Mimicking Gait Dynamics: A Step Toward Precision Learning of Human Activities

We develop a computational protocol for mimicking personal gait dynamics with 12-dimensional time series derived from 4 accelerometer sensors found in the MAREA database and then explore its utilities in line with precision learning of human activities. The foundation of mimicking high dimensional rhythmic dynamics is explicitly established upon deterministic and stochastic structures found on structural representations of evolving biomechanical states hidden within all computed gait cycles. Such a technique enables practitioners to detect and confirm minute structural changes that could last for only a few cycles with high precision. Our computational developments are step-by-step illustrated via one subject’s data, while the other 8 subjects’ data are also analyzed and compared accordingly. A common cyclic composition of evolving biomechanical states of various temporal scales emerges from the 9 subjects’ comparisons. We conclude that mimicking an individual’s gait dynamics offers precise detections of potential multiscale minute differences against gait dynamics of different time periods or of different persons, and further offers clues of efficiency on personal walking activity. This mimicking-based capability is a cornerstone for the proof of concept: dynamics mimicking enables precision learning by improving the efficiency of learning and performing human activities in competitive sports, social dancing, and physical rehabilitation, among many others.