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AbstractFarmers may be at a higher risk of developing hip osteoarthritis (OA) due to the high demands of their occupation. To the authors’ knowledge, the gait patterns of farmers that may be associated with hip joint degeneration have yet to be analyzed. Therefore, this study compares gait mechanics between farmers and non-farmers (controls). It is hypothesized that farmers would exhibit altered lower extremity joint mechanics during walking when compared to matched controls. This exploratory study included five farmers and five sex-, age-, and body mass index (BMI)-matched controls. A 3D gait analysis was performed while study participants walked at a self-selected speed on an instrumented treadmill. Sagittal plane hip, knee, and ankle kinetics and kinematics were assessed. Effect sizes and between-group differences in demographics and gait mechanics were assessed. There were no group differences in walking speed, total stance time as well as hip and knee joint kinematics (p > 0.05). Farmers exhibited statistical trends (p = 0.07–0.08) of lower peak ankle plantarflexion angles, higher plantarflexor moments, higher knee flexion moment impulse, and higher peak vertical ground reaction force during the first and second halves of stance. Additionally, farmers ambulated with a significantly higher knee extensor moment (p = 0.04) and moment impulse (p = 0.05) during the first half of stance and a higher ankle plantarflexion moment impulse (p = 0.04). The results demonstrate a multi-joint gait alteration in farmers compared to non-farmers and may suggest a compensatory gait pattern to optimize hip joint mechanics and mitigate hip joint degeneration. These results provide a preliminary understanding of the impact that agricultural occupations have on joint mechanics that may be associated with the increased prevalence of hip OA in the farming population. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a degenerative nervous system disorder involving motor disturbances. Motor alterations affect the gait according to the progression of PD and can be used by experts in movement disorders to rate the severity of the disease. However, this rating depends on the expertise of the clinical specialist. Therefore, the diagnosis may be inaccurate, particularly in the early stages of PD where abnormal gait patterns can result from normal aging or other medical conditions. Consequently, several classification systems have been developed to enhance PD diagnosis. In this paper, a PD gait severity classification algorithm was developed using vertical ground reaction force (VGRF) signals. The VGRF records used are from a public database that includes 93 PD patients and 72 healthy controls adults. The work presented here focuses on modeling each foot's gait stance phase signals using a modified convolutional long deep neural network (CLDNN) architecture. Subsequently, the results of each model are combined to predict PD severity. The classifier performance was evaluated using ten-fold cross-validation. The best-weighted accuracies obtained were 99.296(0.128)% and 99.343(0.182)%, with the Hoehn-Yahr and UPDRS scales, respectively, outperforming previous results presented in the literature. The classifier proposed here can effectively differentiate gait patterns of different PD severity levels based on gait signals of the stance phase. [ABSTRACT FROM AUTHOR]

Running poses a high risk of developing running-related injuries (RRIs). The majority of RRIs are the result of an imbalance between cumulative musculoskeletal load and load capacity. A general estimate of whole-body biomechanical load can be inferred from ground reaction forces (GRFs). Unfortunately, GRFs typically can only be measured in a controlled environment, which hinders its wider applicability. The advent of portable sensors has enabled training machine-learned models that are able to monitor GRF characteristics associated with RRIs in a broader range of contexts. Our study presents and evaluates a machine-learning method to predict the contact time, active peak, impact peak, and impulse of the vertical GRF during running from three-dimensional sacral acceleration. The developed models for predicting active peak, impact peak, impulse, and contact time demonstrated a root-mean-squared error of 0.080 body weight (BW), 0.198 BW, 0.0073 BW ⋅ seconds, and 0.0101 seconds, respectively. Our proposed method outperformed a mean-prediction baseline and two established methods from the literature. The results indicate the potential utility of this approach as a valuable tool for monitoring selected factors related to running-related injuries.

This systematic review and meta-analysis examined the influence of biomechanical and physical characteristics on ball release speed in cricket using correlation data. Search was conducted across PubMed, Cinhal, Scopus, SportDiscus and Web of Science, with eighteen studies included. The ball release speed had a moderate correlation with overall biomechanical (r = 0.42, p < 0.001) parameters and a strong correlation with physical (r = 0.65, p < 0.001) characteristics. Furthermore, individual biomechanical and physical parameters were also correlated with ball release speed. The run-up speed had a strong correlation (r = 0.50, p < 0.001) and front knee angle at ball release had a moderate correlation (r = 0.40, p < 0.001). Poorer correlations were identified for centre of mass velocity at ball release (r = 0.16, p = 0.134), front knee angle at front foot contact (r = 0.26, p = 0.015) 25 and vertical ground reaction force (r = 0.13, p = 0.659). Strong and significant correlation was found for total arm length (r = 0.65, p < 0.001) and shoulder strength (r = 0.58, p < 0.001). This review highlights the biomechanical and physical parameters that are major determinants of faster ball release speed. [ABSTRACT FROM AUTHOR]

Ankle sprains are common and cause persistent ankle function reduction. To biomechanically evaluate the ankle function after ankle sprains, the ground reaction force (GRF) measurement during the single-legged landing had been used. However, previous studies focused on discrete features of vertical GRF (vGRF), which largely ignored vGRF waveform features that could better identify the ankle function. To identify how the history of ankle sprain affect the vGRF waveform during the single-legged landing with unsupervised machine learning considering the time-series information of vGRF. Eighty-seven currently healthy basketball athletes (12 athletes without ankle sprain, 49 athletes with bilateral, and 26 athletes with unilateral ankle sprain more than 6 months before the test day) performed single-legged landings from a 20 centimeters (cm) high box onto the force platform. Totally 518 trials vGRF data were collected from 87 athletes of 174 ankles, including 259 ankle sprain trials (from previous sprain ankles) and 259 non-ankle sprain trials (from without sprain ankles). The first 100 milliseconds (ms) vGRF waveforms after landing were extracted. Principal component analysis (PCA) was applied to the vGRF data, selecting 8 principal components (PCs) representing 96% of the information. Based on these 8 PCs, k-means method (k = 3) clustered the 518 trials into three clusters. Chi-square test assessed significant differences (p < 0.01) in the distribution of ankle sprain and non-ankle sprain trials among clusters. The ankle sprain trials accounted for a significantly larger percentage (63.9%) in Cluster 3, which exhibited rapidly increased impulse vGRF waveforms with larger peaks in a short time. PCA and k-means method for vGRF waveforms during single-legged landing identified that the history of previous ankle sprains caused a loss of ankle absorption ability lasting at least 6 months from an ankle sprain. • Data analysis was based on ungrouped data. • Combined PCA and k-means methods can cluster the features related to ankle sprain. • In Cluster 3, the vGRF impact phase may be affected by the ankle sprain history. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a widespread neurodegenerative condition that affects many individuals annually. Early identification and monitoring of disease progression are crucial to effectively managing symptoms and preventing motor complications. This research proposes an automated PD diagnosis and severity-grading model based on time-frequency and fuzzy features using improved uni-directional and bi-directional long short-term memory networks with sensitive hyperparameters optimization. We utilize vertical ground reaction force signals collected from Physionet's publicly available dataset recorded during regular and dual-task clinical trials of walking measurements. Only the cumulative signal of both feet was then utilized and segmented into 30-s windows without further pre-processing. Subsequently, we extracted only four key time-frequency and fuzzy features from each segment, effectively capturing the signal's inherent uncertainty. Bayesian optimization is employed in both detection and grading approaches to fine-tune the two critical hyperparameters: the initial learning rate and the number of hidden units in the network. The detection phase yields an exceptional accuracy of 99.19%, surpassing state-of-the-art studies with the same dataset. In the grading phase, classification based on the unified PD rating scale values achieves an accuracy of 92.28%. The proposed study delves into the potential of cumulative gait signals as a powerful diagnostic tool for PD, aiming to extract precise and intricate information by implementing straightforward and minimal processing endeavors. This method demonstrates significant efficiency in terms of complexity, cost, and energy consumption by utilizing a single-dimensional signal, eliminating the need for pre-processing steps, and limiting the features used for training.

