Pastorino M, Cancela J, Arredondo M T, Pansera M, Pastor-Sanz L, Villagra F, Pastor M A, Martin J A
Life Supporting Technologies, Technical University of Madrid, Madrid 28804, Spain.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:1810-3. doi: 10.1109/IEMBS.2011.6090516.
The aim of this paper is to describe and present the results of the automatic detection and assessment of bradykinesia in motor disease patients using wireless, wearable accelerometers. The current work is related to a module of the PERFORM system, a FP7 project from the European Commission, that aims at providing an innovative and reliable tool, able to evaluate, monitor and manage patients suffering from Parkinson's disease. The assessment procedure was carried out through a developed C# library that detects the activities of the patient using an activity recognition algorithm and classifies the data using a Support Vector Machine trained with data coming from previous test phases. The accuracy between the output of the automatic detection and the evaluation of the clinician both expressed with the Unified Parkinson's disease Rating Scale, presents an average value of [68.3 ± 8.9]%. A meta-analysis algorithm is used in order to improve the accuracy to an average value of [74.4 ± 14.9]%. Future work will include a personalized training of the classifiers in order to achieve a higher level of accuracy.
本文旨在描述并展示使用无线可穿戴加速度计对运动疾病患者的运动迟缓进行自动检测和评估的结果。当前工作与PERFORM系统的一个模块相关，该系统是欧盟委员会的一个FP7项目，旨在提供一种创新且可靠的工具，能够评估、监测和管理帕金森病患者。评估过程通过一个已开发的C#库来进行，该库使用活动识别算法检测患者的活动，并使用基于先前测试阶段数据训练的支持向量机对数据进行分类。自动检测输出与临床医生评估之间的准确性均以统一帕金森病评定量表表示，其平均值为[68.3 ± 8.9]%。使用了一种元分析算法将准确性提高到平均值为[74.4 ± 14.9]%。未来的工作将包括对分类器进行个性化训练，以实现更高水平的准确性。
