Posts Tagged Wearable sensors

[Abstract + References] Design and Development of a Robotic Platform Based on Virtual Reality Scenarios and Wearable Sensors for Upper Limb Rehabilitation and Visuomotor Coordination – Conference paper

Abstract

The work reintegration following shoulder biomechanical overload illness is a multidimensional process, especially for those tasks requiring strength, movement control and arm dexterity. Currently different robotic devices used for upper limb rehabilitation are available on the market, but these devices are not based on activities focused on the work reintegration. Furthermore, the rehabilitation programmes aimed to the work reintegration are insufficiently focused on the recovery of the necessary skills for the re-employment.

In this study the details of the design of an innovative robotic platform integrated with wearable sensors and virtual reality scenarios for upper limbs motor rehabilitation and visuomotor coordination is presented. The design of control strategy will also be introduced. The robotic platform is based on a robotic arm characterized by seven degrees of freedom and by an adaptive control, wearable sensorized insoles, virtual reality (VR) scenarios and the Leap Motion device to track the hand gestures during the rehabilitation training. Future works will address the application of deep learning techniques for the analysis of the acquired big amount of data in order to automatically adapt both the difficulty level of the VR serious games and amount of motor assistance provided by the robot.

References

  1. 1.
    MacEachen, E., et al.: Systematic review of the qualitative literature on return to work after injury. Scand. J. Work Environ. Health 32(4), 257–269 (2006)CrossRefGoogle Scholar
  2. 2.
    Franche, R.-L., Krause, N.: Readiness for return to work following injury or illness: conceptualizing the interpersonal impact of healthcare, workplace, and insurance factor. J. Occup. Rehabil. 12(4), 233–256 (2002)CrossRefGoogle Scholar
  3. 3.
    Hou, W.H., Chi, C.C., Lo, H.L.D., Kuo, K.N., Chuang, H.Y.: Vocational rehabilitation for enhancing return-to-work in workers with traumatic upper limb injuries (2013)Google Scholar
  4. 4.
    Shi, Q., Sinden, K., Macdermid, J.C., Walton, D., Grewal, R.: A systematic review of prognostic factors for return to work following work-related traumatic hand injury. J. Hand Ther. 27(1), 55–62 (2014)CrossRefGoogle Scholar
  5. 5.
    Fadyl, J., McPherson, K.: Return to work after injury: a review of evidence regarding expectations and injury perceptions, and their influence on outcome. J. Occup. Rehabil. 18(4), 362–374 (2008)CrossRefGoogle Scholar
  6. 6.
    Krebs, H.I.: Twenty + years of robotics for upper-extremity rehabilitation following a stroke. In: Rehabilitation Robotics (2018)CrossRefGoogle Scholar
  7. 7.
    Buongiorno, D., Sotgiu, E., Leonardis, D., Marcheschi, S., Solazzi, M., Frisoli, A.: WRES: a novel 3 DoF WRist exoskeleton with tendon-driven differential transmission for neuro-rehabilitation and teleoperation. IEEE Robot. Autom. Lett. 3(3), 2152–2159 (2018)CrossRefGoogle Scholar
  8. 8.
    Krebs, H.I., et al.: Robotic applications in neuromotor rehabilitation. Robotica 21(1), 3–11 (2003)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lee, S.-S., Park, S.-A., Kwon, O.-Y., Song, J.-E., Son, K.-C.: Measuring range of motion and muscle activation of flower arrangement tasks and application for improving upper limb function. Korean J. Hortic. Sci. Technol. 30(4), 449–462 (2012)CrossRefGoogle Scholar
  10. 10.
    Spreeuwers, D., et al.: Work-related upper extremity disorders: one-year follow-up in an occupational diseases registry. Int. Arch. Occup. Environ. Health 84(7), 789–796 (2011)CrossRefGoogle Scholar
  11. 11.
    Mehrholz, J., Pohl, M., Platz, T., Kugler, J., Elsner, B.: Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke (2018)Google Scholar
  12. 12.
    Lederer, V., Rivard, M., Mechakra-Tahiri, S.D.: Gender differences in personal and work-related determinants of return-to-work following long-term disability: a 5-year cohort study. J. Occup. Rehabil. 22(4), 522–531 (2012)CrossRefGoogle Scholar
  13. 13.
    Siciliano, B., Lorenzo, S., Villani, L., Orilo, G.: Robotics: Modelling, Planning and Control, 2nd edn. Springer, London (2010).  https://doi.org/10.1007/978-1-84628-642-1CrossRefGoogle Scholar
  14. 14.
    Balasubramanian, S., Melendez-Calderon, A., Roby-Brami, A., Burdet, E.: On the analysis of movement smoothness. J. Neuroeng. Rehabil. 12, 112 (2015).  https://doi.org/10.1186/s12984-015-0090-9CrossRefGoogle Scholar
  15. 15.
    Berger, D.J., d’Avella, A.: Effective force control by muscle synergies. Front. Comput. Neurosci. 8, 46 (2014).  https://doi.org/10.3389/fncom.2014.00046CrossRefGoogle Scholar
  16. 16.
    Holzbaur, K.R.S., Murray, W.M., Delp, S.L.: A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control. Ann. Biomed. Eng. 33(6), 829–840 (2005).  https://doi.org/10.1007/s10439-005-3320-7CrossRefGoogle Scholar
  17. 17.
    Delp, S.L., et al.: OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 54(11), 1940–1950 (2007)CrossRefGoogle Scholar
  18. 18.
    Buongiorno, D., et al.: Evaluation of a pose-shared synergy-based isometric model for hand force estimation: towards myocontrol. In: Biosystems and Biorobotics (2017)Google Scholar
  19. 19.
    Buongiorno, D., Barsotti, M., Barone, F., Bevilacqua, V., Frisoli, A.: A linear approach to optimize an EMG-driven neuromusculoskeletal model for movement intention detection in myo-control: a case study on shoulder and elbow joints. Front. Neurorobot. 12, 74 (2018)CrossRefGoogle Scholar