As people age there is a proximal shift of joint moment generation from ankle plantarflexion and knee extension toward hip extension and flexion moments. This age-related redistribution has been documented in the context of propulsive force generation during the push-off phase with less evidence in the context of weight bearing. Additionally, these sagittal plane joint moments have been a primary focus of studies though the hip frontal plane moment also contributes to vertical support but has received less attention. Furthermore, how aging affects the relationships between changes in sagittal and frontal joint moments and changes in vertical support force as a function of walking speed remains unclear How does aging affect the contributions of sagittal and frontal plane joint moments to weight-bearing across different walking speeds? Gait analysis was performed on 24 young and 17 healthy older subjects walked on the treadmill at their preferred and 30 % faster speeds. Stepwise linear regression analysis was performed to determine the joint moments that predict the peak amplitudes of the vertical ground reaction force (VGRF) across different walking speeds. Hip abduction and knee extension moments were the primary contributors to leading limb weight-bearing in young, whereas hip extension moment was the primary contributor in older adults. Ankle plantarflexion moment was the main contributor to trailing limb weight-bearing in young and hip flexion moment was the main contributor in older adults. From preferred to faster walking speed changes in knee extension moment were the primary contributor to changes in the trailing limb weight-bearing in young whereas changes in hip extension moment were the primary contributor in olderadults. These findings suggested that older and younger adults used different joint moment contributions to produce leading limb and trailing limb vertical support forces across different walking speeds. • Older and younger adults used different weight-bearing strategies during gait. • Hip abduction moment is a key factor to leading limb weight bearing only in young. • Distal to proximal joint moment redistribution was observed in older adults. • Older adults showed reduced trailing limb weight bearing at fast walking speed. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods. [ABSTRACT FROM AUTHOR]

Abstract Background In recent years the use of sample entropy (SampEn) to evaluate the complexity of the locomotor system in human gait data has gained in popularity. However, it has been suggested that SampEn is sensitive to various input parameters and signal preprocessing methods. This study quantified the effects of different temporal and spatial normalization approaches and various lengths of the template vector (m) on SampEn calculations. The discriminatory ability of SampEn was studied by comparing two walking conditions. Methods Twenty-three participants (seven males, 55.7 ± 8.5 years, 165.7 ± 7.9 cm, 80.5 ± 16.7 kg) walked on a treadmill with preferred (Vpref) and maximum (Vmax) speed. Data were segmented and resampled (SEGM), resampled and spatially normalized (NORM), resampled and detrended (ZERO). Results For vertical ground reaction force (vGRF) and center of pressure in anterio-posterior direction (COPap), in both walking conditions, SampEn was generally sensitive to the vector length and not to the data processing, except for COPap in ZERO, m = 2, 4. For the COPml SampEn behaved oppositely, it was sensitive to preprocessing method and not to the m length. The regularity of COPap and vGRF in all processed signals increased in Vmax condition. For the COPml only two signals, WHOLE and ZERO, revealed increased complexity caused by more demanding walking conditions. Conclusions SampEn was able to discriminate between different walking conditions in all analyzed variables, but not in all signals. Depending on evaluated variable, SampEn was susceptible in different way for the m level and processing method. Hence, these should be checked and selected for each variable independently. For future studies evaluating influence of walking velocity on COP and vGRF regularity during treadmill walking it is advised to use raw time series. Furthermore, to maintain template vector which represents biological relevance it is advised to detect highest frequencies present in analyzed signals and evaluate minimal time interval which can reflect change caused by response of a neuromuscular system. During evaluating treadmill walking measured with 100 Hz sampling frequency it is recommended to adopt m from 6 to 10, when average stride time is up to about 1 s.

Introduction: Parkinson’s disease (PD) is characterized by an alteration of the walking gait, frequently including a slower self-selected walking speed (SSWS). Although the reduction of walking speed is inherent to people with PD, such speed reduction also represents a potential confounding factor that might partly explain the observed gait differences between PD and control participants. Methods: In this study, each participant walked along a 25 m level corridor during which vertical ground reaction force signals were recorded using shoes equipped with eight pressure sensors. Vertical ground reaction force signals (using statistical parametric mapping) and temporal and kinetic variables as well as their related variability and asymmetry (using Student’s t-test) were compared between PD (n = 54) and walking-speed-matched control subjects (n = 39). Results: Statistical parametric mapping did not yield significant differences between PD and control groups for the vertical ground reaction force signal along the walking stance phase. Stride time and single support time (equivalent to swing time) were shorter and peak vertical ground reaction force was larger in PD patients compared to controls (p≤ 0.05). However, the single support time was no longer different between people with PD and healthy subjects when expressed relatively to stride time (p = 0.07). While single support, double support, and stance times were significantly more variable and asymmetric for PD than for the control group (p≤ 0.05), stride time was similar (p≥ 0.07). Discussion: These results indicate that at matched SSWS, PD patients adopt a higher cadence than control participants. Moreover, the temporal subdivision of the walking gait of people with PD is similar to healthy individuals but the coordination during the double support phase is different. Hence, this study indicates that isolating the speed factor is crucial in gait analysis for PD. [ABSTRACT FROM AUTHOR]

One of the most important forces generated during gait is the vertical ground reaction force (vGRF). This force can be measured using force plates, but these can limit the scope of gait analysis. This paper presents a method to estimate the vGRF using inertial measurement units (IMU) and machine learning techniques. Four wearable IMUs were used to obtain flexion/extension angles of the hip, knee, and ankle joints, and an IMU placed over the C7 vertebra to measure vertical acceleration. We trained and compared the performance of two machine learning algorithms: feedforward neural networks (FNN) and random forest (RF). We investigated the importance of the inputs introduced into the models and analyzed in detail the contribution of lower limb kinematics and vertical acceleration to model performance. The results suggest that the inclusion of vertical acceleration increases the root mean square error in the FNN, while the RF appears to decrease it. We also analyzed the ability of the models to construct the force signal, with particular emphasis on the magnitude and timing of the vGRF peaks. Using the proposed method, we concluded that FNN and RF models can estimate the vGRF with high accuracy.

