Investigation of the optimal sensor location and classifier for human motion classification
Human motion monitoring by means of wearable technologies is not uncommon nowadays. This demonstrates the growing awareness of the importance of healthy lifestyle. Human body motion involves the movement of multiple muscles and joints. However, the optimal location of sensor placement on the body to...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institute of Electrical and Electronics Engineers Inc.
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39343/ http://umpir.ump.edu.my/id/eprint/39343/1/Investigation%20of%20the%20optimal%20sensor%20location%20and%20classifier%20for%20human.pdf http://umpir.ump.edu.my/id/eprint/39343/2/Investigation%20of%20the%20optimal%20sensor%20location%20and%20classifier%20for%20human%20motion%20classification_ABS.pdf |
| _version_ | 1848825742791540736 |
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| author | Anuar, Mohamed Nur Aqilah, Othman Hamzah, Ahmad Mohd Hasnun Ariff, Hassan |
| author_facet | Anuar, Mohamed Nur Aqilah, Othman Hamzah, Ahmad Mohd Hasnun Ariff, Hassan |
| author_sort | Anuar, Mohamed |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Human motion monitoring by means of wearable technologies is not uncommon nowadays. This demonstrates the growing awareness of the importance of healthy lifestyle. Human body motion involves the movement of multiple muscles and joints. However, the optimal location of sensor placement on the body to record the motion in daily activities has not been well understood. This study aims to find the best sensor location for this purpose among three locations on the body, that is on the back, shank, or wrist. In addition, this study seeks to find the best classification algorithm for human daily activities. The data recorded at these three locations were analysed using several classification algorithms in both Orange software and MATLAB. The results show that the sensor on the wrist provided the best classification result, thereby suggesting that wrist is the best place on the body to place the sensor for human motion monitoring. With regards to classification algorithm, we found that Neural Network provides the most accurate classification as compared to other algorithms. Future development of wearables should look into integrating classification algorithm in the system, thus the human motion monitoring will provide a richer information and not only limited to number of steps and calories burned. |
| first_indexed | 2025-11-15T03:33:46Z |
| format | Conference or Workshop Item |
| id | ump-39343 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:33:46Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-393432023-11-21T01:11:56Z http://umpir.ump.edu.my/id/eprint/39343/ Investigation of the optimal sensor location and classifier for human motion classification Anuar, Mohamed Nur Aqilah, Othman Hamzah, Ahmad Mohd Hasnun Ariff, Hassan T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Human motion monitoring by means of wearable technologies is not uncommon nowadays. This demonstrates the growing awareness of the importance of healthy lifestyle. Human body motion involves the movement of multiple muscles and joints. However, the optimal location of sensor placement on the body to record the motion in daily activities has not been well understood. This study aims to find the best sensor location for this purpose among three locations on the body, that is on the back, shank, or wrist. In addition, this study seeks to find the best classification algorithm for human daily activities. The data recorded at these three locations were analysed using several classification algorithms in both Orange software and MATLAB. The results show that the sensor on the wrist provided the best classification result, thereby suggesting that wrist is the best place on the body to place the sensor for human motion monitoring. With regards to classification algorithm, we found that Neural Network provides the most accurate classification as compared to other algorithms. Future development of wearables should look into integrating classification algorithm in the system, thus the human motion monitoring will provide a richer information and not only limited to number of steps and calories burned. Institute of Electrical and Electronics Engineers Inc. 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39343/1/Investigation%20of%20the%20optimal%20sensor%20location%20and%20classifier%20for%20human.pdf pdf en http://umpir.ump.edu.my/id/eprint/39343/2/Investigation%20of%20the%20optimal%20sensor%20location%20and%20classifier%20for%20human%20motion%20classification_ABS.pdf Anuar, Mohamed and Nur Aqilah, Othman and Hamzah, Ahmad and Mohd Hasnun Ariff, Hassan (2022) Investigation of the optimal sensor location and classifier for human motion classification. In: ICCSCE 2022 - Proceedings: 2022 12th IEEE International Conference on Control System, Computing and Engineering , 21-22 October 2022 , Penang. pp. 142-146. (184194). ISBN 978-166548339-1 (Published) https://doi.org/10.1109/ICCSCE54767.2022.9935635 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Anuar, Mohamed Nur Aqilah, Othman Hamzah, Ahmad Mohd Hasnun Ariff, Hassan Investigation of the optimal sensor location and classifier for human motion classification |
| title | Investigation of the optimal sensor location and classifier for human motion classification |
| title_full | Investigation of the optimal sensor location and classifier for human motion classification |
| title_fullStr | Investigation of the optimal sensor location and classifier for human motion classification |
| title_full_unstemmed | Investigation of the optimal sensor location and classifier for human motion classification |
| title_short | Investigation of the optimal sensor location and classifier for human motion classification |
| title_sort | investigation of the optimal sensor location and classifier for human motion classification |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/39343/ http://umpir.ump.edu.my/id/eprint/39343/ http://umpir.ump.edu.my/id/eprint/39343/1/Investigation%20of%20the%20optimal%20sensor%20location%20and%20classifier%20for%20human.pdf http://umpir.ump.edu.my/id/eprint/39343/2/Investigation%20of%20the%20optimal%20sensor%20location%20and%20classifier%20for%20human%20motion%20classification_ABS.pdf |