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BACKGROUND: The assessment of asymmetric gait quality plays a pivotal role in guiding rehabilitation training; however, the link between gait quality and kinematic-kinetic gait parameters remains ambiguous. OBJECTIVE: To formulate a machine-learning model for evaluating gait quality based on gait parameters, identify factors sensitive to gait quality from asymmetric gait parameters, investigate the relationship between gait indicators and gait quality, and provide guidance for asymmetric gait training and rehabilitation. METHODS: An asymmetric gait database was established through the creation of asymmetric conditions. Kinematic and kinetic data were collected from 8 young and 8 elderly subjects (all male, right dominant population) during gait tests. Gait quality for each test data set was assessed using symmetry indices, resulting in the creation of a gait parameter-gait quality dataset. Utilizing the Random Forest algorithm, a gait quality evaluation model was developed and key quality parameter factors were identified through differential analysis. This model was iteratively refined. The model’s performance was evaluated through 10- fold cross-validation, and its effectiveness was verified using the cross-validation dataset. RESULTS AND CONCLUSION: (1) A gradient test was designed to categorize gait quality into optimal, suboptimal, intermediate, and poor groups, with 759, 329, 133, and 125 instances, respectively. (2) The application of the Random Forest algorithm in gait quality assessment was explored. A relationship model was established between gait indicators and gait quality, yielding a predictive model accuracy of 95.99%. (3) The 13 main parameters significantly influencing asymmetric gait quality were identified through the Random Forest model’s feature importance ranking. (4) An analysis of gait quality sensitivity factors using the 13 important parameters led to the identification of five key sensitivity indexes. The Random Forest model utilizing these sensitivity factors achieved a predictive accuracy of 94.20%. [ABSTRACT FROM AUTHOR]

This study aims to evaluate leg movement by integrating gait analysis with surface electromyography (sEMG) and accelerometer (ACC) data from the lower limbs. We employed a wireless, self-made, and multi-channel measurement system in combination with commercial GaitUp Physilog® 5 shoe-worn inertial sensors to record the walking patterns and muscle activations of 17 participants. This approach generated a comprehensive dataset comprising 1452 samples. To accurately predict gait parameters, a machine learning model was developed using features extracted from the sEMG signals of thigh and calf muscles, and ACCs from both legs. The study utilized evaluation metrics including accuracy (R2), Pearson correlation coefficient (PCC), root mean squared error (RMSE), mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE) to evaluate the performance of the proposed model. The results highlighted the superiority of the CatBoost model over alternatives like XGBoost and Decision Trees. The CatBoost's average PCCs for 17 temporospatial gait parameters of the left and right legs are 0.878 ± 0.169 and 0.921 ± 0.047, respectively, with MSE of 7.65, RMSE of 1.48, MAE of 1.00, MAPE of 0.03, and Accuracy (R2-Score) of 0.91. This research marks a significant advancement by providing a more comprehensive method for detecting and analyzing gait statuses. [ABSTRACT FROM AUTHOR]

The purpose of this study was to compare and examine gait parameters, hip joint angle, and lower extremity muscle activity during normal walking before and after Nordic walking (NW) intervention. Nineteen healthy male participants (age 26.3 ± 2.4 years) were included in the study. During walking, we measured gait parameters using the footprint-measuring gait analysis system. Аngular changes of the hip joint were measured with an IMU-type portable three-dimensional motion analyzer and the EMG activity of each muscle. The measurements above were performed before and after a one-hour NW training course. The hip joint angle, muscle activity, and gait parameters were compared before and after the intervention with the same participants. In gait parameters, the stride length and walking speed were significantly greater after the intervention, while the cadence decreased significantly. Both hip flexion and extension angles were significantly greater after the intervention. In terms of muscle activity, the rectus abdominis, the tibialis anterior, and the gastrocnemius were very different after the intervention. According to previous reports, employing a large stride length during NW would affect the normal gait after the training. The use of the Nordic pole may have stimulated a substantial forward movement in the lower extremities using the large range of motion of the hip joints. The benefits of NW include enhanced forward propulsion during gait and additional stability during the stance phase due to the greater support surface provided by the poles.

