Posture Detection Based on Smart Cushion for Wheelchair Users

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.

Development of a Data Logger for Capturing Human-Machine Interaction in Wheelchair Head-Foot Steering Sensor System in Dyskinetic Cerebral Palsy

“…Other systems are based on the installation of sensors on the wheelchair for the purpose of data logging. In [17][18][19] the installation of pressure sensors on the wheelchair seat is required, making it difficult and uncomfortable for the user. In cases such as in [20], alongside the installation of the data logger, modifications in the wheelchair itself needed to be made, where the seat and the armrest of the wheelchair had to be alternated in order to provide housing for the sensors.…”

“…In wheelchairs, Arduinos have been mainly used for monitoring and/or logging physiological signals from the user. In [17], a posture recognition system based on pressure sensors installed on the seat of the wheelchair was proposed. In [18] the previously mentioned posture recognition system was extended, featuring activity recognition.…”

Determination of Lying Posture through Recognition of Multitier Body Parts

“…al. [15] proposed a cushion-based posture recognition system to process pressure sensor signals for the detection of user's posture in the wheelchair. They compared five different machine learning approaches.…”

Taking the eye road: A new access method for powered mobility in amyotrophic lateral sclerosis

“…20 Shared control of a smart wheelchair extends beyond navigation to include other automated features, integration with AAC, and interaction with other devices in the home or in public spaces. Smart cushions, which incorporate pressure sensing and equalization, 21 could reduce pressure ulcers, potentially increasing independence further.…”