Age-Related Modifications of Muscle Synergies and Their Temporal Activations for Overground Walking.

Healthy ageing modifies neuromuscular control of human overground walking. Previous studies found that ageing changes gait biomechanics, but whether there is concurrent ageing-related modulation of neuromuscular control remains unclear. We analyzed gait kinematics and electromyographic signals (EMGs; 14 lower-limb and trunk muscles) collected at three speeds during overground walking in 11 healthy young adults (mean age of 23.4 years) and 11 healthy elderlies (67.2 years). Neuromuscular control was characterized by extracting muscle synergies from EMGs and the synergies of both groups were k -means-clustered. The synergies of the two groups were grossly similar, but we observed numerous cluster- and muscle-specific differences between the age groups. At the population level, some hip-motion-related synergy clusters were more frequently identified in elderlies while others, more frequent in young adults. Such differences in synergy prevalence between the age groups are consistent with the finding that elderlies had a larger hip flexion range. For the synergies shared between both groups, the elderlies had higher inter-subject variability of the temporal activations than young adults. To further explore what synergy characteristics may be related to this inter-subject variability, we found that the inter-subject variance of temporal activations correlated negatively with the sparseness of the synergies in elderlies but not young adults during slow walking. Overall, our results suggest that as humans age, not only are the muscle synergies for walking fine-tuned in structure, but their temporal activation patterns are also more heterogeneous across individuals, possibly reflecting individual differences in prior sensorimotor experience or ageing-related changes in limb neuro-musculoskeletal properties.

Gait difference between children aged 9 to 12 with and without potential depressive mood.

BACKGROUND: It has been reported that depression has an impact on both temporal spatial parameters and walking kinematics in adults. Given the difference in the walking biomechanics between adults and children, this study aimed to compare the gait difference in children aged 9-12 with and without potential depressive mood (PDM). METHODS: 49 children were recruited from local primary schools. We measured participants' depression level using Depression Anxiety Stress Scale (DASS) and classified them into control (i.e., DASS depression subscale score = 0.6 ± 1.4; n = 23) or PDM group (i.e., DASS depression subscale score = 21.3 ± 5.3; n = 26). Video gait analysis was employed to assess temporal spatial parameters and sagittal plane kinematics during self-paced overground walking. Independent t tests or Mann-Whitney tests were used to compare the gait parameters between the two groups. RESULTS: Participants exhibited similar gait speed, vertical oscillation of the centre of mass, stance time, swing time, step length, upper and lower limb kinematics between the two groups (p > 0.05). However, children with PDM displayed a greater head flexion than controls (p = 0.026; Cohen's d = 0.66; moderate effect). SIGNIFICANCE: Children with PDM may present a more slumped posture during walking when compared with their counterparts. This kinematics difference can potentially be used as a biomechanical marker for detection of mood problems in this cohort.

Sensor-Based Gait Retraining Lowers Knee Adduction Moment and Improves Symptoms in Patients with Knee Osteoarthritis: A Randomized Controlled Trial.

The present study compared the effect between walking exercise and a newly developed sensor-based gait retraining on the peaks of knee adduction moment (KAM), knee adduction angular impulse (KAAI), knee flexion moment (KFM) and symptoms and functions in patients with early medial knee osteoarthritis (OA). Eligible participants (n = 71) with early medial knee OA (Kellgren-Lawrence grade I or II) were randomized to either walking exercise or gait retraining group. Knee loading-related parameters including KAM, KAAI and KFM were measured before and after 6-week gait retraining. We also examined clinical outcomes including visual analog pain scale (VASP) and Knee Injury and Osteoarthritis Outcome Score (KOOS) at each time point. After gait retraining, KAM1 and VASP were significantly reduced (both Ps < 0.001) and KOOS significantly improved (p = 0.004) in the gait retraining group, while these parameters remained similar in the walking exercise group (Ps ≥ 0.448). However, KAM2, KAAI and KFM did not change in both groups across time (Ps ≥ 0.120). A six-week sensor-based gait retraining, compared with walking exercise, was an effective intervention to lower medial knee loading, relieve knee pain and improve symptoms for patients with early medial knee OA.

