Biology subjects

Rashedi, E.

Publications and source records attributed to Rashedi, E..

5 recordsLinked to original sources

bioRxiv2025

Influence of an Exoskeleton on Inter and Intra Task Cycle Variations in Muscle Demands During Intermittent Bending

Temporal effects of exoskeletons can be effectively understood through assessments that incorporate realistic conditions, while using accurate instrumentation in controlled settings. We tracked variations in back and leg muscle activity as twelve participants performed intermittent task cycles involving symmetric and asymmetric bending with a Back-Support Industrial Exoskeleton (BSIE). Comparative assessments were conducted within and across multiple task cycles with fatigue progression. BSIE offered benefits in reducing back demands (9-22%) during bending portion. However, the device increased activity during retraction (80-11%). During sustained bending, asymmetry reduced BSIE benefits in right back region (13% vs. 22% in symmetric postures) and larger benefits of 11-15% were seen in back muscles at low vs. medium fatigue levels. Recommendations indicate having a ratio of dynamic: sustained activities < 30:1 to ensure benefits of BSIEs in back. Overall, outcomes can be helpful for understanding temporal benefits of BSIEs for trunk bending tasks.

bioengineering↗

bioRxiv2022

Lumbar time-varying muscle synergies in trunk flexion and bending movements at different velocities

PurposeAlthough the extent of which the central nervous system uses muscle synergies as a movement control strategy remains an open area of research, it is widely agreed that synergies facilitate the robustness of the neuromuscular system, allowing for effective postural control and flexible movement. This work aimed to investigate the muscle activation patterns of the trunk and time-varying muscle synergies using a novel 18-muscle 3-DOF, 3-D musculoskeletal model of the lumbar spine developed by the authors. Methods24 different biaxial trunk movements were simulated via the optimization of kinetic and kinematic measures towards obtaining the corresponding muscle activation patterns at 3 different velocities. These patterns were subsequently used to extract the principal (phasic and tonic) spatio-temporal synergies associated with the observed muscle activation patterns in the range of simulated movements. ResultsFour dominant synergies were able to explain a considerable percent (about 75%) of the variance of the simulated muscle activities. The extracted synergies were spatially tuned in the direction of the main simulated movements (flexion/extension and right/left lateral bending). The temporal patterns demonstrated gradual monotonic shifts in tonic synergies and biphasic modulatory components in phasic synergies with spatially tuned time-delays. The increase in velocity resulted in an elevated amplitude coefficient and accelerated activation of phasic synergies. ConclusionOur results suggest the plausibility of a time-varying synergies strategy in the dynamic control of trunk movement. Further work is needed to explore leveraging these concepts in various applications, such as rehabilitation and musculoskeletal biomechanics, towards providing more insight into the mechanisms underlying trunk stability and flexibility.

bioengineering↗

bioRxiv2022

Lumbar time-varying muscle synergies in trunk flexion and bending movements at different velocities

PurposeAlthough the extent of which the central nervous system uses muscle synergies as a movement control strategy remains an open area of research, it is widely agreed that synergies facilitate the robustness of the neuromuscular system, allowing for effective postural control and flexible movement. This work aimed to investigate the muscle activation patterns of the trunk and time-varying muscle synergies using a novel 18-muscle 3-DOF, 3-D musculoskeletal model of the lumbar spine developed by the authors. Methods24 different biaxial trunk movements were simulated via the optimization of kinetic and kinematic measures towards obtaining the corresponding muscle activation patterns at 3 different velocities. These patterns were subsequently used to extract the principal (phasic and tonic) spatiotemporal synergies associated with the observed muscle activation patterns in the range of simulated movements. ResultsFour dominant synergies were able to explain a considerable percent (about 75%) of the variance of the simulated muscle activities. The extracted synergies were spatially tuned in the direction of the main simulated movements (flexion/extension and right/left lateral bending). The temporal patterns demonstrated gradual monotonic shifts in tonic synergies and biphasic modulatory components in phasic synergies with spatially tuned time-delays. The increase in velocity resulted in an elevated amplitude coefficient and accelerated activation of phasic synergies. ConclusionOur results suggest the plausibility of a time-varying synergies strategy in the dynamic control of trunk movement. Further work is needed to explore leveraging these concepts in various applications, such as rehabilitation and musculoskeletal biomechanics, towards providing more insight into the mechanisms underlying trunk stability and flexibility.

bioengineering↗

bioRxiv2019

Using a Motion Sensor to Categorize Low Back Pain Patients: A Machine Learning Approach

Low back pain (LBP) remains a critical health issue impacting literally millions of people worldwide. Currently, clinical practitioners rely on subjective measures such as the STarT Back Screening Tool to categorize LBP patients, which then informs specific treatment regimens. This study sought to develop a machine learning model to classify LBP patients into different groups according to kinematic data. Specifically, an inertial measurement unit (IMU) was attached to each patients chest while he performed trunk flexion/extension motions at a self-selected pace. Machine learning algorithms such as support vector machine (SVM) and multi-layer perceptron (MLP) were implemented to evaluate the efficiency of the models. The results showed that the kinematic data we obtained could be used to categorize the patients into two groups: high vs. low-medium risk. We achieved accuracy levels of ~75% and 60% for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, we determined that time-scaled IMU signal resulted in the highest accuracy. Our findings support the use of body-motion measures in developing prognosis tools for healthcare applications. Our results could help overcome the need for objective clinic-based diagnosis approaches, which in turn would lead to assigning better treatment approaches and rehabilitation services for LBP sufferers.

bioengineering↗

bioRxiv2019

A Data-Driven Model to Identify Fatigue Level Based on the Motion Data from a Smartphone

The fatigue due to repetitive and physically challenging jobs may result in workers poor performance and Work-related Musculoskeletal Disorder (WMSD). Thus, it is imperative to frequently monitor fatigue and take necessary recovery actions. Our purpose was to develop a methodology to objectively classify subjects fatigue level in the workplace utilizing the motion sensors embedded in the smartphones. An experiment consisting of twenty-four participants (12 M, 12 F) with a smartphone attached to their right shank was conducted using a fatiguing exercise (squatting), targeted mainly the lower extremity musculature. After each set of an exercise (2-min squatting), participants were asked about their ratings of perceived exertion (RPE), then a reference gait data were collected during a straight walk of 20-32 steps. This process was continued until they reported strong fatigue ([≥]17). Using the RPE to label the gait data, we have developed machine learning algorithms (i.e., binary and multi-class SVM models) to classify the individuals gait into two (no-vs. strong-fatigue) and four levels (no-, low-, medium-, and strong-fatigue). The models reached the accuracies of 91% and 61% for two and four-level classification, respectively. The outcomes of this study may facilitate the implementation of a proactive approach in continuous monitoring of operators fatigue level, which may subsequently increase the workers performance and reduce the risk of WMSDs.

bioengineering↗