Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors

Abstract

This paper presents the results of a pilot study to assess the feasibility of using accelerometer data to estimate the severity of symptoms and motor complications in patients with Parkinson’s disease. A Support Vector Machine (SVM) classifier was implemented to estimate the severity of tremor, bradykinesia and dyskinesia from accelerometer data features. SVM-based estimates were compared with clinical scores derived via visual inspection of video recordings taken while patients performed a series of standardized motor tasks. The analysis of the video recordings was performed by clinicians trained in the use of scales for the assessment of the severity of Parkinsonian symptoms and motor complications. Results derived from the accelerometer time series were analyzed to assess the effect on the estimation of clinical scores of the duration of the window utilized to derive segments (to eventually compute data features) from the accelerometer data, the use of different support vector machine kernels and misclassification cost values, and the use of data features derived from different motor tasks. Results were also analyzed to assess which combinations of data features carried enough information to reliably assess the severity of symptoms and motor complications. Combinations of data features were compared taking into consideration the computational cost associated with estimating each data feature on the nodes of a body sensor network and the effect of using such data features on the reliability of SVM-based estimates of the severity of Parkinsonian symptoms and motor complications.

Intraoperative acceleration measurements to quantify improvement in tremor during deep brain stimulation surgery

“…Accelerometers have been applied outside the operating room (OR) for a wide variety of purposes, e.g., to characterize pathological tremor [8,13], to compare it with physiological tremor [20,35,36], and to evaluate the severity and evolution of tremor [28,29,40] and the tremor-alleviating effect of drugs or DBS [22,36,47]. Pulliam et al [34] used motion sensors during postoperative DBS pulse generator programming to develop automated programming algorithms and concluded that objective assessment can improve patients' outcomes.…”

Pattern Identification of Subthalamic Local Field Potentials in Parkinson’s Disease

“…The accurate identification of PD is of great significance to clinical diagnosis and treatment.At the present stage, machine learning method has been widely used in clinical medical science research.The use of machine learning to diagnose PD had achieved a good diagnostic effect [1]. PD patients with wearable sensor gait data and patients voice signals data also were used to diagnose PD [2].…”

Detecting Parkinsons' symptoms in uncontrolled home environments: A multiple instance learning approach

“…The ultimate goal of these collaborative efforts between the healthcare and engineering communities is to enable unobtrusive autonomous monitoring of the patients' state and generate valuable clinical feedback. In that regard, motor symptom monitoring in Parkinson's Disease (PD) has gained significant attention over the years [11], [15]. In most cases, these symptoms (e.g.…”

“…Several researchers have addressed the problem of quantitative assessment of PD motor symptoms [12], [3], [11], [6]. In [11], researchers explored the feasibility of using accelerometer data to estimate the severity of motor symptoms symptoms using a support vector machine (SVM) classifier [4].…”

“…In [11], researchers explored the feasibility of using accelerometer data to estimate the severity of motor symptoms symptoms using a support vector machine (SVM) classifier [4]. In [19], the authors developed a monitoring system that provided motor assessments by simultaneously analyzing current motor activity of the patients (e.g., sitting, walking) in a simulated daily living environment.…”