The DOI matches its Crossref record; 26 of the 29 references it cites were found in a registry or an index.
Placement Effect of Motion Sensors for Human Activity Recognition using LSTM Network
Abstract
There are various possibilities for wearable sensors to determine the recognition of human condition. In order to theoretically drive with wearable sensor technology, several difficult issues have been studied and solved. We currently succeeded in computing the efficient high performance deep learning model for the sensor-based human activity recognition (HAR). There is, however, an influence study challenge regarding the impact of the body-mounted sensor position on the Long Short-Term Memory (LSTM) network. In this work, we propose to conduct several experiments in order to discover the optimal placement for each physical activity. The experimental results show that by using heterogeneous sensor data fusion, the chest position is ideal for physical activity identification with a maximum accuracy of 94.18%.
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Researchers in [220] discovered that the chest position is ideal for physical activity identification.
“Researchers in [220] explored the placement effect of motion sensors and discovered that the chest position is ideal for physical activity identification.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [220]
Citations
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Flagged first, then by citations
3 of the 29 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- wong, 2020, Location spoofing attack detection with pre-installed sensors in mobile devices, Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications (JoWUA), 11, 16
- 10.1109/ECTI-NCON.2019.8692229 · DOI
Cited by
Sorted by citations
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625
“Researchers in [187] explored the placement effect of motion sensors and discovered that the chest position is ideal for physical activity identification.”
BackgroundResearch on Human Activity Recognition Algorithm Based on LSTM-1DCNN
Zhao, Y., Wang, X., Luo, Y., & Aslam, M. S. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.040528 · Cited by 6
Use of Clustering Algorithms for Sensor Placement and Activity Recognition in Smart Homes
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Metadata
- Authors
- Mekruksavanich, S., Jitpattanakul, A., & Thongkum, P.
- Affiliations
- University of Phayao (TH)
- King Mongkut's University of Technology North Bangkok (TH)
- Venue
- 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering
- Publisher
- IEEE
- Published
- 2021-03-03
- Pages
- 273-276
- OpenAlex
- W3160038997
- Cited by
- 3
- References
- 29
- Citations by year
- 2023: 2 · 2022: 1
- FWCI
- 0.25
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON) (publishedVersion)
- Funders
- University of Phayao
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Keywords
- human activity recognition, wearable sensors, sensor placement, long short-term memory networks, heterogeneous sensor data fusion, physical activity classification
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