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Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors

Sensors (Basel, Switzerland), 2017-05, Vol.17 (6), p.1229 [Peer Reviewed Journal]

2017. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 by the authors. 2017 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s17061229 ;PMID: 28555016

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  • Title:
    Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors
  • Author: Xi, Xugang ; Tang, Minyan ; Miran, Seyed M ; Luo, Zhizeng
  • Subjects: Accidental Falls ; Accuracy ; Activities of Daily Living ; activity monitoring ; Algorithms ; classifier ; Classifiers ; Complexity ; Computer simulation ; Discriminant analysis ; Electromyography ; Fall detection ; Feasibility studies ; Feature extraction ; Feature recognition ; Fuzzy logic ; Humans ; Mathematical analysis ; Monitoring ; Neural networks ; Older people ; Pattern recognition ; Pattern Recognition, Automated ; Permutations ; Sensitivity analysis ; Sensors ; Support Vector Machine ; Support vector machines ; surface electromyography (sEMG) ; Trip estimation ; Wearable Electronic Devices ; Wearable technology
  • Is Part Of: Sensors (Basel, Switzerland), 2017-05, Vol.17 (6), p.1229
  • Description: As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG), with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL). A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP) feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM) with Permutation Entropy (PE) or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN) has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA) with the feature WAMP guarantees a high sensitivity (98.70%) and specificity (98.59%) with a short calculation time (65.586 ms), making it a possible choice for pre-impact fall detection. The thorough quantitative comparison of the features and classifiers in this study supports the feasibility of a wireless, wearable sEMG sensor system for automatic activity monitoring and fall detection.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s17061229
    PMID: 28555016
  • Source: GFMER Free Medical Journals
    MEDLINE
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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