A Vision-Based Approach for Human Activity Intensity Estimation Using Kinematic Features
TL;DR
A novel activity intensity score (AIS) framework that provides a nonintrusive and continuous measure of activity intensity by analyzing video data and enables robust, real-time measurement of movement intensity for applications ranging from healthcare and workplace ergonomics to sports analytics and adaptive HVAC control.
Abstract
Accurate real-time estimation of human activity intensity is essential for diverse applications such as health monitoring, ergonomics, sports science, and adaptive building management. However, existing methods often depend on intrusive wearable sensors, discrete activity classifications, or extensive training datasets, which compromise their practicality and generalizability. To address these gaps, we propose a novel activity intensity score (AIS) framework that provides a nonintrusive and continuous measure of activity intensity by analyzing video data. The proposed method applies pose estimation to video data to extract body landmarks, which are then used to compute kinematic parameters including the angular velocity, angular acceleration, range of motion, peak speed, movement frequency, and rotational energy across defined kinematic chains (e.g., arms, legs, torso). These parameters are then normalized and combined through an optimized weighted summation to produce a continuous activity intensity metric. Experimental validation was conducted with 20 participants performing various activities from low, moderate, and high intensity. Results demonstrated strong correlations between AIS scores and both activity intensity levels (Spearman’s ρ = 0.943 , p < 0.001 ) and participants perceived exertion ratings (Pearson’s r = 0.923 , p < 0.001 ). Statistical comparisons demonstrated that the AIS values effectively discriminate among these three intensity categories (Spearman’s ρ = 0.943 ) and significant group differences confirmed by ANOVA ( p < 0.001 ). Moreover, the AIS exhibited a strong correlation (Pearson’s r = 0.923 ) with self-reported exertion (Borg rating of perceived exertion), indicating consistency with participants’ subjective perceptions. This occupant-invariant and domain-independent method enables robust, real-time measurement of movement intensity for applications ranging from healthcare and workplace ergonomics to sports analytics and adaptive HVAC control.
