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Muscle fatigue detection through wearable sensors: a comparative study using the myo armband

Published: 25 September 2017 Publication History

Abstract

Novel wearable systems allow the measure of very complex physiological phenomena extending their capabilities and maintaining their non-invasiveness. A good example of this is the use of superficial electrodes for recording electromyography signals (also called superficial electromyography- sEMG) which can reveal information regarding muscle force and fatigue. Aiming at demonstrate the accuracy of a commercial grade wearable system for sEMG, the Myo Armband for fatigue measurement, we carried out a comparative study. 3 subjects were used under a standard protocol for fatigue detection using two different sensors: a Base ground-truth sEMG sensor, and the commercial wristband Myo, both connected in the biceps brachii. Time and frequency domain parameters were compared using an ANOVA test and a correlation analysis. Results showed a median correlation for the three subjects between 0.4 and 0.6 between the Base Sensor and the Myo Armband signals exposing significant differences p <0.05 for all three cases. The biomarkers of the sEMG signal of both sensors were consistent research found in the literature. Novel wearables sensors can be used in medical scenarios where high accuracy is not a requirement, instead, non-invasiveness can provide ubiquity for rehabilitation treatments as well as a continuous signal recording and data logging processes.

References

[1]
Fairclough, S. and K. Gilleade. 2014. Advances in physiological computing. Springer Science & Business Media.
[2]
Merletti, R. and D. Farina. 2016. Surface electromyography: physiology, engineering and applications. John Wiley & Sons.
[3]
Montoya, M ; Muñoz, J and Henao, Oscar. 2015. Surface EMG based muscle fatigue detection using a low cost wearable sensor and amplitude frequency analysis. Actas de Ingenieria. Vol 1: p 29--33

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  • (2024)A survey on the state of the art of force myography technique (FMG): analysis and assessmentMedical & Biological Engineering & Computing10.1007/s11517-024-03019-w62:5(1313-1332)Online publication date: 2-Feb-2024
  • (2023)Classification of Hand Movements Using Electromyographic Signals and Machine Learning Models2023 IEEE Colombian Caribbean Conference (C3)10.1109/C358072.2023.10436284(1-6)Online publication date: 22-Nov-2023
  • (2023)Fatigue-aware videogame using biocybernetic adaptation: a pilot study for upper-limb rehabilitation with sEMGVirtual Reality10.1007/s10055-021-00561-y27:1(277-290)Online publication date: 1-Mar-2023
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      cover image ACM Other conferences
      Interacción '17: Proceedings of the XVIII International Conference on Human Computer Interaction
      September 2017
      268 pages
      ISBN:9781450352291
      DOI:10.1145/3123818
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 25 September 2017

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      Author Tags

      1. biostatistical analysis
      2. correlation of signals
      3. muscle fatigue
      4. surface electromyography
      5. wearable sensors

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      Cited By

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      • (2024)A survey on the state of the art of force myography technique (FMG): analysis and assessmentMedical & Biological Engineering & Computing10.1007/s11517-024-03019-w62:5(1313-1332)Online publication date: 2-Feb-2024
      • (2023)Classification of Hand Movements Using Electromyographic Signals and Machine Learning Models2023 IEEE Colombian Caribbean Conference (C3)10.1109/C358072.2023.10436284(1-6)Online publication date: 22-Nov-2023
      • (2023)Fatigue-aware videogame using biocybernetic adaptation: a pilot study for upper-limb rehabilitation with sEMGVirtual Reality10.1007/s10055-021-00561-y27:1(277-290)Online publication date: 1-Mar-2023
      • (2022)Monitoring Neuromuscular Activity during Exercise: A New Approach to Assessing Attentional Focus Based on a Multitasking and Multiclassification Network and an EMG Fitness ShirtBiosensors10.3390/bios1301006113:1(61)Online publication date: 30-Dec-2022
      • (2022)Stress Level Estimation Based on Physiological Signals for Virtual Reality ApplicationsIEEE Access10.1109/ACCESS.2022.318631810(68755-68767)Online publication date: 2022
      • (2022)Method for Music Game Control Using Myoelectric SensorsEntertainment Computing – ICEC 202210.1007/978-3-031-20212-4_19(238-246)Online publication date: 24-Oct-2022
      • (2021)Design, Development, and Testing of an Intelligent Wearable Robotic Exoskeleton Prototype for Upper Limb RehabilitationSensors10.3390/s2116541121:16(5411)Online publication date: 10-Aug-2021
      • (2021)Feasibility Assessment of Muscle Force Estimation Using the Myo Armband During Arm Curl TrainingConverging Clinical and Engineering Research on Neurorehabilitation IV10.1007/978-3-030-70316-5_125(785-789)Online publication date: 2-Oct-2021
      • (2020)Comparison Gestures Recognition Using K-NN and Naïve Bayes2020 International Conference on Applied Science and Technology (iCAST)10.1109/iCAST51016.2020.9557730(677-681)Online publication date: 24-Oct-2020
      • (2020)Enhancing Virtual Rehabilitation in Upper Limbs With Biocybernetic Adaptation: The Effects of Virtual Reality on Perceived Muscle Fatigue, Game Performance and User ExperienceIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2020.296886928:3(740-747)Online publication date: Mar-2020
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