Analysis of Inertial Measurement Unit Data for an AI-Based Physical Function Assessment System Using In-Clinic-like Movements
Assessing objective physical function in patients with cancer is crucial for evaluating their ability to tolerate invasive treatments. Current assessment methods, such as the timed up and go (TUG) test and the short physical performance battery, tend to require additional resources and time, limitin...
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MDPI AG
2024-12-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/11/12/1232 |
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| author | Nobuji Kouno Satoshi Takahashi Ken Takasawa Masaaki Komatsu Naoaki Ishiguro Katsuji Takeda Ayumu Matsuoka Maiko Fujimori Kazuki Yokoyama Shun Yamamoto Yoshitaka Honma Ken Kato Kazutaka Obama Ryuji Hamamoto |
| author_facet | Nobuji Kouno Satoshi Takahashi Ken Takasawa Masaaki Komatsu Naoaki Ishiguro Katsuji Takeda Ayumu Matsuoka Maiko Fujimori Kazuki Yokoyama Shun Yamamoto Yoshitaka Honma Ken Kato Kazutaka Obama Ryuji Hamamoto |
| author_sort | Nobuji Kouno |
| collection | DOAJ |
| description | Assessing objective physical function in patients with cancer is crucial for evaluating their ability to tolerate invasive treatments. Current assessment methods, such as the timed up and go (TUG) test and the short physical performance battery, tend to require additional resources and time, limiting their practicality in routine clinical practice. To address these challenges, we developed a system to assess physical function based on movements observed during clinical consultations and aimed to explore relevant features from inertial measurement unit data collected during those movements. As for the flow of the research, we first collected inertial measurement unit data from 61 patients with cancer while they replicated a series of movements in a consultation room. We then conducted correlation analyses to identify keypoints of focus and developed machine learning models to predict the TUG test outcomes using the extracted features. Regarding results, pelvic velocity variability (PVV) was identified using Lasso regression. A linear regression model using PVV as the input variable achieved a mean absolute error of 1.322 s and a correlation of 0.713 with the measured TUG results during five-fold cross-validation. Higher PVV correlated with shorter TUG test results. These findings provide a foundation for the development of an artificial intelligence-based physical function assessment system that operates without the need for additional resources. |
| format | Article |
| id | doaj-art-8de1d451ef7d4d5faa101ddd6f62909b |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-8de1d451ef7d4d5faa101ddd6f62909b2024-12-27T14:11:34ZengMDPI AGBioengineering2306-53542024-12-011112123210.3390/bioengineering11121232Analysis of Inertial Measurement Unit Data for an AI-Based Physical Function Assessment System Using In-Clinic-like MovementsNobuji Kouno0Satoshi Takahashi1Ken Takasawa2Masaaki Komatsu3Naoaki Ishiguro4Katsuji Takeda5Ayumu Matsuoka6Maiko Fujimori7Kazuki Yokoyama8Shun Yamamoto9Yoshitaka Honma10Ken Kato11Kazutaka Obama12Ryuji Hamamoto13Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, JapanDivision of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, JapanDivision of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, JapanDivision of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, JapanCancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, JapanCancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, JapanDivision of Survivorship Research, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, JapanDivision of Survivorship Research, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, JapanDepartment of Head and Neck, Esophageal Medical Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, JapanDepartment of Head and Neck, Esophageal Medical Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, JapanDepartment of Head and Neck, Esophageal Medical Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, JapanDepartment of Head and Neck, Esophageal Medical Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, JapanDepartment of Surgery, Graduate School of Medicine, Kyoto University, 54 Shogoin-kawahara-cho, Sakyo-ku, Kyoto 606-8507, JapanDivision of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, JapanAssessing objective physical function in patients with cancer is crucial for evaluating their ability to tolerate invasive treatments. Current assessment methods, such as the timed up and go (TUG) test and the short physical performance battery, tend to require additional resources and time, limiting their practicality in routine clinical practice. To address these challenges, we developed a system to assess physical function based on movements observed during clinical consultations and aimed to explore relevant features from inertial measurement unit data collected during those movements. As for the flow of the research, we first collected inertial measurement unit data from 61 patients with cancer while they replicated a series of movements in a consultation room. We then conducted correlation analyses to identify keypoints of focus and developed machine learning models to predict the TUG test outcomes using the extracted features. Regarding results, pelvic velocity variability (PVV) was identified using Lasso regression. A linear regression model using PVV as the input variable achieved a mean absolute error of 1.322 s and a correlation of 0.713 with the measured TUG results during five-fold cross-validation. Higher PVV correlated with shorter TUG test results. These findings provide a foundation for the development of an artificial intelligence-based physical function assessment system that operates without the need for additional resources.https://www.mdpi.com/2306-5354/11/12/1232objective physical function assessmenttimed up and go testshort physical performance batteryinertial measurement unitpatients with cancerartificial intelligence |
| spellingShingle | Nobuji Kouno Satoshi Takahashi Ken Takasawa Masaaki Komatsu Naoaki Ishiguro Katsuji Takeda Ayumu Matsuoka Maiko Fujimori Kazuki Yokoyama Shun Yamamoto Yoshitaka Honma Ken Kato Kazutaka Obama Ryuji Hamamoto Analysis of Inertial Measurement Unit Data for an AI-Based Physical Function Assessment System Using In-Clinic-like Movements Bioengineering objective physical function assessment timed up and go test short physical performance battery inertial measurement unit patients with cancer artificial intelligence |
| title | Analysis of Inertial Measurement Unit Data for an AI-Based Physical Function Assessment System Using In-Clinic-like Movements |
| title_full | Analysis of Inertial Measurement Unit Data for an AI-Based Physical Function Assessment System Using In-Clinic-like Movements |
| title_fullStr | Analysis of Inertial Measurement Unit Data for an AI-Based Physical Function Assessment System Using In-Clinic-like Movements |
| title_full_unstemmed | Analysis of Inertial Measurement Unit Data for an AI-Based Physical Function Assessment System Using In-Clinic-like Movements |
| title_short | Analysis of Inertial Measurement Unit Data for an AI-Based Physical Function Assessment System Using In-Clinic-like Movements |
| title_sort | analysis of inertial measurement unit data for an ai based physical function assessment system using in clinic like movements |
| topic | objective physical function assessment timed up and go test short physical performance battery inertial measurement unit patients with cancer artificial intelligence |
| url | https://www.mdpi.com/2306-5354/11/12/1232 |
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