Fatigue Estimation through Multimodal Data Retrieved from a Commercial Wearable Device

Continuous advancements in sensor technology and the miniaturization of electronic chips have encouraged the exploration and development of wearable device applications. The objective estimation of human fatigue is among the problems that have been recently researched. Existing technological solutions have in the past mainly performed in laboratory settings and using sensors and/or stationary diagnostic equipment requiring the involvement of medical personnel. Consequently, this makes such solutions unfeasible difficult to deploy within application scenarios such as work and home environments and consequently of limited dissemination due to costs. This paper presents a hardware/software platform based on a commercial and low-cost wearable device that combines heart rate monitoring and real-time posture/walking speed classification, the latter obtained through the application of supervised machine learning methodologies. According to the literature, the implemented algorithmic pipeline distinguishes different fatigue levels through pre-established decision rules, usually used as a simple expert system in artificial intelligence, whose output is a score (between 0 and 10) computed from discrete heart rate values and classified activity level. The findings of the preliminary experiments show promising results in the estimation and classification of the intermediate multimodal data used to obtain the score, with a low average error expressed in terms of Mean Absolute Error (4.6 bpm) and Root-Mean-Square Error (6.8 bpm) for heartbeat estimation and high accuracy regarding posture/walking speed classification (about 97.3%).