Osness Emma, Isley Serena, Bertrand Jennifer, Dennett Liz, Bates Jack, Van Decker Nathan, Stanhope Alexis, Omkar Ayushi, Dolgoy Naomi, Ezeugwu Victor E, Tandon Puneeta
Liver Unit, Division of Gastroenterology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2R3, Canada.
Geoffrey and Robyn Sperber Health Sciences Library, University of Alberta, Edmonton, AB T6G 2R3, Canada.
Sensors (Basel). 2025 Sep 15;25(18):5741. doi: 10.3390/s25185741.
Frailty (a syndrome resulting in reduced physical function) assessments and fall risk assessments rely heavily on in-person evaluations and subjective interpretation, limiting scalability and access. Markerless motion capture (MMC) offers a promising solution for remote, objective assessment, but key kinematic parameters associated with frailty and fall risk remain unclear. This scoping review synthesized evidence from MEDLINE, Embase, Scopus, and CINAHL (inception to October 2024). Eligible studies used MMC to assess adults and compared outcomes to validated frailty or fall risk measures. Of 8048 studies, 39 met the inclusion criteria: 30 evaluated fall risk, 7 evaluated frailty, and 2 evaluated both, including 3114 participants (mean age 75.8; 42% male). Microsoft Kinect was used in 75% of the studies. An average of 23 features was extracted per study. Gait analysis was the most common MMC assessment for fall risk, identifying gait speed, stride length, and step width as key parameters. Frailty-related features were less consistent, with two studies identifying power, speed degradation, power reduction, range of motion, and elbow flexion time during a 20 s arm test. Future studies require standardization of methods and improved reporting of data loss. Despite the emerging nature of the field, MMC shows potential for the identification of fall risk and frailty.
衰弱（一种导致身体功能下降的综合征）评估和跌倒风险评估在很大程度上依赖于现场评估和主观解释，这限制了其可扩展性和可及性。无标记运动捕捉（MMC）为远程、客观评估提供了一个有前景的解决方案，但与衰弱和跌倒风险相关的关键运动学参数仍不明确。本综述综合了来自MEDLINE、Embase、Scopus和CINAHL（从创刊到2024年10月）的证据。符合条件的研究使用MMC评估成年人，并将结果与经过验证的衰弱或跌倒风险测量方法进行比较。在8048项研究中，39项符合纳入标准：30项评估跌倒风险，7项评估衰弱，2项两者都评估，包括3114名参与者（平均年龄75.8岁；42%为男性）。75%的研究使用了微软Kinect。每项研究平均提取23个特征。步态分析是评估跌倒风险最常用的MMC评估方法，将步态速度、步幅长度和步宽确定为关键参数。与衰弱相关的特征不太一致，有两项研究在20秒的手臂测试中确定了力量、速度下降、力量降低、运动范围和肘部弯曲时间。未来的研究需要方法标准化并改进数据丢失的报告。尽管该领域尚处于起步阶段，但MMC在识别跌倒风险和衰弱方面显示出潜力。
