How Effective Are Forecasting Models in Predicting Effects of Exoskeletons on Fatigue Progression? DOI Creative Commons
Pranav Madhav Kuber, Abhineet Rajendra Kulkarni, Ehsan Rashedi

и другие.

Sensors, Год журнала: 2024, Номер 24(18), С. 5971 - 5971

Опубликована: Сен. 14, 2024

Forecasting can be utilized to predict future trends in physiological demands, which beneficial for developing effective interventions. This study implemented forecasting models fatigue level progression when performing exoskeleton (EXO)-assisted tasks. Specifically, perceived and muscle activity data were from nine recruited participants who performed 45° trunk flexion tasks intermittently with without assistance until they reached medium-high exertion the low-back region. Two algorithms, Autoregressive Integrated Moving Average (ARIMA) Facebook Prophet, using levels alone, external features of activity. Findings showed that univariate better Prophet model having lowest mean (SD) root squared error (RMSE) across 0.62 (0.24) 0.67 (0.29) EXO-assisted tasks, respectively. Temporal effects BSIE on delaying then evaluated by back up 20 trials. The slope trials was ~48–52% higher vs. assistance. Median benefits 54% 43% observed ARIMA (with features) demonstrates some potential applications workforce health monitoring, intervention assessment, injury prevention.

Язык: Английский

Muscle Fatigue Identification and Prediction in Motion using Wearable Device with Power and Torque-based Features DOI Creative Commons

LI Zhangding,

Xi Wang, Qiao Li

и другие.

Wearable electronics., Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Research on Control Strategy Technology of Upper Limb Exoskeleton Robots: Review DOI Creative Commons
Liying Song, Jin Chen, Hui Cui

и другие.

Machines, Год журнала: 2025, Номер 13(3), С. 207 - 207

Опубликована: Март 3, 2025

Upper limb exoskeleton robots, as highly integrated wearable devices with the human body structure, hold significant potential in rehabilitation medicine, performance enhancement, and occupational safety health. The rapid advancement of high-precision, low-noise acquisition intelligent motion intention recognition algorithms has led to a growing demand for more rational reliable control strategies. Consequently, systems strategies robots are becoming increasingly prominent. This paper innovatively takes hierarchical system entry point comprehensively compares current technologies upper analyzing their applicable scenarios limitations. research still faces challenges such insufficient real-time limited individualized adaptation capabilities. It is recognized that no single traditional algorithm can fully meet interaction requirements between exoskeletons body. integration many advanced artificial intelligence into remains restricted. Meanwhile, quality closely related perception decision-making system. Therefore, combination multi-source information fusion cooperative methods expected enhance efficient human–robot personalized rehabilitation. Transfer learning edge computing enable lightweight deployment, ultimately improving work efficiency life end-users.

Язык: Английский

Процитировано

0
Investigating Spatiotemporal Effects of Back-Support Exoskeletons Using Unloaded Cyclic Trunk Flexion–Extension Task Paradigm DOI Creative Commons
Pranav Madhav Kuber, Ehsan Rashedi

Applied Sciences, Год журнала: 2024, Номер 14(13), С. 5564 - 5564

Опубликована: Июнь 26, 2024

Back-Support Industrial Exoskeletons (BSIEs) are designed to reduce muscle effort during repetitive tasks that involve trunk bending. We recruited twelve participants perform 30 cycles of 45° bending with/without the assistance BSIEs and postural asymmetry, first without any back fatigue, then at medium–high level perceived fatigue. To study benefits BSIEs, effects being in a fatigued state were assessed by comparing demands, kinematics, stability measures bending, retraction, their transition portions per cycle across conditions. Overall, caused minimal decrease lower-back activity (0–1.8%), increased demands retraction portion. A substantial leg was observed (10–18%). Asymmetry right-lower-back demands. Medium–high fatigue an increase (8–12%) retraction. The slower movements improved lowering maximum distance Center Pressure (COP) portion, as well mean velocity COP bending/retraction portions. This controlled demonstrated use cyclic flexion–extension paradigm outcomes can help with understanding temporal using on physiological measures, ultimately benefiting proper implementation.

Язык: Английский

Процитировано

1
How Effective Are Forecasting Models in Predicting Effects of Exoskeletons on Fatigue Progression? DOI Creative Commons
Pranav Madhav Kuber, Abhineet Rajendra Kulkarni, Ehsan Rashedi

и другие.

Sensors, Год журнала: 2024, Номер 24(18), С. 5971 - 5971

Опубликована: Сен. 14, 2024

Forecasting can be utilized to predict future trends in physiological demands, which beneficial for developing effective interventions. This study implemented forecasting models fatigue level progression when performing exoskeleton (EXO)-assisted tasks. Specifically, perceived and muscle activity data were from nine recruited participants who performed 45° trunk flexion tasks intermittently with without assistance until they reached medium-high exertion the low-back region. Two algorithms, Autoregressive Integrated Moving Average (ARIMA) Facebook Prophet, using levels alone, external features of activity. Findings showed that univariate better Prophet model having lowest mean (SD) root squared error (RMSE) across 0.62 (0.24) 0.67 (0.29) EXO-assisted tasks, respectively. Temporal effects BSIE on delaying then evaluated by back up 20 trials. The slope trials was ~48–52% higher vs. assistance. Median benefits 54% 43% observed ARIMA (with features) demonstrates some potential applications workforce health monitoring, intervention assessment, injury prevention.

Язык: Английский

Процитировано

0