IntroductionParkinson’s disease (PD) is characterized by an alteration of the walking gait, frequently including a slower self-selected walking speed (SSWS). Although the reduction of walking speed is inherent to people with PD, such speed reduction also represents a potential confounding factor that might partly explain the observed gait differences between PD and control participants.MethodsIn this study, each participant walked along a 25 m level corridor during which vertical ground reaction force signals were recorded using shoes equipped with eight pressure sensors. Vertical ground reaction force signals (using statistical parametric mapping) and temporal and kinetic variables as well as their related variability and asymmetry (using Student’s t-test) were compared between PD (n = 54) and walking-speed-matched control subjects (n = 39).ResultsStatistical parametric mapping did not yield significant differences between PD and control groups for the vertical ground reaction force signal along the walking stance phase. Stride time and single support time (equivalent to swing time) were shorter and peak vertical ground reaction force was larger in PD patients compared to controls (p ≤ 0.05). However, the single support time was no longer different between people with PD and healthy subjects when expressed relatively to stride time (p = 0.07). While single support, double support, and stance times were significantly more variable and asymmetric for PD than for the control group (p ≤ 0.05), stride time was similar (p ≥ 0.07).DiscussionThese results indicate that at matched SSWS, PD patients adopt a higher cadence than control participants. Moreover, the temporal subdivision of the walking gait of people with PD is similar to healthy individuals but the coordination during the double support phase is different. Hence, this study indicates that isolating the speed factor is crucial in gait analysis for PD.

Background: In recent years the use of sample entropy (SampEn) to evaluate the complexity of the locomotor system in human gait data has gained in popularity. However, it has been suggested that SampEn is sensitive to various input parameters and signal preprocessing methods. This study quantified the effects of different temporal and spatial normalization approaches and various lengths of the template vector (m) on SampEn calculations. The discriminatory ability of SampEn was studied by comparing two walking conditions. Methods: Twenty-three participants (seven males, 55.7 ± 8.5 years, 165.7 ± 7.9 cm, 80.5 ± 16.7 kg) walked on a treadmill with preferred (Vpref) and maximum (Vmax) speed. Data were segmented and resampled (SEGM), resampled and spatially normalized (NORM), resampled and detrended (ZERO). Results: For vertical ground reaction force (vGRF) and center of pressure in anterio-posterior direction (COPap), in both walking conditions, SampEn was generally sensitive to the vector length and not to the data processing, except for COPap in ZERO, m = 2, 4. For the COPml SampEn behaved oppositely, it was sensitive to preprocessing method and not to the m length. The regularity of COPap and vGRF in all processed signals increased in Vmax condition. For the COPml only two signals, WHOLE and ZERO, revealed increased complexity caused by more demanding walking conditions. Conclusions: SampEn was able to discriminate between different walking conditions in all analyzed variables, but not in all signals. Depending on evaluated variable, SampEn was susceptible in different way for the m level and processing method. Hence, these should be checked and selected for each variable independently. For future studies evaluating influence of walking velocity on COP and vGRF regularity during treadmill walking it is advised to use raw time series. Furthermore, to maintain template vector which represents biological relevance it is advised to detect highest frequencies present in analyzed signals and evaluate minimal time interval which can reflect change caused by response of a neuromuscular system. During evaluating treadmill walking measured with 100 Hz sampling frequency it is recommended to adopt m from 6 to 10, when average stride time is up to about 1 s. [ABSTRACT FROM AUTHOR]

This paper presents an actuated spring-loaded inverted pendulum model with a vertically constrained suspended load mass to predict the vertical GRF and energetics of walking and running. Experiments were performed to validate the model prediction accuracy of vertical GRF. The average correlation coefficient was greater than 0.97 during walking and 0.98 during running. The model's predictions of energy cost reduction were compared with experimental data from the literature, and the difference between the experimental and predicted results was less than 7%. The predicted results of characteristic forces and energy cost under different suspension stiffness and damping conditions showed a tradeoff when selecting the suspension parameters of elastically suspended backpacks. [ABSTRACT FROM AUTHOR]

Farmers may be at a higher risk of developing hip osteoarthritis (OA) due to the high demands of their occupation. To the authors' knowledge, the gait patterns of farmers that may be associated with hip joint degeneration have yet to be analyzed. Therefore, this study compares gait mechanics between farmers and non-farmers (controls). It is hypothesized that farmers would exhibit altered lower extremity joint mechanics during walking when compared to matched controls. This exploratory study included five farmers and five sex-, age-, and body mass index (BMI)-matched controls. A 3D gait analysis was performed while study participants walked at a self-selected speed on an instrumented treadmill. Sagittal plane hip, knee, and ankle kinetics and kinematics were assessed. Effect sizes and between-group differences in demographics and gait mechanics were assessed. There were no group differences in walking speed, total stance time as well as hip and knee joint kinematics ( p  > 0.05). Farmers exhibited statistical trends ( p  = 0.07-0.08) of lower peak ankle plantarflexion angles, higher plantarflexor moments, higher knee flexion moment impulse, and higher peak vertical ground reaction force during the first and second halves of stance. Additionally, farmers ambulated with a significantly higher knee extensor moment ( p  = 0.04) and moment impulse ( p  = 0.05) during the first half of stance and a higher ankle plantarflexion moment impulse ( p  = 0.04). The results demonstrate a multi-joint gait alteration in farmers compared to non-farmers and may suggest a compensatory gait pattern to optimize hip joint mechanics and mitigate hip joint degeneration. These results provide a preliminary understanding of the impact that agricultural occupations have on joint mechanics that may be associated with the increased prevalence of hip OA in the farming population.

