Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables’ Data from the Crowd

Elshafei, Mohamed; Costa, Diego Elias; Shihab, Emad

doi:10.3390/s22041454

Cited by 6 publications

(2 citation statements)

References 104 publications

(132 reference statements)

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“…The data were acquired at a sampling frequency of 128 Hz [43]. The IMU at the wrist is essential to measure upper limb-related activities [44]. The IMU in the column can observe how fatigue due to repetitive upper limb tasks can affect neck compensation movements [45].…”

Section: Inertial Sensorsmentioning

confidence: 99%

Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors

Otálora,

Segatto,

Monteiro

et al. 2023

Sensors

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Muscle fatigue is defined as a reduced ability to maintain maximal strength during voluntary contraction. It is associated with musculoskeletal disorders that affect workers performing repetitive activities, affecting their performance and well-being. Although electromyography remains the gold standard for measuring muscle fatigue, its limitations in long-term work motivate the use of wearable devices. This article proposes a computational model for estimating muscle fatigue using wearable and non-invasive devices, such as Optical Fiber Sensors (OFSs) and Inertial Measurement Units (IMUs) along the subjective Borg scale. Electromyography (EMG) sensors are used to observe their importance in estimating muscle fatigue and comparing performance in different sensor combinations. This study involves 30 subjects performing a repetitive lifting activity with their dominant arm until reaching muscle fatigue. Muscle activity, elbow angles, and angular and linear velocities, among others, are measured to extract multiple features. Different machine learning algorithms obtain a model that estimates three fatigue states (low, moderate and high). Results showed that between the machine learning classifiers, the LightGBM presented an accuracy of 96.2% in the classification task using all of the sensors with 33 features and 95.4% using only OFS and IMU sensors with 13 features. This demonstrates that elbow angles, wrist velocities, acceleration variations, and compensatory neck movements are essential for estimating muscle fatigue. In conclusion, the resulting model can be used to estimate fatigue during heavy lifting in work environments, having the potential to monitor and prevent muscle fatigue during long working shifts.

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Section: Inertial Sensorsmentioning

confidence: 99%

Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors

Otálora,

Segatto,

Monteiro

et al. 2023

Sensors

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“…Human activity recognition (HAR) can be used to monitor user's behaviours, analyse them, and consequently assist the user in his/her daily life or provide histories on the activities to specialists for evaluation. The applications of HAR include health monitoring [1,2], rehabilitation [3], fitness [4], home automation [5], and safety [6].…”

Section: Introductionmentioning

confidence: 99%

DeepHAR: a deep feed-forward neural network algorithm for smart insole-based human activity recognition

D'Arco

Wang

Zheng

2023

Neural Comput & Applic

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Health monitoring, rehabilitation, and fitness are just a few domains where human activity recognition can be applied. In this study, a deep learning approach has been proposed to recognise ambulation and fitness activities from data collected by five participants using smart insoles. Smart insoles, consisting of pressure and inertial sensors, allowed for seamless data collection while minimising user discomfort, laying the baseline for the development of a monitoring and/or rehabilitation system for everyday life. The key objective has been to enhance the deep learning model performance through several techniques, including data segmentation with overlapping technique (2 s with 50% overlap), signal down-sampling by averaging contiguous samples, and a cost-sensitive re-weighting strategy for the loss function for handling the imbalanced dataset. The proposed solution achieved an Accuracy and F1-Score of 98.56% and 98.57%, respectively. The Sitting activities obtained the highest degree of recognition, closely followed by the Spinning Bike class, but fitness activities were recognised at a higher rate than ambulation activities. A comparative analysis was carried out both to determine the impact that pre-processing had on the proposed core architecture and to compare the proposed solution with existing state-of-the-art solutions. The results, in addition to demonstrating how deep learning solutions outperformed those of shallow machine learning, showed that in our solution the use of data pre-processing increased performance by about 2%, optimising the handling of the imbalanced dataset and allowing a relatively simple network to outperform more complex networks, reducing the computational impact required for such applications.

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Kinect-Based Evaluation of Severity of Facial Paresis: Pilot Study

Kovařík

Schätz²,

Ciler³

et al. 2023

Software Engineering Application in Systems Design

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Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables’ Data from the Crowd

Cited by 6 publications

References 104 publications

Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors

Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors

DeepHAR: a deep feed-forward neural network algorithm for smart insole-based human activity recognition

Kinect-Based Evaluation of Severity of Facial Paresis: Pilot Study

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