Stride length (SL), foot clearance (FC), and foot progression angle (FPA) are the key parameters for diagnosing gait disorders. This study used the distance data between two feet measured by ultra-wideband (UWB) sensors installed on shoes and proposed a method for estimating the three gait parameters. Here, a method of compensating the offset of the UWB sensor and estimating the distances between a base sensor installed on one foot during the stance phase and three UWB sensors on the other during the swing phase was applied. Foot trajectory was acquired in a gait experiment with ten healthy adults walking on a treadmill. The results were compared with those obtained using a motion capture system (MCS). The UWBs sensor displayed average errors of 45.84 mm, 7.60 mm, and 2.82° for SL, FC, and FPA, respectively, compared with the MCS. A similar accuracy level was achieved in a previous study that used an inertial measurement unit (IMU). Thus, these results suggest that UWB sensors can be extensively applied to sensor systems used to analyze mobile gait systems. [ABSTRACT FROM AUTHOR]

BACKGROUND: Stroke is one of the major diseases endangering the health of Chinese citizens, characterized by high morbidity, disability, mortality and recurrence. Gait dysfunction or impairment is considered to be one of the most common and devastating physiological consequences of stroke. Active lower extremity rehabilitation can promote the recovery of gait function, improve the ability to perform activities of daily living, and improve the quality of life of patients. OBJECTIVE: To review and summarize research findings applicable to post-stroke gait quantification and analysis, with a focus on the latest gait analysis system technologies, post-stroke gait data processing and analysis techniques, and their feasibility and potential value in the clinical setting. METHODS: Search terms included “stroke, gait analysis, assessment, lower limb, spatiotemporal, kinematics, kinetics, plantar pressure, electromyography (EMG), machine learning, statistics” in Chinese and English. CNKI and PubMed databases were searched for relevant articles published from January 2000 to December 2021. A few of classical distant documents were also included. The initial screening was performed by reading the titles and abstracts of the articles. Duplicate studies, low-quality journals and irrelevant documents were excluded and 62 articles were finally included for review. RESULTS AND CONCLUSION: Traditional qualitative gait analysis is primarily based on observational gait and is therefore subjective and largely influenced by the observer’s experience. Quantitative gait analysis, on the other hand, provides measured parameters with good accuracy and repeatability for diagnostic and comparative assessment throughout the rehabilitation process. The rapid development of smart wearable technologies and artificial intelligence is increasingly drawing more attention to gait research. Although their use in clinical settings is not yet well exploited, these tools are expected to change the status quo in stroke gait quantification by providing the means to acquire, store, and analyze multifactorial complex gait data while capturing its nonlinear dynamic variability and providing the valuable benefit of predictive analysis. Gait analysis is often characterized by a number of specific parameters that describe normal or abnormal gait, including temporal, kinematic, kinetic, and electromyographic parameters. Understanding the factors associated with walking function can help clinicians and researchers identify gait-related parameters that should focus on when assessing walking function. As conventional statistical methods are no longer adequate to handle the large data volumes with high heterogeneity and complexity generated by instrumented gait analysis, and gait analysis involves a large number of interdependent parameters that are difficult to interpret due to the large amount of data and their interrelationships. To simplify the evaluation, the application of machine learning to gait analysis is a promising solution. [ABSTRACT FROM AUTHOR]

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We propose three novel digital biomarkers for the longitudinal performance monitoring and movement evaluation of hemiparetic patients, e.g. after stroke. We devised convergence points (CP) for the bilateral walking analysis based on gait parameters, e.g. stride duration using regression- modelling to estimate similarity between body sides. The physical activity (PA) was devised to evaluate the energy expenditure of all extremities during training and free-living. The functional range of motion (fROM) is a digital biomarker to quantify the upper arm reaching ability, represented in 3D visualisations. In this work, we detail CP, PA, and fROM to derive rehabilitation insights for personalising therapies. We evaluated the proposed digital biomarkers in a clinical observation study with 11 patients after stroke during their rehabilitation including therapy and self-paced daily routines.

