Classification of Hemodynamics Scenarios from a Public Radar Dataset Using a Deep Learning Approach

Abstract

Contact-free sensors offer important advantages compared to traditional wearables. Radio-frequency sensors (e.g., radars) offer the means to monitor cardiorespiratory activity of people without compromising their privacy, however, only limited information can be obtained via movement, traditionally related to heart or breathing rate. We investigated whether five complex hemodynamics scenarios (resting, apnea simulation, Valsalva maneuver, tilt up and tilt down on a tilt table) can be classified directly from publicly available contact and radar input signals in an end-to-end deep learning approach. A series of robust k-fold cross-validation evaluation experiments were conducted in which neural network architectures and hyperparameters were optimized, and different data input modalities (contact, radar and fusion) and data types (time and frequency domain) were investigated. We achieved reasonably high accuracies of 88% for contact, 83% for radar and 88% for fusion of modalities. These results are valuable in showing large potential of radar sensing even for more complex scenarios going beyond just heart and breathing rate. Such contact-free sensing can be valuable for fast privacy-preserving hospital screenings and for cases where traditional werables are impossible to use.

Radar Vital Signs Detection by Using Optimized CNN+GRU Model

“…designed a deep learning model to detect vital signals with contact and non-contact methods. With the proposed method, they achieved 88% accuracy in contact measurements, 83% in non-contact measurements, and 88% with fusion processes [18]. Savage et al analyzed vital signals obtained from 75 patient individuals by using pattern recognition techniques.…”

Tensor computing for big data analytic

“…These systems have different pros and cons during operations [230,231,232,291], thanks to their dedicated hardware systems which result in high resolution or sensing range / performance, they have been deployed for various applications such as indoor healthcare monitoring [292,293,294,295,296,297,298,257,273,274,256,258,299,275,276,239,242,244,245,271,300,254,301], in-vehicle monitoring [302,278,253,285,259,303], and continuous user authentication [304,284].…”