Publications by authors named "T Rikakis"

Article Synopsis
  • The study developed a Hierarchical Bayesian Model (HBM) to quantify the relationship between movement quality and functionality in stroke survivors undergoing upper extremity therapy.
  • Clinicians rated videos of 478 stroke patients performing therapy tasks, and the HBM was built to analyze the effectiveness of these movements in relation to their functional abilities.
  • Results showed that the HBM resolved discrepancies in clinician ratings 95% of the time and aligned kinematic data with therapy tasks in over 90% of cases, indicating its potential for automated therapy assessments across various neurorehabilitation settings.
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For upper extremity rehabilitation, quantitative measurements of a person's capabilities during activities of daily living could provide useful information for therapists, including in telemedicine scenarios. Specifically, measurements of a person's upper body kinematics could give information about which arm motions or movement features are in need of additional therapy, and their location within the home could give context to these motions. To that end, we present a new algorithm for identifying a person's location in a region of interest based on a Bluetooth received signal strength (RSS) and present an experimental evaluation of this and a different Bluetooth RSS-based localization algorithm via fingerprinting.

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We are developing a system for long term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We present a cyber-human methodology used by the SARAH system for automated assessment of upper extremity stroke rehabilitation at the home. We propose a hierarchical model for automatically segmenting stroke survivor's movements and generating training task performance assessment scores during rehabilitation.

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We introduce a novel in-home hand rehabilitation system for monitoring hand motions and assessing grip forces of stroke patients. The overall system is composed of a sensing device and a computer vision system. The sensing device is a lightweight cylindrical object for easy grip and manipulation, which is covered by a passive sensing layer called "Smart Skin.

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This paper proposes a computational framework for movement quality assessment using a decision tree model that can potentially assist a physical therapist in a telerehabilitation context. Using a dataset of key kinematic attributes collected from eight stroke survivors, we demonstrate that the framework can be reliably used for movement quality assessment of a reach-to-grasp cone task, an activity commonly used in upper extremity stroke rehabilitation therapy. The proposed framework is capable of providing movement quality scores that are highly correlated to the ratings provided by therapists, who used a custom rating rubric created by rehabilitation experts.

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