Deans' stroke musings

Changing stroke rehab and research worldwide now.Time is Brain!Just think of all the trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 493 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It's quite disgusting that this information is not available from every stroke association and doctors group.
My back ground story is here:http://oc1dean.blogspot.com/2010/11/my-background-story_8.html

Monday, October 31, 2016

Self-Paced Reaching after Stroke: A Quantitative Assessment of Longitudinal and Directional Sensitivity Using the H-Man Planar Robot for Upper Limb Neurorehabilitation

It would seem to be much simpler and faster to use wearable sensors to determine base measurements. But that makes the assumption that our researchers are reading research in their field.
http://journal.frontiersin.org/article/10.3389/fnins.2016.00477/full?utm_source=newsletter&utm_medium=email&utm_campaign=Neuroscience-w45-2016
Asif Hussain1*, Aamani Budhota1,2, Charmayne Mary Lee Hughes1,3,4, Wayne D. Dailey1,5, Deshmukh A. Vishwanath6, Christopher W. K. Kuah6, Lester H. L. Yam6, Yong J. Loh6, Liming Xiang7, Karen S. G. Chua6, Etienne Burdet5 and Domenico Campolo1*
  • 1Robotics Research Centre, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
  • 2Interdisciplinary Graduate School, Nanyang Technological University, Singapore, Singapore
  • 3Department of Kinesiology, San Francisco State University, San Francisco, CA, USA
  • 4Health Equity Institute, San Francisco State University, San Francisco, CA, USA
  • 5Department of Bioengineering, Imperial College of Science, Technology and Medicine, London, UK
  • 6Centre for Advanced Rehabilitation Therapeutics, Tan Tock Seng Hospital Rehabilitation Centre, Singapore, Singapore
  • 7School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore
Technology aided measures offer a sensitive, accurate and time-efficient approach for the assessment of sensorimotor function after neurological insult compared to standard clinical assessments. This study investigated the sensitivity of robotic measures to capture differences in planar reaching movements as a function of neurological status (stroke, healthy), direction (front, ipsilateral, contralateral), movement segment (outbound, inbound), and time (baseline, post-training, 2-week follow-up) using a planar, two-degrees of freedom, robotic-manipulator (H-Man). Twelve chronic stroke (age: 55 ± 10.0 years, 5 female, 7 male, time since stroke: 11.2 ± 6.0 months) and nine aged-matched healthy participants (age: 53 ± 4.3 years, 5 female, 4 male) participated in this study. Both healthy and stroke participants performed planar reaching movements in contralateral, ipsilateral and front directions with the H-Man, and the robotic measures, spectral arc length (SAL), normalized time to peak velocities (TpeakN), and root-mean square error (RMSE) were evaluated. Healthy participants went through a one-off session of assessment to investigate the baseline. Stroke participants completed a 2-week intensive robotic training plus standard arm therapy (8 × 90 min sessions). Motor function for stroke participants was evaluated prior to training (baseline, week-0), immediately following training (post-training, week-2), and 2-weeks after training (follow-up, week-4) using robotic assessment and the clinical measures Fugl-Meyer Assessment (FMA), Activity-Research-Arm Test (ARAT), and grip-strength. Robotic assessments were able to capture differences due to neurological status, movement direction, and movement segment. Movements performed by stroke participants were less-smooth, featured longer TpeakN, and larger RMSE values, compared to healthy controls. Significant movement direction differences were observed, with improved reaching performance for the front, compared to ipsilateral and contralateral movement directions. There were group differences depending on movement segment. Outbound reaching movements were smoother and featured longer TpeakN values than inbound movements for control participants, whereas SAL, TpeakN, and RMSE values were similar regardless of movement segment for stroke patients. Significant change in performance was observed between initial and post-assessments using H-Man in stroke participants, compared to conventional scales which showed no significant difference. Results of the study indicate the potential of H-Man as a sensitive tool for tracking changes in performance compared to ordinal scales (i.e., FM, ARAT).

