Development of a Framework for Compensation Detection during Robotic Stroke Rehabilitation Therapy using Convex Geometrical Decision Boundaries

Stroke therapy is essential to reduce impairments of the motor movements of stroke survivors, however therapy sessions have draw- backs which makes in inaccessible for most. Robotic stroke therapy seeks to remedy these limitations but it is hampered by the development incorrect movements during the...

Full description

Saved in:
Bibliographic Details
Main Author: Uy, Sean Rich
Format: text
Published: Archīum Ateneo 2020
Subjects:
n/a
Online Access:https://archium.ateneo.edu/theses-dissertations/404
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Ateneo De Manila University
id ph-ateneo-arc.theses-dissertations-1530
record_format eprints
spelling ph-ateneo-arc.theses-dissertations-15302021-09-27T03:00:04Z Development of a Framework for Compensation Detection during Robotic Stroke Rehabilitation Therapy using Convex Geometrical Decision Boundaries Uy, Sean Rich Stroke therapy is essential to reduce impairments of the motor movements of stroke survivors, however therapy sessions have draw- backs which makes in inaccessible for most. Robotic stroke therapy seeks to remedy these limitations but it is hampered by the development incorrect movements during the session. Incorrect movements, called as compensatory movements, can cause problems that can hamper the re- covery. Thus, there is a need to develop compensatory detection systems to assist patients doing autonomous therapy sessions. Previous studies on automatic detection using depth sensors and machine learning clas- sifiers did not yield satisfactory results. This study initially explores class imbalance as a possible reason for the low F1-scores. Different methods to address class imbalance were employed to improve and to analyze the performance of the classifiers and the dataset. Addressing class imbalance marginally improves the performance which led to employing additional preprocessing steps and feature extracting. Splitting the dataset by the therapy movements and removal of extra joints improved some scores however using joint angles degraded performance. The study proposes a method of having a compensatory detection system in a real-time visual feedback system using convex geometry re- moving the need for training classifiers and getting compensatory data from patients. The proposed method was shown that it can be implemented and can detect compensation but additional studies are needed for verification. 2020-01-01T08:00:00Z text https://archium.ateneo.edu/theses-dissertations/404 Theses and Dissertations (All) Archīum Ateneo n/a
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic n/a
spellingShingle n/a
Uy, Sean Rich
Development of a Framework for Compensation Detection during Robotic Stroke Rehabilitation Therapy using Convex Geometrical Decision Boundaries
description Stroke therapy is essential to reduce impairments of the motor movements of stroke survivors, however therapy sessions have draw- backs which makes in inaccessible for most. Robotic stroke therapy seeks to remedy these limitations but it is hampered by the development incorrect movements during the session. Incorrect movements, called as compensatory movements, can cause problems that can hamper the re- covery. Thus, there is a need to develop compensatory detection systems to assist patients doing autonomous therapy sessions. Previous studies on automatic detection using depth sensors and machine learning clas- sifiers did not yield satisfactory results. This study initially explores class imbalance as a possible reason for the low F1-scores. Different methods to address class imbalance were employed to improve and to analyze the performance of the classifiers and the dataset. Addressing class imbalance marginally improves the performance which led to employing additional preprocessing steps and feature extracting. Splitting the dataset by the therapy movements and removal of extra joints improved some scores however using joint angles degraded performance. The study proposes a method of having a compensatory detection system in a real-time visual feedback system using convex geometry re- moving the need for training classifiers and getting compensatory data from patients. The proposed method was shown that it can be implemented and can detect compensation but additional studies are needed for verification.
format text
author Uy, Sean Rich
author_facet Uy, Sean Rich
author_sort Uy, Sean Rich
title Development of a Framework for Compensation Detection during Robotic Stroke Rehabilitation Therapy using Convex Geometrical Decision Boundaries
title_short Development of a Framework for Compensation Detection during Robotic Stroke Rehabilitation Therapy using Convex Geometrical Decision Boundaries
title_full Development of a Framework for Compensation Detection during Robotic Stroke Rehabilitation Therapy using Convex Geometrical Decision Boundaries
title_fullStr Development of a Framework for Compensation Detection during Robotic Stroke Rehabilitation Therapy using Convex Geometrical Decision Boundaries
title_full_unstemmed Development of a Framework for Compensation Detection during Robotic Stroke Rehabilitation Therapy using Convex Geometrical Decision Boundaries
title_sort development of a framework for compensation detection during robotic stroke rehabilitation therapy using convex geometrical decision boundaries
publisher Archīum Ateneo
publishDate 2020
url https://archium.ateneo.edu/theses-dissertations/404
_version_ 1712577846490169344