Stride length (SL), foot clearance (FC), and foot progression angle (FPA) are the key parameters for diagnosing gait disorders. This study used the distance data between two feet measured by ultra-wideband (UWB) sensors installed on shoes and proposed a method for estimating the three gait parameters. Here, a method of compensating the offset of the UWB sensor and estimating the distances between a base sensor installed on one foot during the stance phase and three UWB sensors on the other during the swing phase was applied. Foot trajectory was acquired in a gait experiment with ten healthy adults walking on a treadmill. The results were compared with those obtained using a motion capture system (MCS). The UWBs sensor displayed average errors of 45.84 mm, 7.60 mm, and 2.82° for SL, FC, and FPA, respectively, compared with the MCS. A similar accuracy level was achieved in a previous study that used an inertial measurement unit (IMU). Thus, these results suggest that UWB sensors can be extensively applied to sensor systems used to analyze mobile gait systems.

Objectives: The aim of the present study was to examine the effects of a rehabilitation program combined with a home-based vibration-assisted therapy on gait parameters in children with cerebral palsy (CP)., Methods: In a retrospective study, 180 children, 101 boys and 79 girls, (mean age 7.2 ± 3.3 years) with CP at Gross Motor Function Classification System (GMFCS) Level I and Level II were examined using gait analyses with the Leonardo Mechanograph® Gangway at three measurement points. The measurements were conducted before (M0) and after a six-month rehabilitation period (M6), as well as 12 months after the commencement of rehabilitation (M12). The difference between measurement points M6-M0 (treatment interval) and M12-M6 (follow-up interval) were compared, and significance was determined using the Wilcoxon test., Results: Children with CP at GMFCS Level I and II demonstrated a significant improvement in gait efficiency (pathlength/distance M6-M0: -0.053 (SD 0.25) vs M12-M6: -0.008 (0.36), p=0.038). There were no significant difference in change of mean velocity and average step length between M6-M0 and M12-M6 (p=0.964 and p=0.611)., Conclusions: The rehabilitation program seems to enhance gait efficiency in children with CP. German Clinical Trial Registry: DRKS0001131 at www.germanctr.de., Competing Interests: The authors have no conflict of interest.

Hisashi Kawai, Yu Taniguchi, Satoshi Seino, Ryota Sakurai, Yosuke Osuka, Shuichi Obuchi, Yutaka Watanabe, Hunkyung Kim, Hiroki Inagaki, Akihiko Kitamura, Shuichi Awata, Shoji ShinkaiTokyo Metropolitan Institute of Gerontology, Tokyo 173-0015, JapanBackground: Gait measures such as gait speed, stride length, step width, and stance duration change with advanced age and are associated with adverse health outcomes among older adults. The stride-to-stride variabilities of gait measures are also related to falls and cognitive decline in older adults; however, reference values of these gait parameters in older Japanese adults do not exist. This study aimed to determine the reference values of gait parameters as measured by a plantar pressure platform in community-dwelling older Japanese adults.Methods: Community-dwelling adults (N=1,212) who were independent in basic activities of daily living and aged 70–96 years (491 men, 721 women) completed the gait performance measurement in a geriatric health assessment. We assessed 10 gait performance measures with a plantar pressure platform system (P-WALK, BTS Bioengineering) and calculated means and coefficient of variations (CVs) of the gait measures as well as quintiles for those gait parameters per age group among men and women.Results: Mean (SDs) of gait speed, stride length, step width, and stance durations were 1.26 (0.24) meters per second (m/s), 121.9 (19.8) cm, 24.0 (3.2) cm, and 552.4 (60.4) milliseconds (ms), respectively, in men, and 1.27 (0.21) m/s, 115.7 (16.3) cm, 17.9 (2.8) cm, and 517.6 (59.8) ms, respectively, in women. Mean of CVs (SD) of stride length, step width, and single-stance duration were 2.76 (1.35), 12.06 (3.98), and 5.74 (2.66), respectively, in men and 2.69 (1.24), 15.65 (4.53), and 5.77 (2.40), respectively, in women. Gait parameters (except CVs of step width) declined significantly with age regardless of gender (P< 0.01 for trends).Conclusion: This study determined age group dependent gait parameter reference values, presented as means with quintile ranges, in community-dwelling older Japanese adults. These reference values may be useful metrics for gait assessment in the elderly.Keywords: gait parameter, variation coefficient, community-dwelling, older adult, reference value

