Brahad Balaji Kokad supervised by Dr. Aftab M. Hussain received his Master of Science – Dual Degree in Electronics and Communication Engineering (ECD). Here’s a summary of his research work on A Multi-Scale Spatio-Temporal Ensemble Method with Physics-Informed Features for Pressure-Based Sitting Posture Recognition

The rapid evolution of modern professional and educational environments has precipitated a global transition toward a predominantly sedentary lifestyle. In industrialized and developing nations alike, individuals now spend upward of ten hours daily in a seated position; Toko et al. called it out as “the modern epidemic”. While the digitalization of labor has enhanced productivity, it has simultaneously introduced significant risks to musculoskeletal and metabolic health. Modern workstations, despite their ergonomic advancements, facilitate prolonged static loading that the human musculoskeletal system is not biologically designed to sustain. As desk jobs are becoming more normalized, making people comfortably numb. Thus, the development of intelligent, non-intrusive, and reliable systems for monitoring and classifying sitting posture has emerged as a critical frontier in proactive healthcare, workplace ergonomics, and personal well-being. This is referred to as Sitting Posture Recognition (SPR). Researchers have tried many different approaches and deployed a diverse range of technologies to build systems for Human Activity Recognition (HAR) and SPR. These include but are not limited to RGB imaging and feature extraction, depth sensing and 3D joint tracking, wearable inertial sensors, smart textiles, and pressuresensitive mats (PSMs). PSMs have become increasingly popular as they address several drawbacks and concerns with the other technologies–they are non-intrusive, generalizable to multiple users, do not invade privacy by involving a camera, aren’t limited by objects blocking a camera FOV, do not meddle with general bodily movement, etc. Many different studies have tried different Machine Learning (ML) and Deep Learning (DL) techniques for SPR, and in order to do that, researchers have created their own datasets. However, one of the primary concerns with existing research has been lack of generalizability due to not having a diverse dataset that can be a good proxy for the general population. This thesis outlines the creation of a diverse dataset from 33 different participants, both male and female, with a wide range of physiological attributes such as height, weight, and age. The data comprises pressure readings from a high-resolution PSM. With over 40,000 datapoints, a thorough data analysis is performed, and then several ML and DL techniques are applied to evaluate the performance of different models for SPR. It is found that traditional models fail to surpass a particular accuracy level, and thus, a Multi-Scale Spatio-Temporal Ensemble Method with Physics-Informed Features is proposed, which outperforms conventional models. 

June 2026