Bächlin Marc, Plotnik Meir, Roggen Daniel, Maidan Inbal, Hausdorff Jeffrey M, Giladi Nir, Tröster Gerhard
Wearable Computing Laboratory, Swiss Federal Institute of Technology Zürich, Zürich CH-8092, Switzerland.
IEEE Trans Inf Technol Biomed. 2010 Mar;14(2):436-46. doi: 10.1109/TITB.2009.2036165. Epub 2009 Nov 10.
In this paper, we present a wearable assistant for Parkinson's disease (PD) patients with the freezing of gait (FOG) symptom. This wearable system uses on-body acceleration sensors to measure the patients' movements. It automatically detects FOG by analyzing frequency components inherent in these movements. When FOG is detected, the assistant provides a rhythmic auditory signal that stimulates the patient to resume walking. Ten PD patients tested the system while performing several walking tasks in the laboratory. More than 8 h of data were recorded. Eight patients experienced FOG during the study, and 237 FOG events were identified by professional physiotherapists in a post hoc video analysis. Our wearable assistant was able to provide online assistive feedback for PD patients when they experienced FOG. The system detected FOG events online with a sensitivity of 73.1% and a specificity of 81.6%. The majority of patients indicated that the context-aware automatic cueing was beneficial to them. Finally, we characterize the system performance with respect to the walking style, the sensor placement, and the dominant algorithm parameters.
在本文中，我们展示了一款针对患有步态冻结（FOG）症状的帕金森病（PD）患者的可穿戴辅助设备。该可穿戴系统使用身体上的加速度传感器来测量患者的运动。它通过分析这些运动中固有的频率成分来自动检测FOG。当检测到FOG时，该辅助设备会提供有节奏的听觉信号，刺激患者恢复行走。十名PD患者在实验室中执行多项行走任务时对该系统进行了测试。记录了超过8小时的数据。八名患者在研究过程中经历了FOG，专业物理治疗师在事后视频分析中识别出237次FOG事件。我们的可穿戴辅助设备能够在PD患者经历FOG时提供在线辅助反馈。该系统在线检测FOG事件的灵敏度为73.1%，特异性为81.6%。大多数患者表示情境感知自动提示对他们有益。最后，我们针对行走方式、传感器放置和主要算法参数对系统性能进行了表征。