Stride length estimation using inertial measurement unit (IMU) sensors is getting popular recently as one representative gait parameter for health care and sports training. The traditional estimation method requires some explicit calibrations and design assumptions. Current deep learning methods suffer from few labeled data problem. To solve above problems, this article proposes a single convolutional neural network (CNN) model to predict stride length of running and walking and classify the running or walking type per stride. The model trains its pretext task with self-supervised learning on a large unlabeled dataset for feature learning, and its downstream task on the stride length estimation and classification tasks with supervised learning with a small labeled dataset. The proposed model can achieve better average percent error, 4.78%, on running and walking stride length regression and 99.83% accuracy on running and walking classification, when compared to the previous approach, 7.44% on the stride length estimation. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the effect of Kinesio taping facilitating technique on Vastus medialis oblique and gracilis muscles regarding gait parameters in osteoarthritic (OA) knee patients. Method: Twenty-six OA knee patients randomly assigned into two groups; Kinesio taping group (50% tension facilitating technique applied on vastus medialis oblique and gracilis muscles) and placebo groups. Subjects were assessed gait parameters with the modified six-minute walk test by self-adjusted speed on the force distribution measurement systems (FDM-T) for three occasions: before, immediately and 48 hours after taping. Two-way repeated ANOVA was used to analyze the data. Results: Significant increases of step length, stride length and cadence were found immediately after Kinesio taping with p-value 0.008, 0.015, 0.042 respectively. As well as after 48 hours the Kinesio taping group had significant improvement (p-value < 0.001). Conclusions: The results of this study demonstrated that the Kinesio taping for osteoarthritic knee can improve walking pattern as shown in gait parameters and speed. [ABSTRACT FROM AUTHOR]

An inertial measurement system for estimating gait parameters indicating cognitive impairment is developed for use during large-scale health checkups. Current health checkups conduct a 10 m fastest gait examination to assess signs of cognitive impairment and physical performance. Earlier methods require examiners to follow a subject and measure the gait time using a stopwatch. The method proposed herein reduces burdens on examiners. Several gait parameters in addition to the gait time of many subjects can be measured simultaneously and quantitatively from three-dimensional foot movements estimated using inertial sensors comprising two accelerometers and two gyroscopes with different measurement range attached to both feet. Gait parameters derived from the inertial sensor are gait time, stride length, gait cycle, gait velocity, toe angle, toe height, and the percentage of swing phase. The gait condition, such as walking or running, is distinguished from the moment of toe off and heel contact of both feet. In all, 1406 subjects with ages of 19-93 years old were given instructions to walk at their highest velocity on a straight 16-m-long walking course. Mini-Mental State Examination (MMSE) is conducted on subjects. The score is used as a reference valuation of the cognitive impairment level. Experimental results show that the proposed measurement system provides equal performance to that obtained using a stopwatch and improves correlation between the MMSE score and the fastest 10 m gait time of subjects who did not run. Furthermore, it is confirmed that the proposed measurement system using inertial sensors can quantitatively provide spatiotemporal gait parameters to evaluate the physical performance in a short time during the large-scale health checkups.

Background: Gait stability during dual tasks is important for elderly persons, especially for elderly individuals in need of care. A study was conducted to assess gait stability by using Lyapunov exponents (λS) during single task and dual task conditions in independently living elderly people (Go-Goes) and elderly people in need of care (No-Goes).Material and methods: This study was conducted with 26 participants (average age 82 ± 9.4 years) who were allocated to the Go-Goes or No-Goes group. Outcomes were mediolateral and vertical Lyapunov exponents (λS) from accelerometer data and gait speed under single task and dual task conditions.Results: In both groups significantly higher mediolateral and vertical Lyapunov exponent values as well as significantly lower walking speeds under dual task conditions were found in both groups. The effect sizes were small to moderate for mediolateral λS and large for vertical λS and these differences remained when the analyses were adjusted for walking speed.Conclusion: Elderly people showed lower gait stability and gait speed under dual task conditions compared to single task conditions. [ABSTRACT FROM AUTHOR]