via Design and Development of a Robotic Platform Based on Virtual Reality Scenarios and Wearable Sensors for Upper Limb Rehabilitation and Visuomotor Coordination | SpringerLink

, , , , , , , , , ,

Leave a comment

[ARTICLE] Gait analysis with the Kinect v2: normative study with healthy individuals and comprehensive study of its sensitivity, validity, and reliability in individuals with stroke – Full Text

Abstract

Background

Gait is usually assessed by clinical tests, which may have poor accuracy and be biased, or instrumented systems, which potentially solve these limitations at the cost of being time-consuming and expensive. The different versions of the Microsoft Kinect have enabled human motion tracking without using wearable sensors at a low-cost and with acceptable reliability. This study aims: First, to determine the sensitivity of an open-access Kinect v2-based gait analysis system to motor disability and aging; Second, to determine its concurrent validity with standardized clinical tests in individuals with stroke; Third, to quantify its inter and intra-rater reliability, standard error of measurement, minimal detectable change; And, finally, to investigate its ability to identify fall risk after stroke.

Methods

The most widely used spatiotemporal and kinematic gait parameters of 82 individuals post-stroke and 355 healthy subjects were estimated with the Kinect v2-based system. In addition, participants with stroke were assessed with the Dynamic Gait Index, the 1-min Walking Test, and the 10-m Walking Test.

Results

The system successfully characterized the performance of both groups. Significant concurrent validity with correlations of variable strength was detected between all clinical tests and gait measures. Excellent inter and intra-rater reliability was evidenced for almost all measures. Minimal detectable change was variable, with poorer results for kinematic parameters. Almost all gait parameters proved to identify fall risk.

Conclusions

Results suggest that although its limited sensitivity to kinematic parameters, the Kinect v2-based gait analysis could be used as a low-cost alternative to laboratory-grade systems to complement gait assessment in clinical settings.

Background

The physiological basis of cerebrovascular accidents make gait deficits a common sequelae after stroke [1]. More than 60% of stroke survivors are unable to walk independently after the injury [2] and, even after rehabilitation, more than half of the cases still present gait-related deficits [3]. Most prevailing deficits after stroke include reduced speed [4] and increased gait inter-limb asymmetry [5]. These gait impairments can be aggravated in the elderly, due to the natural musculoskeletal and cognitive decline with age [67], where the incidence of stroke is higher [8]. Importance of these deficits relies on their great impact on independence [9], quality of life [10], and fall risk [11]. Consequently, their adequate assessment is necessary for a proper diagnosis and to plan, if required, customized interventions to each individual’s condition and evaluate the effectiveness of these interventions.