Biomechanical effects following footstrike pattern modification using wearable sensors.

OBJECTIVES: This study sought to examine the biomechanical effects of an in-field sensor-based gait retraining program targeting footstrike pattern modification during level running, uphill running and downhill running. DESIGN: Quasi-experimental design. METHODS: Sixteen habitual rearfoot strikers were recruited. All participants underwent a baseline evaluation on an instrumented treadmill at their preferred running speeds on three slope settings. Participants were then instructed to modify their footstrike pattern from rearfoot to non-rearfoot strike with real-time audio biofeedback in an 8-session in-field gait retraining program. A reassessment was conducted to evaluate the post-training biomechanical effects. Footstrike pattern, footstrike angle, vertical instantaneous loading rate (VILR), stride length, cadence, and knee flexion angle at initial contact were measured and compared. RESULTS: No significant interaction was found between training and slope conditions for all tested variables. Significant main effects were observed for gait retraining (p-values≤0.02) and slopes (p-values≤0.01). After gait retraining, 75% of the participants modified their footstrike pattern during level running, but effects of footstrike pattern modification were inconsistent between slopes. During level running, participants exhibited a smaller footstrike angle (p≤0.01), reduced VILR (p≤0.01) and a larger knee flexion angle (p=0.01). Similar effects were found during uphill running, together with a shorter stride length (p=0.01) and an increased cadence (p≤0.01). However, during downhill running, no significant change in VILR was found (p=0.16), despite differences found in other biomechanical measurements (p-values=0.02-0.05). CONCLUSION: An 8-session in-field gait retraining program was effective in modifying footstrike pattern among runners, but discrepancies in VILR, stride length and cadence were found between slope conditions.

Classification of runners' performance levels with concurrent prediction of biomechanical parameters using data from inertial measurement units.

Identification of runner's performance level is critical to coaching, performance enhancement and injury prevention. Machine learning techniques have been developed to measure biomechanical parameters with body-worn inertial measurement unit (IMU) sensors. However, a robust method to classify runners is still unavailable. In this paper, we developed two models to classify running performance and predict biomechanical parameters of 30 subjects. We named the models RunNet-CNN and RunNet-MLP based on their architectures: convolutional neural network (CNN) and multilayer perceptron (MLP), respectively. In addition, we examined two validation approaches, subject-wise (leave-one-subject-out) and record-wise. RunNet-MLP classified runner's performance levels with an overall accuracy of 97.1%. Our results also showed that RunNet-CNN outperformed RunNet-MLP and gradient boosting decision tree in predicting biomechanical parameters. RunNet-CNN showed good agreement (R2 > 0.9) with the ground-truth reference on biomechanical parameters. The prediction accuracy for the record-wise method was better than the subject-wise method regardless of biomechanical parameters or models. Our findings showed the viability of using IMUs to produce reliable prediction of runners' performance levels and biomechanical parameters.

Real-Time Estimation of Knee Adduction Moment for Gait Retraining in Patients With Knee Osteoarthritis.

Previous clinical studies have reported that gait retraining is an effective non-invasive intervention for patients with medial compartment knee osteoarthritis. These gait retraining programs often target a reduction in the knee adduction moment (KAM), which is a commonly used surrogate marker to estimate the loading in the medial compartment of the tibiofemoral joint. However, conventional evaluation of KAM requires complex and costly equipment for motion capture and force measurement. Gait retraining programs, therefore, are usually confined to a laboratory environment. In this study, machine learning techniques were applied to estimate KAM during walking with data collected from two low-cost wearable sensors. When compared to the traditional laboratory-based measurement, our mobile solution using artificial neural network (ANN) and XGBoost achieved an excellent agreement with R2 of 0.956 and 0.947 respectively. With the implementation of a real-time audio feedback system, the present algorithm may provide a viable solution for gait retraining outside laboratory. Clinical treatment strategies can be developed using the continuous feedback provided by our system.