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An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence

arXiv 2103.15990 ↗First posted 29 Mar 2021cs.HC cs.LG eess.SPcited by 11

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Abstract (arXiv)

With the rapid development of the internet of things (IoT) and artificial intelligence (AI) technologies, human activity recognition (HAR) has been applied in a variety of domains such as security and surveillance, human-robot interaction, and entertainment. Even though a number of surveys and review papers have been published, there is a lack of HAR overview papers focusing on healthcare applications that use wearable sensors. Therefore, we fill in the gap by presenting this overview paper. In particular, we present our projects to illustrate the system design of HAR applications for healthcare. Our projects include early mobility identification of human activities for intensive care unit (ICU) patients and gait analysis of Duchenne muscular dystrophy (DMD) patients. We cover essential components of designing HAR systems including sensor factors (e.g., type, number, and placement location), AI model selection (e.g., classical machine learning models versus deep learning models), and feature engineering. In addition, we highlight the challenges of such healthcare-oriented HAR systems and propose several research opportunities for both the medical and the computer science community.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 8 of its 33 references that are in the map. First posted on arXiv 29 Mar 2021.

Builds on

8 of 33 references.

References · 8

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  3. 2017Interpretable Convolutional Neural Networkscited by 161
  4. 2017Deep Learning for Sensor-based Activity Recognition: A Surveycited by 133
  5. 2019Few-Shot Learning-Based Human Activity Recognitioncited by 11
  6. 2020BWCNN: Blink to Word, a Real-Time Convolutional Neural Network Approachcited by 6
  7. 2018Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modelingcited by 3
  8. 2020Learning-to-Learn Personalised Human Activity Recognition Modelscited by 1

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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 4; 2024: 1; 2025: 3.

11 papers.

  1. 2022Transformer-based Models to Deal with Heterogeneous Environments in Human Activity Recognitioncited by 12
  2. 2023MAiVAR-T: Multimodal Audio-image and Video Action Recognizer using Transformerscited by 4
  3. 2021Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approachescited by 2
  4. 2025mmExpert: Integrating Large Language Models for Comprehensive mmWave Data Synthesis and Understandingcited by 2
  5. 2022A Prospective Approach for Human-to-Human Interaction Recognition from Wi-Fi Channel Data using Attention Bidirectional Gated Recurrent Neural Network with GUI Application Implementationcited by 1
  6. 2023Walk4Me: Telehealth Community Mobility Assessment, An Automated System for Early Diagnosis and Disease Progressioncited by 1
  7. 2025Physically Plausible Data Augmentations for Wearable IMU-based Human Activity Recognition Using Physics Simulationcited by 1
  8. 2023Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learningcited by 0
  9. 2023Unified Contrastive Fusion Transformer for Multimodal Human Action Recognitioncited by 0
  10. 2024MU-MAE: Multimodal Masked Autoencoders-Based One-Shot Learningcited by 0
  11. 2025Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensorscited by 0