Posts Tagged Monitoring
[ARTICLE] Adaptive Treadmill-Assisted Virtual Reality-Based Gait Rehabilitation for Post-Stroke Physical Reconditioning—a Feasibility Study in Low-Resource Settings – Full Text
Neurological disorders, such as stroke is a leading cause of disability with a prevalence rate of 424 in 100,000 individuals in India . Often, these patients suffer from functional disabilities, heterogeneous physical deconditioning along with deteriorated cardiac functioning ,  and a sedentary lifestyle immediately following stroke . A deconditioned patient requires reconditioning of his/her cardiac capacity and ambulation capabilities that can be achieved through individualized rehabilitation . This needs to be done under the supervision of a clinician who can monitor one’s functional capability, cardiac capacity and gait performance thereby recommending an appropriate dosage of the gait rehabilitation exercise intensity to the patient along with feedback. Such gait rehabilitation is crucial since about 80% of these patients have been reported to suffer from gait-related disorders  along with more energy expenditure than able-bodied individuals  often accompanied with reduced cardiac capacity , . However, given the low doctor-to-patient ratio , lack of rehabilitation facilities and patients being released early from rehabilitation clinics followed by home-based exercise , particularly in developing countries like India, availing individualized rehabilitation services becomes difficult. Again, undergoing home-based exercises under clinician’s one-on-one supervision becomes difficult given the restricted healthcare resources, thereby limiting the rehabilitation outcomes . Again, given the restricted healthcare resources, getting a clinician visiting the homes for delivering therapy sessions to patients is often costly causing the patients to miss the expert inputs on the exercise intensity suiting his/her exercise capability along with motivational feedback from the clinician . This necessitates the use of a complementary technology-assisted rehabilitation platform that can be availed by the patient at his/her home  following a short stay at the rehabilitation clinic . Again, it is preferred that this platform be capable of offering individualized gait exercise while varying the dosage of exercise intensity (based on the patient’s exercise capability) along with motivational feedback . Additionally, exercise administered by this platform can be complemented with intermediate clinician-mediated assessments of rehabilitation outcomes, thereby reducing continuous demands on the restricted clinical resources. Thus, it is important to investigate the use of such technology-assisted gait exercise platforms that are capable of offering exercise based on one’s individualized capability along with motivational feedback.
Researchers have explored the use of technology-assisted solutions to offer rehabilitative gait exercises to these patients, along with presenting motivational feedback –. Specifically, investigators have used Virtual Reality (VR) coupled with a treadmill (having a limited footprint and making it suitable for home-based settings) while delivering individualized feedback  to the patient during exercise. Again, VR can help to project scenarios that can make the exercise engaging and interactive for a user –. In fact, Finley et al. have shown that the visual feedback offered by VR provides an optical flow that can induce changes in the gait performance (quantified in terms of gait parameters, e.g., Step Length, Step Symmetry, etc.) of such patients during treadmill-assisted walk . Further, Jaffe et al. have reported positive implications of VR-based treadmill-assisted walking exercise on the gait performance of individuals with stroke , leading to improvement in their community ambulation . These studies have shown the efficacy of the VR-based treadmill-assisted gait exercise platform to contribute towards gait rehabilitation of individuals suffering from stroke. Though promising, none of these platforms are sensitive to one’s individualized exercise capability and thus, in turn, could not decide an optimum dosage of exercise intensity suiting one’s capability, e.g., cardiac capacity and ambulation capability. This is particularly critical for individuals with stroke since they possess diminished exercise ability along with deteriorated cardiac functioning , .
From literature review, we find that after stroke, treadmill-assisted cardiac exercise programs can lead to one’s improved fitness and exercise capability . For example, researchers have presented studies on Moderate-Intensity Continuous Exercise and High-Intensity Interval Training in which exercise protocols are individualized by a clinician based on one’s cardiac capacity while contributing to effective gait rehabilitation –. Though promising, these have not offered a progressive and adaptive exercise environment in which the dosage of exercise intensity is varied based on one’s cardiac capacity in real-time. Thus, the choice of optimum dosage of exercise intensity that can be individualized in real-time for a patient, still remains as inadequately explored . For deciding the optimal dosage of rehabilitative exercise intensity, clinicians often refer to the guidelines recommended by the American College of Sports Medicine (ACSM) . These guidelines suggest thresholds to decide the intensity of the exercise based on one’s metabolic energy consumption in terms of oxygen intake, heart rate, etc. Deciding the dosage of exercise intensity is crucial, particularly for individuals with stroke since their energy requirements have been reported to be 55-100% higher than that of their able-bodied counterparts . Specifically, higher energy requirement often limits the capabilities of these patients and challenges their rehabilitation outcomes. This can be addressed if the technology-assisted gait exercise platform can offer individualized exercise (maintaining the safe exercise thresholds) based on the energy expenditure of the patients acquired in real-time during the exercise.
