for Human Activity Recognition: Overview, Advances, and Challenges

for Human Activity Recognition: Overview, Advances, and Challenges

for Human Activity Recognition: Overview, Advances, and Challenges

for Human Activity Recognition: Overview, Advances, and Challenges

Advanced Human Activity Recognition Using Wearable Sensors

This study reviews pre-2018 developments and proposes an improved HAR framework incorporating advanced feature engineering, sensor fusion, and ensemble learning techniques, which shows strong potential in healthcare, fitness, and smart environments.

From Inertial to Ambient: A Systematic Review of Sensor Modalities and Data Acquisition Strategies in Human Activity Recognition

- 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.

A Review of TinyML for Human Activity Recognition on Edge Devices

The integration of Tiny Machine Learning (TinyML) into human activity recognition (HAR) represents a paradigm shift in artificial intelligence, enabling real-time, efficient, and privacy-preserving analysis on resource-constrained edge devices. This paper presents a comprehensive review of TinyML for HAR, covering foundational concepts, methodologies, and applications. the review examines the state of the art of existing works and approaches that combine TinyML and HAR, providing a detailed review and comparison of models, algorithms, and frameworks. This comparison sheds light on the effectiveness and limitations of different methodologies. Key contributions include a systematic taxonomy of HAR systems leveraging TinyML, a detailed analysis of optimization techniques like pruning, quantization, and knowledge distillation, and insights into state-of-the-art frameworks and datasets. Challenges such as scalability, energy efficiency, and generalization to diverse environments are critically examined, alongside solutions like federated learning, multimodal data fusion, the integration of generative AI, neuromorphic hardware, 5G/6G and Internet Of Things (IoT) connectivity, which are highlighted as transformative enablers for advancing HAR applications. This review serves as a foundational resource for researchers and practitioners aiming to harness TinyML’s potential in activity recognition systems.