Edge Intelligence for Human Activity Recognition Using Resource-Constrained Sensors

Abstract: Human activity recognition (HAR) systems are evolving beyond traditional high-bandwidth sensors toward resource-efficient alternatives that can operate on edge devices while preserving user privacy. This survey examines HAR approaches using low-resource sensing modalities including inertial measurement units (IMUs), structural vibrations, acoustic emissions, capacitive sensing, and pressure sensors. We analyze signal processing pipelines for these modalities, feature extraction techniques optimized for limited computational resources, and lightweight machine learning models suitable for microcontroller deployment. The survey covers data augmentation strategies for small datasets, transfer learning approaches from simulated to real environments, and sensor fusion techniques that combine multiple low-cost sensors. Additionally, we discuss energy harvesting for self-powered operation, real-time constraints in edge computing, and the trade-offs between model complexity and recognition accuracy in resource-constrained settings.