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

arXiv: https://arxiv.org/abs/2103.15990 · HTML: https://mlsanity.com/paper/2103.15990/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 8 of 8)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Interpretable Convolutional Neural Networks](https://mlsanity.com/paper/1710.00935/) (2017)
- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [Few-Shot Learning-Based Human Activity Recognition](https://mlsanity.com/paper/1903.10416/) (2019)
- [BWCNN: Blink to Word, a Real-Time Convolutional Neural Network Approach](https://mlsanity.com/paper/2006.01232/) (2020)
- [Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling](https://mlsanity.com/paper/1805.07233/) (2018)
- [Learning-to-Learn Personalised Human Activity Recognition Models](https://mlsanity.com/paper/2006.07472/) (2020)

## Built on by (most cited first, 11 of 11)

- [Transformer-based Models to Deal with Heterogeneous Environments in Human Activity Recognition](https://mlsanity.com/paper/2209.11750/) (2022)
- [MAiVAR-T: Multimodal Audio-image and Video Action Recognizer using Transformers](https://mlsanity.com/paper/2308.03741/) (2023)
- [Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches](https://mlsanity.com/paper/2105.06295/) (2021)
- [mmExpert: Integrating Large Language Models for Comprehensive mmWave Data Synthesis and Understanding](https://mlsanity.com/paper/2509.16521/) (2025)
- [A Prospective Approach for Human-to-Human Interaction Recognition from Wi-Fi Channel Data using Attention Bidirectional Gated Recurrent Neural Network with GUI Application Implementation](https://mlsanity.com/paper/2202.08146/) (2022)
- [Walk4Me: Telehealth Community Mobility Assessment, An Automated System for Early Diagnosis and Disease Progression](https://mlsanity.com/paper/2305.05543/) (2023)
- [Physically Plausible Data Augmentations for Wearable IMU-based Human Activity Recognition Using Physics Simulation](https://mlsanity.com/paper/2508.13284/) (2025)
- [Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning](https://mlsanity.com/paper/2308.14843/) (2023)
- [Unified Contrastive Fusion Transformer for Multimodal Human Action Recognition](https://mlsanity.com/paper/2309.05032/) (2023)
- [MU-MAE: Multimodal Masked Autoencoders-Based One-Shot Learning](https://mlsanity.com/paper/2408.04243/) (2024)
- [Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensors](https://mlsanity.com/paper/2512.03287/) (2025)

## 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.