A Close Look into Human Activity Recognition Models using Deep Learning
Not on the map. Why?
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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 8 of its 46 references that are in the map. First posted on arXiv 26 Apr 2022.
Builds on
8 of 46 references.
References · 8
- 2017Deep Learning for Sensor-based Activity Recognition: A Surveycited by 133
- 2017Ensembles of Deep LSTM Learners for Activity Recognition using Wearablescited by 61
- 2017Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensorscited by 24
- 2021Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detectioncited by 15
- 2022Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learningcited by 12
- 2021Machine Learning Algorithms In User Authentication Schemescited by 11
- 2021A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methodscited by 10
- 2020Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensorscited by 7
Built on by
Citing papers per year: 2023: 6; 2024: 1; 2025: 1.
8 papers.
- 2023Leveraging Deep Learning Approaches for Deepfake Detection: A Reviewcited by 2
- 2023Hybrid Deepfake Detection Utilizing MLP and LSTMcited by 1
- 2023Your Identity is Your Behavior -- Continuous User Authentication based on Machine Learning and Touch Dynamicscited by 1
- 2024Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learningcited by 1
- 2023Machine Learning Based Approach to Recommend MITRE ATT&CK Framework for Software Requirements and Design Specificationscited by 0
- 2023Recent Advancements In The Field Of Deepfake Detectioncited by 0
- 2023Augmenting Vision-Based Human Pose Estimation with Rotation Matrixcited by 0
- 2025SOS: A Shuffle Order Strategy for Data Augmentation in Industrial Human Activity Recognitioncited by 0
- 2025SOS: A Shuffle Order Strategy for Data Augmentation in Industrial Human Activity Recognition15 May 2025
- 2024Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning6 Mar 2024
- 2023Augmenting Vision-Based Human Pose Estimation with Rotation Matrix9 Oct 2023
- 2023Recent Advancements In The Field Of Deepfake Detection10 Aug 2023
- 2023Your Identity is Your Behavior -- Continuous User Authentication based on Machine Learning and Touch Dynamics24 Apr 2023
- 2023Hybrid Deepfake Detection Utilizing MLP and LSTM21 Apr 2023
- 2023Leveraging Deep Learning Approaches for Deepfake Detection: A Review4 Apr 2023
- 2023Machine Learning Based Approach to Recommend MITRE ATT&CK Framework for Software Requirements and Design Specifications10 Feb 2023