Low-Memory-Footprint CNN-Based Biomedical Signal Processing for Wearable Devices

Low-Memory-Footprint CNN-Based Biomedical Signal Processing for Wearable Devices

The rise of wearable devices has enabled real-time processing of sensor data for critical health monitoring applications, such as human activity recognition (HAR) and cardiac disorder classification (CDC). However, the limited computational and memory resources of wearables necessitate lightweight y...

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Bibliographic Details
Main Authors: Zahra Kokhazad, Dimitrios Gkountelos, Milad Kokhazadeh, Charalampos Bournas, Georgios Keramidas, Vasilios Kelefouras
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:IoT
Subjects:
biomedical applications
human activity recognition
cardiac disorder classification
filter-based pruning
low-rank factorization
quantization
Online Access:https://www.mdpi.com/2624-831X/6/2/29
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