German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University, Munich, Germany.
Motognosis GmbH, Berlin, Germany.
PLoS One. 2023 Jan 26;18(1):e0279697. doi: 10.1371/journal.pone.0279697. eCollection 2023.
Quantitative assessment of motor function is increasingly applied in fall risk stratification, diagnosis, and disease monitoring of neuro-geriatric disorders of balance and gait. Its broad application, however, demands for low-cost and easy to use solutions that facilitate high-quality assessment outside laboratory settings. In this study, we validated in 30 healthy adults (12 female, age: 32.5 [22 - 62] years) the performance and accuracy of the latest generation of the Microsoft RGB-D camera, i.e., Azure Kinect (AK), in tracking body motion and providing estimates of clinical measures that characterise static posture, postural transitions, and locomotor function. The accuracy and repeatability of AK recordings was validated with a clinical reference standard multi-camera motion capture system (Qualisys) and compared to its predecessor Kinect version 2 (K2). Motion signal quality was evaluated by Pearson's correlation and signal-to-noise ratios while the accuracy of estimated clinical parameters was described by absolute and relative agreement based on intraclass correlation coefficients. The accuracy of AK-based body motion signals was moderate to excellent (RMSE 89 to 20 mm) and depended on the dimension of motion (highest for anterior-posterior dimension), the body region (highest for wrists and elbows, lowest for ankles and feet), and the specific motor task (highest for stand up and sit down, lowest for quiet standing). Most derived clinical parameters showed good to excellent accuracy (r .84 to .99) and repeatability (ICC(1,1) .55 to .94). The overall performance and limitations of body tracking by AK were comparable to its predecessor K2 in a cohort of young healthy adults. The observed accuracy and repeatability of AK-based evaluation of motor function indicate the potential for a broad application of high-quality and long-term monitoring of balance and gait in different non-specialised environments such as medical practices, nursing homes or community centres.
运动功能的定量评估越来越多地应用于神经老年平衡和步态障碍的跌倒风险分层、诊断和疾病监测。然而，其广泛应用需要低成本且易于使用的解决方案，以方便在实验室环境之外进行高质量的评估。在这项研究中，我们在 30 名健康成年人（12 名女性，年龄：32.5[22-62]岁）中验证了最新一代 Microsoft RGB-D 相机，即 Azure Kinect（AK）在跟踪身体运动和提供特征静态姿势、姿势转换和运动功能的临床测量估计方面的性能和准确性。AK 记录的准确性和可重复性使用临床参考标准多摄像机运动捕捉系统（Qualisys）进行了验证，并与前代 Kinect 版本 2（K2）进行了比较。通过 Pearson 相关系数和信噪比评估运动信号质量，而基于 ICC（1,1）的绝对和相对一致性描述了估计临床参数的准确性。基于 AK 的身体运动信号的准确性为中等至优秀（RMSE 为 89 至 20 毫米），取决于运动的维度（前后维度最高）、身体区域（手腕和肘部最高，脚踝和脚部最低）和特定的运动任务（站立和坐下最高，安静站立最低）。大多数衍生的临床参数都具有良好到优秀的准确性（r.84 到.99）和可重复性（ICC(1,1).55 到.94）。在年轻健康成年人的队列中，AK 的身体跟踪整体性能和局限性与其前代 K2 相当。AK 基于运动功能评估的观察准确性和可重复性表明，它具有在不同非专业环境中广泛应用高质量和长期监测平衡和步态的潜力，例如医疗实践、养老院或社区中心。
