Dataset Information

Objective Arm Swing Analysis in Early-Stage Parkinson's Disease Using an RGB-D Camera (Kinect®).

ABSTRACT:

Background

Arm swing changes are common even in the early stages of Parkinson's disease (PD). We hypothesized that arm swing changes decrease with age and can be detected using a low-cost, RGB-D depth-sensing camera.

Objective

This study aimed to assess the differences in arm swing between PD patients and healthy participants and to investigate the possible effects of aging on these differences.

Methods

Twenty-five PD patients (aged 45-87 years) and 25 age-matched, healthy subjects (aged 46-88 years) were included. Clinical variables were evaluated using a descriptive analysis. No spatiotemporal variables were normally distributed; therefore, we used a Mann-Whitney U test to compare the continuous variables between groups and to perform age-stratified analysis. A receiver operating characteristic analysis was generated to evaluate the discrimination activity of arm swing asymmetry (ASA).

Results

The PD group showed significant reductions in arm swing magnitude (left, p = 0.002; right, p = 0.006) and arm swing speed (left, p = 0.002; right, p = 0.004) and significantly greater ASA (p < 0.001). The age-stratified analysis showed significant differences in ASA in the 40-59-year group (p = 0.001) and bilateral arm swing magnitude in the 60-66-year group. No differences were found in those aged >67 years.

Conclusions

The camera detected differences in ASA, arm swing speed, and arm swing magnitude between PD patients and healthy individuals. Analysis of arm swing variables should be stratified by age, and the validity of the analysis may be questionable in patients aged >67 years.

SUBMITTER: Ospina BM

PROVIDER: S-EPMC6218143 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Objective Arm Swing Analysis in Early-Stage Parkinson's Disease Using an RGB-D Camera (Kinect®).

Ospina Beatriz Muñoz BM Chaparro Jaime Andrés Valderrama JAV Paredes Juan David Arango JDA Pino Yor Jaggy Castaño YJC Navarro Andrés A Orozco Jorge Luis JL

Journal of Parkinson's disease 20180101 4

<h4>Background</h4>Arm swing changes are common even in the early stages of Parkinson's disease (PD). We hypothesized that arm swing changes decrease with age and can be detected using a low-cost, RGB-D depth-sensing camera.<h4>Objective</h4>This study aimed to assess the differences in arm swing between PD patients and healthy participants and to investigate the possible effects of aging on these differences.<h4>Methods</h4>Twenty-five PD patients (aged 45-87 years) and 25 age-matched, healthy ...[more]

PMID: 30149466

Similar Datasets

Project description:BackgroundAccurately measuring hypokinetic arm swing during free-living gait in Parkinson's disease (PD) is challenging due to other concurrent arm activities. We developed a method to isolate gait segments without these arm activities.MethodsWrist accelerometer and gyroscope data were collected from 25 individuals with PD and 25 age-matched controls while performing unscripted activities in their home environment. This was done after overnight withdrawal of dopaminergic medication ('pre-medication') and approximately one hour after intake ('post-medication'). Using video annotations as ground truth, we trained and evaluated two classifiers: one for detecting gait and one for detecting gait segments without other arm activities. Based on the filtered gait segments, arm swing was quantified using the median and 95th percentile range of motion (RoM). These arm swing parameters were evaluated in three ways: (1) the agreement between predicted and video-annotated gait segments without other arm activities, (2) the sensitivity to differences between PD and controls, and (3) the sensitivity to the effects of dopaminergic medication.ResultsOn the most affected side, the mean (SD) balanced accuracy for detecting gait without other arm activities was 0.84 (0.10) pre-medication and 0.88 (0.09) post-medication. The agreement between arm swing parameters of predicted and video-annotated gait segments without other arm activities was high irrespective of medication state (intra-class correlation coefficients: median RoM: 0.99; 95th percentile RoM: 0.97). Both the median and 95th percentile RoM were smaller in PD pre-medication compared to controls (median: Δ = - 18 . 80 ∘ , 95% CI [ - 30.63, - 10.60], p < 0.001; 95th percentile: Δ = - 28 . 34 ∘ , 95% CI [ - 38.26, - 18.18], p < 0.001), and smaller in pre- compared to post-medication (median: Δ = - 12 . 31 ∘ , 95% CI [ - 21.35, - 5.59], p < 0.001; 95th percentile: Δ = - 19 . 04 ∘ , 95% CI [ - 28.48, - 11.14], p < 0.001). The differences in RoM between pre- and post-medication were larger after filtering gait for the median (p < 0.01) and 95th percentile RoM (p = 0.01).ConclusionsFiltering out gait segments with other concurrent arm activities is feasible and increases the change in arm swing parameters following dopaminergic medication in free-living conditions. This approach may be used to monitor treatment effect and disease progression in daily life.

Dataset Information

Objective Arm Swing Analysis in Early-Stage Parkinson's Disease Using an RGB-D Camera (Kinect®).

Background

Objective

Methods

Results

Conclusions

Publications

Objective Arm Swing Analysis in Early-Stage Parkinson's Disease Using an RGB-D Camera (Kinect®).

Similar Datasets

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