Assessment of gait is commonly performed in the clinical setting using standardized scales and tests that evaluate different aspects of human locomotion and, in some cases, compare the results of the person being tested with those obtained by a matched healthy sample [12]. Although these tools are easy to administer and, in general, not time-consuming, they can present lack of specificity and, more importantly, may have poor accuracy and be biased by subjective evaluations [13]. Over the years, different technological solutions have been proposed to overcome these limitations. Accurate estimation of spatiotemporal parameters has been enabled by instrumented walkways [14] and force plates [15], generally, from ground reaction forces during walking. Estimation of kinematic parameters, however, require the position of several joints to be tracked during the test, which has been indirectly facilitated by different technological solutions that estimate the position of some sensors that are attached to specific body parts [16,17,18]. Among them, optical motion tracking has become the most common alternative for accurate investigation of kinematic gait parameters [19]. Although instrumented systems allow for accurate spatiotemporal and kinematic analysis, their high cost and large size have restricted their use to research laboratories and large clinical centers with high economic resources [20].

In the last years, the Microsoft Kinect (Microsoft, Redmond, WA), a portable off-the-shelf infrared camera originally intended for entertainment, has enabled human motion tracking without using wearable sensors at a very low-cost. Reliability studies have shown comparable performance of the Kinect to laboratory-grade gait analysis systems, for both the first [2122] and the second version of the device [23], known as the Kinect v2, which features improved depth accuracy and number of joints tracked [24]. Characteristics of the Kinect v2 have motivated their use for assessing spatiotemporal [25,26,27] and kinematic parameters of gait [2628] with promising results in healthy individuals, even on treadmills [2829]. Its reliability in stroke population, however, remains almost unexplored. Little evidence suggests that data retrieved from the Kinect v2 can be used to differentiate healthy subjects from individuals with stroke [30] and to complement clinical assessment [31]. Despite of the existing data supporting the reliability of the Kinect v2 to assess spatiotemporal and kinematic gait parameters, the unavailability of the software, the limited investigation in individuals with stroke, and the unknown psychometric properties of Kinect-based tests in this population could compromise the clinical relevance of these results.

The objective of this study was fourfold. First, to compare a cohort of individuals with stroke with respect to a group of healthy controls to determine the sensitivity of an open-access Kinect v2-based gait analysis system to motor disability and aging. Second, to determine the concurrent validity of the system with standardized clinical tests in individuals with stroke. Third, to quantify its reliability as defined by the inter and intra-rater reliability, the standard error of measurement, and the minimal detectable change. And, finally, to investigate the ability of the system to identify risk of falls after stroke.

[…]

 

Continue —>  Gait analysis with the Kinect v2: normative study with healthy individuals and comprehensive study of its sensitivity, validity, and reliability in individuals with stroke | Journal of NeuroEngineering and Rehabilitation | Full Text

, , , , , , , ,

Leave a comment

[Abstract] Wearable Movement Sensors for Rehabilitation: A Focused Review of Technological and Clinical Advances – PM&R

Abstract

Recent technologic advancements have enabled the creation of portable, low-cost, and unobtrusive sensors with tremendous potential to alter the clinical practice of rehabilitation. The application of wearable sensors to track movement has emerged as a promising paradigm to enhance the care provided to patients with neurologic or musculoskeletal conditions. These sensors enable quantification of motor behavior across disparate patient populations and emerging research shows their potential for identifying motor biomarkers, differentiating between restitution and compensation motor recovery mechanisms, remote monitoring, telerehabilitation, and robotics. Moreover, the big data recorded across these applications serve as a pathway to personalized and precision medicine. This article presents state-of-the-art and next-generation wearable movement sensors, ranging from inertial measurement units to soft sensors. An overview of clinical applications is presented across a wide spectrum of conditions that have potential to benefit from wearable sensors, including stroke, movement disorders, knee osteoarthritis, and running injuries. Complementary applications enabled by next-generation sensors that will enable point-of-care monitoring of neural activity and muscle dynamics during movement also are discussed.

