Butt A H, Rovini E, Dolciotti C, Bongioanni P, De Petris G, Cavallo F
IEEE Int Conf Rehabil Robot. 2017 Jul;2017:116-121. doi: 10.1109/ICORR.2017.8009232.
The main goal of this study is to investigate the potential of the Leap Motion Controller (LMC) for the objective assessment of motor dysfunctioning in patients with Parkinson's disease (PwPD). The most relevant clinical signs in Parkinson's Disease (PD), such as slowness of movements, frequency variation, amplitude variation, and speed, were extracted from the recorded LMC data. Data were clinically quantified using the LMC software development kit (SDK). In this study, 16 PwPD subjects and 12 control healthy subjects were involved. A neurologist assessed the subjects during the task execution, assigning them a score according to the MDS/UPDRS-Section III items. Features of motor performance from both subject groups (patients and healthy controls) were extracted with dedicated algorithms. Furthermore, to find out the significance of such features from the clinical point of view, machine learning based methods were used. Overall, our findings showed the moderate potential of LMC to extract the motor performance of PwPD.
本研究的主要目标是探究Leap Motion控制器（LMC）用于客观评估帕金森病患者（PwPD）运动功能障碍的潜力。从记录的LMC数据中提取帕金森病（PD）最相关的临床体征，如运动迟缓、频率变化、幅度变化和速度。使用LMC软件开发工具包（SDK）对数据进行临床量化。本研究纳入了16名PwPD受试者和12名健康对照受试者。一名神经科医生在任务执行期间对受试者进行评估，根据MDS/UPDRS第三部分项目为他们打分。使用专用算法提取两组受试者（患者和健康对照）的运动表现特征。此外，为了从临床角度找出这些特征的意义，使用了基于机器学习的方法。总体而言，我们的研究结果表明LMC在提取PwPD运动表现方面具有一定潜力。
