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Sensors 2017, 17(4), 719; doi:10.3390/s17040719

Posture Detection Based on Smart Cushion for Wheelchair Users

1
School of Logistics Engineering, Wuhan University of Technology, Wuhan, 430070, China
2
Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Rende, 87036, Italy
*
Author to whom correspondence should be addressed.
Received: 1 March 2017 / Revised: 20 March 2017 / Accepted: 25 March 2017 / Published: 29 March 2017
(This article belongs to the Special Issue Advances in Body Sensor Networks: Sensors, Systems, and Applications)
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Abstract

The postures of wheelchair users can reveal their sitting habit, mood, and even predict health risks such as pressure ulcers or lower back pain. Mining the hidden information of the postures can reveal their wellness and general health conditions. In this paper, a cushion-based posture recognition system is used to process pressure sensor signals for the detection of user’s posture in the wheelchair. The proposed posture detection method is composed of three main steps: data level classification for posture detection, backward selection of sensor configuration, and recognition results compared with previous literature. Five supervised classification techniques—Decision Tree (J48), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Naive Bayes, and k-Nearest Neighbor (k-NN)—are compared in terms of classification accuracy, precision, recall, and F-measure. Results indicate that the J48 classifier provides the highest accuracy compared to other techniques. The backward selection method was used to determine the best sensor deployment configuration of the wheelchair. Several kinds of pressure sensor deployments are compared and our new method of deployment is shown to better detect postures of the wheelchair users. Performance analysis also took into account the Body Mass Index (BMI), useful for evaluating the robustness of the method across individual physical differences. Results show that our proposed sensor deployment is effective, achieving 99.47% posture recognition accuracy. Our proposed method is very competitive for posture recognition and robust in comparison with other former research. Accurate posture detection represents a fundamental basic block to develop several applications, including fatigue estimation and activity level assessment. View Full-Text
Keywords: smart wheelchair; posture detection; smart cushion; pressure sensor; activity level assessment smart wheelchair; posture detection; smart cushion; pressure sensor; activity level assessment
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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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Ma, C.; Li, W.; Gravina, R.; Fortino, G. Posture Detection Based on Smart Cushion for Wheelchair Users. Sensors 2017, 17, 719.

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