Posts Tagged biomechanics

[ARTICLE] Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback – Full Text

Abstract

Background

Add-on robot-mediated therapy has proven to be more effective than conventional therapy alone in post-stroke gait rehabilitation. Such robot-mediated interventions routinely use also visual biofeedback tools. A better understanding of biofeedback content effects when used for robotic locomotor training may improve the rehabilitation process and outcomes.

Methods

This randomized cross-over pilot trial aimed to address the possible impact of different biofeedback contents on patients’ performance and experience during Lokomat training, by comparing a novel biofeedback based on online biological electromyographic information (EMGb) versus the commercial joint torque biofeedback (Rb) in sub-acute non ambulatory patients.

12 patients were randomized into two treatment groups, A and B, based on two different biofeedback training. For both groups, study protocol consisted of 12 Lokomat sessions, 6 for each biofeedback condition, 40 min each, 3 sessions per week of frequency. All patients performed Lokomat trainings as an add-on therapy to the conventional one that was the same for both groups and consisted of 40 min per day, 5 days per week. The primary outcome was the Modified Ashworth Spasticity Scale, and secondary outcomes included clinical, neurological, mechanical, and personal experience variables collected before and after each biofeedback training.

Results

Lokomat training significantly improved gait/daily living activity independence and trunk control, nevertheless, different effects due to biofeedback content were remarked. EMGb was more effective to reduce spasticity and improve muscle force at the ankle, knee and hip joints. Robot data suggest that Rb induces more adaptation to robotic movements than EMGb. Furthermore, Rb was perceived less demanding than EMGb, even though patient motivation was higher for EMGb. Robot was perceived to be effective, easy to use, reliable and safe: acceptability was rated as very high by all patients.

Conclusions

Specific effects can be related to biofeedback content: when muscular-based information is used, a more direct effect on lower limb spasticity and muscle activity is evidenced. In a similar manner, when biofeedback treatment is based on joint torque data, a higher patient compliance effect in terms of force exerted is achieved. Subjects who underwent EMGb seemed to be more motivated than those treated with Rb.

Background

Stroke is the leading cause of acquired disability throughout the world, with increasing survival rates as medical care and treatment techniques improve [1]. Post-stroke disability often affects mobility, balance, and walking [2]. The majority of stroke survivors rank walking recovery among their top rehabilitation goals [3,4,5]. Furthermore, the ability to walk is one of the most important determining factors for returning home after stroke [4].

Recovery of walking mainly occurs within the first 11 weeks after a stroke [6]; indeed, further recovery after that time is rare [7]. Overall, between 30 and 40% of stroke survivors are not able to regain a functional gait after rehabilitation [48]. These data have stimulated advances in many different innovative technological approaches to improve the gait rehabilitation efficacy.

Modern concepts favour task-specific repetitive rehabilitation approaches [9], with high intensity [10] and early multisensory stimulation [11]. These requirements are met by robot assisted gait training (RAGT) approaches. Recent studies on stroke patients have reported that when conventional therapy and RAGT are combined, compared to conventional therapy alone, gait recovery significantly improves [12] and patients are more likely to recover independent walking [13]. In particular, non-ambulatory patients in the sub-acute phase are the group most likely to benefit from this type of training [13].

This high interest in robotic therapy has attracted attention to human robot interactions in the rehabilitation framework, and a consensus is forming on the importance of top-down approaches in rehabilitation, particularly when dealing with robotic devices [14]. The critical aspects of top-down approaches are multifarious and include motivation, active participation [15], learning skills [16] and error-driven-learning [17], evidencing the key aspects of biofeedback information to guide and improve patient robot interactions.

Thus, biofeedback is, at present, the main approach to guide top-down control mechanisms, which represents a powerful tool to drive recovery. To this aim, the patient has to be aware of the differences between on-line performance and the desired performance [18]. In this scenario, many different error signals can be used, and at present, no indication exists for their specific effects on performances [1819]. Many biological parameters have been used to feed biofeedback information to patients in different stroke gait rehabilitation scenarios [20].

In general, in spite of the information content, biofeedback has been associated with improved outcomes in several gait pathologies [21,22,23,24]. Among diverse types of biofeedback, the most generally employed in gait rehabilitation paradigms have been electromyographic (EMG), kinematic as well as robot generated indexes [25], although no comparisons have been made among these approaches.

At present, many robotic devices for gait rehabilitation in stroke are commercially available [26]. Two main classes can be identified, those based on body weight support systems (BWSS) and over ground exoskeletons. Overall, BWSS are the most widely used in rehabilitation centres, with Lokomat, Gait Trainer and GEO systems being the most popular. The present study focuses on the biofeedback content effects during Lokomat gait training in stroke survivors. Commercially available Lokomat biofeedback tools are based either on navigational or robot-generated information. The latter approach focuses on the forces that assist patients to follow the predefined gait pattern due to force transducers built into the robot drives [25].

