In the sport context, recovery has been characterized as a multifactor process (physiological, psychological, behavioral, social, etc.). This study takes a multidisciplinary approach to find psychophysiological markers of the stress-recovery process. It aims to determine how athletes’ specific recovery actions relate to their perceptions of recovery, and Heart Rate Variability (HRV). A total of 196 assessments were analyzed from 6 players on a men’s professional basketball team within the Liga LEB Oro basketball federation (2012/2013 season). Perceptions of recovery, recovery strategies, and HRV were recorded. The results show a pattern of individual differences in behavior related to athletes’ recovery actions and HRV profiles throughout the season (p < .05). Moreover, we observed that each player had different recovery needs. In light of these results, we suggest an individualistic approach to evaluating and monitoring recovery to attend more accurately to each player’s recovery needs.

Robots are expected to be pervasive in the society in a not too distant future where they will work extensively as assistants of humans in various activities. With this in view, a novel affect-sensitive architecture for human-robot cooperation is presented in this paper where the robot is expected to recognize human psychological states. As a demonstration, an online heart rate variability analysis to infer the mental stress of a human engaged in a task is presented. This technique involves real-time heart rate monitoring, signal processing using both Fourier Transforrn and Wavelet Transform, and inferring the stress condition based on the level of activation of the sympathetic and parasympathetic nervous systems using fuzzy logic. Results from human subject trials are presented to validate the presented methodology. This stress detection technique is expected to be useful in the future human-robot cooperation activities, where the robot will recognize human stress and respond appropriately.