 

via Wearable Movement Sensors for Rehabilitation: A Focused Review of Technological and Clinical Advances – PM&R

, , , , , , , , ,

Leave a comment

[BLOG POST] Advantages of incorporating tele-rehabilitation into healthcare

The technology applied to the field of rehabilitation provides multiple benefits for both the health system and the patient.  Next, we explain why tele-rehabilitation will help optimize healthcare resources and improve patients’ quality of life.

A better picture: lack of specialists and long waiting lists in rehabilitation

“The Spanish health system is not prepared to respond to the advance of medical rehabilitation and the epidemic of disability due to ageing,” concluded the Spanish Society for Rehabilitation and Physical Medicine (Sermef) in its latest edition held from 30 May to 1 June in Gijón (Asturias).

This shortage throughout the country requires incorporating tele-rehabilitation into the Spanish health system.  However, Manuel Rodríguez-Piñero, from Sermef’s board of directors, stated, “the Rehabilitation services suffer from technological obsolescence, which, if not taken care of, will become obsolete and out of the 21st century”. Such as this news from ABC in Seville underlines, the director added: “The integration of robotics or virtual reality systems into rehabilitation assistance, to give two examples, are common to all European rehabilitation centers and is, unfortunately, the story of our hospitals”.

Sermef calls for unified action to improve the detection and treatment of disability situations and to promote a reorganization of rehabilitation for rational care, as well as an adequate definition of portfolios of services and procedures that allow efficient management. 

Recently, La Sexta Noticias also announced that the lack of physiotherapists, the number of chronically ill, and the current model of care contribute to the congestion of the centers and, consequently, a delay of more than two months for rehabilitation in public health care. Professionals stress the importance of receiving physical therapy on time, and more so after an operation. Failure to do so can have lifelong consequences.

How to solve it: distance rehabilitation therapies

Remote rehabilitation or tele-rehabilitation consists, in the first place, of diagnosing possible musculoskeletal pathologies through wearable sensors that record movements in real time and in a very precise way. The medical report is then shared with the physical therapist to determine the type of exercises the patient should follow. Through the internet connection, the patient can access a space, where he can easily find the exercises so he can do them whenever and wherever he wants, and he can consult with the doctor whenever the need.

DyCare wanted to bet on ReHub, a new solution based on an online platform that allows connecting the patient, the physiotherapist and the doctor. It facilitates the execution of the rehabilitation treatment from home, and it is always monitored by the rehabilitation professional.

Silvia Raga, CEO of the company, comments: “Our goal is to offer products of value to the patient. We want to show objective data to offer more personalized treatments for the patient, and, at the same time, contribute to savings in the health system”. With this in mind, DyCare does not lose focus on transforming the future of rehabilitation by developing the first digital solution for distance physical rehabilitation therapies.

Eight advantages of tele-rehabilitation

  1. Storage of and access to the patient’s medical records from any location
  2. A personalized program of the rehabilitation exercises, specifically adapted to the patient’s physical condition
  3. Real-time control and monitoring of the patient by the expert
  4. Remote adaptation of the exercises
  5. Continuous interaction between doctor, physiotherapist and patient
  6. Patient empowerment and adherence to treatment thanks to the biofeedback they receive in real time during the execution of the exercises
  7. Comfort when performing the exercises as they can be done where and when the patient wants
  8. Savings in travel costs and waiting time

If you have any questions or if you would like to receive more information from DyCare ReHub, please do not hesitate to contact us, we will be happy to contact you.

 

via Advantages of incorporating tele-rehabilitation into healthcare – Dycare

, , , ,

Leave a comment

[ARTICLE] Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training – Full Text

A conceptual representation of the wrist-worn sensor system for home-based upper-limb rehabilitation. The system consists of two wearable sensors, a tablet computer to be… View more

Abstract:

