# Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey

arXiv: https://arxiv.org/abs/2004.08821 · HTML: https://mlsanity.com/paper/2004.08821/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2020.

## Built on by (most cited first, 26 of 26)

- [Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-Worn Inertial Sensors](https://mlsanity.com/paper/2305.13124/) (2023)
- [Progressive Cross-modal Knowledge Distillation for Human Action Recognition](https://mlsanity.com/paper/2208.08090/) (2022)
- [B-HAR: an open-source baseline framework for in depth study of human activity recognition datasets and workflows](https://mlsanity.com/paper/2101.10870/) (2021)
- [Scaling laws in wearable human activity recognition](https://mlsanity.com/paper/2502.03364/) (2025)
- [Estimating indoor occupancy through low-cost BLE devices](https://mlsanity.com/paper/2102.03351/) (2021)
- [HAROOD: A Benchmark for Out-of-distribution Generalization in Sensor-based Human Activity Recognition](https://mlsanity.com/paper/2512.10807/) (2025)
- [Inertia-1: An Open Exploration of Wearable Motion Foundation Models](https://mlsanity.com/paper/2607.06617/) (2026)
- [Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity Recognition](https://mlsanity.com/paper/2207.04367/) (2022)
- [MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction](https://mlsanity.com/paper/2306.10164/) (2023)
- [A Comprehensive Review of Automated Data Annotation Techniques in Human Activity Recognition](https://mlsanity.com/paper/2307.05988/) (2023)
- [SoK: Behind the Accuracy of Complex Human Activity Recognition Using Deep Learning](https://mlsanity.com/paper/2405.00712/) (2024)
- [CNN Autoencoders for Hierarchical Feature Extraction and Fusion in Multi-sensor Human Activity Recognition](https://mlsanity.com/paper/2502.04489/) (2025)
- [WaveGlove: Transformer-based hand gesture recognition using multiple inertial sensors](https://mlsanity.com/paper/2105.01753/) (2021)
- [A Survey on Multi-Resident Activity Recognition in Smart Environments](https://mlsanity.com/paper/2304.12304/) (2023)
- [A Multi-Stage Temporal Convolutional Network for Volleyball Jumps Classification Using a Waist-Mounted IMU](https://mlsanity.com/paper/2310.13097/) (2023)
- [LLM-Guided Exemplar Selection for Few-Shot Wearable-Sensor Human Activity Recognition](https://mlsanity.com/paper/2512.22385/) (2025)
- [Latent Space Unsupervised Semantic Segmentation](https://mlsanity.com/paper/2207.11067/) (2022)
- [Video-based Pose-Estimation Data as Source for Transfer Learning in Human Activity Recognition](https://mlsanity.com/paper/2212.01353/) (2022)
- [rWISDM: Repaired WISDM, a Public Dataset for Human Activity Recognition](https://mlsanity.com/paper/2305.10222/) (2023)
- [Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review](https://mlsanity.com/paper/2311.06311/) (2023)
- [Elevator, Escalator, or Neither? Classifying Conveyor State Using Smartphone under Arbitrary Pedestrian Behavior](https://mlsanity.com/paper/2405.03218/) (2024)
- [ALS-HAR: Harnessing Wearable Ambient Light Sensors to Enhance IMU-based Human Activity Recogntion](https://mlsanity.com/paper/2408.09527/) (2024)
- [Classification Filtering](https://mlsanity.com/paper/2509.13975/) (2025)
- [Human Activity Recognition Based on Electrocardiogram Data Only](https://mlsanity.com/paper/2509.19328/) (2025)
- [WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognition](https://mlsanity.com/paper/2606.13194/) (2026)
- [From Compression to Deployment: Real-Time and Energy-Efficient FastGRNN on Ultra-Constrained Microcontrollers](https://mlsanity.com/paper/2606.17249/) (2026)

## Abstract (arXiv)

In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.