Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions
Falls represent a significant risk factor, necessitating accurate classification methods. This study aims to identify the optimal placement of wearable sensors—specifically accelerometers, gyroscopes, and magnetometers—for effective fall-direction classification. Although previous research identifie...
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MDPI AG
2024-10-01
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| author | Sokea Teng Jung-Yeon Kim Seob Jeon Hyo-Wook Gil Jiwon Lyu Euy Hyun Chung Kwang Seock Kim Yunyoung Nam |
| author_facet | Sokea Teng Jung-Yeon Kim Seob Jeon Hyo-Wook Gil Jiwon Lyu Euy Hyun Chung Kwang Seock Kim Yunyoung Nam |
| author_sort | Sokea Teng |
| collection | DOAJ |
| description | Falls represent a significant risk factor, necessitating accurate classification methods. This study aims to identify the optimal placement of wearable sensors—specifically accelerometers, gyroscopes, and magnetometers—for effective fall-direction classification. Although previous research identified optimal sensor locations for distinguishing falls from non-falls, limited attention has been given to the classification of fall direction across different body regions. This study assesses inertial measurement unit (IMU) sensors placed at 12 distinct body locations to determine the most effective positions for capturing fall-related data. The research was conducted in three phases: first, comparing classifiers across all sensor locations to identify the most effective; second, evaluating performance differences between sensors placed on the left and right sides of the body; and third, exploring the efficacy of combining sensors from the upper and lower body regions. Statistical analyses of the results for the most effective classifier model demonstrate that the support vector machine (SVM) is more effective than other classifiers across all sensor locations, with statistically significant differences in performance. At the same time, the comparison between the left and right sensor locations shows no significant performance differences within the same anatomical areas. Regarding optimal sensor placement, the findings indicate that sensors positioned on the pelvis and upper legs in the lower body, as well as on the shoulder and head in the upper body, were the most effective results for accurate fall-direction classification. The study concludes that the optimal sensor configuration for fall-direction classification involves strategically combining sensors placed on the pelvis, upper legs, and lower legs. |
| format | Article |
| id | doaj-art-6b038c2cd9e24e3d943a0adbd195cdcb |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-6b038c2cd9e24e3d943a0adbd195cdcb2025-08-20T01:47:37ZengMDPI AGSensors1424-82202024-10-012419643210.3390/s24196432Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall DirectionsSokea Teng0Jung-Yeon Kim1Seob Jeon2Hyo-Wook Gil3Jiwon Lyu4Euy Hyun Chung5Kwang Seock Kim6Yunyoung Nam7Department of ICT Convergence, Soonchunhyang University, Asan 31538, Republic of KoreaICT Convergence Research Center, Soonchunhyang University, Asan 31538, Republic of KoreaDepartment of Obstetrics and Gynecology, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Republic of KoreaDepartment of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Republic of KoreaDivision of Respiratory Medicine, Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Republic of KoreaDepartment of Dermatology, College of Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Republic of KoreaFuture Innovation Medical Research Center, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Republic of KoreaDepartment of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Republic of KoreaFalls represent a significant risk factor, necessitating accurate classification methods. This study aims to identify the optimal placement of wearable sensors—specifically accelerometers, gyroscopes, and magnetometers—for effective fall-direction classification. Although previous research identified optimal sensor locations for distinguishing falls from non-falls, limited attention has been given to the classification of fall direction across different body regions. This study assesses inertial measurement unit (IMU) sensors placed at 12 distinct body locations to determine the most effective positions for capturing fall-related data. The research was conducted in three phases: first, comparing classifiers across all sensor locations to identify the most effective; second, evaluating performance differences between sensors placed on the left and right sides of the body; and third, exploring the efficacy of combining sensors from the upper and lower body regions. Statistical analyses of the results for the most effective classifier model demonstrate that the support vector machine (SVM) is more effective than other classifiers across all sensor locations, with statistically significant differences in performance. At the same time, the comparison between the left and right sensor locations shows no significant performance differences within the same anatomical areas. Regarding optimal sensor placement, the findings indicate that sensors positioned on the pelvis and upper legs in the lower body, as well as on the shoulder and head in the upper body, were the most effective results for accurate fall-direction classification. The study concludes that the optimal sensor configuration for fall-direction classification involves strategically combining sensors placed on the pelvis, upper legs, and lower legs.https://www.mdpi.com/1424-8220/24/19/6432fallsoptimal sensorfall directionclassificationmachine learningfeature extraction |
| spellingShingle | Sokea Teng Jung-Yeon Kim Seob Jeon Hyo-Wook Gil Jiwon Lyu Euy Hyun Chung Kwang Seock Kim Yunyoung Nam Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions Sensors falls optimal sensor fall direction classification machine learning feature extraction |
| title | Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions |
| title_full | Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions |
| title_fullStr | Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions |
| title_full_unstemmed | Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions |
| title_short | Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions |
| title_sort | analyzing optimal wearable motion sensor placement for accurate classification of fall directions |
| topic | falls optimal sensor fall direction classification machine learning feature extraction |
| url | https://www.mdpi.com/1424-8220/24/19/6432 |
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