Person identification using deep neural networks on physiological biomarkers during exercise

Abstract

Much progress has been made in wearable sensors that provide real-time continuous physiological data from non-invasive 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.

Multisensing Wearables for Real-Time Monitoring of Sweat Electrolyte Biomarkers During Exercise and Analysis on Their Correlation With Core Body Temperature

“…With the application of state-of-art deep learning tools on biosignals, this type of real-time biomarker data collected from athletes could also be used for other tasks such as person identification [40]. These networks or algorithms can then be deployed on an edge hardware platform using energyefficiency architectures [41], [42].…”