Generally effectiveness of Lokomat training is assessed with gait functional outcome measures. Specific data about spasticity effects of Lokomat training are rare, and mainly focused on spinal cord injury (SCI) patients and on ankle muscles. In this framework few studies addressed positive effects of Lokomat training on reducing spasticity and improving volitional control of the spastic ankle in persons with incomplete SCI [27], and on reducing the abnormal modulation of neuromuscular properties that arises as secondary effects after SCI [2829]. To our knowledge, as concern stroke population, a single study compared conventional rehabilitation versus Lokomat add-on training selecting spasticity as a secondary outcome, demonstrating no significant robotic gait training effects [30].

Furthermore, no studies have either analysed the use of an electromyographic -based biofeedback (EMGb) of hip, knee and ankle muscles during training with the Lokomat robot, or compared the impact of different biofeedback types on Lokomat robotic gait training. To this end, we designated a randomized controlled trial, because this type of study is the most rigorous and robust research method of determining whether a cause-effect relation exists between an intervention and an outcome [31]. In this pilot study we compared two different types of biofeedback: a robot generated joint torque biofeedback (Rb) versus a novel on-line EMGb. Thus, a randomized cross-over clinical trial using the Lokomat RAGT device, was conducted focusing on patients’ performances, personal experience and robot forces data in sub-acute non ambulatory patients. In particular the main outcome measure was considered the lower limb spasticity. Considering that in stroke population, spasticity may affect quality-of-life and can be highly detrimental to daily function [32], we also analysed patients’ personal experience related to training gait with the Lokomat system.[…]

 

Continue —> Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback | Journal of NeuroEngineering and Rehabilitation | Full Text

figure3

Representative image of visual biofeedback provided to the patient (PT6) according to on-line EMG activity during first (a) and last (b) EMGb training session. EMG data were displayed on the screen with 4 colour stripes partitioned into 16 stages within the gait cycle. First stripe referred to VL-RF, second stripe refers to BF, third stripe referred to GM-SOL and last stripe referred to TA. Coloured lines in the patient’s feedback were generated as follows: i) Red colour means that the signal is higher than in the template, or ii) Blue means that the signal is lower than in the template. From Fig. 3-b is evident a more physiological muscle activity during the whole gait cycle

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[Abstract] Improving walking ability in people with neurological conditions: A theoretical framework for biomechanics driven exercise prescription

Abstract

The purpose of this paper is to discuss how knowledge of the biomechanics of walking can be used to inform the prescription of resistance exercises for people with mobility limitations. Muscle weakness is a key physical impairment that limits walking in commonly occurring neurological conditions such as cerebral palsy, traumatic brain injury and stroke. Few randomised trials to date have shown conclusively that strength training improves walking in people living with these conditions. This appears to be because

1) the most important muscle groups for forward propulsion when walking have not been targeted for strengthening, and

2) strength training protocols have focused on slow and heavy resistance exercises, which do not improve the fast muscle contractions required for walking.

We propose a theoretical framework to improve exercise prescription by integrating the biomechanics of walking with the principles of strength training outlined by the American College of Sports Medicine (ACSM), to prescribe exercises that are specific to improving the task of walking. The high angular velocities that occur in the lower limb joints during walking indicate that resistance exercises targeting power generation would be most appropriate. Therefore, we propose the prescription of plyometric and ballistic resistance exercise, applied using the ACSM guidelines for task-specificity, once people with neurological conditions are ambulating, to improve walking outcomes. This new theoretical framework for resistance training ensures that exercise prescription matches how the muscles work during walking.

via Improving walking ability in people with neurological conditions: A theoretical framework for biomechanics driven exercise prescription – Archives of Physical Medicine and Rehabilitation

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[Abstract] Gait rehabilitation using functional electrical stimulation induces changes in ankle muscle coordination in stroke survivors: a preliminary study

Background: Previous studies have demonstrated that post-stroke gait rehabilitation combining functional electrical stimulation applied to the ankle muscles during fast treadmill walking (FastFES) improves gait biomechanics and clinical walking function. However, there is considerable inter-individual variability in response to FastFES. Although FastFES aims to sculpt ankle muscle coordination, whether changes in ankle muscle activity underlie observed gait improvements is unknown. The aim of this study was to investigate three cases illustrating how FastFES modulates ankle muscle recruitment during walking.

