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Sensors 2017, 17(12), 2712; doi:10.3390/s17122712

Early Detection of the Initiation of Sit-to-Stand Posture Transitions Using Orthosis-Mounted Sensors

1
Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
2
Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
*
Author to whom correspondence should be addressed.
Received: 24 August 2017 / Revised: 8 November 2017 / Accepted: 22 November 2017 / Published: 23 November 2017
(This article belongs to the Special Issue Wearable and Ambient Sensors for Healthcare and Wellness Applications)
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Abstract

Assistance during sit-to-stand (SiSt) transitions for frail elderly may be provided by powered orthotic devices. The control of the powered orthosis may be performed by the means of electromyography (EMG), which requires direct contact of measurement electrodes to the skin. The purpose of this study was to determine if a non-EMG-based method that uses inertial sensors placed at different positions on the orthosis, and a lightweight pattern recognition algorithm may accurately identify SiSt transitions without false positives. A novel method is proposed to eliminate false positives based on a two-stage design: stage one detects the sitting posture; stage two recognizes the initiation of a SiSt transition from a sitting position. The method was validated using data from 10 participants who performed 34 different activities and posture transitions. Features were obtained from the sensor signals and then combined into lagged epochs. A reduced number of features was selected using a minimum-redundancy-maximum-relevance (mRMR) algorithm and forward feature selection. To obtain a recognition model with low computational complexity, we compared the use of an extreme learning machine (ELM) and multilayer perceptron (MLP) for both stages of the recognition algorithm. Both classifiers were able to accurately identify all posture transitions with no false positives. The average detection time was 0.19 ± 0.33 s for ELM and 0.13 ± 0.32 s for MLP. The MLP classifier exhibited less time complexity in the recognition phase compared to ELM. However, the ELM classifier presented lower computational demands in the training phase. Results demonstrated that the proposed algorithm could potentially be adopted to control a powered orthosis. View Full-Text
Keywords: orthosis; extreme learning machine; physical activity; posture recognition; sit-to-stand transition orthosis; extreme learning machine; physical activity; posture recognition; sit-to-stand transition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Doulah, A.; Shen, X.; Sazonov, E. Early Detection of the Initiation of Sit-to-Stand Posture Transitions Using Orthosis-Mounted Sensors. Sensors 2017, 17, 2712.

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