This paper presents an intelligent system containing FSR-based posture detection using machine learning algorithms. This paper is aimed at detecting the sitting posture of a wheelchair user. Individuals using wheelchairs are at increased risk of pressure ulcers when they hold an incorrect position for too long because the blood supply desists at some points of their skin due to increased pressure. The main objective of this research is to find a better configuration combined with the best machine learning algorithm for the detection of posture to prevent pressure ulcers. In the proposed monitoring system, two configurations consisting of a 3 × 3 matrix configuration (9 sensors) and a crossconfiguration (5 sensors) of FSR sensors are embedded on a wheelchair seat to get pressure data generated and collected in a real-time processing unit and then compared. The posture recognition is performed for five sitting positions: ideal, backward-leaning, forward-leaning, right-leaning, and left-leaning based on five machine learning algorithms: K -nearest neighbors ( K -NN), logistic regression (LR), decision tree (DT), support vector machines (SVM), and LightGBM. The research study provides a system to detect a real-time pressure sitting posture on a processing unit (laptop) wirelessly using the ESP32 module. Consequently, a posture classification accuracy of up to 95.41% is accomplished using a 3 × 3 matrix configuration. The proposed system helps prevent pressure ulcers and is valuable in risk assessment related to pressure ulcers. This system describes the relationship between accuracy, different sensor configurations, and performance of the multiple machine learning algorithms.
Patients with cognitive difficulties and impairments must be given innovative wheelchair systems to ease navigation and safety in today’s technologically evolving environment. This study presents a novel system developed to convert a manual wheelchair into an electric wheelchair. A portable kit has been designed so that it may install on any manual wheelchair with minor structural changes to convert it into an electric wheelchair. The multiple modes include the Joystick module, android app control, and voice control to provide multiple features to multiple disabled people. The proposed system includes a cloud-based data conversion model for health sensor data to display on an android application for easy access for the caretaker. A novel arrangement of sensors has been applied according to the accurate human body weight distribution in a sitting position that has greatly enhanced the accuracy of the applied model. Furthermore, seven different machine learning algorithms are applied to compare the accuracy, i.e., KNN, SVM, logistic regression, decision tree, random forest, XG Boost, and NN. The proposed system uses force-sensitive resistance (FSR) sensors with prescribed algorithms incorporated into wheelchair seats to detect users’ real-time sitting positions to avoid diseases, such as pressure ulcers and bed sores. Individuals who use wheelchairs are more likely to develop pressure ulcers if they remain in an inappropriate posture for an extended period because the blood supply to specific parts of their skin is cut off owing to increased pressure. Two FSR configurations are tested using seven algorithms of machine learning techniques to discover the optimal fit for a high-efficiency and high-accuracy posture detection system. Additionally, an obstacle detection facility enables one to drive safely in unknown and dynamic environments. An android application is also designed to provide users of wheelchairs with the ease of selecting the mode of operation of the wheelchair and displaying real-time posture and health status to the user or caretaker.
In this research, an intelligent and cost-efficient system has been proposed to detect the improper sitting posture of a person working at a desk, mostly in offices, using machine learning classification techniques. The current era demands to avoid the harms of an improper posture as it, when prolonged, is very painful and can be fatal sometimes. This study also includes a comparison of two arrangements. Arrangement 01 includes six force-sensitive resistor (FSR) sensors alone, and it is less expensive. Arrangement 02 consists of two FSR sensors and one ultrasonic sensor embedded in the back seat of a chair. The K-nearest neighbor (KNN), Naive Bayes, logistic regression, and random forest algorithms are used to augment the gain and enhanced accuracy for posture detection. The improper postures recognized in this study are backward-leaning, forward-leaning, left-leaning, and right-leaning. The presented results validate the proposed system as the accuracy of 99.8% is achieved using a smaller number of sensors that make the proposed prototype cost-efficient with improved accuracy and lower execution time. The proposed model is of a dire need for employees working in offices or even at the residential level to make it convenient to work for hours without having severe effects of improper posture and prolonged sitting.
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