Sarcopenia can cause various senile diseases and is a major factor associated with the quality of life in old age. To diagnose, assess, and monitor muscle loss in daily life, 10 sarcopenia and 10 normal subjects were selected using lean mass index and grip strength, and their gait signals obtained from inertial sensor-based gait devices were analyzed. Given that the inertial sensor can measure the acceleration and angular velocity, it is highly useful in the kinematic analysis of walking. This study detected spatial-temporal parameters used in clinical practice and descriptive statistical parameters for all seven gait phases for detailed analyses. To increase the accuracy of sarcopenia identification, we used Shapley Additive explanations to select important parameters that facilitated high classification accuracy. Support vector machines (SVM), random forest, and multilayer perceptron are classification methods that require traditional feature extraction, whereas deep learning methods use raw data as input to identify sarcopenia. As a result, the input that used the descriptive statistical parameters for the seven gait phases obtained higher accuracy. The knowledge-based gait parameter detection was more accurate in identifying sarcopenia than automatic feature selection using deep learning. The highest accuracy of 95% was achieved using an SVM model with 20 descriptive statistical parameters. Our results indicate that sarcopenia can be monitored with a wearable device in daily life.

Ambulation is a fundamental requirement of human beings for enjoying healthy community life. A neurological disorder such as stroke can significantly affect one's gait thereby restricting one's active community participation. To quantify one's gait, spatiotemporal gait parameters are widely used in clinical context with different tests such as 10 meter walk test, 6 min walk test, etc. Though these conventional observation-based methods are powerful, yet they often suffer from subjectivity, a scarcity of adequately trained therapists and frequent clinical visits for assessment. Researchers have been exploring the technology-assisted solutions for gait characterization. There are laboratory-based stereophotogrammetric methods and walk mats that are powerful tools as far as gait characterization is concerned. However, these suffer from issues with portability, accessibility due to high cost, labor-intensiveness, etc. Faced with these issues, our present research tries to investigate and quantify the gait abnormalities in individuals with neurological disorder by using a portable and cost-effective instrumented shoes (ShoesFSRhenceforth). The in-house developed ShoesFSR comprised of a pair of shoes instrumented with Force Sensing Resistors (FSR) and a wireless data acquisition unit. The real-time FSR data was acquired wirelessly and analyzed by a central console to offer quantitative indices of one's gait. Studies were conducted with 15 healthy participants and 9 post-stroke survivors. The spatiotemporal gait parameters of healthy participants measured using ShoesFSR were validated with standard methods such as stereophotogrammetric system and paper-based setup. Statistical analysis showed good agreement between the gait parameters measured using ShoesFSR and the standard methods. Specifically, the mean absolute error of the spatial parameters measured by the ShoesFSR, in the worst case, was 1.24% and that for the temporal parameters was 1.12% with that measured by standard methods for healthy gait. This research shows the potential of the ShoesFSR to quantify gait abnormality of post-stroke hemiplegic patients. In turn, the results show a promise for the future clinical use of the ShoesFSR.

Mobile, sensor-based gait analysis in Parkinson’s disease (PD) facilitates the objective measurement of gait parameters in cross-sectional studies. Besides becoming outcome measures for clinical studies, the application of gait parameters in personalized clinical decision support is limited. Therefore, the aim of this study was to evaluate whether the individual response of PD patients to dopaminergic treatment may be measured by sensor-based gait analysis. 13 PD patients received apomorphine every 15 min to incrementally increase the bioavailable apomorphine dose. Motor performance (UPDRS III) was assessed 10 min after each apomorphine injection. Gait parameters were obtained after each UPDRS III rating from a 2 × 10 m gait sequence, providing 41.2 ± 9.2 strides per patient and injection. Gait parameters and UPDRS III ratings were compared cross-sectionally after apomorphine titration, and more importantly between consecutive injections for each patient individually. For the individual response, the effect size Cohen’s d for gait parameter changes was calculated based on the stride variations of each gait sequence after each injection. Cross-sectionally, apomorphine improved stride speed, length, gait velocity, maximum toe clearance, and toe off angle. Between injections, the effect size for individual changes in stride speed, length, and maximum toe clearance correlated to the motor improvement in each patient. In addition, significant changes of stride length between injections were significantly associated with UPDRS III improvements. We therefore show, that sensor-based gait analysis provides objective gait parameters that support clinical assessment of individual PD patients during dopaminergic treatment. We propose clinically relevant instrumented gait parameters for treatment studies and especially clinical care. [ABSTRACT FROM AUTHOR]

