# Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects

arXiv: https://arxiv.org/abs/2202.03274 · HTML: https://mlsanity.com/paper/2202.03274/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 205 references that are in the map.
- First posted on arXiv 2 Feb 2022.

## Builds on (most cited first, 22 of 22)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [YouTube-8M: A Large-Scale Video Classification Benchmark](https://mlsanity.com/paper/1609.08675/) (2016)
- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding](https://mlsanity.com/paper/1703.07475/) (2017)
- [Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors](https://mlsanity.com/paper/1505.04868/) (2015)
- [UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones](https://mlsanity.com/paper/1611.07688/) (2016)
- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [Multi-task Deep Learning for Real-Time 3D Human Pose Estimation and Action Recognition](https://mlsanity.com/paper/1912.08077/) (2019)
- [EmbraceNet: A robust deep learning architecture for multimodal classification](https://mlsanity.com/paper/1904.09078/) (2019)
- [HuGaDB: Human Gait Database for Activity Recognition from Wearable Inertial Sensor Networks](https://mlsanity.com/paper/1705.08506/) (2017)
- [DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors](https://mlsanity.com/paper/2006.14435/) (2020)
- [Attention-based Convolutional Neural Network for Weakly Labeled Human Activities Recognition with Wearable Sensors](https://mlsanity.com/paper/1903.10909/) (2019)
- [A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches](https://mlsanity.com/paper/2010.07051/) (2020)
- [AHAR: Adaptive CNN for Energy-efficient Human Activity Recognition in Low-power Edge Devices](https://mlsanity.com/paper/2102.01875/) (2021)
- [Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques](https://mlsanity.com/paper/1811.01627/) (2018)
- [Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark](https://mlsanity.com/paper/2101.01665/) (2021)
- [Landscaping Systematic Mapping Studies in Software Engineering: A Tertiary Study](https://mlsanity.com/paper/1909.10751/) (2019)
- [A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network](https://mlsanity.com/paper/2101.00702/) (2021)

## Built on by (most cited first, 10 of 10)

- [MVSA-Net: Multi-View State-Action Recognition for Robust and Deployable Trajectory Generation](https://mlsanity.com/paper/2311.08393/) (2023)
- [SoK: Behind the Accuracy of Complex Human Activity Recognition Using Deep Learning](https://mlsanity.com/paper/2405.00712/) (2024)
- [MuJo: Multimodal Joint Feature Space Learning for Human Activity Recognition](https://mlsanity.com/paper/2406.03857/) (2024)
- [CNN Autoencoders for Hierarchical Feature Extraction and Fusion in Multi-sensor Human Activity Recognition](https://mlsanity.com/paper/2502.04489/) (2025)
- [Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2502.16708/) (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)
- [Advancements In Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-The-Art](https://mlsanity.com/paper/2308.03907/) (2023)
- [A Lightweight 3D-CNN for Event-Based Human Action Recognition with Privacy-Preserving Potential](https://mlsanity.com/paper/2511.03665/) (2025)
- [Online Human Action Detection during Escorting](https://mlsanity.com/paper/2506.23573/) (2025)
- [Multilevel neural networks with dual-stage feature fusion for human activity recognition](https://mlsanity.com/paper/2604.16577/) (2026)

## Abstract (arXiv)

Human Activity Recognition (HAR) plays a significant role in the everyday life of people because of its ability to learn extensive high-level information about human activity from wearable or stationary devices. A substantial amount of research has been conducted on HAR and numerous approaches based on deep learning and machine learning have been exploited by the research community to classify human activities. The main goal of this review is to summarize recent works based on a wide range of deep neural networks architecture, namely convolutional neural networks (CNNs) for human activity recognition. The reviewed systems are clustered into four categories depending on the use of input devices like multimodal sensing devices, smartphones, radar, and vision devices. This review describes the performances, strengths, weaknesses, and the used hyperparameters of CNN architectures for each reviewed system with an overview of available public data sources. In addition, a discussion with the current challenges to CNN-based HAR systems is presented. Finally, this review is concluded with some potential future directions that would be of great assistance for the researchers who would like to contribute to this field.