Advances in human sensing and machine learning are paving the way for new applications of robotics in sports and fitness, making skill coaching smarter, easier and more accessible. Physical and social human robot interaction in particular has received special attention as a feedback mechanism for human performance augmentation. A core challenge in deploying robots that interact physically with humans in dynamic environments such as sports, relates to modeling human skills and designing appropriate interaction schemes. We present the first ML-based HRI platform for physical robot to human skill coaching in real-time in Martial Arts which can be extended to various sports. Our system comprises of the Sawyer robot, our specially developed IoT katana and a skill-training program for the Martial Art of Iaido. We built and deployed in real-time a ML-based Iaido strike recognition model trained on expert and beginner data, and achieved accuracies ranging between 94.8% and 99.97%. We assessed the system’s effectiveness in coaching skills through robot interaction in a sparring experiment and a survey involving 12 participants practicing key Iaido techniques with guided training from Sawyer. Our results demonstrated improvement in all participants’ Iaido strike skill after training with Sawyer, and they responded positively to robot-assisted skill coaching.

Human and machine interaction is shaping the future of work in a growing body of applications ranging from big data analysis to healthcare. While many employer-centered solutions have emerged in recent years in areas such as crowd-sourcing and big-data analysis, worker-centered approaches have received less attention. In this work, we explore a worker-centered application of Human-Robot Interaction (HRI) for improving human well-being at work through actively sensing and recognising sitting posture habits. We present a platform for data-driven human sitting posture training based on adaptive HRI feedback using a novel robot called Owro. Owro is an owl-shaped emotive desktop robot that connects with the LifeChair, which is an IoT cushion for sitting posture training. The robot displays various emotive feedback to correct poor sitting habits based on the actively recognised human sitting posture. Our results demonstrated that the HRI feedback was effective in improving sitting posture by significantly increasing the percentage of time spent sitting upright from 15.62% to 47.96%, significantly reducing the percentage of amount of time spent slouching from 84.38% to 52.04%, and promoting a healthier back pressure distribution. We also show preliminary survey results on the human perception of the HRI feedback using Owro.

Humans spend on average more than half of their day sitting down. The ill-effects of poor sitting posture and prolonged sitting on physical and mental health have been extensively studied, and solutions for curbing this sedentary epidemic have received special attention in recent years. With the recent advances in sensing technologies and Artificial Intelligence (AI), sitting posture monitoring and correction is one of the key problems to address for enhancing human well-being using AI. We present the application of a sitting posture training smart cushion called LifeChair that combines a novel pressure sensing technology, a smartphone app interface and machine learning (ML) for real-time sitting posture recognition and seated stretching guidance. We present our experimental design for sitting posture and stretch pose data collection using our posture training system. We achieved an accuracy of 98.93% in detecting more than 13 different sitting postures using a fast and robust supervised learning algorithm. We also establish the importance of taking into account the divergence in user body mass index in posture monitoring. Additionally, we present the first ML-based human stretch pose recognition system for pressure sensor data and show its performance in classifying six common chair-bound stretches.