The purpose was to compare vertical ground reaction forces and sagittal-plane energy dissipation patterns of the lower extremity during a single-limb jump-stabilization task between individuals with chronic ankle instability who did or did not attend rehabilitation after their initial ankle sprain. Cross-sectional. Research laboratory. Eight participants with chronic ankle instability who did and 12 participants who did not attend rehabilitation were enrolled. Normalized vertical ground reaction force data were used to calculate the average loading rate, time to peak force, and the peak force. Sagittal plane kinematics and joint moments at the ankle, knee, and hip, were used to calculate each joint's relative energy dissipation at 50, 100, 150, and 200 ms post-landing. Participants who attended rehabilitation had a slower average loading rate (P = 0.025) and smaller peak vertical ground reaction force (P = 0.025). The average relative energy dissipation at the knee was higher in those who attended rehabilitation at the 100 ms (P = 0.041), 150 ms (P = 0.046), and 200 ms (P = 0.042) time intervals. Attending rehabilitation after an ankle sprain may have a beneficial effect on jump-stabilization outcomes among individuals with chronic ankle instability. • Attending rehabilitation was associated with lesser vertical ground reaction forces. • Attending rehabilitation was associated with higher energy dissipation at the knee. • Ankle sprain rehabilitation has a beneficial effect in those with chronic ankle instability. [ABSTRACT FROM AUTHOR]

Increased vertical ground reaction force (vGRF) and dynamic knee valgus contribute to non-contact anterior cruciate ligament (ACL) injuries. We examined feedback’s influence during landing and transfer to a game-specific drill, measured by deceleration. Thirty-one female athletes performed 30 drop landings with augmented feedback and dual-task conditions, with a game-specific drill before and after. Differences were shown across time (baseline, feedback, post-feedback) and between conditions (with or without dual-task) in peak vGRF and knee to ankle ratio (K:A ratio). K:A ratio is the ratio of the frontal plane distance between the knees relative to the frontal plane distance between the ankles. This measure serves as a surrogate for knee valgus where a ratio closer to 1 indicates less knee valgus. There were reductions in peak vGRF (p < 0.05) and improvements in K:A ratio (p < 0.05) across time, improvements in K:A ratio across time and by condition (p < 0.05), and reduction in deceleration during landing in a game-specific drill (p < 0.05). Feedback may improve landing mechanics and transfer to a game-specific drill that can influence ACL injury in sport. [ABSTRACT FROM AUTHOR]

Introduction: Despite automation, manual load lifting is unavoidable in occupations like Industry, healthcare, defence, etc. Ergonomics studies on manual lifting conducted across the globe explored few aspects of biomechanical and physiological responses. It was hypothesised that, 'holistic ergonomics approach' involving simultaneous recording of these responses under single study, would elucidate potential injury causing factors more effectively and results could be validated by applying meta-analysis technique of literature review. Aim: To identify the potential determinants of manual lifting by applying multivariate statistics to existing data and correlate the results with determinants as per literature review applying meta-Analysis. Materials and Methods: Clustering and principal component analysis tools of factor analysis was applied on the data obtained under the pilot study (n=11) undertaken by same authors previously on manual lifting that assessed 26 dependent parameters simultaneously for load magnitudes (10 kg, 20 kg), lifting heights (floor-knuckle, knuckle-shoulder and floorshoulder) and lifting frequencies (1lift.min-1, 4lifts.min-1). Further, extensive scoping literature review on determinants of manual load lifting was done applying text mining tool of meta-analysis technique on R software platform using 921 pubmed abstracts published between 1991 and 2018. Results: Salient findings of factor analysis corroborated with that of scoping literature review. Accordingly, dependent variable 'Vertical Ground Reaction Force (VGRF)' and independent variable 'vertical height of lift' changed most significantly during manual lifting, showing significant positive correlations. Newton's third law of motion states that while bipedal standing/walking/running on floor, two forces (with three vector components) are acting upon a person: the force of gravity (downward force, equivalent to body weight) and the Ground Reaction Force (GRF, an equal upward force exerted by floor). However, while standing still at one place for lifting load, only the largest vector component of GRF, i.e., VGRF acts on the body (='body weight'+'load magnitude') through the vertical height of lift. Conclusion: It may be concluded that 'vertical height of lift' and VGRF are possible indicators of injury potential of manual lifting. However, studies on larger sample size and meta-analysis of relevant full papers instead of abstracts need to be done in future. [ABSTRACT FROM AUTHOR]

Introduction: Despite automation, manual load lifting is unavoidable in occupations like industry, healthcare, defence, etc. Ergonomics studies on manual lifting conducted across the globe explored few aspects of biomechanical and physiological responses. It was hypothesised that, ‘holistic ergonomics approach’ involving simultaneous recording of these responses under single study, would elucidate potential injury causing factors more effectively and results could be validated by applying meta-analysis technique of literature review. Aim: To identify the potential determinants of manual lifting by applying multivariate statistics to existing data and correlate the results with determinants as per literature review applying meta-analysis. Materials and Methods: Clustering and principal component analysis tools of factor analysis was applied on the data obtained under the pilot study (n=11) undertaken by same authors previously on manual lifting that assessed 26 dependent parameters simultaneously for load magnitudes (10 kg, 20 kg), lifting heights (floor-knuckle, knuckle-shoulder and floor-shoulder) and lifting frequencies (1 lift/min, 4 lift/min). Further, extensive scoping literature review on determinants of manual load lifting was done applying text mining tool of meta-analysis technique on R software platform using 921 Pubmed abstracts published between 1991 and 2018. Results: Salient findings of factor analysis corroborated with that of scoping literature review. Accordingly, dependent variable ‘Vertical Ground Reaction Force (VGRF)’ and independent variable ‘vertical height of lift’ changed most significantly during manual lifting, showing significant positive correlations. Newton’s third law of motion states that while bipedal standing/walking/running on floor, two forces (with three vector components) are acting upon a person: the force of gravity (downward force, equivalent to body weight) and the Ground Reaction Force (GRF, an equal upward force exerted by floor). However, while standing still at one place for lifting load, only the largest vector component of GRF, i.e., VGRF acts on the body (=‘body weight’+‘load magnitude’) through the vertical height of lift. Conclusion: It may be concluded that ‘vertical height of lift’ and VGRF are possible indicators of injury potential of manual lifting. However, studies on larger sample size and meta-analysis of relevant full papers instead of abstracts need to be done in future.

