High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity

Abstract

Parkinson's disease (PD) can present with a variety of motor disorders that fluctuate throughout the day, making assessment a challenging task. Paper-based measurement tools can be burdensome to the patient and clinician and lack the temporal resolution needed to accurately and objectively track changes in motor symptom severity throughout the day. Wearable sensor-based systems that continuously monitor PD motor disorders may help to solve this problem, although critical shortcomings persist in identifying multiple disorders at high temporal resolution during unconstrained activity. The purpose of this study was to advance the current state of the art by (1) introducing hybrid sensor technology to concurrently acquire surface electromyographic (sEMG) and accelerometer data during unconstrained activity and (2) analyzing the data using dynamic neural network algorithms to capture the evolving temporal characteristics of the sensor data and improve motor disorder recognition of tremor and dyskinesia. Algorithms were trained (n=11 patients) and tested (n=8 patients; n=4 controls) to recognize tremor and dyskinesia at 1-second resolution based on sensor data features and expert annotation of video recording during 4-hour monitoring periods of unconstrained daily activity. The algorithms were able to make accurate distinctions between tremor, dyskinesia, and normal movement despite the presence of diverse voluntary activity. Motor disorder severity classifications averaged 94.9% sensitivity and 97.1% specificity based on 1 sensor per symptomatic limb. These initial findings indicate that new sensor technology and software algorithms can be effective in enhancing wearable sensor-based system performance for monitoring PD motor disorders during unconstrained activities.

Keywords: EMG; Parkinson's disease quantification; accelerometer; dyskinesia; motor disorder; tremor.

FIG. 1.

Block diagram of the procedures used to detect and analyze surface electromyographic (sEMG) and accelerometer (ACC) signals from hybrid sensors. The sensor is configured with parallel sEMG detection bars on the bottom of the sensor and a triaxial accelerometer to provide X, Y, Z outputs to the data acquisition system. sEMG and ACC features are extracted from these data to serve as inputs to a dynamic neural network (DNN) for tremor (7 input nodes for each feature, 4 hidden nodes, and 1 output node). Features extracted from the ACC signal serve as inputs to the DNN for dyskinesia (4 input nodes for each feature, 2 hidden nodes, and 1 output node). Severity of each disorder is identified through the use of a maximum a posteriori probability (MAP) classifier.

FIG. 2.

Raw surface electromyographic (sEMG) and accelerometer signal patterns characteristic of tremor (A), subclinical tremor (B), dyskinesia (C), and normal voluntary movement (D) are shown for data acquired from the wrist extensor location of a patient with PD. Tremor is characterized by repetitive sEMG signal bursts at a fixed frequency related to cyclic movements of the limb. The periodic sEMG activity is preserved during subclinical tremor but produces no observable limb movement. In contrast, dyskinesia is characterized by irregular sEMG activity and rapid chaotic movements. Normal voluntary activities such as feeding oneself (D) may produce rapid accelerations and bursts of muscle activation that can mimic dyskinesia (C), making accurate recognition challenging for the algorithms.

FIG. 3.

Plot of dyskinesia and tremor severity as a function of time following administration of the first dose of anti-Parkinson’s medication (l-dopa). Transitions between different movement disorders and different severities are plotted using a 1-second scale. For every second of data, there is only 1 movement disorder severity score. Black-diamond data points represent results from the algorithm, and gray-diamond data points represent results from the video annotations. The results were analyzed from a sensor on the right forearm of a 62-year-old subject with a 16-year history of PD complicated by motor fluctuations.