Unconstrained detection of freezing of Gait in Parkinson's disease patients using smartphone

• Published in: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2015; pp. 3751 - 3754
• Main Authors: Hanbyul Kim, Hong Ji Lee, Woongwoo Lee, Sungjun Kwon, Sang Kyong Kim, Hyo Seon Jeon, Hyeyoung Park, Chae Won Shin, Won Jin Yi, Jeon, Beom S., Park, Kwang S.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.08.2015
• Subjects: Acceleration; Accelerometers; Accelerometry - instrumentation; Accidental Falls; Aged; Algorithms; Classification algorithms; Female; Gait; Gait Disorders, Neurologic - diagnosis; Gait Disorders, Neurologic - physiopathology; Humans; Machine learning algorithms; Male; Parkinson Disease - diagnosis; Parkinson Disease - physiopathology; Parkinson's disease; Sensitivity; Sensors; Smartphone; Walking
• ISSN: 1094-687X; 1557-170X; 1558-4615
• DOI: 10.1109/EMBC.2015.7319209

Freezing of gait (FOG) is a common motor impairment to suffer an inability to walk, experienced by Parkinson's disease (PD) patients. FOG interferes with daily activities and increases fall risk, which can cause severe health problems. We propose a novel smartphone-based system to detect FOG symptoms in an unconstrained way. The feasibility of single device to sense gait characteristic was tested on the various body positions such as ankle, trouser pocket, waist and chest pocket. Using measured data from accelerometer and gyroscope in the smartphone, machine learning algorithm was applied to classify freezing episodes from normal walking. The performance of AdaBoost.M1 classifier showed the best sensitivity of 86% at the waist, 84% and 81% in the trouser pocket and at the ankle respectively, which is comparable to the results of previous studies.