Mechanical factors have been implicated in the development of osteoarthritis after anterior cruciate ligament (ACL) reconstruction. This study tested for associations between ambulatory joint loading (total joint moment [TJM] and vertical ground reaction force [vGRF]) and changes in serum levels of cartilage oligomeric matrix protein (COMP) in response to a mechanical stimulus (30‐min walk) in individuals with ACL reconstruction. Twenty‐five subjects (mean age: 34.5 ± 9.8 years; 2.2 ± 0.2 years post‐surgery) with primary unilateral ACL reconstruction underwent gait analysis for assessment of peak vGRF and TJM first (TJM1) and second (TJM2) peaks. Serum COMP concentrations were measured by enzyme‐linked immunosorbent assay immediately before, 3.5 h, and 5.5 h after a 30‐min walk. Pearson correlation coefficients and backward stepwise multiple linear regression analysis, with adjustments for age, sex, body mass index, and between‐limb speed difference, assessed associations between changes in COMP and between‐limb differences in joint loading parameters. Greater TJM1 (R = 0.542, p = 0.005), TJM2 (R = 0.460, p = 0.021), and vGRF (R = 0.577, p = 0.003) in the ACL‐reconstructed limb as compared to the contralateral limb were associated with higher COMP values 3.5 h following the 30‐min walk. Change in COMP at 5.5 h became a significant predictor of the between‐limb difference in TJM1 and vGRF in multivariate analyses after accounting for the between‐limb speed difference. These results demonstrate that higher TJM and vGRF in the ACLR limb as compared to the contralateral limb are associated with higher relative COMP levels 3.5 and 5.5 h after a 30‐min walk. Future work should investigate the effect of therapies to alter joint loading on the biological response in individuals after ACL reconstruction. [ABSTRACT FROM AUTHOR]

Background: Anterior cruciate ligament (ACL) reconstruction is performed as the gold standard of care for patients with ACL rupture. ACL reconstruction provides successful clinical outcomes, however, it cannot repair faulty lower limb mechanics. The loading asymmetry between right and left limbs during movement is often used to measure the effectiveness of surgical interventions and the potential risks of re-injury. Objectives: This study aims to evaluate the interlimb asymmetry of vertical ground reaction in patients with ACL reconstruction. Methods: An online search was done in the following databases: Science Direct, Scopus, PubMed, and Google Scholar. Then, studies from 2000 to 2022 were extracted. Selected articles were screened in a 4-step process according to the inclusion and exclusion criteria. Two researchers evaluated the methodological quality of the articles with the modified Downs and Black checklist. Finally, studies were classified into 3 categories: low (< 40%), medium (40% - 69%), and high quality (= 70%). Results: Finally, 9 studies were selected for the systematic review. The average methodological quality was 68.33% (range 54% - 80%), which shows the medium quality of the studies. A total of 4 articles had high methodological quality and 5 articles had medium quality. The results of 8 studies reported asymmetric vertical ground reaction force after ACL reconstruction. Only 1 study reported no significant difference in the asymmetrical vertical ground reaction force. Conclusion: Patients undergoing ACL reconstruction have knee flexion limitation and decreased quadriceps muscle strength. Eventually, this mechanism of energy absorption by the active tissue decreases, and mainly the shock absorption by the passive tissue increases. As a result, this mechanism leads to ACL re-injury and tibiofemoral joint cartilage damage. [ABSTRACT FROM AUTHOR]

Parkinson’s disease (PD) causes gait impairments resulting in tremor, balance instabilities, increased fall risk, and disability. The current clinical diagnosis success rate is around 80%. Thus, automated classification of these impairments in gait with machine learning techniques can serve as an assessment tool for identification of PD. The primary focus of the study is to investigate anthropometric parameter-based models for classification of non-PD and PD subjects together with their severity. The proposed work performs the computation of clinically relevant features using Vertical Ground Reaction Force (VGRF) data from a total of 165 individuals’ database consisting 93 Parkinson’s and 72 healthy controls. All extracted features are tested for significance and redundancy among other gait characteristics. The optimal combination of features is selected using Recursive Feature Elimination technique with 10-fold cross validation for classification. In this study, wide range of machine learning techniques from different domains are used and their performance is evaluated based on accuracy, specificity, recall, precision and F1-score. Both gender and age-gender specific models outperformed the generalized model for PD as well as PD severity assessment. The highest prediction accuracy reported for age-gender specific models is 98.50% (non-PD and PD) using Support Vector Machine (SVM) classifier and 97.76% (non-PD and PD with severity scales) using k-Nearest Neighbor (kNN) and SVM. This study demonstrates integration of gait data with machine learning techniques as a potential biomarker for assessment of PD severity.

Running poses a high risk of developing running-related injuries (RRIs). The majority of RRIs are the result of an imbalance between cumulative musculoskeletal load and load capacity. A general estimate of whole-body biomechanical load can be inferred from ground reaction forces (GRFs). Unfortunately, GRFs typically can only be measured in a controlled environment, which hinders its wider applicability. The advent of portable sensors has enabled training machine-learned models that are able to monitor GRF characteristics associated with RRIs in a broader range of contexts. Our study presents and evaluates a machine-learning method to predict the contact time, active peak, impact peak, and impulse of the vertical GRF during running from three-dimensional sacral acceleration. The developed models for predicting active peak, impact peak, impulse, and contact time demonstrated a root-mean-squared error of 0.080 body weight (BW), 0.198 BW, 0.0073 BW ⋅ seconds, and 0.0101 seconds, respectively. Our proposed method outperformed a mean-prediction baseline and two established methods from the literature. The results indicate the potential utility of this approach as a valuable tool for monitoring selected factors related to running-related injuries., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Bogaert, Davis and Vanwanseele.)

