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.
Highlights
This research presented the standalone system comprising five classification capable of of identifying identifyingsitting sittingposture postureabnormalities abnormalitiesonona a classification algorithms algorithms that are capable wheelchair with a prediction performance of up to 99.03%
As a result of the pressure data that are processed by machine learning algorithms, our system is able to classify four leaning positions by means of some effective classification algorithms using machine learning
The sitting posture recognition system offers an effective way for rehabilitation and pressure ulcer prevention for wheelchair users and it is beneficial for able-bodied users
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The endurance of pressure varies from person to person; a number of research studies have found that a pressure value greater than arterial capillary pressure (32 mm Hg) may lead to occlusion in blood vessels [7,8] Those who are unable to have any sort of movement without the help of others, which may usually be due to spinal cord injuries, Parkinson’s and osteoporotic fracture, are at significantly higher risk of developing pressure ulcers [9,10,11]. A textile pressure sensor is proposed in [16] and a sitting posture detection uses gyroscope readings through mobile devices attached to human spinal points and incorporates decision tree algorithm [17]. We present an intuitive method of sitting posture recognition system using pressure sensors and machine learning algorithms.
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