Person identification using deep neural networks on physiological biomarkers during exercise

Much progress has been made in wearable sensors that provide real-time continuous physiological data from noninvasive measurements including heart rate and biofluids such as sweat. This information can potentially be used to identify the health condition of a person by applying machine learning algorithms on the physiological measurements. We present a person identification task that uses machine learning algorithms on a set of biomarkers collected from 30 subjects carrying out a cycling experiment. We compared an SVM and a gated recurrent neural network (RNN) for real-time accuracy using different window sizes of the measured data. Results show that using all biomarkers gave the best results from any of the models. With all biomarkers, the gated RNN model achieved ~ 90% accuracy even in a 30 s time window; and ~92.3% accuracy in a 150 s time window. Excluding any of the biomarkers leads to at least 7.4% absolute accuracy drop for the RNN model. The RNN implementation on the Jetson Nano incurs a low latency of ~ 45 ms per inference.