Introduction: Ballet demands diverse physical prowess, requiring dancers to execute movements symmetrically, irrespective of their dominant leg. Ballet often includes jumps, including the basic open-leg leap-the grand jeté-which requires uniform performance regardless of the leg on which the leap is initiated. However, no studies have simultaneously evaluated the effects of dominant leg or movement variation on jump height, leg split angle, jump time, and floor reaction forces during take-off and landing, which are related to the feeling of floating in the grand jeté. This study aimed to determine whether the high-level, stable, and beautiful performance required of professional ballet dancers in the grand jeté is affected by the dominant leg. Methods: Twelve female ballet dancers, all right leg dominant, performed the grand jeté 3 times on each side, distinguishing between dominant (right leg landing) and non-dominant (left leg landing) grand jetés. Utilising 3D movement analysis, we measured jump height, jump time, maximum leg split angle, and maximum vertical ground reaction force (VGRF) during take-off and landing. Mean values and coefficients of variation were calculated for each analysed parameter. Paired sample t -tests were conducted to assess differences between left and right grand jetés, with a significance level set at P  < .05. Results: Statistically significant differences were observed in jump height ( P  = .028) and jump time ( P  = .001) when comparing the average of three trials for each side. However, no significant differences were found in maximum leg split angle ( P  = 0.643), maximum VGRF at take-off ( P  = .200), and maximum VGRF at landing ( P  = .109). In addition, no significant differences in coefficients of variation were identified for all items. Conclusion: Ballet dancers showed consistent performance on dominant and non-dominant legs but higher and longer jumps for grand jetés landing on the dominant leg, which may have affected overall performance., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Disruptions in knee biomechanics during walking following anterior cruciate ligament (ACL) injury have been suggested to lead to the development of premature knee osteoarthritis (OA) and to be potential markers of OA risk and targets for intervention. This study investigated if side‐to‐side differences in early stance peak vertical ground reaction force (vGRF) during walking 2 years after ACL reconstruction are associated with longer‐term (10 years post‐reconstruction) changes in patient‐reported outcomes. Twenty‐eight participants (mean age: 28.7 ± 6.4 years) with primary unilateral ACL reconstruction underwent gait analysis for assessment of peak vGRF and completed Knee Injury and Osteoarthritis Outcome Score (KOOS) and International Knee Documentation Committee (IKDC) surveys at 2 years post‐surgery (2.2 ± 0.3 years) and completed surveys at follow‐up 10 years post‐surgery (10.5 ± 0.9 years). Associations between changes (10–2 years) in patient‐reported outcomes and between limb‐differences in vGRF were assessed with Pearson or Spearman's ρ correlation coefficients and exploratory backwards elimination multiple linear regression analyses. Differences in vGRF between symptomatic progressors and non‐progressors were also assessed. The side‐to‐side difference in vGRF was related to the variability in longer‐term changes in patient‐reported outcome metrics and distinguished symptomatic progressors from non‐progressors. Participants with higher vGRF in the reconstructed (ACLR) limb versus the contralateral limb had worsening of IKDC (R = −0.391, p = 0.040), KOOS pain (ρ = −0.396, p = 0.037), KOOS symptoms (ρ = −0.572, p = 0.001), and KOOS quality of life (R = −0.458, p = 0.014) scores at follow‐up. Symptomatic progressors had greater vGRF in the ACLR limb as compared to the contralateral limb at baseline than non‐progressors (p = 0.023). These data highlight associations between a simple‐to‐measure gait metric and the development of long‐term clinical symptoms after an ACL injury. [ABSTRACT FROM AUTHOR]

Step marching is the rhythmic movement of the lower extremities which combines hip, knee, and ankle joint movements. The rhythmic movement requires standing on one leg involving the voluntary action of moving the center of mass over the upcoming stance leg. Step marching has been used for body balance training methods among the elderly because it is relatively simple and safe amid practice. The amount and direction of ground reaction force during step marching affecting standing balance has not been thoroughly explored. The objective of this study then, was to compare the effect of two different types of marching (comfortable marching and marching incorporating 90-degree hip and knee flexion) walking on vertical ground reaction force (VGRF). Ten healthy subjects (5 males and 5 females) aged 27.6±3.9 years (mean ± SD) participated. Each subject underwent testing with step marching under the two conditions for 2 minutes, and 10 rounds of preferred-speed walking on 2 force platforms. VGRF was measured with Smart analyzer®, BTS Bioengineering software. Paired t-test revealed significant differences in VGRF among the two conditions of step marching and walking whereby step marching incorporating 90-degree hip and knee flexion showed the highest mean value (11.23±0.5 N/kg), whereas the walking condition showed the lowest (10.09±0.6 N/kg). Two types of step marching provide slightly higher VGRF than walking. Hence, they could be applied as a challenge amid balance training among the elderly in case of weight bearing on the bones and joints is required for progressive strength training of the lower limbs. [ABSTRACT FROM AUTHOR]

Vertical Ground Reaction Force (VGRF) is a force obtained during gait cycle beneath the feet and is used to screen the severity of Parkinson's disease (PD) patient's in clinical environment. This article investigates the VGRF signals (left and right) semblance nature among PD patients and control subjects as a function of time and possibility of reconstructing dual tasking VGRF signal from normal walking VGRF signals using radial basis function (RBF) based artificial intelligence (AI). There are many traditional methods for gait analysis and these methods are purely subjective and none made semblance analysis of same subjects gait pattern in different tasking. In order to overcome the difficulties faced by PD patients, RBF based AI is proposed in this research to reconstruct the dual tasking VGRF signal from normal walking VGRF signal. 93 PD patients with mean age: 66.3 years (63% men), and 73 healthy controls with mean age: 66.3 years (55% men) datasets are used in this work. Proposed RBF network is trained on VGRF signals obtained in normal walking and dual tasking conditions from control. The network was trained with 60% of VGRF data and tested on remaining 40% data. Semblance analysis results are encouraging, and it shows that semblance is high in PD patients than control subjects during dual tasking (P < 0.05). In order to test the findings of semblance analysis, we explicitly reconstruct VGRF signal of clinically significant dual tasking from VGRF signal of normal walking by the proposed RBF method. Findings proved that the proposed RBF network can reconstruct dual tasking VGRF signal of PD patients from their normal walking VGRF signal with high cross correlation (P < 0.0001). These findings pave way for a new adjunct tool to diagnose the gait dynamics of PD patients using the proposed reconstruction method. [ABSTRACT FROM AUTHOR]

Context: Gait biomechanics are linked to biochemical changes that contribute to the development of posttraumatic knee osteoarthritis in individuals with anterior cruciate ligament reconstruction (ACLR). It remains unknown if modifying peak loading during gait using real-time biofeedback will result in acute biochemical changes related to cartilage metabolism. Objective: To determine if acutely manipulating peak vertical ground reaction force (vGRF) during gait influences acute changes in serum cartilage oligomeric matrix protein concentration (sCOMP) among individuals with ACLR. Design: Crossover study. Patients or Other Participants: Thirty individuals with unilateral ACLR participated (70% female, age = 20.43 ± 2.91 years old, body mass index = 24.42 ± 4.25, months post-ACLR = 47.83 ± 26.97). Additionally, we identified a subgroup of participants who demonstrated an increase in sCOMP after the control or natural loading condition (sCOMPCHANGE > 0 ng/mL, n = 22, 70% female, age = 20.32 ± 3.00 years old, body mass index = 24.73 ± 4.33, months post-ACLR = 47.27 ± 29.32). Main Outcome Measure(s): Serum was collected both prior to and immediately after each condition to determine SCOMPCHANGE. Intervention(s): All participants attended 4 sessions that involved 20 minutes of walking on a force-measuring treadmill consisting of a control condition (natural loading) followed by random ordering of 3 loading conditions with real-time biofeedback: (1) symmetric vGRF between limbs, (2) a 5% increase in vGRF (high loading) and (3) a 5% decrease in vGRF (low loading). A general linear mixed model was used to determine differences in sCOMPchange between altered loading conditions and the control group in the entire cohort and the subgroup. Results: The sCOMPCHANGE was not different across loading conditions for the entire cohort (F3.29 = 1.34, P = .282). Within the subgroup, sCOMPCHANGE was less during high loading (1.95 ± 24.22 ng/mL, t21 = -3.53, P = .005) and symmetric loading (9.93 ± 21.45 ng/mL, t21 =-2.86, P= .025) compared with the control condition (25.79 ± 21.40 ng/mL). Conclusions: Increasing peak vGRF during gait decreased sCOMP in individuals with ACLR who naturally demonstrated an increase in sCOMP after 20 minutes of walking. [ABSTRACT FROM AUTHOR]

