A Proposal of Implementation of Sitting Posture Monitoring System for Wheelchair Utilizing Machine Learning Methods

Abstract

This paper presents a posture recognition system aimed at detecting sitting postures of a wheelchair user. The main goals of the proposed system are to identify and inform irregular and improper posture to prevent sitting-related health issues such as pressure ulcers, with the potential that it could also be used for individuals without mobility issues. In the proposed monitoring system, an array of 16 screen printed pressure sensor units was employed to obtain pressure data, which are sampled and processed in real-time using read-out electronics. The posture recognition was performed for four sitting positions: right-, left-, forward- and backward leaning based on k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), decision tree (DT) and LightGBM machine learning algorithms. As a result, a posture classification accuracy of up to 99.03 percent can be achieved. Experimental studies illustrate that the system can provide real-time pressure distribution value in the form of a pressure map on a standard PC and also on a raspberry pi system equipped with a touchscreen monitor. The stored pressure distribution data can later be shared with healthcare professionals so that abnormalities in sitting patterns can be identified by employing a post-processing unit. The proposed system could be used for risk assessments related to pressure ulcers. It may be served as a benchmark by recording and identifying individuals’ sitting patterns and the possibility of being realized as a lightweight portable health monitoring device.

A systematic review of integrated machine learning in posture recognition

“…Position recognition was performed for four positions in the sitting position: tilted to the right, left, forward and backward using as machine learning algorithms k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), decision tree (DT) and LightGBM. As a result, a posture classification accuracy of up to 99.03 percent was observed [23]. Kumar S. compared the performance of different classifiers in detecting 3 positions, sitting, standing and lying down, using the Microsoft Kinect sensor for data collection.…”

Classification of the Sidewalk Condition Using Self-Supervised Transfer Learning for Wheelchair Safety Driving

“…For example, Favey et al and Arnay et al [ 3 , 4 ] developed new sensors to increase the driving quality of electric wheelchairs, while studies from [ 5 , 6 , 7 ] focused on the development of motors and controllers to address various issues while driving through uphill, ramp, and stairs. In addition, there have been studies to facilitate wheelchair control by sensing surface electromyography (sEMG) signals from the human arm to detect gestures [ 8 ] or by using printed pressure sensor units to identify and inform irregular and improper posture to prevent sitting-related health issues [ 9 ]. Moreover, in [ 10 ], the muscular activity of the user was measured through electromyography (EMG) sensors, which were then processed and utilized to control both the wheelchair and robotic manipulator.…”

An Intelligent Cost-Efficient System to Prevent the Improper Posture Hazards in Offices Using Machine Learning Algorithms

“…The maximum propagation delay for the ANN and ADC control logic is 8.714 ns [ 11 ]. Ahmad et al employed the J48 algorithm to identify the five types of sitting postures using the pressure readings of twelve pressure sensors as features, achieving 99.47 percent experimental classification accuracy [ 12 ]. A center of pressure, contact area proportion, and pressure ratios are used in another article to identify five common trunk postures, two common left foot postures, and three common right foot postures.…”

“…Using a specific hardware system that interprets video in real-time using convolutional neural networks, a system based on the worker's postural detection is created, constructed, and tested. This device can identify the worker's neck, shoulders, and arms position and provide advice to help them avoid health problems caused by bad posture [ 12 ]. Many additional efforts, such as clinical implication assessment for diabetes mellitus [ 27 ], are built on machine learning techniques.…”

“…e system includes a DR step based on the principal component analysis (PCA) [6]. For pose detection between the matrix stored (in the system) and new data obtained by pressure and distance sensors, K-nearest neighbours (KNN) classifiers are used [7][8][9][10][11][12][13]. Noninvasive optical fiber sensor architecture can be fitted to the bottom of a shoe for remote plantar pressure monitoring, which might be utilized in an IoT e-Health system to track people's health.…”