Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors

Abstract

Background: Parkinson's disease (PD) is a progressive neurological disease, with characteristic motor symptoms such as tremor and bradykinesia. There is a growing interest to continuously monitor these and other symptoms through body-worn sensor technology. However, limited battery life and memory capacity hinder the potential for continuous, long-term monitoring with these devices. There is little information available on the relative value of adding sensors, increasing sampling rate, or computing complex signal features, all of which may improve accuracy of symptom detection at the expense of computational resources. Here we build on a previous study to investigate the relationship between data measurement characteristics and accuracy when using wearable sensor data to classify tremor and bradykinesia in patients with PD. Methods: Thirteen individuals with PD wore a flexible, skin-mounted sensor (collecting tri-axial accelerometer and gyroscope data) and a commercial smart watch (collecting tri-axial accelerometer data) on their predominantly affected hand. The participants performed a series of standardized motor tasks, during which a clinician scored the severity of tremor and bradykinesia in that limb. Machine learning models were trained on scored data to classify tremor and bradykinesia. Model performance was compared when using different types of sensors (accelerometer and/or gyroscope), different data sampling rates (up to 62.5 Hz), and different categories of pre-engineered features (up to 148 features). Performance was also compared between the flexible sensor and smart watch for each analysis. Results: First, there was no effect of device type for classifying tremor symptoms (p > 0.34), but bradykinesia models incorporating gyroscope data performed slightly better (up to 0.05 AUROC) than other models (p = 0.01). Second, model performance decreased with sampling frequency (p < 0.001) for tremor, but not bradykinesia (p > 0.47). Finally, model performance for both symptoms was maintained after substantially reducing the feature set.

Challenges and opportunities for improving the landscape for Lewy body dementia clinical trials

“…Parkinsonism has typically been measured by the UPDRS Part 3 or MDS-UPDRS Part 3, but these clinical measurements do not fully capture the sensitivity and type of motor symptoms (e.g., gait, balance, falls) in LBDs or everyday motor function at home. Opportunities to assess motor features and response to intervention in broader, more continuous, and real-life environments may include use of quantitative measures and remote assessment with wearable sensor technology [ 49 , 50 ].…”

Wearable Skin Sensors and Their Challenges: A Review of Transdermal, Optical, and Mechanical Sensors

“…The human gait is also considered as biometric property, tracking physical activity and periodic analysis of it can lead to detection of any abnormal patterns such as sudden tremors that are precursor of various diseases like Parkinson's and Alzheimer's disease. Hence, these mechanical sensors can contribute towards early detection of such fatal diseases [117]. Moreover, these sensors also have potential applications in rehabilitation, sports training, and prothesis [118].…”

Visualizing and Evaluating Finger Movement Using Combined Acceleration and Contact-Force Sensors: A Proof-of-Concept Study

“…The types of sensors and devices were used to capture hand movements with nine-axis inertial measurement unit (IMU), acceleration, pressure and bending sensors, electromyography, magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), leap motion, virtual reality (VR), motion capture, and tablets [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ].…”

“…Based on the data, they assessed the CSM patient’s hand dexterity [ 39 ]. Moreover, other studies have combined a smartwatch with a flexible hand-mounted sensor (with built-in triaxial acceleration and gyroscopes) to detect tremor symptoms in Parkinson’s disease [ 40 ], an infrared imaging device [ 41 ], a rehabilitation robot [ 42 ], and other devices for evaluating hand dexterity [ 43 , 44 ].…”