A Framework for Real-Time Estimation of Human Activity Intensity in Indoor Environments
Moein Younesi Heravi, Youjin Jang, Inbae Jeong, Israt Sharmin Dola · Proceedings of the ... ISARC · 2025
The real-time estimation of human activity level in indoor environments is crucial for thermal comfort prediction and optimizing HVAC systems, as well as supporting health monitoring and ergonomic assessments.Existing methods typically categorize activities into predefined types and therefore are not efficient for new activities.Furthermore, the transition between activities is not captured in these classification methods.To address these limitations, we propose a novel Activity Intensity Score (AIS) framework that provides a continuous, non-intrusive assessment of human activity intensity.The proposed AIS framework uses kinematic parameters derived from video-based pose estimation to calculate a dynamic intensity score.Key kinematic parameters, such as angular speed, angular acceleration, range of motion, movement frequency, and rotational energy, are extracted from pose landmarks using MediaPipe.These parameters are then normalized and combined to generate a continuous AIS that reflects real-time variations in movement intensity.The proposed approach was validated through controlled experiments with participants performing activities of varying intensities, including low (e.g., sitting), moderate (e.g., walking), and high (e.g., jumping jacks).Results showed that the AIS effectively distinguishes between different activity intensities, with higher AIS values corresponding to more intensive activities.The proposed method addresses the need for a more granular understanding of activity levels beyond simple categorical classification.This research contributes to real-time activity estimation, which has applications in optimizing indoor environmental conditions, health monitoring, and dynamic ergonomic assessments.