Iurato Matteo, Dondero Paolo, Job Mirko, Stanzani Ronny, Leuzzi Gaia, Ingegnosi Igor, Testa Marco
REHELab, Dipartimento di Neuroscienze, Riabilitazione, Oftalmologia, Genetica e Scienze Materno-Infantili, Università degli studi di Genova, Genova, Italy.
Swhard s.r.l., Genova, Italy.
Front Digit Health. 2025 Sep 1;7:1576031. doi: 10.3389/fdgth.2025.1576031. eCollection 2025.
Telerehabilitation improves accessibility and accelerates recovery: in this context, Inertial Measurement Units (IMUs) are promising wearable sensors for remote movement data collection, which allows to evaluate how closely exercise repetitions align with a prescribed trajectory. Current data processing methods for this purpose include data-driven approaches, requiring exercise-specific training through large amount of data, or distance-based methods with unbounded output, not easy to interpret. This study proposes a novel algorithm which combines the versatility of a bounded output score with numerical stability of quaternions. Data from an IMU-based device were acquired during the execution of human functional shoulder movements by both a young and elderly group of participants. Outputs from the application of the proposed methodology on collected data from same or different movements were statistically compared, revealing ability of discriminating repetitions of the same or of different movements ( , effect size = 0.97, contrast ratio 1.7). The proposed algorithm was also confronted with the traditional approaches by statistically comparing outputs from comparison matrices rescaled in equal range of values, and results indicated mild differences in performance ( effect size < 0.5). Future works may involve integrating this approach into a functioning telerehabilitation system and obtaining feedback on the usability from real users.
在此背景下，惯性测量单元（IMU）是用于远程运动数据收集的很有前景的可穿戴传感器，它能够评估运动重复与规定轨迹的匹配程度。当前用于此目的的数据处理方法包括数据驱动方法，该方法需要通过大量数据进行特定运动训练，或者是基于距离的方法，其输出无界且不易解释。本研究提出了一种新颖的算法，该算法将有界输出分数的通用性与四元数的数值稳定性相结合。在年轻和老年参与者执行人体功能性肩部运动期间，从基于IMU的设备获取了数据。对所提出方法应用于相同或不同运动的收集数据的输出进行了统计比较，结果表明该方法能够区分相同或不同运动的重复情况（ ，效应大小 = 0.97，对比率1.7）。通过对在相等值范围内重新缩放的比较矩阵的输出进行统计比较，将所提出的算法与传统方法进行了对比，结果表明性能上存在轻微差异（效应大小 < 0.5）。未来的工作可能包括将这种方法集成到一个有效的远程康复系统中，并从实际用户那里获得关于可用性的反馈。
