Dataset Information

Capturing Upper Limb Gross Motor Categories Using the Kinect® Sensor.

ABSTRACT: IMPORTANCE:Along with growth in telerehabilitation, a concurrent need has arisen for standardized methods of tele-evaluation. OBJECTIVE:To examine the feasibility of using the Kinect sensor in an objective, computerized clinical assessment of upper limb motor categories. DESIGN:We developed a computerized Mallet classification using the Kinect sensor. Accuracy of computer scoring was assessed on the basis of reference scores determined collaboratively by multiple evaluators from reviewing video recording of movements. In addition, using the reference score, we assessed the accuracy of the typical clinical procedure in which scores were determined immediately on the basis of visual observation. The accuracy of the computer scores was compared with that of the typical clinical procedure. SETTING:Research laboratory. PARTICIPANTS:Seven patients with stroke and 10 healthy adult participants. Healthy participants intentionally achieved predetermined scores. OUTCOMES AND MEASURES:Accuracy of the computer scores in comparison with accuracy of the typical clinical procedure (immediate visual assessment). RESULTS:The computerized assessment placed participants' upper limb movements in motor categories as accurately as did typical clinical procedures. CONCLUSIONS AND RELEVANCE:Computerized clinical assessment using the Kinect sensor promises to facilitate tele-evaluation and complement telehealth applications. WHAT THIS ARTICLE ADDS:Computerized clinical assessment can enable patients to conduct evaluations remotely in their homes without therapists present.

SUBMITTER: Seo NJ

PROVIDER: S-EPMC6638702 | biostudies-literature | 2019 Jul/Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Capturing Upper Limb Gross Motor Categories Using the Kinect® Sensor.

Seo Na Jin NJ Crocher Vincent V Spaho Egli E Ewert Charles R CR Fathi Mojtaba F MF Hur Pilwon P Lum Sara A SA Humanitzki Elizabeth M EM Kelly Abigail L AL Ramakrishnan Viswanathan V Woodbury Michelle L ML

The American journal of occupational therapy : official publication of the American Occupational Therapy Association 20190701 4

<h4>Importance</h4>Along with growth in telerehabilitation, a concurrent need has arisen for standardized methods of tele-evaluation.<h4>Objective</h4>To examine the feasibility of using the Kinect sensor in an objective, computerized clinical assessment of upper limb motor categories.<h4>Design</h4>We developed a computerized Mallet classification using the Kinect sensor. Accuracy of computer scoring was assessed on the basis of reference scores determined collaboratively by multiple evaluators ...[more]

PMID: 31318673

Similar Datasets

Project description:IntroductionLongitudinal observations of upper limb motor recovery after stroke have suggested that certain subgroups may exhibit distinct recovery patterns. Here we sought to examine whether the predictive ability for post-stroke upper limb motor outcomes could be enhanced by applying conventional stratification strategies.MethodFor 60 individuals who suffered the first stroke, upper limb motor impairment was assessed with the upper extremity Fugl-Meyer assessment (UE-FMA) at 2 weeks as a baseline and then 3 months post-stroke. Brain structural damage at baseline was assessed by MRI data-derived markers ranging from traditional lesion size to the lesion load and to the disconnectome. Linear regression models for predicting upper limb motor outcomes (UE-FMA score at 3 months post-stroke) based on baseline upper limb motor impairment (UE-FMA score at 2 weeks post-stroke), brain structural damage, and their combinations were generated, and those with the best predictive performance were determined for individual subgroups stratified according to initial impairment (severe and non-severe), lesion location (cortical and non-cortical), and neurophysiological status (motor evoked potential-positive and motor evoked potential-negative).ResultsThe best predictions were made by baseline upper limb motor impairment alone for subgroups with less functional impairment (non-severe) or less structural involvement (non-cortical), but by the combination of baseline upper limb motor impairment and brain structural damage for the other subgroups. The predictive models tailored for subgroups determined according to initial impairment and neurophysiological status yielded a smaller overall error than that for the whole group in upper limb motor outcome predictions.DiscussionThe predictive ability for upper limb motor outcomes could be enhanced beyond the one-size-fits-all model for all individuals with stroke by applying specific stratification strategies, with stratification according to initial impairment being the most promising. We expect that predictive models tailored for individual subgroups could lead closer to the personalized prognosis of upper limb motor outcomes after stroke.

Dataset Information

Capturing Upper Limb Gross Motor Categories Using the Kinect® Sensor.

Publications

Capturing Upper Limb Gross Motor Categories Using the Kinect® Sensor.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets