Human activity recognition using CNN and long term short term memory
Bibliographic Data
| ID | 15797885 |
|---|---|
| Authors | Rohith Krishna Murthy (corresponding author), S Dhanraj, T N Manjunath, Achyutha Prasad N (0000-0002-2356-4146), Achyutha N Prasad, Piyush Kumar Pareek (0000-0003-2287-0122, corresponding author), Harish N Kumar (Dr. Hari Singh Gour University) |
| Year | 2022 |
| Pages | 10797-10809 |
| Publication date | 2022-09-22 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Health Sciences (JOURNAL) |
| Journal identifiers | ISSN: 2550-696X • E-ISSN: 2550-6978 |
| Publisher | Universidad Tecnica de Manabi (PUBLISHER • EC) |
| DOI | 10.53730/ijhs.v6ns6.12919 |
| OpenAlex | W4296617360 |
| Language | EN |
| References cited | 8 |
Human activity recognition aims to work out the activities performed by someone in a picture or video. Examples of actions are running, sitting, sleeping, and standing. Complex movement patterns and harmful occurrences like falling may be a part of these activities. The suggested ConvLSTM network can be created by successively combining fully connected layers, long immediate memory (LSTM) networks, and convolutional neural networks (CNN). The acquisition system will pre calculate skeleton coordinates using human detection and pose estimation from the image/video sequence. The ConvLSTM model builds new controlled features from the raw skeleton coordinates and their distinctive geometric and kinematic properties. Raw skeleton coordinates are utilized to generate geometric and kinematic properties supported by relative joint position values, joint differences, and their angular velocities. By utilizing a multi-player trained CNN-LSTM combination, novel spatiotemporal directed features can be obtained. The classification head with completely connected layers is then utilized. The suggested model was tested using the KinectHAR dataset, which consists of 130,000 samples with 81 attribute variables and was compiled using the Kinect (v2) sensor Experimental data is used to compare the performance of independent CNN and LSTM networks
Computer vision · Convolutional neural network · Human skeleton · Joint (building · Kinematics · Pattern recognition (psychology · Position (finance · Skeleton (computer programming · Term (time · Anomaly Detection Techniques and Applications · Computer Science · Context-Aware Activity Recognition Systems · Human Pose and Action Recognition · Artificial Intelligence
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| Highly cited | No |
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