From Inertial to Ambient: A Systematic Review of Sensor Modalities and Data Acquisition Strategies in Human Activity Recognition
TL;DR
This systematic review provides a comprehensive examination of the full spectrum of sensor modalities employed in HAR research spanning wearable inertial sensors, physiological sensors, vision-based sensors, depth cameras, ambient sensing systems, and emerging multimodal fusion frameworks tracing, technical characteristics, application domains, and comparative strengths and limitations.
Abstract
- Human Activity Recognition (HAR) is a rapidly advancing research domain with transformative applications across healthcare, smart homes, rehabilitation, elderly monitoring, sports analytics, and context-aware computing. The performance and deployability of HAR systems are fundamentally determined by the sensor modalities through which human motion and behavioral data are captured. This systematic review provides a comprehensive examination of the full spectrum of sensor modalities employed in HAR research spanning wearable inertial sensors, physiological sensors, vision-based sensors, depth cameras, ambient sensing systems, and emerging multimodal fusion frameworks tracing, technical characteristics, application domains, and comparative strengths and limitations. The review further examines data acquisition protocols, benchmark datasets, and the persistent challenges of inter-subject variability, sensor placement sensitivity, privacy constraints, and computational efficiency that continue to shape the field. Emerging directions including federated sensing, edge-optimized data acquisition, self-supervised learning from unlabeled sensor streams, and privacy-preserving modalities are critically evaluated as promising pathways toward robust, scalable, and ethically responsible HAR deployment. By synthesizing evidence across more than a decade of HAR sensor research, this review provides a structured reference for researchers and practitioners designing next-generation activity recognition systems, and identifies the most consequential open challenges and future directions for the field.