Posts Tagged Monitoring

[Thesis] Monitoring stroke rehabilitation of arm movement outside of the clinical setting – Full Text PDF

Monitoring Stroke Rehabilitation of Arm Movement Outside of the Clinical Setting

by

Juan Pablo Gómez Arrunátegui

B.A.Sc, The University of British Columbia, 2015

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR
THE DEGREE OF

Master of Applied Science

in

THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES

(Biomedical Engineering)

The University of British Columbia

(Vancouver)

October 2018

© Juan Pablo Gómez Arrunátegui, 2018

 

Abstract

Stroke is the leading cause of disability in North America. Fifty-four percent of stroke survivors suffer from upper body hemiparesis, a weakness that limits the client’s ability to perform functional tasks with the affected side of the body. Stroke rehabilitation aims to recover limb mobility through thousands of repeated functional movements that lead to neural regeneration.
However, time constraints in clinical rehabilitation lead to an average of 32 arm repetitions per session, which is insufficient for optimal recovery. Accurate monitoring of client activity outside of the clinical setting could enable therapists to track what they do, improving recovery. To address this problem, we have designed the Arm Rehabilitation Monitor (ARM), a wrist-worn device that collects movement data in unconstrained environments, and processes it offline to identify reach actions. Reach actions were identified as functionally meaningful tasks that lead to better
rehabilitation.

We enrolled 15 participants with mild to moderate hemiparesis due to stroke to perform two activities: (1) a functional assessment of the arm, and (2) an activity of daily living (ADL) task that consisted of making a pizza. The data recorded by the IMU on both activities was used to train three different machine learning algorithms (Random Forest, Convolutional Neural Networks and Shapelets) to detect reaching gestures.

We found that the ARM obtained the best results with the Random Forest and CNN algorithms. The CNN algorithm had the best F1-score (0.523) for the Clinic-Home inter-subject tests, while the RF algorithm obtained the best score (0.486) in the Clinic-Home intra-subject configuration. We used the ARM to estimate the time spent reaching and the number of reach counts. The CNN algorithm predicted the reach time for the Clinic-Home inter-subject tests to be 1.07x ( 0.55x) the true reach time and the reach counts to be 1.28x ( 0.40x) the true number of reach gestures. In turn, the RF algorithm predicted the reach time for the Clinic-Home intra-subject configuration to be 1.16x ( 0.84x) and the reach counts to be 1.26x (0.40x). Both results have a smaller standard deviation when estimating reach counts than a comparable commercial accelerometer worn on the wrist.

Full Text PDF —>  Monitoring stroke rehabilitation of arm movement outside of the clinical setting – UBC Library Open Collections

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[Abstract] A novel smartphone camera-LED Communication for clinical signal transmission in mHealth-rehabilitation system – IEEE Conference Publication

Abstract:

In this paper, an implementation of mobile-Visible Light Communication (mVLC) technology for clinical data transmission in home-based mobile-health (mHealth) rehabilitation system is introduced. Mobile remote rehabilitation program is the solutions for improving the quality of care of the clinicians to the patients with chronic condition and disabilities. Typically, the program inquires routine exercise which obligate patients to wear wearable electronic sensors for hours in a specific range of time. Thus it motivate us to develop a novel harmless biomedical communicating system since most of the device’s protocol was based on RF communication technology which risky for a human body in term of long term usage due to RF exposure and electromagnetic interference (EMI). The proposed system are designed to utilize a visible light as a medium for hazardless-communication between wearable sensors and a mobile interface device (smartphone). Multiple clinical data such as photoplethysmogram (PPG), electrocardiogram (ECG), and respiration signal are transmitted through LED and received by a smartphone camera. Furthermore, a smartphone also used for local interface and data analyzer henceforth sent the data to the cloud for further clinician’s supervision.

I. Introduction

Home-based rehabilitation are focused to improve the care quality of the clinicians to the patients. It helps the medical experts and clinicians to monitor their patients without direct interaction to the patients. For patients, it helps them to keep the intense care of their clinical states while being at home and also helps some patients with inability to leave their home to easily interact with their doctor for treatment. Basically each individual patients and diseases have different rehabilitation treatment, such as smart exercise bike for Parkinson’s disease [1], cycling exercise for chronic disease [2], seated exercises for older adults [3], and movement disorders patients [4], also hand exercise for postStroke patients [5]. Most of the mentioned rehabilitation program are required a regular time of exercise treatment, for example based on American Heart Association / American Stroke Association (AHA/ASA) guideline [6], for inpatient rehabilitation facilities (IRFs) at least 3 hours/day with 5 days/week is required. Moreover other researcher [7], mentioned the same treatment timeline requirement for their proposed home stroke rehabilitation and monitoring system.