The energy expenditure can be defined as the cost of physical activity  and it is often expressed in terms of oxygen consumption or heart rate . Thus, investigators have monitored the oxygen consumption and heart rate to estimate the energy expenditure of individuals with stroke during their walk , . However, monitoring oxygen consumption during exercise requires a cumbersome setup , making it unsuitable for home-based rehabilitation. On the other hand, one’s heart rate (HR) can be monitored using portable solutions  that can be integrated with a treadmill in home-based settings. Researchers have explored treadmill-assisted gait exercise platforms that are sensitive to the user’s heart rate. For example, researchers have offered treadmill training to subjects with stroke in which some of them varied treadmill speed to achieve 45%-50% , while others varied speed to achieve 85% to 95% ,  of one’s age-related maximum heart rate. Again, Pohl et al. have offered treadmill-assisted exercise to subjects with stroke while ensuring that the user’s heart rate settled to the respective resting-state heart rate . Again of late, there had been advanced treadmills, available off-the-shelf, that can monitor one’s heart rate and vary the treadmill speed to maintain the user’s heart rate at a predefined level , . Though one’s heart rate is an important indicator that needs to be considered during treadmill-assisted exercise, one’s walking speed while using the treadmill also offers important information on one’s exercise capability. This is because gait rehabilitation aims to improve one’s community ambulation that is related to one’s walking speed . Thus, it would be interesting to explore the composite effect of one’s walking speed along with working and resting-state heart rates during treadmill-assisted gait exercise to study one’s energy expenditure, quantified in terms of a proxy index, namely Physiological Cost Index (PCI) .
Given that there are no existing studies that have used a treadmill-assisted gait exercise platform deciding the dosage of exercise intensity based on one’s PCI estimated in real-time during exercise, it might be interesting to explore the use of such an individualized gait exercise platform for individuals with stroke. Thus, we wanted to extend a treadmill-assisted gait exercise platform by making it adaptive to one’s individualized PCI. Additionally, we wanted to augment this platform with VR-based user interface to offer visual feedback to the user undergoing gait exercise. We hypothesized that such a gait exercise platform can recondition a patient’s exercise capability in terms of cardiac and gait performance to achieve improved community ambulation. The objectives of our research were three-fold, namely to (i) implement a novel PCI-sensitive Adaptive Response Technology (PCI-ART) offering VR-based treadmill-assisted gait exercise, (ii) investigate the safety and feasibility of use of this platform among able-bodied individuals before applying it to subjects with stroke and (iii) examine implications of undergoing gait exercise with this platform on the patients’ (a) cardiac and gait performance along with energy expenditure, (b) clinical measures estimating the physical reconditioning and (c) views on their community ambulation capabilities.
The rest of the paper is organized as follows: Section II presents our system design. Section III explains the experiments and procedures of this study. Section IV discusses the results. In Section V, we summarize our findings, limitations, and scope of future research.[…]
[Abstract] Hand Sensory Rehabilitation System Which Incorporated Visual and Tactile Feedback – IEEE Conference Publication
[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
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
THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES
The University of British Columbia
© Juan Pablo Gómez Arrunátegui, 2018
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
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.
[Abstract] A novel smartphone camera-LED Communication for clinical signal transmission in mHealth-rehabilitation system – IEEE Conference Publication
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 , cycling exercise for chronic disease , seated exercises for older adults , and movement disorders patients , also hand exercise for postStroke patients . 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 , for inpatient rehabilitation facilities (IRFs) at least 3 hours/day with 5 days/week is required. Moreover other researcher , mentioned the same treatment timeline requirement for their proposed home stroke rehabilitation and monitoring system.
[Master’s thesis] Tracking, monitoring and feedback of patient exercises using depth camera technology for home based rehabilitation – ANNA RIDDERSTOLPE – Full Text PDF
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.
[ARTICLE] Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review – Full Text HTML
To perform a systematic review of studies using remote physical activity monitoring in neurological diseases, highlighting advances and determining gaps.
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.
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.
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.
[ARTICLE] Monitoring of Upper Limb Rehabilitation and Recovery after Stroke: An Architecture for a Cloud-Based Therapy Platform
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.