Han Byul Kim1, Hong Ji Lee1, Woong Woo Lee2, Sang Kyong Kim1, Hyo Seon Jeon1, Hye Young Park3, Chae Won Shin4, Won Jin Yi5, Beomseok Jeon3, Kwang S Park6. 1. 1 Graduate Program of Bioengineering, College of Engineering, Seoul National University, Seoul, Republic of Korea. 2. 2 Department of Neurology, Eulji General Hospital , Seoul, Republic of Korea. 3. 3 Department of Neurology and Movement Disorder Center, Seoul National University Hospital, Seoul, Republic of Korea. 4. 4 Department of Neurology, Kyung Hee University Medical Center , Seoul, Republic of Korea. 5. 5 Department of Oral and Maxillofacial Radiology, School of Dentistry, Seoul National University , Seoul, Republic of Korea. 6. 6 Department of Biomedical Engineering, College of Medicine, Seoul National University , Seoul, Republic of Korea.
Abstract
BACKGROUND: Freezing of gait (FOG) is a commonly observed motor symptom for patients with Parkinson's disease (PD). The symptoms of FOG include reduced step lengths or motor blocks, even with an evident intention of walking. FOG should be monitored carefully because it not only lowers the patient's quality of life, but also significantly increases the risk of injury. INTRODUCTION: In previous studies, patients had to wear several sensors on the body and another computing device was needed to run the FOG detection algorithm. Moreover, the features used in the algorithm were based on low-level and hand-crafted features. In this study, we propose a FOG detection system based on a smartphone, which can be placed in the patient's daily wear, with a novel convolutional neural network (CNN). METHODS: The walking data of 32 PD patients were collected from the accelerometer and gyroscope embedded in the smartphone, located in the trouser pocket. The motion signals measured by the sensors were converted into the frequency domain and stacked into a 2D image for the CNN input. A specialized CNN model for FOG detection was determined through a validation process. RESULTS: We compared our performances with the results acquired by the previously reported settings. The proposed architecture discriminated the freezing events from the normal activities with an average sensitivity of 93.8% and a specificity of 90.1%. CONCLUSIONS: Using our methodology, the precise and continuous monitoring of freezing events with unconstrained sensing can assist patients in managing their chronic disease in daily life effectively.
BACKGROUND: Freezing of gait (FOG) is a commonly observed motor symptom for patients with Parkinson's disease (PD). The symptoms of FOG include reduced step lengths or motor blocks, even with an evident intention of walking. FOG should be monitored carefully because it not only lowers the patient's quality of life, but also significantly increases the risk of injury. INTRODUCTION: In previous studies, patients had to wear several sensors on the body and another computing device was needed to run the FOG detection algorithm. Moreover, the features used in the algorithm were based on low-level and hand-crafted features. In this study, we propose a FOG detection system based on a smartphone, which can be placed in the patient's daily wear, with a novel convolutional neural network (CNN). METHODS: The walking data of 32 PDpatients were collected from the accelerometer and gyroscope embedded in the smartphone, located in the trouser pocket. The motion signals measured by the sensors were converted into the frequency domain and stacked into a 2D image for the CNN input. A specialized CNN model for FOG detection was determined through a validation process. RESULTS: We compared our performances with the results acquired by the previously reported settings. The proposed architecture discriminated the freezing events from the normal activities with an average sensitivity of 93.8% and a specificity of 90.1%. CONCLUSIONS: Using our methodology, the precise and continuous monitoring of freezing events with unconstrained sensing can assist patients in managing their chronic disease in daily life effectively.
Entities:
Keywords:
Parkinson's disease; convolutional neural network; e-health; freezing of gait; home monitoring; smartphone
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