지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.12
- 수록면
- 611 - 620 (10page)
- DOI
- 10.5143/JESK.2023.42.6.611
이용수
초록· 키워드
오류제보하기
Objective: The aim of this study is to analyze and validate cervical spine range of motion (ROM) using a 3D depth camera.
Background: Measuring the ROM of the cervical spine is crucial for diagnosing and managing spine-related diseases. However, the conventional manual measurement method for cervical spine ROM has several issues, including measurement errors depending on the person conducting the measurement and the need for human involvement. Furthermore, existing motion capture systems are costly and require specialized technology. Therefore, this study proposes a method for measuring cervical spine ROM using a 3D depth camera and conducts a study to assess its accuracy.
Method: In this study, we defined a method for measuring cervical spine ROM using a human body model provided by a 3D depth camera manufacturer (Azure Kinect, MS, USA) and developed accompanying software. We verified the accuracy of three cervical spine movements: flexion-extension, left/right rotation, and left/right bending by comparing the measurements with a motion capture system (as reference data, Vicon Inc., UK) and manual measurements. We also proposed a data correction process to enhance accuracy.
Results: ① In the flexion-extension results, the raw data exhibited an accuracy of 67.54% (±6.6%) compared to the reference values. However, after applying the correction algorithm, the accuracy improved to 95.92% (±4.71%). ② In the rotation results, the raw data demonstrated an accuracy of 89.05% (±6.39%) compared to the reference values. As a result of analysis through data correction, the accuracy improved to 95.53% (±4.71%). ③ In the case of lateral bending, the raw data demonstrated an accuracy of 87.77% (±4.72%) compared to the reference values. As a result of analysis through data correction, the accuracy improved to 95.25% (±3.13%).
Conclusion: This study yielded consistent data when compared to reference data obtained from a motion capture system. The accuracy of the 3D depth camera was enhanced through the applied correction algorithm, surpassing the accuracy of manual measurements.
Application: Digital anthropometric technology based on 3D depth cameras has broad applications in clinical settings, exercise facilities, welfare centers, and homes. It is anticipated to play a significant role in the digital healthcare sector, in conjunction with webcam and smartphone camera technologies that are expected to develop in the future.
Background: Measuring the ROM of the cervical spine is crucial for diagnosing and managing spine-related diseases. However, the conventional manual measurement method for cervical spine ROM has several issues, including measurement errors depending on the person conducting the measurement and the need for human involvement. Furthermore, existing motion capture systems are costly and require specialized technology. Therefore, this study proposes a method for measuring cervical spine ROM using a 3D depth camera and conducts a study to assess its accuracy.
Method: In this study, we defined a method for measuring cervical spine ROM using a human body model provided by a 3D depth camera manufacturer (Azure Kinect, MS, USA) and developed accompanying software. We verified the accuracy of three cervical spine movements: flexion-extension, left/right rotation, and left/right bending by comparing the measurements with a motion capture system (as reference data, Vicon Inc., UK) and manual measurements. We also proposed a data correction process to enhance accuracy.
Results: ① In the flexion-extension results, the raw data exhibited an accuracy of 67.54% (±6.6%) compared to the reference values. However, after applying the correction algorithm, the accuracy improved to 95.92% (±4.71%). ② In the rotation results, the raw data demonstrated an accuracy of 89.05% (±6.39%) compared to the reference values. As a result of analysis through data correction, the accuracy improved to 95.53% (±4.71%). ③ In the case of lateral bending, the raw data demonstrated an accuracy of 87.77% (±4.72%) compared to the reference values. As a result of analysis through data correction, the accuracy improved to 95.25% (±3.13%).
Conclusion: This study yielded consistent data when compared to reference data obtained from a motion capture system. The accuracy of the 3D depth camera was enhanced through the applied correction algorithm, surpassing the accuracy of manual measurements.
Application: Digital anthropometric technology based on 3D depth cameras has broad applications in clinical settings, exercise facilities, welfare centers, and homes. It is anticipated to play a significant role in the digital healthcare sector, in conjunction with webcam and smartphone camera technologies that are expected to develop in the future.
목차
1. Introduction
2. Method
3. Results
4. Discussion
5. Conclusion
References
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