Dataset Information

Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson's disease.

ABSTRACT: Parkinson'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.

SUBMITTER: Vergara-Diaz G

PROVIDER: S-EPMC7864964 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson's disease.

Vergara-Diaz Gloria G Daneault Jean-Francois JF Parisi Federico F Admati Chen C Alfonso Christina C Bertoli Matilde M Bonizzoni Edoardo E Carvalho Gabriela Ferreira GF Costante Gianluca G Fabara Eric Eduardo EE Fixler Naama N Golabchi Fatemah Noushin FN Growdon John J Sapienza Stefano S Snyder Phil P Shpigelman Shahar S Sudarsky Lewis L Daeschler Margaret M Bataille Lauren L Sieberts Solveig K SK Omberg Larsson L Moore Steven S Bonato Paolo P

Scientific data 20210205 1

Parkinson'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 subject ...[more]

PMID: 33547317

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Project description:Distinct gait characteristics like short steps and shuffling gait are prototypical signs commonly observed in Parkinson's disease. Routinely assessed by observation through clinicians, gait is rated as part of categorical clinical scores. There is an increasing need to provide quantitative measurements of gait, e.g. to provide detailed information about disease progression. Recently, we developed a wearable sensor-based gait analysis system as diagnostic tool that objectively assesses gait parameter in Parkinson's disease without the need of having a specialized gait laboratory. This system consists of inertial sensor units attached laterally to both shoes. The computed target of measures are spatiotemporal gait parameters including stride length and time, stance phase time, heel-strike and toe-off angle, toe clearance, and inter-stride variation from gait sequences. To translate this prototype into medical care, we conducted a cross-sectional study including 190 Parkinson's disease patients and 101 age-matched controls and measured gait characteristics during a 4x10 meter walk at the subjects' preferred speed. To determine intraindividual changes in gait, we monitored the gait characteristics of 63 patients longitudinally. Cross-sectional analysis revealed distinct spatiotemporal gait parameter differences reflecting typical Parkinson's disease gait characteristics including short steps, shuffling gait, and postural instability specific for different disease stages and levels of motor impairment. The longitudinal analysis revealed that gait parameters were sensitive to changes by mirroring the progressive nature of Parkinson's disease and corresponded to physician ratings. Taken together, we successfully show that wearable sensor-based gait analysis reaches clinical applicability providing a high biomechanical resolution for gait impairment in Parkinson's disease. These data demonstrate the feasibility and applicability of objective wearable sensor-based gait measurement in Parkinson's disease reaching high technological readiness levels for both, large scale clinical studies and individual patient care.

Dataset Information

Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson's disease.

Publications

Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson's disease.

Similar Datasets

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