A Tool to Assist in the Analysis of Gaze Patterns in Upper Limb Prosthetic Use.
Gaze-tracking, where the point of regard of a subject is mapped onto the image of the scene the subject sees, can be employed to study the visual attention of the users of prosthetic hands. It can show whether the user pays greater attention to the actions of their prosthetic hand as they use it to...
| Publicado en: | Prosthesis (2673-1592) Vol. 5; no. 3; pp. 898 - 916 |
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| Autores principales: | , , |
| Formato: | algorithm pictorial research tables/charts Journal Article |
| Publicado: |
MDPI
Sep2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=172394146&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 172394146 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 26731592 MVQF jtl: Prosthesis (2673-1592) issn: 26731592 maglogo: N pubinfo: dt: Sep2023 vid: 5 iid: 3 pid: 97109 pub: MDPI artinfo: ui: 172394146 172394146 172394146 10.3390/prosthesis5030063 172394146 ppf: 898 ppct: 18 formats: tig: atl: A Tool to Assist in the Analysis of Gaze Patterns in Upper Limb Prosthetic Use. aug: au: Kyberd, Peter Popa, Alexandru Florin Cojean, Théo affil: School of Engineering, College of Science and Engineering, University of Derby, Derby DE22 3AW, UK sug: subj: Eye Movements Evaluation Upper Extremity Pathology Limb Prosthesis Utilization Task Performance and Analysis Human Manipulation, Orthopedic Visual Fields Software Myoelectric Prosthesis Clinical Assessment Tools Simulations Image Processing, Computer Assisted Male Female Adolescence Adult Middle Age Descriptive Statistics Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Gaze-tracking, where the point of regard of a subject is mapped onto the image of the scene the subject sees, can be employed to study the visual attention of the users of prosthetic hands. It can show whether the user pays greater attention to the actions of their prosthetic hand as they use it to perform manipulation tasks, compared with the general population. Conventional analysis of the video data requires a human operator to identify the key areas of interest in every frame of the video data. Computer vision techniques can assist with this process, but fully automatic systems require large training sets. Prosthetic investigations tend to be limited in numbers. However, if the assessment task is well-controlled, it is possible to make a much simpler system that uses the initial input from an operator to identify the areas of interest and then the computer tracks the objects throughout the task. The tool described here employs colour separation and edge detection on images of the visual field to identify the objects to be tracked. To simplify the computer's task further, this test uses the Southampton Hand Assessment Procedure (SHAP) to define the activity spatially and temporarily, reducing the search space for the computer. The work reported here concerns the development of a software tool capable of identifying and tracking the points of regard and areas of interest throughout an activity with minimum human operator input. Gaze was successfully tracked for fourteen unimpaired subjects and was compared with the gaze of four users of myoelectric hands. The SHAP cutting task is described and the differences in attention observed with a greater number of shorter fixations by the prosthesis users compared to unimpaired subjects. There was less looking ahead to the next phase of the task by the prosthesis users. pubtype: Academic Journal doctype: algorithm pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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