Source: A novel smartphone camera-LED Communication for clinical signal transmission in mHealth-rehabilitation system – IEEE Conference Publication

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[Abstract] Kinect V2 as a tool for stroke recovery: Pilot study of motion scale monitoring

Abstract:

This paper investigates Kinect device application during rehabilitation of people with an ischemic stroke. There are many similar application using Kinect as a tool during rehabilitation. This paper is focused on measurement of Kinect’s spatial accuracy and proposition of body states and exercises according to the Motor assessment scale for stroke (MAS). The system observes the whole rehabilitation process and objectively compares ranges of movement during each exercise. Angles between limbs are computed in the skeletal body joints projection to three anatomical planes, which enables a better insight to subject performance. The system is easily implemented with a consumer-grade computer and a low-cost Kinect device. Selected exercises are presented together with the angles evolution, body states recognition and the MAS Scale after the stroke classification.

Source: Kinect V2 as a tool for stroke recovery: Pilot study of motion scale monitoring – IEEE Xplore Document

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[Master’s thesis] Tracking, monitoring and feedback of patient exercises using depth camera technology for home based rehabilitation – ANNA RIDDERSTOLPE – Full Text PDF

Abstract

Neurological and chronic diseases have profound impacts on a person’s life. Rehabilitation is essential in order to maintain and promote maximal level of recovery by pushing the bounds of physical, emotional and cognitive impairments. However, due to the low physical mobility and poor overall condition of many patients, traveling back and forth to doctors, nurses and rehabilitation centers can be exhausting tasks. In this thesis a game-based rehabilitation platform for home usage, supporting stroke and COPD rehabilitation is presented. The main goal is to make rehabilitation more enjoyable, individualized and easily accessible for the patients.

The game-based rehabilitation tool consists of three systems with integrated components: the caregiver’s planning and follow-up system, the patient’s gaming system and the connecting server system. The server back end components allow the storage of patient specific information that can be transmitted between the patient and the caregiver system for planning, monitoring and feedback purposes. The planning and follow-up system is a server system accessed through a web-based front-end, where the caregiver schedules the rehabilitation program adjusted for each individual patient and follow up on the rehabilitation progression. The patient system is the game platform developed in this project, containing 16 different games and three assessment tests. The games are based on specific motion patterns produced in collaboration with rehabilitation specialists. Motion orientation and guidance functions is implemented specifically for each exercise to provide feedback to the user of the performed motion and to ensure proper execution of the desired motion pattern.

The developed system has been tested by several people and with three real patients. The participants feedback supported the use of the game-based platform for rehabilitation as an entertaining alternative for rehabilitation at home. Further implementation work and evaluation with real patients are necessary before the product can be used for commercial purpose.

Full Text PDF

 

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[ARTICLE] Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review – Full Text HTML

Abstract

Objective

To perform a systematic review of studies using remote physical activity monitoring in neurological diseases, highlighting advances and determining gaps.

Methods

Studies were systematically identified in PubMed/MEDLINE, CINAHL and SCOPUS from January 2004 to December 2014 that monitored physical activity for ≥24 hours in adults with neurological diseases. Studies that measured only involuntary motor activity (tremor, seizures), energy expenditure or sleep were excluded. Feasibility, findings, and protocols were examined.

Results

137 studies met inclusion criteria in multiple sclerosis (MS) (61 studies); stroke (41); Parkinson’s Disease (PD) (20); dementia (11); traumatic brain injury (2) and ataxia (1). Physical activity levels measured by remote monitoring are consistently low in people with MS, stroke and dementia, and patterns of physical activity are altered in PD. In MS, decreased ambulatory activity assessed via remote monitoring is associated with greater disability and lower quality of life. In stroke, remote measures of upper limb function and ambulation are associated with functional recovery following rehabilitation and goal-directed interventions. In PD, remote monitoring may help to predict falls. In dementia, remote physical activity measures correlate with disease severity and can detect wandering.

Conclusions

These studies show that remote physical activity monitoring is feasible in neurological diseases, including in people with moderate to severe neurological disability. Remote monitoring can be a psychometrically sound and responsive way to assess physical activity in neurological disease. Further research is needed to ensure these tools provide meaningful information in the context of specific neurological disorders and patterns of neurological disability.

Continue —> PLOS ONE: Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review

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[ARTICLE] Monitoring of Upper Limb Rehabilitation and Recovery after Stroke: An Architecture for a Cloud-Based Therapy Platform

Abstract

Amongst the therapies available to stroke sufferers, one that is gaining attention is the application of video games to encourage therapeutic movement. The Limbs Alive project at Newcastle University has developed a system that gathers therapeutic game data from patients, uses statistical tools to estimate a number of performance metrics and presents the results to patients and clinicians via web applications. This paper describes the architecture of this system and outlines the various technical challenges that were overcome, including in security and deployment.

Source: IEEE Xplore Abstract (Abstract) – Monitoring of Upper Limb Rehabilitation and Recovery after Stroke: An Architecture for a Cloud-Based…

 

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