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Related Concept Videos

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
441

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Related Experiment Video

Updated: Aug 29, 2025

The Impact of Motor Task Conditions on Goal-Directed Arm Reaching Kinematics and Trunk Compensation in Chronic Stroke Survivors
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A real-time algorithm for the detection of compensatory movements during reaching.

Edward Averell1, Don Knox1, Frederike van Wijck1

  • 1Glasgow Caledonian University, Glasgow, UK.

Journal of Rehabilitation and Assistive Technologies Engineering
|September 9, 2022
PubMed
Summary

This study developed a real-time motion tracking algorithm to detect compensatory movements during upper limb rehabilitation exercises for stroke survivors. The system accurately quantifies movement, aiding professionals in monitoring recovery progress during game-based therapy.

Keywords:
Assistive TechnologyMotion Analysis SystemsMotion/ Posture AnalysisStroke RehabilitationVirtual Reality

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Area of Science:

  • Rehabilitation Engineering
  • Biomedical Signal Processing
  • Movement Science

Background:

  • Stroke survivors often exhibit compensatory movements during rehabilitation, potentially hindering recovery.
  • Existing interactive game systems for stroke rehabilitation lack robust motion tracking to detect incorrect movements.
  • Real-time monitoring of exercise performance is crucial for effective stroke recovery.

Purpose of the Study:

  • To develop and validate a real-time algorithm for detecting compensatory movements during upper limb reaching tasks.
  • To provide quantitative metrics for health professionals to track stroke survivor progress.
  • To enable integration of accurate performance monitoring into home-based game systems for stroke rehabilitation.

Main Methods:

  • A real-time algorithm was created to analyze upper limb reaching motions using a low-cost depth camera.
  • The algorithm segments cyclical movements, identifying compensatory patterns and providing performance data.
  • Algorithm accuracy was validated by comparing real-time data collection against offline analysis in healthy participants.

Main Results:

  • The algorithm successfully segmented cyclical reaching motions and detected movement components in real-time.
  • Real-time detection of movement types achieved high accuracy, with a maximum error of 1.71%.
  • The system provides a graphical representation of task performance for clinical assessment.

Conclusions:

  • Real-time detection and quantification of compensatory movements are feasible using the developed algorithm.
  • The system can be integrated into home-based game systems for stroke survivors.
  • This technology supports effective, repetitive task practice and progress monitoring in stroke rehabilitation.