Firstly, to make our results comparable with previous literature, we aimed to cross-sectionally compare 31 predefined gait features between children with DMD and a typically developing (TD) database (1). Secondly, we aimed to explore the longitudinal changes in the 31 predefined gait features in growing boys with DMD using follow-up 3DGA sessions (2). 3DGA-sessions (n = 124) at self-selected speed were collected in 27 boys with DMD (baseline age: 4.6–15 years). They were repeatedly measured over a varying follow-up period (range: 6 months–5 years). The TD group consisted of 27 children (age: 5.4–15.6 years). Per measurement session, the spatiotemporal parameters, and the kinematic and kinetic waveforms were averaged over the selected gait cycles. From the averaged waveforms, discrete gait features (e.g., maxima and minima) were extracted. Mann-Whitney U tests were performed to cross-sectionally analyze the differences between DMD at baseline and TD (1). Linear mixed effect models were performed to assess the changes in gait features in the same group of children with DMD from both a longitudinal (i.e., increasing time) as well as a cross-sectional perspective (i.e., increasing baseline age) (2). At baseline, the boys with DMD differed from the TD children in 17 gait features. Additionally, 21 gait features evolved longitudinally when following-up the same boys with DMD and 25 gait features presented a significant cross-sectional baseline age-effect. The current study quantitatively described the longitudinal alterations in gait features in boys with DMD, thereby providing detailed insight into how DMD gait deteriorates. Additionally, our results highlight that gait features extracted from 3DGA are promising outcome measures for future clinical trials to quantify the efficacy of novel therapeutic strategies. [ABSTRACT FROM AUTHOR]

Prolonging ambulation is an important treatment goal in children with Duchenne muscular dystrophy (DMD). Three-dimensional gait analysis (3DGA) could provide sensitive parameters to study the efficacy of clinical trials aiming to preserve ambulation. However, quantitative descriptions of the natural history of gait features in DMD are first required. The overall goal was to provide a full delineation of the progressive gait pathology in children with DMD, covering the entire period of ambulation, by performing a so-called mixed cross-sectional longitudinal study. Firstly, to make our results comparable with previous literature, we aimed to cross-sectionally compare 31 predefined gait features between children with DMD and a typically developing (TD) database (1). Secondly, we aimed to explore the longitudinal changes in the 31 predefined gait features in growing boys with DMD using follow-up 3DGA sessions (2). 3DGA-sessions (n = 124) at self-selected speed were collected in 27 boys with DMD (baseline age: 4.6–15 years). They were repeatedly measured over a varying follow-up period (range: 6 months–5 years). The TD group consisted of 27 children (age: 5.4–15.6 years). Per measurement session, the spatiotemporal parameters, and the kinematic and kinetic waveforms were averaged over the selected gait cycles. From the averaged waveforms, discrete gait features (e.g., maxima and minima) were extracted. Mann-Whitney U tests were performed to cross-sectionally analyze the differences between DMD at baseline and TD (1). Linear mixed effect models were performed to assess the changes in gait features in the same group of children with DMD from both a longitudinal (i.e., increasing time) as well as a cross-sectional perspective (i.e., increasing baseline age) (2). At baseline, the boys with DMD differed from the TD children in 17 gait features. Additionally, 21 gait features evolved longitudinally when following-up the same boys with DMD and 25 gait features presented a significant cross-sectional baseline age-effect. The current study quantitatively described the longitudinal alterations in gait features in boys with DMD, thereby providing detailed insight into how DMD gait deteriorates. Additionally, our results highlight that gait features extracted from 3DGA are promising outcome measures for future clinical trials to quantify the efficacy of novel therapeutic strategies.

Abstract Machines that are sensitive to environmental fluctuations, such as autonomous and pet robots, are currently in demand, rendering the ability to control huge and complex systems crucial. However, controlling such a system in its entirety using only one control device is difficult; for this purpose, a system must be both diverse and flexible. Herein, we derive and analyze the feature values of robot sensor and actuator data, thereby investigating the role that each feature value plays in robot locomotion. We conduct experiments using a developed quadruped robot from which we acquire multi-point motion information as the movement data; we extract the features of these movement data using an autoencoder. Next, we decompose the movement data into three features and extract various gait patterns. Despite learning only the “walking” movement, the movement patterns of trotting and bounding are also extracted herein, which suggests that movement data obtained via hardware contain various gait patterns. Although the present robot cannot locomote with these movements, this research suggests the possibility of generating unlearned movements.

