Template-Matching-Based Detection of Freezing of Gait Using Wearable Sensors

Parkinson's disease (PD) lead to lots of injuries associated with fall incidences every year, causing lots of human suffering and assets loss for patients. Freezing of Gait (FOG), which is one of the most common symptoms of PD, is quite responsible for most incidents. Although lots of research have been done on characterize analysis and detection methods of FOG, large room for improvement still exists in the high accuracy and high efficiency examination of FOG. In view of the above requirements, this paper presents a template-matching-based improved subsequence Dynamic Time Warping (IsDTW) method, and experimental tests were carried out on typical open source datasets. Results show that proposed IsDTW not only embodies higher experimental accuracy (92%), but also has a significant runtime efficiency.