Assessing abnormal gaits of Parkinson’s disease patients using a wearable motion detector

Accelerometers have been widely used in wearable systems for gait analysis. Several gait cycle parameters are provided to quantify the level of gait regularity and symmetry. This study attempts to assess abnormal gaits of Parkinson disease (PD) patients based on the gait cycle parameters derived in real-time from an accelerometry-based wearable motion detector. The results of an experiment with 25 healthy young adults showed that there were significant differences between gait cycle parameters of normal gaits and abnormal gaits derived from the wearable motion detector. Five PD patients diagnosed as Hoehn & Yahr stage I to II were recruited. It is difficult to collect data of abnormal gaits of the PD patients; therefore, ranges of the gait cycle parameters of abnormal gaits of PD patients were estimated statistically based on the “lower confidence limit” of the gait cycle parameters of their normal gaits. These results may lead to the future development of wearable sensors enabling real-time recognition of abnormal gaits of PD patients. Ambulatory rehabilitation, gait assessment and personal telecare for people with gait disorders are also possible applications.