High-dosage motor practice can significantly contribute to achieving functional recovery after a stroke. Performing rehabilitation exercises at home and using, or attempting to use, the stroke-affected upper limb during Activities of Daily Living (ADL) are effective ways to achieve high-dosage motor practice in stroke survivors. This paper presents a novel technological approach that enables 1) detecting goal-directed upper limb movements during the performance of ADL, so that timely feedback can be provided to encourage the use of the affected limb, and 2) assessing the quality of motor performance during in-home rehabilitation exercises so that appropriate feedback can be generated to promote high-quality exercise. The results herein presented show that it is possible to detect 1) goal-directed movements during the performance of ADL with a c -statistic of 87.0% and 2) poorly performed movements in selected rehabilitation exercises with an F -score of 84.3%, thus enabling the generation of appropriate feedback. In a survey to gather preliminary data concerning the clinical adequacy of the proposed approach, 91.7% of occupational therapists demonstrated willingness to use it in their practice, and 88.2% of stroke survivors indicated that they would use it if recommended by their therapist.

Introduction

Stroke is a leading cause of severe long-term disability. In the US alone, nearly 800,000 people suffer a stroke each year [1]. The number of individuals who suffer a stroke each year is expected to rise in the coming years because the prevalence of stroke increases with age and the world population is aging [2]. Approximately 85% of individuals who have a stroke survive, but they often experience significant motor impairments. Upper-limb paresis is the most common impairment following a stroke. It affects 75% of stroke survivors and leads to limitations in the performance of Activities of Daily Living (ADL) [4].

Inability to use the stroke-affected upper limb for ADL often leads to a phenomenon that is referred to as learned non-use [5]. As patients rely more and more on the unaffected (or less impaired) upper limb [5] they progressively lose motor abilities of the stroke-affected upper limb that they may have recovered as a result of a rehabilitation intervention [6].

A high dosage of motor practice using the stroke-affected upper limb during the performance of ADL, despite considerable difficulty, stimulates neuroplasticity and motor function recovery [7]–[8][9]. Thus, it is clinically important to encourage stroke survivors to continue making appropriate use of the affected upper limb [10]–[11][12][13], in addition to engaging in rehabilitation exercises that focus on range-of-motion and functional abilities [14]–[15][16].

The use of wearable sensors has recently emerged as an efficient way to monitor the amount of upper-limb use after a stroke [17]–[18][19][20][21][22]. However, despite growing evidence of the clinical potential of these devices [23], their widespread clinical deployment has been hindered by technical limitations. A shortcoming of currently available wrist-worn devices is that they cannot distinguish between Goal-Directed (GD) movements (i.e., movements performed for a specific purposeful task) and non-Goal-Directed (non-GD) movements (e.g., the arm swinging during gait). Instead, these sensors focus on recording the number and/or intensity of any type of arm movements [10]. Consequently, non-GD movements are reflected as part of the measurements with equal importance as GD movements. This results in an overestimation of the amount of actual arm use [24]. Furthermore, monitoring the aggregate number of stroke-affected upper limb movements is not sufficient for the purpose of providing timely feedback to encourage the use of the affected limb during the performance of ADL. To promote the use of the stroke-affected limb, it is critical that feedback reflects the relative use of the affected upper limb compared to the contralateral one.

Wrist-worn movement sensors have also been applied to monitoring rehabilitation exercises in the home setting [25]–[26][27][28]. However, existing systems primarily focus on quantifying the dosage/intensity of the exercises (e.g., the duration of the exercises and the number of movement repetitions) and do not monitor if the quality of the performed exercise is appropriate. Ensuring good quality of movement during the performance of rehabilitation exercises is critical for maximizing functional recovery after a stroke [29]. Moreover, providing customized feedback regarding the quality of exercise movements can increase motivation, promote long-term adherence to a prescribed exercise regimen, and ultimately maximize clinical outcomes [30]. One of the reasons for limited exercise participation by stroke survivors is the lack of access to resources to support exercise including performance feedback from rehabilitation specialists [31]. There are no technical solutions that provide feedback regarding the quality of exercise performance for upper-limb rehabilitation after stroke.

