Resource-Efficient Hybrid CNN-HMM Approach for Heart Rate Classification

There is a growing interest in using Deep learning (DL) to analyse sensor data for monitoring heart-rate, aiming for earlier and accurate prediction of cardiovascular diseases (CvD). However, the resource-intensive nature of DL pose challenges, particularly on memory-limited devices like smartphones and other edge devices. To address this issue, we propose a novel approach that combines a Convolutional Neural Network-Hidden Markov Model (CNN-HMM). In this model, features from signals on smartphones or other edge devices are extracted using a CNN block, while classification is performed using an HMM. This CNN-HMM model offers advantages in processing non-stationary data and effectively extracting heart rate information. Additionally, we aim to address the challenge of Deep Neural Network (DNN) inference on resource-constrained edge devices by systematically comparing different light-weight models on several commercial hardware platforms (Raspberry Pi 4, high-performance computing (HPC) platforms) across popular frameworks, highlighting their performance and limitations. The proposed approach has achieved an accuracy of 96.39%, a precision of 96.27%, a recall of 97.20%, and an F1-score of 96.73% on NVIDIA Tesla T4 GPU (Graphics Processing Unit). Additionally, our model has demonstrated enhanced throughput, outperforming fine-tuned MobileNetV2, NASNet-Mobile, and MobileNetV3Small models by 77.62%, 138.65%, and 39.92% respectively on the Raspberry Pi 4 hardware platform.