Evaluation of Parkinson’s Disease at Home: Predicting Tremor from Wearable Sensors

2019 ◽  
Author(s):  
Margot Heijmans ◽  
Jeroen Habets ◽  
Mark Kuijf ◽  
Pieter Kubben ◽  
Christian Herff
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Wearable Sensors ◽  
At Home

scholarly journals Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson’s disease

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Gloria Vergara-Diaz ◽  
Jean-Francois Daneault ◽  
Federico Parisi ◽  
Chen Admati ◽  
Christina Alfonso ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Symptom Severity ◽  
Wearable Sensors ◽  
Motor Fluctuations ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Motor Tasks ◽  
Specific Symptom ◽  
At Home

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks.

scholarly journals Freezing of gait and fall detection in Parkinson’s disease using wearable sensors: a systematic review

2017 ◽  
Vol 264 (8) ◽  
pp. 1642-1654 ◽  
Author(s):  
Ana Lígia Silva de Lima ◽  
Luc J. W. Evers ◽  
Tim Hahn ◽  
Lauren Bataille ◽  
Jamie L. Hamilton ◽  
...  
Keyword(s):  
Systematic Review ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Fall Detection

scholarly journals Quantification of Arm Swing during Walking in Healthy Adults and Parkinson’s Disease Patients: Wearable Sensor-Based Algorithm Development and Validation

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5963 ◽  
Author(s):  
Elke Warmerdam ◽  
Robbin Romijnders ◽  
Julius Welzel ◽  
Clint Hansen ◽  
Gerhard Schmidt ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Angular Velocity ◽  
Wearable Sensors ◽  
Healthy Adults ◽  
Wearable Sensor ◽  
Arm Swing ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Development And Validation

Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson’s disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from −0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson’s disease.

Towards a personal at-home lab for motion video tracking in patients with Parkinson's disease

2017 ◽  
Author(s):  
Alexander Yu. Meigal ◽  
Kirill S. Prokhorov ◽  
Nikita A. Bazhenov ◽  
Liudmila I. Gerasimova-Meigal ◽  
Dmitry G. Korzun
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Video Tracking ◽  
Motion Video ◽  
At Home

scholarly journals Design of a virtual longitudinal observational study in Parkinson’s disease (AT‐HOME PD)

2020 ◽  
Author(s):  
Ruth B. Schneider ◽  
Larsson Omberg ◽  
Eric A. Macklin ◽  
Margaret Daeschler ◽  
Lauren Bataille ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Observational Study ◽  
Longitudinal Observational Study ◽  
At Home

scholarly journals Validity and Responsiveness of At-Home Touch Screen Assessments in Advanced Parkinson's Disease

2015 ◽  
Vol 19 (6) ◽  
pp. 1829-1834 ◽  
Author(s):  
Mevludin Memedi ◽  
Dag Nyholm ◽  
Anders Johansson ◽  
Sven Palhagen ◽  
Thomas Willows ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Touch Screen ◽  
Advanced Parkinson’S Disease ◽  
At Home

scholarly journals Wearable sensors objectively measure gait parameters in Parkinson’s disease

PLoS ONE ◽  
2017 ◽  
Vol 12 (10) ◽  
pp. e0183989 ◽  
Author(s):  
Johannes C. M. Schlachetzki ◽  
Jens Barth ◽  
Franz Marxreiter ◽  
Julia Gossler ◽  
Zacharias Kohl ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Wearable Sensors ◽  
Gait Parameters
Sign in / Sign up

Export Citation Format

Share Document