We propose a system for aiding in functional recovery after a stroke that consists of two wearable sensors, one worn on the stroke-affected upper limb and the other on the contralateral upper limb [32] (Fig. 1). The proposed system can be used to provide timely feedback when ADL are performed. If the system detects that the patient consistently performs GD movements with the unaffected upper limb, and rarely uses the stroke-affected upper limb, then a visual or vibrotactile reminder can be triggered to encourage the patient to attempt GD movements with the stroke-affected limb. A benefit of this approach is that if a movement is critical (e.g., signing a check), patients can use the unaffected upper limb without receiving negative feedback as long as they have performed a sufficient number of movements with the affected upper limb throughout the day. Furthermore, the system promotes high-dosage motor practice with appropriate feedback to extend components of rehabilitation interventions into the home environment.[…]

via Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training – IEEE Journals & Magazine

, , , , , , , , , ,

Leave a comment

[Abstract] Tele-health, wearable sensors and the Internet. Will they improve stroke outcomes through increased intensity of therapy, motivation and adherence to rehabilitation programs?

Provisional Abstract
Background and Purpose
Stroke, predominantly a condition of older age, is a major cause of acquired disability in the global population and puts an increasing burden on healthcare resources. Clear evidence for the importance of intensity of therapy in optimizing functional outcomes is founded in animal models, supported by neuroimaging and behavioral research, and strengthened by recent meta-analyses from multiple clinical trials. However, providing intensive therapy using conventional treatment paradigms is expensive and sometimes not feasible due to patients’ environmental factors. This paper addresses the need for cost-effective increased intensity of practice and suggests potential benefits of telehealth (TH) as an innovative model of care in physical therapy.

Summary of Key Points
We provide an overview of TH and present evidence that a web-supported program used in conjunction with Constraint Induced Therapy (CIT), can increase intensity and adherence to a rehabilitation regimen. The design and feasibility testing of this web-based program, ‘LifeCIT’ is presented. We describe how wearable sensors can monitor activity and provide feedback to patients and therapists. The methodology for the development of a wearable device with embedded inertial measurement units and mechanomyography sensors, algorithms to classify functional movement, and a graphical user interface to present meaningful data to patients to support a home exercise program is explained.

Recommendations for Clinical Practice
We propose that wearable sensor technologies and TH programs have the potential to provide cost-effective, intensive, home-based stroke rehabilitation.

Source: JUST ACCEPTED: “Tele-health, wearable sensors and the Internet. Will they improve stroke outcomes through increased intensity of therapy, motivation and adherence to rehabilitation programs?” |

, , , , , , , , ,

Leave a comment

[ARTICLE] A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training – Full Text

Although motor learning theory has led to evidence-based practices, few trials have revealed the superiority of one theory-based therapy over another after stroke. Nor have improvements in skills been as clinically robust as one might hope. We review some possible explanations, then potential technology-enabled solutions.

Over the Internet, the type, quantity, and quality of practice and exercise in the home and community can be monitored remotely and feedback provided to optimize training frequency, intensity, and progression at home. A theory-driven foundation of synergistic interventions for walking, reaching and grasping, strengthening, and fitness could be provided by a bundle of home-based Rehabilitation Internet-of-Things (RIoT) devices.

A RIoT might include wearable, activity-recognition sensors and instrumented rehabilitation devices with radio transmission to a smartphone or tablet to continuously measure repetitions, speed, accuracy, forces, and temporal spatial features of movement. Using telerehabilitation resources, a therapist would interpret the data and provide behavioral training for self-management via goal setting and instruction to increase compliance and long-term carryover.

On top of this user-friendly, safe, and conceptually sound foundation to support more opportunity for practice, experimental interventions could be tested or additions and replacements made, perhaps drawing from virtual reality and gaming programs or robots. RIoT devices continuously measure the actual amount of quality practice; improvements and plateaus over time in strength, fitness, and skills; and activity and participation in home and community settings. Investigators may gain more control over some of the confounders of their trials and patients will have access to inexpensive therapies.

Neurologic rehabilitation has been testing a motor learning theory for the past quarter century that may be wearing thin in terms of leading to more robust evidence-based practices. The theory has become a mantra for the field that goes like this. Repetitive practice of increasingly challenging task-related activities assisted by a therapist in an adequate dose will lead to gains in motor skills, mostly restricted to what was trained, via mechanisms of activity-dependent induction of molecular, cellular, synaptic, and structural plasticity within spared neural ensembles and networks.