Purpose This study was designed to examine the effects of a single corrective exercise (CEX) and corrective kinesio taping (CKT) on gait patterns, plantar pressure, balance, and pain in 20~30s female patients with moderate hallux valgus. Methods Twenty-one participants (age: 30.1±5.1 yrs; height: 164.1±4.8 cm; body weight: 56.7±6.8 kg; body mass index: 21.2±5.7 kg·m-2; hallux valgus angle: 27.2±6.1°) with hallux valgus was recruited and participated in three trials, i.e., CEX trial, CKT trial, and combined CEX and CKT (CEX+CKT) trial, repeatedly in a counter-balanced order. One week of wash-out period was placed between the trials to minimize the effect of the previous treatment on the next treatment. Variables related to gait pattern, plantar pressure, balance, and pain were measured during each treatment. We carried out repeated two way ANOVA on measured variables. Results 1) Regarding gait patterns, CEX treatment and CEX+CKT treatments showed significant increases in the length of patients strides, the single support line during the stance phase, and significant reduction of the cadence. 2) Regarding gait cycle, CEX treatment and CEX+CKT treatments showed significant reductions in the contact times of forefoot, midfoot, and heel. There was a significant reduction of double stance phase in CEX treatment. 3) Regarding foot pressure on gait, CEX+CKT treatments significantly increased the maximum pressure of midfoot and heel. CEX treatments significantly increased the maximum pressure of forefoot. 4) Regarding balance, CEX treatment and CKT treatments significantly increased one leg standing with eyes closed. 5) Pain was significantly reduced in CKT treatment and CEX+CKT treatments. Conclusions According to the aforementioned results, it was concluded that a single CKT treatment was effective in reducing pain when walking and that plantar pressure, gait pattern, gait cycle, and balance were improved through a single bout of CEX treatments. Therefore, treatments by stage, starting with CKT treatments to reduce the pain, and then treating CEX to improve the gait pattern, gait cycle, foot pressure when walking, and balance ability, would be effective. Future research is warranted to identify the effects of long-term treatments.

Background: Autism spectrum disorders are a type of developmental disorder that primarily disrupt social interactions and communications. Autism has no treatment, but early diagnosis of it is crucial to reduce these effects. The incidence of autism is represented in repetitive patterns of children's motion. When walking, these children tighten their muscles and cannot control and maintain their body position. Autism is not only a mental health disorder but also a movement disorder., Method: This study aims to identify autistic children based on data recorded from their gait patterns using a Kinect sensor. The database used in this study comprises walking information, such as joint positions and angles between joints, of 50 autistic and 50 healthy children. Two groups of features were extracted from the Kinect data in this study. The first one was statistical features of joints' position and angles between joints. The second group was the features based on medical knowledge about autistic children's behaviors. Then, extracted features were evaluated through statistical tests, and optimal features were selected. Finally, these selected features were classified by naïve Bayes, support vector machine, k-nearest neighbors, and ensemble classifier., Results: The highest classification accuracy for medical knowledge-based features was 87% with 86% sensitivity and 88% specificity using an ensemble classifier; for statistical features, 84% of accuracy was obtained with 86% sensitivity and 82% specificity using naïve Bayes., Conclusion: The dimension of the resulted feature vector based on autistic children's medical knowledge was 16, with an accuracy of 87%, showing the superiority of these features compared to 42 statistical features., Competing Interests: There are no conflicts of interest., (Copyright: © 2024 Journal of Medical Signals & Sensors.)