Purpose: Small knee flexion angles are associated with increased non-contact anterior cruciate ligament (ACL) injury risks. The purpose of this study was to provide insights into how ankle plantar flexion angles influenced knee flexion angles at initial contact during single-leg drop landings. Method: Thirteen male recreational basketball players performed single-leg drop landings from a 30-cm high platform using three randomized foot-landing positions (natural, fore-foot, and flat-foot). A motion capture system and a force plate were used to measure lower extremity kinematics and vertical ground reaction force (VGRF). A one-way repeated measures Analysis of Variance and the Friedman test were conducted (α = .05). Results: Foot-landing position had a significant effect on knee flexion angles at initial contact (p < .001). As compared to flat-foot landings (18° (SD 5°), significantly smaller knee flexion angles were found for natural (mean 12° (SD 5°), p = .004) and fore-foot landings (mean 12° (SD 3°), p = .001). There was however significantly smaller knee flexion range of motion in flat-foot landings (mean 42° (SD 3°), as compared to natural landings (mean 48° (SD 4°) (p < .001). Flat-foot landings also resulted in a 1.4 times higher maximum VGRF than fore-foot landings (p < .001) and 1.3 times higher than natural landings (p = .005). Conclusions: Natural and fore-foot landings are better for force absorption but are associated with smaller knee flexion angles at initial contact. These findings have important implications for non-contact ACL injuries. [ABSTRACT FROM AUTHOR]

Supplemental digital content is available in the text. Purpose: To compare gait biomechanics throughout stance phase 6 and 12 months after unilateral anterior cruciate ligament reconstruction (ACLR) between ACLR and contralateral limbs and compared with controls. Methods: Vertical ground reaction force (vGRF), knee flexion angle (KFA), and internal knee extension moment (KEM) were collected bilaterally 6 and 12 months post-ACLR in 30 individuals (50% female, 22 ± 3 yr, body mass index = 23.8 ± 2.2 kg·m−2) and at a single time point in 30 matched uninjured controls (50% female, 22 ± 4 yr, body mass index = 23.6 ± 2.1 kg·m−2). Functional analyses of variance were used to evaluate the effects of limb (ACLR, contralateral, and control) and time (6 and 12 months) on biomechanical outcomes throughout stance. Results: Compared with the uninjured controls, the ACLR group demonstrated bilaterally lesser vGRF (ACLR, 9% body weight [BW]; contralateral, 4%BW) during early stance and greater vGRF during midstance (ACLR, 5%BW; contralateral, 4%BW) 6 months post-ACLR. Compared to the uninjured controls, the ACLR group demonstrated bilaterally lesser vGRF (ACLR, 10%BW; contralateral, 8%BW) during early stance and greater vGRF during midstance (ACLR, 5%BW; contralateral, 5%BW) 12 months post-ACLR. Compared with controls, the ACLR limb demonstrated lesser KFA during early stance at 6 (2.3°) and 12 months post-ACLR (2.0°), and the contralateral limb demonstrated lesser KFA during early stance at 12 months post-ACLR (2.8°). Compared with controls, the ACLR limb demonstrated lesser KEM during early stance at both 6 months (0.011BW × height) and 12 months (0.007BW × height) post-ACLR, and the contralateral limb demonstrated lesser KEM during early stance only at 12 months (0.006BW × height). Conclusions: Walking biomechanics are altered bilaterally after ACLR. During the first 12 months post-ACLR, both the ACLR and contralateral limbs demonstrate biomechanical differences compared with control limbs. Differences between the contralateral and control limbs increase from 6 to 12 months post-ACLR. [ABSTRACT FROM AUTHOR]

The notion of limb dominance has been commonly used in the upper extremity, yet the two lower extremities are often treated as equal for analytical purposes. Attempts to determine the effects of limb laterality on gait have produced conflicting results. The purpose of this study was to determine if limb dominance affects the vertical ground reaction force and center of pressure (COP) during able-bodied gait. The Parotec system (Paromed GmbH, Germany) was used to collect plantar foot pressure data. Fifteen subjects volunteered to participate in this study. The coefficient of variation of the COP displacement in the mediolateral direction and the variability of peak force beneath the lateral forefoot in the nondominant foot were significant greater than in the dominant foot. Moreover, COP velocity in the anterior-posterior direction during the terminal stance phase showed greater value in the dominant foot. Our study provides support for limb laterality by showing limb dominance affected the vertical ground reaction force and center of pressure during walking gait. This finding suggests it is an important issue in movement science for clinicians and would assist in improving sports performance and rehabilitation program. [ABSTRACT FROM AUTHOR]

Objective: comparing the effects of gait at different speeds on spatiotemporal and kinetic parameters can result in better perception of gait pattern differences between young and older adults. Thus the purpose of this study was to compare the percentage of gait stance and swing phases and vertical ground reaction force between young and older adults during walking with different speeds. Methods: 20 young and 20 older adult men with mean age of 25.73 and 66.54 years old respectively, were selected via nonrandom sampling. the percentage of stance and swing phases gait cycle and vertical ground reaction force (VGRF) in stance subphases including weight acceptance, midstance and push off during walking at a 10-meter pathway with slow, medium, and fast speeds, were measured by Kistler Force plate. The data were analyzed running ANOVA with repeated measures and Bonferronii tests. Results: The results showed that in both age groups, with increasing walking speed, the percent of stance phase would decrease and the percent of swing phase was increased (P0.05). Also, at slow speed, there was no significant difference in vertical ground reaction force between two age groups (p>0.05). But, there were significant differences in the VGRFs of weight acceptance and push off phases at medium and fast speeds, between two groups (p

