Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair

Abstract

Many modern jobs require long periods of sitting on a chair that may result in serious health complications. Dynamic chairs are proposed as alternatives to the traditional sitting chairs; however, previous studies have suggested that most users are not aware of their postures and do not take advantage of the increased range of motion offered by the dynamic chairs. Building a system that identifies users’ postures in real time, as well as forecasts the next few postures, can bring awareness to the sitting behavior of each user. In this study, machine learning algorithms have been implemented to automatically classify users’ postures and forecast their next motions. The random forest, gradient decision tree, and support vector machine algorithms were used to classify postures. The evaluation of the trained classifiers indicated that they could successfully identify users’ postures with an accuracy above 90%. The algorithm can provide users with an accurate report of their sitting habits. A 1D-convolutional-LSTM network has also been implemented to forecast users’ future postures based on their previous motions, the model can forecast a user’s motions with high accuracy (97%). The ability of the algorithm to forecast future postures could be used to suggest alternative postures as needed.

Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion

“…Jeong et al (2021) used FSRs and distance sensors and k-NN and achieved an accuracy of 59%, 82%, and 92% using the pressure sensors only, distance sensors only, and mixed sensor systems, respectively [ 41 ]. Finally, Farhani et al (2022) used force-sensitive resistors (FSRs) and random forest and achieved an accuracy of 94% [ 42 ].…”

“…Farhani et al (2022) used FSRs attached to the seat pan of a Formid dynamic chair and RF, SVM, and GDTs to classify seven basic sitting postures with an accuracy of around 90% [ 42 ].…”

Deep Neural Network-Based Visual Feedback System for Nasopharyngeal Swab Sampling

“…In addition, convolutional neural networks (CNN), which are widely known to detect the local characteristics of data, have been applied to nonlinear one-dimensional time-series data [28][29][30][31][32]. Furthermore, machine learning models that combine multiple sensor inputs have shown promising results in the prediction of nonlinear outputs [33,34]. Hence, owing to the loadcell data in this study containing time-series contact force data from multi-channel sensors, a one-dimensional CNN (1DCNN) with multiple filters is suitable for capturing concealed patterns within the measured force.…”

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

“…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. Lower-resolution mapping characteristics were compared to high-resolution sensor pressure mats on the backrest and seat pan features [21][22][23][24][25][26]. To recognize the postures of each body component, five distinct supervised machine-learning approaches are used [13].…”