An adaptable human fatigue evaluation system

Back-Support Industrial Exoskeletons (BSIEs) can be beneficial in reducing the risk of injury due to overexertion during trunk flexion tasks. Most real-world tasks include complex body movements, leading to mixed outcomes that necessitate field-based methods for detecting overall physical demands. Monitoring fatigue can be beneficial in this regard to ensure that benefits of BSIEs are translated to the real world. Our experiment included 14 participants, who performed 30 repetitions of 45° trunk-flexion while assisted by a BSIE, first without fatigue and then at medium-high back fatigue (7/10 in the Borg scale). We extracted 135 features from recorded muscle activity, trunk motion, and whole-body stability across bending, transition, and retraction portions of each trunk-flexion cycle. Four classification algorithms, namely Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB), were implemented to assess fatigue prediction. XGB (Accuracy: 86.1%, Recall: 86%, Specificity: 86.3%) was effective in classifying fatigue with data obtained from a single EMG sensor located on the lower back (erector spinae) muscle. Meanwhile, stability measures showed high predictability with both RF (92.9%, 91.9%, 94.1%) and XGB (93.5, 94.1%, 93.1%). Findings demonstrate the success of force plates, and when replaced by pressure insoles, they can facilitate real-world fatigue detection during BSIE-assisted trunk-flexion tasks.

Section: Introductionmentioning

confidence: 99%

Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach

Kuber,

Godbole,

Rashedi

2024

“…Fatigue and its incomplete recovery have been linked to longer reaction time, impaired concentration, coordination, and information processing, and poor judgment [1]. This can adversely affect task performance and increase the risk of suffering an injury [2]. The prevalence of fatigue costs U.S. employers ~$130 billion annually in lost productive time, with ~38% of workers reporting fatigue in their previous 2 weeks of work [3].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Fatigue Level Prediction for Exoskeleton-Assisted Trunk Flexion Tasks Using Wearable Sensors

Kuber,

Kulkarni,

Rashedi

2024

Monitoring physical demands during task execution with exoskeletons can be instrumental in understanding their suitability for industrial tasks. This study aimed at developing a fatigue level prediction model for Back-Support Industrial Exoskeletons (BSIEs) using wearable sensors. Fourteen participants performed a set of intermittent trunk-flexion task cycles consisting of static, sustained, and dynamic activities, until they reached medium-high fatigue levels, while wearing BSIEs. Three classification algorithms, Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB), were implemented to predict perceived fatigue level in the back and leg regions using features from four wearable wireless Electromyography (EMG) sensors with integrated Inertial Measurement Units (IMUs). We examined the best grouping and sensor combinations by comparing prediction performance. The findings showed best performance in binary classification of leg and back fatigue with 95% (2 EMG + IMU sensors) and 82% (single IMU sensor) accuracy, respectively. Tertiary classification for back and leg fatigue level prediction required four sensor setups with both EMG and IMU measures to perform at 79% and 67% accuracy, respectively. The efforts presented in our article demonstrate the feasibility of an accessible fatigue level detection system, which can be beneficial for objective fatigue assessment, design selection, and implementation of BSIEs in real-world scenarios.

“…Besides suffering from muscle strain/sprain, when fatigued, a worker may be more vulnerable to injury due to the affected neuromusculoskeletal systems. High demands on the body compared to the body's capacity to generate vital forces can lead to failure, poor work quality, and performance errors, and they can cause injury [16]. Therefore, estimating the impacts of fatigue on the human body during the performance of tasks can be beneficial.…”

Section: Introductionmentioning

confidence: 99%

Investigating Spatiotemporal Effects of Back-Support Exoskeletons Using Unloaded Cyclic Trunk Flexion–Extension Task Paradigm

Kuber,

Rashedi

2024

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