# A Close Look into Human Activity Recognition Models using Deep Learning

arXiv: https://arxiv.org/abs/2204.13589 · HTML: https://mlsanity.com/paper/2204.13589/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 26 Apr 2022.

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

- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [Ensembles of Deep LSTM Learners for Activity Recognition using Wearables](https://mlsanity.com/paper/1703.09370/) (2017)
- [Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors](https://mlsanity.com/paper/1708.08989/) (2017)
- [Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection](https://mlsanity.com/paper/2109.05701/) (2021)
- [Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning](https://mlsanity.com/paper/2201.08564/) (2022)
- [Machine Learning Algorithms In User Authentication Schemes](https://mlsanity.com/paper/2110.07826/) (2021)
- [A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods](https://mlsanity.com/paper/2110.07832/) (2021)
- [Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors](https://mlsanity.com/paper/2005.03948/) (2020)

## Built on by (most cited first, 8 of 8)

- [Leveraging Deep Learning Approaches for Deepfake Detection: A Review](https://mlsanity.com/paper/2304.01908/) (2023)
- [Hybrid Deepfake Detection Utilizing MLP and LSTM](https://mlsanity.com/paper/2304.14504/) (2023)
- [Your Identity is Your Behavior -- Continuous User Authentication based on Machine Learning and Touch Dynamics](https://mlsanity.com/paper/2305.09482/) (2023)
- [Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning](https://mlsanity.com/paper/2403.03832/) (2024)
- [Machine Learning Based Approach to Recommend MITRE ATT&CK Framework for Software Requirements and Design Specifications](https://mlsanity.com/paper/2302.05530/) (2023)
- [Recent Advancements In The Field Of Deepfake Detection](https://mlsanity.com/paper/2308.05563/) (2023)
- [Augmenting Vision-Based Human Pose Estimation with Rotation Matrix](https://mlsanity.com/paper/2310.06068/) (2023)
- [SOS: A Shuffle Order Strategy for Data Augmentation in Industrial Human Activity Recognition](https://mlsanity.com/paper/2505.10312/) (2025)

## Abstract (arXiv)

Human activity recognition using deep learning techniques has become increasing popular because of its high effectivity with recognizing complex tasks, as well as being relatively low in costs compared to more traditional machine learning techniques. This paper surveys some state-of-the-art human activity recognition models that are based on deep learning architecture and has layers containing Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), or a mix of more than one type for a hybrid system. The analysis outlines how the models are implemented to maximize its effectivity and some of the potential limitations it faces.