Leg problems have become more frequent in fast-growing turkeys. The objective of this study was to evaluate the effects of common leg defects on kinetic parameters of gait and biomechanical properties of bone. Nine hundred, day-old, male, Large White turkeys were raised in 48 floor pens. At 42 d of age, turkeys were divided into four categories of leg condition as determined by visual evaluation: Normal, Crooked toes, Shaky legs, and Valgus. Fifteen toms were selected from each group and trained to walk on a pressure sensitive walkway. Gait kinetic data were collected at 92, 115 and 144 d of age. At 145 d of age, turkeys were sacrificed and bones were collected and frozen until analysis. Morphological measurements of femur, tibia and tarsus-metatarsus were recorded. Bone mineral density (BMD) and content (BMC) were obtained using DEXA. Bone strength of tibias was evaluated using a four-point bending test and femurs with a torsion test. ANOVA was used to detect differences among groups, and Tukey's test used for mean separation. There were no differences in BW among different leg conditions. Gait parameters changed as turkeys aged, and age-group interactions were observed on peak vertical force (PVF), contact time, step length (SL) and bipedal cycle. No differences (P > 0.05) were detected in morphological measurements of femur or tibia. Relative asymmetry of femur length was lower (P < 0.05) in Normal and Valgus turkeys than in toms with Crooked toes. There were no differences (P > 0.05) among groups for femur BMD, BMC or strength. Tibia BMD and the area moment of inertia of turkeys with Crooked toes were lower (P < 0.05) than in toms with Valgus. With all four leg conditions, femur strength was positively correlated with PVF and negatively correlated with SL; BMD and BMC were correlated with tibia strength and gait kinetic parameters. In conclusion, only crooked toes caused consistent differences in gait patterns, bone properties and bone strength, but in general, gait kinetics was correlated with bone biomechanics in turkeys. [ABSTRACT FROM AUTHOR]

Ambulation is a fundamental requirement of human beings for enjoying healthy community life. A neurological disorder such as stroke can significantly affect one's gait thereby restricting one's active community participation. To quantify one's gait, spatiotemporal gait parameters are widely used in clinical context with different tests such as 10 meter walk test, 6min walk test, etc. Though these conventional observation-based methods are powerful, yet they often suffer from subjectivity, a scarcity of adequately trained therapists and frequent clinical visits for assessment. Researchers have been exploring the technology-assisted solutions for gait characterization. There are laboratory-based stereophotogrammetric methods and walk mats that are powerful tools as far as gait characterization is concerned. However, these suffer from issues with portability, accessibility due to high cost, labor-intensiveness, etc. Faced with these issues, our present research tries to investigate and quantify the gait abnormalities in individuals with neurological disorder by using a portable and cost-effective instrumented shoes (ShoesFSR henceforth). The in-house developed ShoesFSR comprised of a pair of shoes instrumented with Force Sensing Resistors (FSR) and a wireless data acquisition unit. The real-time FSR data was acquired wirelessly and analyzed by a central console to offer quantitative indices of one's gait. Studies were conducted with 15 healthy participants and 9 post-stroke survivors. The spatiotemporal gait parameters of healthy participants measured using ShoesFSR were validated with standard methods such as stereophotogrammetric system and paper-based setup. Statistical analysis showed good agreement between the gait parameters measured using ShoesFSR and the standard methods. Specifically, the mean absolute error of the spatial parametersmeasured by the ShoesFSR, in the worst case, was 1.24%and that for the temporal parameters was 1.12%with thatmeasured by standardmethods for healthy gait. This research shows the potential of the ShoesFSR to quantify gait abnormality of post-stroke hemiplegic patients. In turn, the results show a promise for the future clinical use of the ShoesFSR. [ABSTRACT FROM AUTHOR]

