A low-latency deep learning approach for human action recognition in medical internet of things applications
Year: 2026
Type: article
Abstract: Real-time human action recognition (HAR) plays a vital role in healthcare monitoring, particularly for elderly care and assistive environments. However, existing HAR systems often struggle with high computational demands, large model sizes, and vulnerability to background noise, limiting their use on edge devices in the Internet of Medical Things (IoMT) settings. This study proposes Temporal-SpatialCNN, a lightweight framework built on a novel CNNBlock architecture that integrates convolution, batch normalization, and Spatial-Dropout to enhance both efficiency and generalization. The study systematically analyzes various layer arrangements within CNNBlock and identifies an optimal configuration that improves recognition performance while maintaining minimal computational overhead. The model incorporates diverse skeletal input modalities – joint coordinates, joint collection distances, slow motion, and velocity – to capture enriched spatio-temporal features. Extensive experiments on five well-known benchmark datasets validate the effectiveness of the proposed approach, achieving state-of-the-art accuracy (up to 99.66% on the Florence-3D dataset) with an inference time of 9.6 ms. To demonstrate real-world applicability, a real-time Save Our Soul (SOS) system was implemented on standard hardware, capable of detecting emergency gestures such as calls for assistance, thereby highlighting the model’s practical potential in real-time, resource-constrained healthcare scenarios.
Authors: Hoangcong Le, Cheng-Kai Lu
Institution: National Taiwan Normal University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,880
Awards: Cognitive Psychology and Deep Learning-Based Cognitive Learning Robot and Its Industrial Application, Multi-Objective Crowd-Aware Robot Navigation System Using Deep Reinforcement Learning, Deep-Learning and Federated Learning Framework Based Retinal Fluid Detection and Segmentation in Optical Coherence Tomography
OpenAlex ID: W7127985322