Introduction

The rehabilitation of neurological disorders such as stroke and cerebral palsy is a labor -intensive process that requires daily one-on-one interactions with therapists. The significant burden placed on the health care providers and the overall health care system have stimulated particular interest in technology assisted systems for neurorehabilitation (Maciejasz et al., 2014), with the underlying objective of decreasing the workload of the therapist and to facilitate training with minimal supervision at an affordable cost. A significant amount of this work has focused on the development of robotic devices to train upper extremity (UE) task-related movements (Riener et al., 2005; Prange et al., 2006; Brewer et al., 2007; Balasubramanian et al., 2010). The advantages of robot-assisted therapy include the ability to actively assist or resist human motions, to acquire accurate measurements of the dynamic and kinematic performance of participants during training using integrated sensors, and to administer repetitive task-specific training with limited supervision from a therapist. To date, clinical studies have shown that robot-assisted therapy of the UE is at least as effective as conventional rehabilitation therapy in terms of reducing motor impairments over a short-term period (Prange et al., 2006; Kwakkel et al., 2008; Lo et al., 2010; Norouzi-Gheidari et al., 2012) and thus can effectively complement conventional therapy. Although conventional therapy, itself is not very productive/ efficient, Duncan et al. reported that only 33–70% of the stroke patients recover useful arm ability, and initial paresis severity remains the best predictor of arm function recovery over 6 months (Duncan et al., 1992; Huang and Krakauer, 2009). It is possible that the limited recovery success for UE dysfunction after stroke is hampered by the limited amount of training offered to the affected population. As such, increasing the frequency and intensity of training could significantly improve performance (Harvey, 2009). However, an arguably equal, if not more important factor contributing to this limited improvement can be attributed to the partial understanding and incomplete assessment of the disability itself, which in technology intervention systems has been explored less thoroughly. Clear knowledge of the level of sensorimotor deficits is required for devising a comprehensive and efficient training regime (Balasubramanian et al., 2012a).
Conventionally, assessment of motor functions is carried out by therapists by means of ordinal clinical scales to examine specific aspects of a subject's motor behavior and devise an appropriate treatment strategy accordingly (Fugl-Meyer et al., 1974; Lyle, 1981; Gladstone et al., 2002). For example, the Action Research Arm Test (ARAT) scores performance on various tasks using a 4-point scale, where 0 indicates no movement and 3 indicates the task is completed with normal performance (Lyle, 1981). Although the ARAT and other post-stroke motor assessments are widely accepted and have high test-retest and interrater reliability, their reliance on ordinal scoring renders them insensitive to subtle differences in deficit and changes over the rehabilitation lifespan. Furthermore, the additional time required to perform manual assessment discourages their regular use in clinical practice to track and understand motor recovery in the affected population.
It is apparent that stroke rehabilitation would benefit if clinicians had a complete understanding of the specific sensorimotor deficits exhibited by the patient (Balasubramanian et al., 2012a). Robotic technology has the potential to augment the assessment process by using integrated sensors to record continuous, high-resolution data. These sensory measurements are collected during normal use of the system and do not require additional time for a discrete assessment protocol. These systems are (semi-) autonomous, potentially more objective than functional assessments, and less prone to human error/subjectivity (Bosecker et al., 2010; Lambercy et al., 2010). However, this form of assessment has yet to be fully established and validated when compared to the gold standards, and is expensive due to the high cost of (most) robotic systems for use in standard clinical practice.
At Nanyang Technological University (NTU) we have designed a novel low-cost, planar, table-top robot for decentralized neurorehabilitation (hereafter called H-Man) (Campolo et al., 2014; Hussain et al., 2015a). It can benefit participants with limited access to a therapist for rehabilitation, and with properly validated assessment protocols, can provide continuous updates about motor progress to the patient, their caregivers, and the therapy team. In this study, we evaluated the ability of the H-Man to detect differences in planar self-paced reaching as a function of neurological status (stroke, age-matched healthy control), direction (front, ipsilateral, contralateral), and movement segment (outbound, inbound). In addition, we investigated the longitudinal sensitivity of these performance metrics to capture motor performance changes in stroke patients, and examined the relationship between robotic measures and conventional scales. Multiple studies have previously addressed variations in performance metrics on workspace. However, due to variations in protocols/task definitions (for example point to point vs. path reaching, free reaching vs. supported movements) and varying outcomes, the reliability, and validity of reaching movements as measures of upper limb motor functionality is still limited (Levin, 1996; Archambault et al., 1999; Kamper et al., 2002; Sukal et al., 2007). Moreover, most of these studies focus on developing relations to clinical scales and/or inter-relationships between performance metrics. In this paper, we focus on a more fundamental question: the distribution/variation of performance outcomes within a control group and across stroke participants for different directions, and for different segments of movements [outbound movements (i.e., away from the body) and inbound movement (i.e., toward the body)]. Multiple papers briefly address this question but not as a major focus of study for-example, Kamper et al. and Levin presented studies on free reaching in 3D and planar supported reaching tasks, respectively (Levin, 1996; Kamper et al., 2002), which showed modest variations across directions but pre-dominantly focused on results (of all directions) to establish relationships with performance matrices and or clinical scales. Here, we report variations for all directions and performance matrices, for both control and stroke participants, along with comparisons between inbound and outbound movement segments. These results help build a clearer understanding of the characteristics of reaching movements and how they differ across stroke and healthy participants. Further, we also show the sensitivity of selected performance measures compared to clinical scales by analysing longitudinal changes in metrics by assessing performance over a 2-week period, which adds weight to the potential of the H-Man as an effective assessment tool.

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