Evaluation of physical effort by IoT-based wearable sensors

Assessment of physical effort commonly addressed in research through acceleration signals can show significant measurement errors. The approach presented in this paper formulates a proper measurement system to evaluate the physical effort by wearable devices. Specifically, the paper proposes the joint use of sensors for skin conductance (SC) and electromyography (EMG). The EMG sensor is introduced uniquely with the aim of properly identifying the effort level. The classification is, instead, entirely entrusted to the SC signal alone. The proposed approach is then tested in the evaluation of muscular fatigue felt by arms. The experimental results show good performance, as the obtained values in terms of classification accuracy and sensitivity are, respectively, 84.68 % and 89.75 %.

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Publication Details

Journal
Measurement Sensors
Published
2024-12-31
DOI
https://doi.org/10.1016/j.measen.2024.101648
Citations
3
Primary Topic
Sports Performance and Training
Type
article
Field-Weighted Citation Impact
1.43
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article

Evaluation of physical effort by IoT-based wearable sensors

Grazia Iadarola, Susanna Spinsante, Alessandro Mengarelli, Sandro Fioretti
3 citations
Measurement Sensors
Sports Performance and Training
1.43
article

Evaluation of physical effort by IoT-based wearable sensors

Grazia Iadarola, Susanna Spinsante, Alessandro Mengarelli, Sandro Fioretti
article en
3 citations

Abstract

Assessment of physical effort commonly addressed in research through acceleration signals can show significant measurement errors. The approach presented in this paper formulates a proper measurement system to evaluate the physical effort by wearable devices. Specifically, the paper proposes the joint use of sensors for skin conductance (SC) and electromyography (EMG). The EMG sensor is introduced uniquely with the aim of properly identifying the effort level. The classification is, instead, entirely entrusted to the SC signal alone. The proposed approach is then tested in the evaluation of muscular fatigue felt by arms. The experimental results show good performance, as the obtained values in terms of classification accuracy and sensitivity are, respectively, 84.68 % and 89.75 %.

Measurement SensorsVol. 38
Marche Polytechnic University (IT)
Affordable and clean energy
Openalex Percentile: Top 18%
Sports Performance and Training
1.43
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