Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors
Shyamal Patel,Konrad Lorincz,R. Hughes,N. Huggins,John H. Growdon,David G. Standaert,Metin Akay,Jennifer G. Dy,Matt Welsh,Paolo Bonato +9 more
- Vol. 13, Iss: 6, pp 864-873
TLDR
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, and a support vector machine (SVM) classifier was implemented to estimateThe severity of tremor, bradykinesia and dyskinesian symptoms from accelerometers data features.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 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.read more
Citations
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Wearable Sensors for Human Activity Monitoring: A Review
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TL;DR: A comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition, making a primary distinction in this paper between data-driven and knowledge-driven approaches.
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Technology in Parkinson's disease: Challenges and opportunities.
Alberto J. Espay,Paolo Bonato,Fatta B. Nahab,Walter Maetzler,John Dean,Jochen Klucken,Bjoern M. Eskofier,Aristide Merola,Fay B. Horak,Anthony E. Lang,Ralf Reilmann,Joseph P. Giuffrida,Alice Nieuwboer,Malcolm K. Horne,Max A. Little,Irene Litvan,Tanya Simuni,E. Ray Dorsey,Michelle A. Burack,Ken Kubota,Anita Kamondi,Catarina Godinho,Jean-Francois Daneault,Georgia Mitsi,Lothar Krinke,J.M. Hausdorff,Bastiaan R. Bloem,Spyros Papapetropoulos +27 more
TL;DR: The work carried out by the task force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and the quality of life of individuals with PD is summarized.
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Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications
TL;DR: This paper analyzes the most important requirements for an effective BSN-specific software framework, enabling efficient signal-processing applications and presents signal processing in node environment (SPINE), an open-source programming framework, designed to support rapid and flexible prototyping and management of BSN applications.
Proceedings ArticleDOI
Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks
Terry Taewoong Um,Franz M. J. Pfister,Daniel Pichler,Satoshi Endo,Muriel Lang,Sandra Hirche,Urban Fietzek,Dana Kulic +7 more
TL;DR: The proposed methods and CNNs are applied to the classification of the motor state of Parkinson’s Disease patients, which is challenging due to small dataset size, noisy labels, and large intra-class variability.
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