Improved Spatiotemporal Framework for Human Activity Recognition in Smart Environment

Publication from Materials
Smart Connected Lighting

Ziad Salem, Andreas Peter Weiss

Sensors 2023 23(1), 132;, 12/2022


The rapid development of microsystems technology with the availability of various machine learning algorithms facilitates human activity recognition (HAR) and localization by low-cost and low-complexity systems in various applications related to industry 4.0, healthcare, ambient assisted living as well as tracking and navigation tasks. Previous work, which provided a spatiotemporal framework for HAR by fusing sensor data generated from an inertial measurement unit (IMU) with data obtained by an RGB photodiode for visible light sensing (VLS), already demonstrated promising results for real-time HAR and room identification. Based on these results, we extended the system by applying feature extraction methods of the time and frequency domain to improve considerably the correct determination of common human activities in industrial scenarios in combination with room localization. This increases the correct detection of activities to over 90% accuracy. Furthermore, it is demonstrated that this solution is applicable to real-world operating conditions in ambient light.

Keywords: human activity recognition; inertial measurement unit; visible light sensing; sensor data fusion; features extraction; machine learning