This theory has led to a range of evidence-based therapies, as well as to caricatures of the mantra (eg, a therapist says to patient, “Do those plasticity reps!”). A mantra can become too automatic, no longer apt to be reexamined as a testable theory. A recent Cochrane review of upper extremity stroke rehabilitation found “adequately powered, high-quality randomized clinical trials (RCTs) that confirmed the benefit of constraint-induced therapy paradigms, mental practice, mirror therapy, virtual reality paradigms, and a high dose of repetitive task practice.”1 The review also found positive RCT evidence for other practice protocols. However, they concluded, no one strategy was clearly better than another to improve functional use of the arm and hand. The ICARE trial2 for the upper extremity after stroke found that both a state-of-the-art Accelerated Skill Acquisition Program (motor learning plus motivational and psychological support strategy) compared to motor learning-based occupational therapy for 30 hours over 10 weeks led to a 70% increase in speed on the Wolf Motor Function Test, but so did usual care that averaged only 11 hours of formal but uncharacterized therapy. In this well-designed RCT, the investigators found no apparent effect of either the dose or content of therapy. Did dose and content really differ enough to reveal more than equivalence, or is the motor-learning mantra in need of repair?

Walking trials after stroke and spinal cord injury,38 such as robot-assisted stepping and body weight-supported treadmill training (BWSTT), were conceived as adhering to the task-oriented practice mantra. But they too have not improved outcomes more than conventional over-ground physical therapy. Indeed, the absolute gains in primary outcomes for moderate to severely impaired hemiplegic participants after BWSTT and other therapies have been in the range of only 0.12 to 0.22 m/s for fastest walking speed and 50 to 75 m for 6-minute walking distance after 12 to 36 training sessions over 4 to 12 weeks.3,9 These 15% to 25% increases are just as disappointing when comparing gains in those who start out at a speed of <0.4 m/s compared to >0.4 to 0.8 m/s.3

Has mantra-oriented training reached an unanticipated plateau due to inherent limitations? Clearly, if not enough residual sensorimotor neural substrate is available for training-induced adaptation or for behavioral compensation, more training may only fail. Perhaps, however, investigators need to reconsider the theoretical basis for the mantra, that is, whether they have been offering all of the necessary components of task-related practice, such as enough progressively difficult practice goals, the best context and environment for training, the behavioral training that motivates compliance and carryover of practice beyond the sessions of formal training, and blending in other physical activities such as strengthening and fitness exercise that also augment practice-related neural plasticity? These questions point to new directions for research….

Continue —> A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training – Mar 01, 2017

Figure 1. Components of a Rehabilitation-Internet-of-Things: wireless chargers for sensors (1), ankle accelerometers with gyroscopes (2) and Android phone (3) to monitor walking and cycling, and a force sensor (4) in line with a stretch band (5) to monitor resistance exercises.

 

, , , , , , , , , ,

Leave a comment

[Abstract] A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training

Abstract

Although motor learning theory has led to evidence-based practices, few trials have revealed the superiority of one theory-based therapy over another after stroke. Nor have improvements in skills been as clinically robust as one might hope.

We review some possible explanations, then potential technology-enabled solutions. Over the Internet, the type, quantity, and quality of practice and exercise in the home and community can be monitored remotely and feedback provided to optimize training frequency, intensity, and progression at home. A theory-driven foundation of synergistic interventions for walking, reaching and grasping, strengthening, and fitness could be provided by a bundle of home-based Rehabilitation Internet-of-Things (RIoT) devices. A RIoT might include wearable, activity-recognition sensors and instrumented rehabilitation devices with radio transmission to a smartphone or tablet to continuously measure repetitions, speed, accuracy, forces, and temporal spatial features of movement.

Using telerehabilitation resources, a therapist would interpret the data and provide behavioral training for self-management via goal setting and instruction to increase compliance and long-term carryover. On top of this user-friendly, safe, and conceptually sound foundation to support more opportunity for practice, experimental interventions could be tested or additions and replacements made, perhaps drawing from virtual reality and gaming programs or robots. RIoT devices continuously measure the actual amount of quality practice; improvements and plateaus over time in strength, fitness, and skills; and activity and participation in home and community settings. Investigators may gain more control over some of the confounders of their trials and patients will have access to inexpensive therapies.

