Towards Enhanced Human Activity Recognition for Real-World Human-Robot Collaboration

Abstract

This research contributes to the field of Human-Robot Collaboration (HRC) within dynamic and unstructured environments by extending the previously proposed Fuzzy State-Long Short-Term Memory (FS-LSTM) architecture to handle the uncertainty and irregularity inherent in real-world sensor data. Recognising the challenges posed by low-cost sensors, which are highly susceptible to environmental conditions and often fail to provide regular periodic readings, this paper introduces additional pre-processing blocks. These include two indirect Kalman filters and an additional LSTM network, which together enhance the input variables for the fuzzification process. The enhanced FS-LSTM approach is evaluated using real-world data, demonstrating its effectiveness in extracting meaningful information and accurately recognising human activities. This work underscores the potential of robotics in addressing global challenges, particularly in labour-intensive and hazardous tasks. By improving the integration of humans and robots in unstructured environments, this research contributes to the broader exploration of robotics in new societal applications, fostering connections and collaborations across diverse fields.