Walking detection for Parkinson’s disease patients and healthy control subjects measured with a smartphone accelerometer using mean amplitude deviation algorithm

Authors

• Milla Juutinen Faculty of Medicine and Health Technology, Tampere University
• Jari Ruokolainen Faculty of Business and Management, Tampere University; Unit of Neurology, Satasairaala Central Hospital, Wellbeing Services County of Satakunta; School of Technology and Innovations, University of Vaasa
• Juha Puustinen Unit of Neurology, Satasairaala Central Hospital, Wellbeing Services County of Satakunta
• Anu Holm Satakunta University of Applied Sciences
• Mark van Gils Faculty of Medicine and Health Technology, Tampere University
• Antti Vehkaoja Faculty of Medicine and Health Technology, Tampere University

DOI: https://doi.org/10.23996/fjhw.156622

Keywords:

Parkinson's disease, gait, neurodegenerative diseases, smartphone, acceleration

Abstract

Parkinson's disease is a neurodegenerative disorder that affects mobility, leading to a decline in the patient's quality of life. Analyzing gait for these patients aims to improve the mobility of the Parkinson’s disease patients. The goal of this study was to validate mean amplitude deviation for detecting gait in Parkinson’s disease patients and healthy controls. This method is robust and orientation-independent and has accurate results on physical activity detection for different study populations. The novelty of this study is to use and evaluate a previously validated method with Parkinson’s disease patients instead of healthy young subjects as in earlier studies. We utilized inertial measurement unit data measured using smartphones from pre-existing datasets with pre-defined and labeled activities and free-living data containing continuously collected over three consecutive days. One dataset included 30 healthy adults, and the other two included in total of 62 and 68 Parkinson’s disease patients and 40 and 39 healthy controls, respectively. The sensitivity of the algorithm in a controlled measurement setting was 100% and 98.7% for healthy adults and a combined dataset of Parkinson’s disease patients and control subjects, respectively. Correspondingly, the specificity was 74.9% and 81.6%. Visual inspection of the free-living data showed that the algorithm provided durations and timings of walking activities, and walking took place during the daytime as anticipated for subjects with a typical daily rhythm. Median walking times were under ten minutes per hour. The results reached the same performance range as earlier studies with an orientation-independent approach, justifying the feasibility of this method. Therefore, this study validated the use of mean amplitude deviation for walking detection in Parkinson’s disease patients and healthy adults. Future research will utilize the detected walking segments in analyzing the motor symptoms of the disease aiming to improve patient well-being through identifying the needs for additional healthcare interventions.