As humans, we constantly change our movement strategies to adapt to changes in physical functions and the external environment. We have to walk very slowly in situations with a high risk of falling, such as walking on slippery ice, carrying an overflowing cup of water, or muscle weakness owing to aging or motor deficit. However, previous studies have shown that a normal gait pattern at low speeds results in reduced efficiency and stability in comparison with those at a normal speed. Another possible strategy is to change the gait pattern from normal to step-to gait, in which the other foot is aligned with the first swing foot. However, the efficiency and stability of the step-to gait pattern at low speeds have not been investigated yet. Therefore, in this study, we compared the efficiency and stability of the normal and step-to gait patterns at intermediate, low, and very low speeds. Eleven healthy participants were asked to walk with a normal gait and step-to gait on a treadmill at five different speeds (i.e., 10, 20, 30, 40, and 60 m/min), ranging from very low to normal walking speed. The efficiency parameters (percent recovery and walk ratio) and stability parameters (center of mass lateral displacement) were analyzed from the motion capture data and then compared for the two gait patterns. The results suggested that step-to gait had a more efficient gait pattern at very low speeds of 10–30 m/min, with a larger percent recovery, and was more stable at 10–60 m/min in comparison with a normal gait. However, the efficiency of the normal gait was better than that of the step-to gait pattern at 60 m/min. Therefore, step-to gait is effective in improving gait efficiency and stability when faced with situations that force us to walk slowly or hinder quick walking because of muscle weakness owing to aging or motor deficit along with a high risk of falling.

As humans, we constantly change our movement strategies to adapt to changes in physical functions and the external environment. We have to walk very slowly in situations with a high risk of falling, such as walking on slippery ice, carrying an overflowing cup of water, or muscle weakness owing to aging or motor deficit. However, previous studies have shown that a normal gait pattern at low speeds results in reduced efficiency and stability in comparison with those at a normal speed. Another possible strategy is to change the gait pattern from normal to step-to gait, in which the other foot is aligned with the first swing foot. However, the efficiency and stability of the step-to gait pattern at low speeds have not been investigated yet. Therefore, in this study, we compared the efficiency and stability of the normal and step-to gait patterns at intermediate, low, and very low speeds. Eleven healthy participants were asked to walk with a normal gait and step-to gait on a treadmill at five different speeds (i.e., 10, 20, 30, 40, and 60 m/min), ranging from very low to normal walking speed. The efficiency parameters (percent recovery and walk ratio) and stability parameters (center of mass lateral displacement) were analyzed from the motion capture data and then compared for the two gait patterns. The results suggested that step-to gait had a more efficient gait pattern at very low speeds of 10–30 m/min, with a larger percent recovery, and was more stable at 10–60 m/min in comparison with a normal gait. However, the efficiency of the normal gait was better than that of the step-to gait pattern at 60 m/min. Therefore, step-to gait is effective in improving gait efficiency and stability when faced with situations that force us to walk slowly or hinder quick walking because of muscle weakness owing to aging or motor deficit along with a high risk of falling. [ABSTRACT FROM AUTHOR]

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In this article, a simple prototype and control system of a lower limb exoskeleton driven by stepper motors was proposed. To study kinematic parameters of the device, a general, three-dimensional simulation model of the exoskeleton was developed. Moreover, a gait pattern was modeled for hip, knee, and ankle joints. It represents sagittal plane angular positions of joints during normal gait and consists of three piecewise-defined sine-square-based functions. It is based on the experimental gait data recorded with an optoelectronic motion capture system for a healthy subject walking on a treadmill. Although the developed pattern is intended to be implemented in a control system of a lower limb exoskeleton for gait rehabilitation, it can be used to control bipedal robots as well. Last but not least, the control approach was tested with the exoskeleton prototype. [ABSTRACT FROM AUTHOR]

Insects have millimeter-sized bodies but excellent autonomous control. Therefore, numerous researchers have been bio-inspired by their attractive features to create highly functional robots. In this research, we investigated a neural network integrated circuit (NNIC) that generated the driving waveform of an insect-type microrobot. Previously, we discussed an NNIC that had a switching mechanism for the gait pattern. It could switch between the tripod gait pattern and ripple gait pattern typical of insects. The simulation results revealed that the gait was unstable when changed by varying the coupling coefficient voltage of the synaptic model. In addition, the order of the ripple gait pattern had an error under certain conditions. In this paper, we compare the measurement results with the simulation results. As unstable gaits do not appear in the measurement results, the fabricated NNIC generated a more stable gait pattern than the simulation. [ABSTRACT FROM AUTHOR]