Source: A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training

, , , , , , , , , , ,

Leave a comment

[Abstract] A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training

Abstract

Although motor learning theory has led to evidence-based practices, few trials have revealed the superiority of one theory-based therapy over another after stroke. Nor have improvements in skills been as clinically robust as one might hope. We review some possible explanations, then potential technology-enabled solutions. Over the Internet, the type, quantity, and quality of practice and exercise in the home and community can be monitored remotely and feedback provided to optimize training frequency, intensity, and progression at home. A theory-driven foundation of synergistic interventions for walking, reaching and grasping, strengthening, and fitness could be provided by a bundle of home-based Rehabilitation Internet-of-Things (RIoT) devices. A RIoT might include wearable, activity-recognition sensors and instrumented rehabilitation devices with radio transmission to a smartphone or tablet to continuously measure repetitions, speed, accuracy, forces, and temporal spatial features of movement. Using telerehabilitation resources, a therapist would interpret the data and provide behavioral training for self-management via goal setting and instruction to increase compliance and long-term carryover. On top of this user-friendly, safe, and conceptually sound foundation to support more opportunity for practice, experimental interventions could be tested or additions and replacements made, perhaps drawing from virtual reality and gaming programs or robots. RIoT devices continuously measure the actual amount of quality practice; improvements and plateaus over time in strength, fitness, and skills; and activity and participation in home and community settings. Investigators may gain more control over some of the confounders of their trials and patients will have access to inexpensive therapies.

Source: A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training

, , , , , , , , , , , ,

Leave a comment

[THESIS] Pose Estimation and Segmentation for Rehabilitation

Abstract

The global population is getting older and the aging demographic is increasing demands on health-care industry. This will drive the demand for post stroke, joint replacement, and chronic disease management rehabilitation. Currently physiotherapists rely on mostly subjective and observational tools for patient assessment and progress tracking. This thesis proposes methods to enable the use of non-intrusive, small, wearable, wireless sensors to estimate the pose of the lower body during rehabilitation and extract objective performance measures useful for therapists.

Two different kinematic models of the human lower body are introduced. The first approach expresses the body position and orientation in the world frame using three prismatic and revolute joints, while the second switches the model’s base between the right and the left ankle during gait. An Extended Kalman Filter (EKF) is set up to estimate the joint angles, velocities, and accelerations of the models using measurements from inertial measurement units. The state update model assumes constant joint acceleration and is linear. Measurement prediction, relating the joint positions, velocities and accelerations to the measured angular velocity and linear acceleration at each IMU, is done using forward kinematics, using one of the two proposed kinematic models. The approach is validated on healthy participant gait using motion capture studio data for ground truth comparison. The prismatic and revolute model achieves better Cartesian position accuracy in the swing leg due to a shorter kinematic chain, while the switching base model improves the stance leg Cartesian estimate and does not allow measurement noise to accumulate as drift in global position, knee joint angle root mean squared errors (RMSE) of 6.1° and 5.6° are attained respectively by the models.

Next the Rhythmic Extended Kalman Filter (R-EKF) algorithm is developed to improve pose estimation. It learns a model of rhythmic movement over time based on harmonic Fourier series and removes the constant acceleration assumption. The estimated phase and frequency of the motion also allow the proposed approach to segment the motion into repetitions and extract useful features such as gait symmetry, step length, and mean joint movement and variance. The algorithm is shown to outperform the EKF in simulation, on healthy participant data, and stroke patient monthly assessments. For the healthy participant marching dataset, the R-EKF improves joint acceleration and velocity estimates over regular EKF by 40% and 37% respectively, estimates joint angles with 2.4° RMSE, and segments the motion into repetitions with 96% accuracy.

While the proposed R-EKF effectively segments rhythmic rehabilitation movement such as gait, not all rehabilitation motions are rhythmic or may have uneven delays between repetitions by regimen design or due to fatigue. For such motions a time-series iv segmentation as data point classification algorithm is proposed. Common dimensionality reduction and classification techniques are applied to estimated joint angle data to classify each time-step as a segment or non-segment point. The algorithm is tested on five common rehabilitation exercises performed by healthy participants and achieves a segmentation accuracy of 82%.

, , , , ,

Leave a comment

%d bloggers like this: