# Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark

arXiv: https://arxiv.org/abs/2101.01665 · HTML: https://mlsanity.com/paper/2101.01665/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 5 Jan 2021.

## Builds on (most cited first, 5 of 5)

- [Multi-task Self-Supervised Learning for Human Activity Detection](https://mlsanity.com/paper/1907.11879/) (2019)
- [Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art](https://mlsanity.com/paper/1806.05226/) (2018)
- [Human Activity Recognition with Convolutional Neural Netowrks](https://mlsanity.com/paper/1906.01935/) (2019)
- [PerceptionNet: A Deep Convolutional Neural Network for Late Sensor Fusion](https://mlsanity.com/paper/1811.00170/) (2018)
- [Personalized Activity Recognition with Deep Triplet Embeddings](https://mlsanity.com/paper/2001.05517/) (2020)

## Built on by (most cited first, 5 of 5)

- [Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects](https://mlsanity.com/paper/2202.03274/) (2022)
- [BenchHAR: Benchmarking Self-Supervised Learning for Generalizable Sensor-based Activity Recognition](https://mlsanity.com/paper/2605.08296/) (2026)
- [Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series](https://mlsanity.com/paper/2212.06146/) (2022)
- [Invisible-to-Visible: Privacy-Aware Human Instance Segmentation using Airborne Ultrasound via Collaborative Learning Variational Autoencoder](https://mlsanity.com/paper/2204.07280/) (2022)
- [KAN-MLP-Mixer: A comprehensive investigation of the usage of Kolmogorov-Arnold Networks (KANs) for improving IMU-based Human Activity Recognition](https://mlsanity.com/paper/2605.19031/) (2026)

## Abstract (arXiv)

Recognizing human activity plays a significant role in the advancements of human-interaction applications in healthcare, personal fitness, and smart devices. Many papers presented various techniques for human activity representation that resulted in distinguishable progress. In this study, we conduct an extensive literature review on recent, top-performing techniques in human activity recognition based on wearable sensors. Due to the lack of standardized evaluation and to assess and ensure a fair comparison between the state-of-the-art techniques, we applied a standardized evaluation benchmark on the state-of-the-art techniques using six publicly available data-sets: MHealth, USCHAD, UTD-MHAD, WISDM, WHARF, and OPPORTUNITY. Also, we propose an experimental, improved approach that is a hybrid of enhanced handcrafted features and a neural network architecture which outperformed top-performing techniques with the same standardized evaluation benchmark applied concerning MHealth, USCHAD, UTD-MHAD data-sets.