Monitoring Motor Fluctuations in Patients with Parkinson’s Disease Using Wearable Sensors

By Shyamal Patel, Konrad Lorincz, R. Hughes, N. Huggins, J. Growdon, D. Standaert, M. Akay, Jennifer G. Dy, Matt Welsh, Paolo Bonato. IEEE Transactions on Information Technology in Biomedicine, 2009

606 citations recorded by the source corpus.

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 SVM 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.

Subfields: Biomedical Engineering, Neurology

View Paper: https://www.semanticscholar.org/paper/fa5e5ece78098b1acd6ff53749bb54592166663a

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In Biomedical Engineering

Citations in the corpus

Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications (2013, background): “In [18], a pilot study for monitoring Parkinson’s symptoms and motor complications using wearable sensors is described.”
Fusion of Inertial and Depth Sensor Data for Robust Hand Gesture Recognition (2014, methodology): “In [16], a support vector machine (SVM) classifier was used as part of a body sensor network to estimate the severity of Parkinsonian symptoms.”
Smart Health Monitoring Systems: An Overview of Design and Modeling (2013, methodology): “A pilot study has been conducted to estimate the severity of the symptoms (tremor, bradykinesia and dyskinesia) and motor complications in Parkinson’s patients using a support vector machine (SVM) classifier [16].”
Wearable Sensors and Systems (2010, methodology): “The software application provides clinicians with access to wearable sensor data and measures the severity of parkinsonian symptoms [45].”
From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare (2015, methodology): “Feet Accelerometer [24, 27] Gyroscopes force, bend and pressure, electric field height, air pressure [28] Posture/activity from heel acceleration and planar pressure.”
Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks (2017, methodology): “Several researchers have attempted to automate PD motor state classification using machine learning techniques based on wearable sensor data (e.g. [25], [10], [7]).”
Sensor-Based Activity Recognition (2012, methodology): “[168] used accelerometer sensor data to analyze and assess activities of patients with Parkinson’s disease.”
PD Disease State Assessment in Naturalistic Environments Using Deep Learning (2015, neutral): “Systems are based on laboratory environments, where participants engage in a series of movement tasks that are part of the clinical assessment procedure in PD (Goetz et al. 2008; Patel et al. 2009).”
Wearable Sensors for Human Activity Monitoring: A Review (2015, background): “The use of wearable sensors has made it possible to have the necessary treatment at home for patients after an attack of diseases such as heart-attacks, sleep apnea, Parkinson disease and so on [10]–[12].”