Comparative Study of Wearable Sensors, Video, and Handwriting to Detect Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder that affects the extrapyramidal motor system. The initial clinical symptoms can appear long before the retirement age, affecting the patients' ability to continue their work. However, the modern healthcare lacks the apparatus to detect the early signs of the disease, with only selected experts being able to spot the onset. Moreover, even if the disease is detected on time, the current clinical routine has no means of monitoring the progression of the disease, nor the effect of the therapy. In this work, we perform a comparative study of three patient-driven monitoring approaches, including wearable sensors data, video of the routine exercises performed by the patients, and their handwriting - all tine-tuned to detect PD. Aspiring to create an inexpensive clinical workflow, we collect the sensor data using a commercial wearable wireless sensor, and the video and the handwriting data are recorded using a basic smartphone. In the analysis stage, we rely on machine and deep learning methods, using f1-score for gauging the performance of the patient-driven data acquisition approaches. The sensor data analysis demonstrates the best f1-score of 0.93. At the same time, the handwriting and remote video recordings are more comfortable and less time-consuming, offering a sensible trade-off for the patients at the later stages of PD.

Automated summary

Parkinson's disease is a common neurodegenerative disorder, and current healthcare lacks the means to detect early symptoms and monitor disease progression. This study compares three patient-driven monitoring approaches, using wearable sensor data, video, and handwriting data, analyzed with machine and deep learning methods. Sensor data shows the best performance, while video and handwriting data are more convenient and time-saving.