Posts Tagged biomedical monitoring

[Abstract] A wearable monitoring system for at-home stroke rehabilitation exercises: A preliminary study

Abstract:

When stroke survivors perform rehabilitation exercises in clinical settings, experienced therapists can evaluate the associated quality of movements by observing only the initial part of the movement execution so that they can discourage therapeutically undesirable movements effectively and reinforce desirable ones as much as possible in the limited therapy time. This paper introduces a novel monitoring platform based on wearable technologies that can replicate the capability of skilled therapists. Specifically, we propose to deploy five wearable sensors on the trunk, and upper and forearm of the two upper limbs, analyze partial to complete observation data of reaching exercise movements, and employ supervised machine learning to estimate therapists’ evaluation of movement quality. Estimation performance was evaluated using F-Measure, Receiver Operating Characteristic Area, and Root Mean Square Error, showing that the proposed system can be trained to evaluate the movement quality of the entire exercise movement using as little as the initial 5s of the exercise performance. The proposed platform may help ensure high quality exercise performance and provide virtual feedback of experienced therapists during at-home rehabilitation.

I. Introduction

Stroke is a leading cause of death and disabilities in adults, and the majority of its survivors suffer from upper extremity paresis [1]. There is scientific evidence that repetitive rehabilitation exercises and training could improve motor abilities as a result of motor learning processes [2]. Among many, a reaching movement is a fundamental component of daily movement that requires the coordination of multiple upper extremity segments [3]. It is shown that repetitive reaching exercises improve the smoothness, precision, and speed of arm movements [4]. To continue to improve and to sustain motor function, it is clinically important that patients continue to engage in rehabilitation exercises even outside the clinical settings [5], which emphasizes the importance of the home-based therapy.

 

via A wearable monitoring system for at-home stroke rehabilitation exercises: A preliminary study – IEEE Conference Publication

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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] Fatigue detection and estimation using auto-regression analysis in EEG

Abstract:

Estimation of fatigue is a required criteria in the field of physiology. The estimation of muscle fatigue and its development in the brain signals can provide a level of endurance among athletes and limits of a persons in doing physical tasks. In this paper a technique for detecting and estimating the fatigue development using regression parameters for EEG signals is discussed. The study of 14 subjects was undertaken and analysed for the fatigue development using Auto-Regression(AR) model. The behaviour of the error function obtained is analysed for the prediction of the stages and limits of muscle fatigue development.

I. Introduction

Muscle fatigue is a phenomenon associated with the muscle contraction. It is understood as the reduction in the ability of maximal force generation by the muscle with time, during its stressing, as the muscle contraction keeps on increasing. The nervous system’s limitation to generate sustainable signals and the reduction of ability of muscle fiber to contract are two major factors contributing to fatigue development [1]. Fatigue development limits the performance and capability of the individual in sports, long stretch driving conditions and in rigourous day to day activities. Hence a parameter that can estimate the fatigue levels and provide a break point for maximum fatigue can be useful for physiology and in other areas such as labour. People working under mines can be monitored for the fatigue break point and the overall productivity of such areas can be increased by proper analysis. The fatigue development in a person can be analysed via number of methods based on physiological changes. These include Electroencephalogram (EEG), Elec-tromyography(EMG), and Heart Rate Variability(HRV). Zadry et.al. [2] reported the increase in alpha band power level of EEG with time for fatigue development [3]. Ali et.al. also reported increase in RMS values of different bands in EEG [4]. Few studies measure brain activity in light repetitive task using EEG [5] to measure drowsiness or fatigue on drivers [6] [7] and night work [8] [9]. The EEG analysis for overall fatigue has been the focus of research, but research for specific muscle fatigue detection has been limited. The EEG based detection of fatigue has the advantage of quantitative based assessment. But, for real time application perspective faster computational power and signal processing methods are required. One of the challenges based on EEG based approach is the disturbances and contamination of the signal from eyes blinking action, muscle noise by movements and instrumental noises like line noise, electronic interferences [10]. Another problem is imposed by the inter-variability and intra-variability in EEG dynamics accompanying loss of alertness [11].

Source: Fatigue detection and estimation using auto-regression analysis in EEG – IEEE Xplore Document

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[Conference Paper] Monitoring Game-Based Motor Rehabilitation of Patients at Home for Better Plans of Care and Quality of Life – Full Text PDF

Abstract

This paper describes the biomedical, remote monitoring infrastructure developed and currently tested in the EU REHAB@HOME project to support home rehabilitation of the upper extremity of persons post-stroke and in persons with other neurological disorders, such as Multiple Sclerosis patients, in order to track their progress over therapy and improve their Quality of Life. The paper will specifically focus on describing the initial testing of the tele-rehabilitation system’s components for patients’ biomedical monitoring over therapy, which support the delivery and monitoring of more personalized, engaging plans of care by rehabilitation centers and services.

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