Gait analysis is the systematic study of human motion and aims at quantifying the gait characteristics with various temporal and spatial gait parameters. Gait has proven to be linked to certain diseases, such as hemiplegic paralysis and Parkinson’s disease (PD). In this work, we present our designed and developed gait sensing platform that can capture human movement, called uGait, and we further present its application in discriminating patients with Parkinson’s disease from healthy subjects. First, we show the overall system architecture of uGait. Second, we detail how to extract and select meaningful spatial-temporal gait parameters and present an effective wrapper-based feature selection method. Typically, we consider the gait parameters associated with the situation where one walks on a u-shape walkway rather than only on a straight walkway, and we consider the influence of human body height on gait parameters extraction. We then apply our proposed feature selector to identify a discriminant subset of gait features and construct a classifier with better generalization ability for PD detection. Finally, we collect experimental data with uGait and conduct extensive experiments with three different classifiers and three different feature selectors. Experimental results demonstrate power of the quantitative gait analysis in classifying PD patients. [ABSTRACT FROM AUTHOR]

Gait parameters are key information to know the physical health of elderly people and if necessary, the parameters are used to avoid unnecessary injury due to unexpected falling resulting from weakened muscle. Many methods to estimate gait parameters have been studied such as the one using motion capture cameras. However, most existing methods require expensive sensors and markers attached to a person. This type of method is only feasible when it is used in a testing room. Thus it is difficult to obtain the gait parameters during normal life. In this paper, an algorithm for a home robot to acquire gait parameters while chasing a resident in a house is proposed. The home robot has a Kinect v2 sensor on its top to measure the distance. As this is a home robot always standing by the resident, the gait parameters will be always acquired and stored in the database. This robot does not need a special testing room. The home robot chases the resident from behind to estimate the physical health of the resident. The gait parameters such as knee joint angle, stride time, stride length and walking speed are precisely obtained by this algorithm. The algorithm of acquiring the gait parameters and chasing has been tuned to be used in a house with an elderly person. Several tests have been conducted considering scenarios of using this robot in a daily life. [ABSTRACT FROM AUTHOR]

Carpal sinus hairs on the forearms are assumed to have evolved within the stem lineage of Theria. The presence and similar position of these specialized tactile hairs in scansorial and terrestrial species as well as earlier studies on rats indicate a biological role in sensing substrate irregularities in high structured environments to ensure the dynamic stability of the body during locomotion. While these sensors were considered as one functional unit until so far, the present study deals with the biological role of the single tactile hairs of the trident, assuming a role in sensing substrate diameters and adapting limb coordination and body posture to different arboreal inclinations. To investigate the influence of each hair, we studied the locomotion of rats on poles of two different diameters whereby we selectively removed individual carpal sinus hairs. The rats walked at speeds ranging from 0.12 m/s to 0.58 m/s. Normal-light high-speed cameras and x-ray fluoroscopy visualized the hairs and body dynamics during locomotion. The time lag between first contact of the hairs to the branch until contact of the forepaw was 56–108 ms. Within this time window the pronation/supination of the paw and anterior body posture are adjusted to the substrate diameter. We presume that the most proximal sinus hair (located between the medial and lateral one) senses the paw-substrate distance through the increasing bend from its first branch-contact until the contact of the paw. The medial and the lateral hairs touch the pole sides and thereby, may collect information about the properties of the small-diameter substrate. The removal of single hairs from the group results in minor changes of kinematic parameters, but locomotor stability is seriously impaired when more than one hair is cut. The kinematic responses span from a more crouched body posture and higher forearm pronation to paw slipping, muscle tremor or complete refusal to walk on the narrow substrate. • Single carpal sinus hairs are assumed to have different biological roles. • Sensory input of carpal sinus hairs essential for dynamic stability of the trunk during walking on small diameters. • Each carpal sinus hair has its own position during substrate contact on small diameter branches. • Sensorimotor connection between sinus hairs and limbs is proposed. [ABSTRACT FROM AUTHOR]