Unsupervised Learning of Physical Effort: proposal of a simple metric for wearable devices

Detection of physical effort through the analysis of physiological parameters is a challenging endeavor, due to the intrinsically complex nature of the physiological condition of fatigue status. Indeed, the possibility to categorize physical effort is critical to reduce the associated risks, particularly among people exposed to heavy workload. On the other hand, as the effects of physical effort on physiological signals are not univocal, data labeling commonly applied in machine learning is not always possible, and unsupervised methods must be investigated. This paper presents a method for unsupervised learning of physical effort. The method is based on a metric exploiting two physiological signals that can be easily acquired by wearable devices, namely, heart rate and skin conductance. Such metric has the same unit of measurement of heart rate, i.e., the beats per minute, but it is characterized by greater information content related to effort levels. Then, the experimental analysis is carried out on signals acquired before, during and after the execution of physical activity. The Davies-Bouldin index, chosen as figure of merit for performance analysis, shows that the proposed method is able to identify the proper number of clusters, corresponding to the different levels of physical effort.