Abstract
This paper presents a solution for capturing physical activities in a hybrid work environment and controlling posture using a single multimodal sensor. The sensor system is attached to the subject's Upper Body (chest). The sensor collects accelerometer data during eight different activities and processes it using MATLAB for data analysis. In MATLAB, the data is fed to a machine learning algorithm for classification applications. Additionally, real-life data were collected from the office environment and the home environment. By doing so, we were able to condense the data into the type of physical activity, capture highly desirable points of interest, and obtain accuracy from the MATLAB classification learner. Using this knowledge, we were able to test how accurately our simulation can predict outcomes and assess its capabilities in a real-world scenario. The proposed system can be used to detect various motions, including good sitting posture, twisting and turning, and walking, all from a single sensor.