Development of a real-time RGB-D visual feedback-assisted pulmonary rehabilitation system.
| Autor: | Tang WR; Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, Tainan, Taiwan., Su W; Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan., Lien JJ; Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan., Chang CC; Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, Tainan, Taiwan., Yen YT; Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, Tainan, Taiwan., Tseng YL; Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, Tainan, Taiwan. |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | Heliyon [Heliyon] 2023 Dec 14; Vol. 10 (1), pp. e23704. Date of Electronic Publication: 2023 Dec 14 (Print Publication: 2024). |
| DOI: | 10.1016/j.heliyon.2023.e23704 |
| Abstrakt: | Background: Following surgery, perioperative pulmonary rehabilitation (PR) is important for patients with early-stage lung cancer. However, current inpatient programs are often limited in time and space, and outpatient settings have access barriers. Therefore, we aimed to develop a background-free, zero-contact thoracoabdominal movement-tracking model that is easily set up and incorporated into a pre-existing PR program or extended to home-based rehabilitation and remote monitoring. We validated its effectiveness in providing preclinical real-time RGB-D (colour-depth camera) visual feedback. Methods: Twelve healthy volunteers performed deep breathing exercises following audio instruction for three cycles, followed by audio instruction and real-time visual feedback for another three cycles. In the visual feedback system, we used a RealSense™ D415 camera to capture RGB and depth images for human pose-estimation with Google MediaPipe. Target-tracking regions were defined based on the relative position of detected joints. The processed depth information of the tracking regions was visualised on a screen as a motion bar to provide real-time visual feedback of breathing intensity. Pulmonary function was simultaneously recorded using spirometric measurements, and changes in pulmonary volume were derived from respiratory airflow signals. Results: Our movement-tracking model showed a very strong correlation (r = 0.90 ± 0.05) between thoracic motion signals and spirometric volume, and a strong correlation (r = 0.73 ± 0.22) between abdominal signals and spirometric volume. Displacement of the chest wall was enhanced by RGB-D visual feedback (23 vs 20 mm, P = 0.034), and accompanied by an increased lung volume (2.58 vs 2.30 L, P = 0.003). Conclusion: We developed an easily implemented thoracoabdominal movement-tracking model and reported the positive impact of real-time RGB-D visual feedback on self-promoted external chest wall expansion, accompanied by increased internal lung volumes. This system can be extended to home-based PR. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2023 The Authors.) |
| Databáze: | MEDLINE |
| Externí odkaz: |
|