PerMTL: A Multi-Task Learning Framework for Skilled Human Performance Assessment

Abstract

Intelligent and complex human motion analysis can help design the next generation IoT and AR/VR systems for automated human performance assessment. Such an automated system can help advocate the interpretability and translatability of complex human motions, intelligent motion feedback, and fine-grained motion skill assessment to design next-generation interactive human-machine teaming systems. Motivated by this, we design a wearable sensing framework for assessing the players’ performance and consider a live badminton game as our use case. Generally, the players on the field try to improve their performance by focusing on fast and synchronous coordination of their limbs’ reflex actions to have the ideal body postures to perform the desired shot. Learning the minute dissimilarities and distinctive traits from each limb of the players simultaneously can help assess the players’ performance and specific skillsets during a game. This paper proposes a multi-task learning framework, PerMTL to learn the shared features from each player’s limb. The PerMTL comprises a task-specific regressor output layer that helps to determine the dissimilarities and distinctive traits between the player’s limbs for collective inference in a body sensor network (BSN) environment. We evaluate the PerMTL framework using publicly available Badminton Activity Recognition (BAR) and Daily and Sports Activities (DSA) datasets. Empirical results indicate that PerMTL achieves R² Score of ≈ 82% in predicting the players’ performance.