Stack Emma, Agarwal Veena, King Rachel, Burnett Malcolm, Tahavori Fatemeh, Janko Balazs, Harwin William, Ashburn Ann, Kunkel Dorit
Faculty of Health Sciences, University of Southampton, Southampton, UK.
Faculty of Health Sciences, University of Southampton, Southampton, UK; Southampton Centre for Biomedical Research, University Hospital Southampton NHS Foundation Trust, UK.
Gait Posture. 2018 May;62:321-326. doi: 10.1016/j.gaitpost.2018.03.047. Epub 2018 Mar 28.
Falls and near falls are common among people with Parkinson's (PwP). To date, most wearable sensor research focussed on fall detection, few studies explored if wearable sensors can detect instability.
Can instability (caution or near-falls) be detected using wearable sensors in comparison to video analysis?
Twenty-four people (aged 60-86) with and without Parkinson's were recruited from community groups. Movements (e.g. walking, turning, transfers and reaching) were observed in the gait laboratory and/or at home; recorded using clinical measures, video and five wearable sensors (attached on the waist, ankles and wrists). After defining 'caution' and 'instability', two researchers evaluated video data and a third the raw wearable sensor data; blinded to each other's evaluations. Agreement between video and sensor data was calculated on stability, timing, step count and strategy.
Data was available for 117 performances: 82 (70%) appeared stable on video. Ratings agreed in 86/117 cases (74%). Highest agreement was noted for chair transfer, timed up and go test and 3 m walks. Video analysts noted caution (slow, contained movements, safety-enhancing postures and concentration) and/or instability (saving reactions, stopping after stumbling or veering) in 40/134 performances (30%): raw wearable sensor data identified 16/35 performances rated cautious or unstable (sensitivity 46%) and 70/82 rated stable (specificity 85%). There was a 54% chance that a performance identified from wearable sensors as cautious/unstable was so; rising to 80% for stable movements.
Agreement between wearable sensor and video data suggested that wearable sensors can detect subtle instability and near-falls. Caution and instability were observed in nearly a third of performances, suggesting that simple, mildly challenging actions, with clearly defined start- and end-points, may be most amenable to monitoring during free-living at home. Using the genuine near-falls recorded, work continues to automatically detect subtle instability using algorithms.
跌倒和跌倒未遂在帕金森病患者中很常见。迄今为止，大多数可穿戴传感器研究都集中在跌倒检测上，很少有研究探讨可穿戴传感器能否检测到不稳定性。
与视频分析相比，可穿戴传感器能否检测到不稳定性（警惕或跌倒未遂）？
从社区团体中招募了24名有或没有帕金森病的人（年龄在60 - 86岁之间）。在步态实验室和/或家中观察他们的动作（如行走、转身、转移和伸手）；使用临床测量、视频和五个可穿戴传感器（附着在腰部、脚踝和手腕上）进行记录。在定义“警惕”和“不稳定性”后，两名研究人员评估视频数据，第三名研究人员评估可穿戴传感器的原始数据；彼此不知道对方的评估结果。计算视频和传感器数据在稳定性、时间、步数和策略方面的一致性。
获得了117次动作的数据：82次（70%）在视频中看起来是稳定的。在86/117例（74%）中评级一致。在椅子转移、定时起立行走测试和3米步行中一致性最高。视频分析人员在40/134次动作（30%）中注意到警惕（动作缓慢、包含特定动作、安全增强姿势和专注）和/或不稳定性（挽救反应、绊倒或转向后停止）：可穿戴传感器的原始数据识别出16/35次被评为警惕或不稳定的动作（敏感性46%），以及70/82次被评为稳定的动作（特异性85%）。可穿戴传感器识别为警惕/不稳定的动作有54%的可能性确实如此；稳定动作的这一比例上升到80%。
可穿戴传感器和视频数据之间的一致性表明，可穿戴传感器可以检测到细微的不稳定性和跌倒未遂。在近三分之一的动作中观察到了警惕和不稳定性，这表明简单的、具有轻度挑战性的、具有明确起点和终点的动作，在家中自由活动期间可能最适合进行监测。利用记录的真实跌倒未遂情况，研究仍在继续使用算法自动检测细微的不稳定性。