Context: Slower habitual walking speed and aberrant gait biomechanics are linked to clinically significant knee-related symptoms and articular cartilage composition changes linked to posttraumatic osteoarthritis (PTOA) following anterior cruciate ligament reconstruction (ACLR)., Objective: To determine specific gait biomechanical variables that can accurately identify individuals with clinically significant knee-related symptoms post-ACLR, and the corresponding threshold values, sensitivity, specificity, and odds ratios for each biomechanical variable., Design: Cross-sectional analysis., Setting: Laboratory., Patients or Other Participants: Seventy-one individuals (n=38 female; age=21±4 years; height=1.76±0.11 m; mass=75.38±13.79 kg) who were 6 months post-primary unilateral ACLR (6.2±0.4 months)., Main Outcome Measures: 3D motion capture of 5 overground walking trials was used to calculate discrete gait biomechanical variables of interest during stance phase (1st and 2nd peak vertical ground reaction force [vGRF]; midstance minimum vGRF; peak internal knee abduction and extension moments; and peak knee flexion angle), along with habitual walking speed. Knee Injury and Osteoarthritis Outcome Scores (KOOS) was used to dichotomize patients as symptomatic (n=51) or asymptomatic (n=20) using the Englund et al. 2003 KOOS guidelines for defining clinically significant knee-related symptoms. Separate receiver operating characteristic (ROC) curves and respective areas under the curve (AUC) were used to evaluate the capability of each biomechanical variable of interest for identifying individuals with clinically significant knee-related symptoms., Results: Habitual walking speed (AUC=0.66), vGRF at midstance (AUC=0.69), and 2nd peak vGRF (AUC=0.76), demonstrated low-to-moderate accuracy for identifying individuals with clinically significant knee-related symptoms. Individuals who exhibited habitual walking speeds ≤1.27 m/s, midstance vGRF ≥0.82 BW, and 2nd peak vGRF ≤1.11 BW, demonstrated 3.13, 6.36, and 9.57 times higher odds of experiencing clinically significant knee-related symptoms, respectively., Conclusions: Critical thresholds for gait variables may be utilized to identify individuals with increased odds of clinically significant knee-related symptoms and potential targets for future interventions.

Gait analysis provides valuable motor deficit quantitative information about Parkinson's disease patients. Detection of gait abnormalities is key to preserving healthy mobility. The goal of this paper is to propose a novel gait analysis and continuous wavelet transform-based approach to diagnose idiopathic Parkinson's disease. First, we eliminate the noise resulting from orientation changes of test subjects by filtering the continuous wavelet transform output below 0.8 Hz. Next, we analyze the complex plot output above 0.8 Hz, which takes an ellipse, and calculate the area using 95 % confidence level. We found out that this ellipse area, along with the mean continuous wavelet transform output value, and the peak of the temporal signal are excellent features for classification. Experiments using Artificial Neural Networks on the Physionet database produced an accuracy of 97.6 % . Furthermore, we have shown an association between the Parkinson's disease severity stage and the ellipse complex plot area with a 97.8% overall accuracy. Based on the results, we could effectively recognize the gait patterns and distinguish apart Parkinson's disease patients with varying severity from healthy individuals. [ABSTRACT FROM AUTHOR]

Objective: To compare sagittal walking gait biomechanics between participants with knee osteoarthritis (KOA) who increased quadriceps strength following a lower-extremity strengthening intervention (responders) and those who did not increase strength following the same strengthening protocol (non-responders) both at baseline and following the lower extremity strengthening protocol.Design: Fifty-three participants with radiographic KOA (47% female, 62.3 ± 7.1 years, BMI = 28.5 ± 3.9 kg/m2) were enrolled in 10 sessions of lower extremity strengthening over a 28-day period. Maximum isometric quadriceps strength and walking gait biomechanics were collected on the involved limb at baseline and 4-weeks following the strengthening intervention. Responders were classified as individuals who increased quadriceps strength greater than the upper limit of the 95% confidence interval (CI) for the minimal detectable change (MDC) in quadriceps strength (29 Nm) determined in a previous study. 2 × 2 functional analyses of variance were used to evaluate the effects of group (responders and non-responders) and time (baseline and 4-weeks) on time-normalized waveforms for knee flexion angle (KFA), vertical ground reaction force (vGRF), and internal knee extension moment (KEM).Results: A significant group x time interaction for KFA demonstrated greater KFA in the first half of stance at baseline and greater knee extension in the second half of stance at 4-weeks in responders compared to non-responders. There was no significant group x time interaction for vGRF or internal KEM.Conclusions: Quadriceps strengthening may be used to stimulate small changes in KFA in individuals with KOA. [ABSTRACT FROM AUTHOR]

Kinetic models of human motion rely on boundary conditions which are defined by the interaction of the body with its environment. In the simplest case, this interaction is limited to the foot contact with the ground and is given by the so called ground reaction force (GRF). A major challenge in the reconstruction of GRF from kinematic data is the double support phase, referring to the state with multiple ground contacts. In this case, the GRF prediction is not well defined. In this work we present an approach to reconstruct and distribute vertical GRF (vGRF) to each foot separately, using only kinematic data. We propose the biomechanically inspired force shadow method (FSM) to obtain a unique solution for any contact phase, including double support, of an arbitrary motion. We create a kinematic based function, model an anatomical foot shape and mimic the effect of hip muscle activations. We compare our estimations with the measurements of a Zebris pressure plate and obtain correlations of 0.39≤r≤0.94 for double support motions and 0.83≤r≤0.87 for a walking motion. The presented data is based on inertial human motion capture, showing the applicability for scenarios outside the laboratory. The proposed approach has low computational complexity and allows for online vGRF estimation.

Although numerous studies have analyzed the relationship between manual material handling (MMH) and the forces acting on the lumbar spine, the difference in the MMH between experts and novices through the analysis of measured data has not been well studied. The purpose of this paper was to analyze the difference in the MMH positions between ten skilled experts working at a freight transport company (Group 1) and five unskilled novices without any experience (Group 2) during asymmetric lifting. All the human subjects performed asymmetric lifting experiments with closed eyes; the experiments involved moving loads (6 and 18 kg) to the left side. Time series data of the vertical ground reaction force were measured, using a Wii Balance Board, and then, the center-of-pressure (CoP) trajectories were calculated. The balance board was used for the measurement, because it was reliable, inexpensive, and portable and provided good repeatability even on rough surfaces, and all the information pertaining to the load and worker under various conditions was captured without any omissions. Under the 18 kg load condition, the CoP positions for Group 2 were located on the same side during left asymmetric lifting; however, those for Group 1 were located on the opposite side during left asymmetric lifting ($P