Convolutional Neural Network-Based Human Activity Recognition for Edge Fitness and Context-Aware Health Monitoring Devices

The vital signs can vary significantly depending on the daily physical activities, which may not be due to defects of the organs. Under remote human health-monitoring applications, for reliable disease diagnosis, recorded biomedical signals such as an electrocardiogram (ECG) and a photoplethysmogram (PPG) must be indexed with physical activity information, as it is unknown to the physicians and also to the computer-aided diagnostic system. Since deep-learning (DL) networks were explored for various vital signs extraction and disease classification, we present an effective convolutional neural network (CNN)-based human activity recognition (HAR) method by exploring suitable hyperparameters. CNN-based methods are evaluated using the acceleration signals taken from the standard HAR benchmark database, University of California, Irvine (UCI) with accuracy (ACC), model size (in kB), and processing time (PT), and also implemented on the Raspberry Pi 4 (R-Pi-4) to study real-time feasibility. Evaluation results showed that higher HAR accuracy can be achieved with the activation function of the exponential linear unit (ELU) for the 2-layer CNN with a segment size of 2.5 s (ACC of 89.05% and PT of 0.142 ms), the 4-layer CNN with a segment size of 1 s (ACC of 91.66% and PT of 0.541 ms), and the 6-layer CNN with a segment size of 2 s (ACC of 91.18% and PT of 1.672 ms). Results demonstrated that the selection of an optimal number of layers and hyperparameters plays a major role in achieving higher accuracy with lower computational time on both personal computer central processing unit (PC-CPU) and R-Pi computing platforms, which were not addressed in past studies.

Automated summary

The variation in vital signs may be attributed to daily physical activities rather than organ defects. This study proposes a convolutional neural network-based human activity recognition method and evaluates its performance using acceleration signals from a standard benchmark database. The results demonstrate the importance of selecting optimal hyperparameters and the number of layers to achieve higher accuracy and shorter computational time.