Methods: We conducted a preliminary case series study on three individuals (53-70y; 2M; 35-60 months post-stroke; 19-22 lower extremity Fugl-Meyer) who participated in 18 sessions of FastFES (3 sessions/week; ClinicalTrials.gov: NCT01668602). Clinical walking function (speed, six-minute walk test, and Timed-Up-and-Go test), gait biomechanics (paretic propulsion and ankle angle at initial-contact), and plantarflexor (soleus) / dorsiflexor (tibialis anterior) muscle recruitment were assessed pre- and post-FastFES while walking without stimulation.
Results: Two participants (R1, R2) were categorized as responders based on improvements in clinical walking function. Consistent with heterogeneity of clinical and biomechanical changes commonly observed following gait rehabilitation, how muscle activity was altered with FastFES differed between responders.R1 exhibited improved plantarflexor recruitment during stance accompanied by increased paretic propulsion. R2 exhibited improved dorsiflexor recruitment during swing accompanied by improved paretic ankle angle at initial-contact. In contrast, the third participant (NR1), classified as a non-responder, demonstrated increased ankle muscle activity during inappropriate phases of the gait cycle. Across all participants, there was a positive relationship between increased walking speeds after FastFES and reduced SOL/TA muscle coactivation.
Conclusion: Our preliminary case series study is the first to demonstrate that improvements in ankle plantarflexor and dorsiflexor muscle recruitment (muscles targeted by FastFES) accompanied improvements in gait biomechanics and walking function following FastFES in individuals post-stroke. Our results also suggest that inducing more appropriate (i.e., reduced) ankle plantar/dorsi-flexor muscle coactivation may be an important neuromuscular mechanism underlying improvements in gait function after FastFES training, suggesting that pre-treatment ankle muscle status could be used for inclusion into FastFES. The findings of this case-series study, albeit preliminary, provide the rationale and foundations for larger-sample studies using similar methodology.

 

via Frontiers | Gait rehabilitation using functional electrical stimulation induces changes in ankle muscle coordination in stroke survivors: a preliminary study | Neurology

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[Thesis] Recovery of arm-hand function after stroke: developing neuromechanical biomarkers to optimize rehabilitation strategies. – Leiden University

Abstract

The aim of this thesis was to explore the neuromechanics of recovery of arm-hand function after stroke. A literature review revealed six articles that measured biomechanical and electromyographical outcome measures simultaneously, while applying active and passive tasks and multiple movement velocities to separate neural and non-neural contributors to movement disorders after stroke. Therefore, a neuromechanic assessment protocol was developed. Parameters were responsive to clinical status and had good to excellent test-retest reliability. Selective muscle activation was assessed with high measurement reliability and was significantly lower in chronic stroke patients compared to healthy participants. Longitudinally, neuromechanical parameters were combined with data on arm-hand function at six months after stroke. Paresis and diminished modulation of reflexes were associated with poor functional outcome. Changes in tissue properties were represented by a shift in wrist rest angle towards flexion and decline in passive range of motion. Increase in active range of motion and steady rest angle contributed most to prediction of functional outcome. The precision diagnostics provided by a neuromechanical assessment protocol could support clinical decision making and should be used in prediction models and as biomarkers in recovery of arm-hand function after stroke, for example by improving the selection of time-window and patients.

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via Recovery of arm-hand function after stroke: developing neuromechanical biomarkers to optimize rehabilitation strategies.

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[Abstract] Bio-inspired upper limb soft exoskeleton to reduce stroke-induced complications.

Abstract

Stroke has become the leading cause of disability and the second-leading cause of mortality worldwide. Dyskinesia complications are the major reason of these high death and disability rates. As a tool for rapid motion function recovery in stroke patients, exoskeleton robots can reduce complications and thereby decrease stroke mortality rates. However, existing exoskeleton robots interfere with the wearer’s natural motion and damage joints and muscles due to poor human-machine coupling. In this paper, a novel ergonomic soft bionic exoskeleton robot with 7 degrees of freedom was proposed to address these problems based on the principles of functional anatomy and sports biomechanics. First, the human motion system was analysed according to the functional anatomy, and the muscles were modelled as tension lines. Second, a soft bionic robot was established based on the musculoskeletal tension line model. Third, a robot control method mimicking human muscle control principles was proposed and optimized on a humanoid platform manufactured using 3D printing. After the control method was optimized, the motion trajectory similarities between humans and the platform exceeded 87%. Fourth, the force-assisted effect was tested based on electromyogram signals, and the results showed that muscle signals decreased by 58.17% after robot assistance. Finally, motion-assistance experiments were performed with stroke patients. The joint movement level increased by 174% with assistance, which allowed patients to engage in activities of daily living. With this robot, stroke patients could recover their motion functions, preventing complications and decreasing fatality and disability rates.

 

via Bio-inspired upper limb soft exoskeleton to reduce stroke-induced complications. – PubMed – NCBI

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[Abstract + References] Towards a framework for rehabilitation and assessment of upper limb motor function based on Serious Games – IEEE Conference Publication

Abstract

 Serious Games and Virtual Reality (VR) are being considered at present as an alternative to traditional rehabilitation therapies. In this paper, the ongoing development of a framework focused on rehabilitation and assessment of the upper limb motor function based on serious games as a source of entertainment for physiotherapy patients is described. A set of OpenSource Serious Games for rehabilitation has been developed, using the last version of Microsoft1® Kinect™ as low cost monitoring sensor and the software Unity. These Serious Games captures 3D human body data and it stored them in the patient database to facilitate a later clinical analysis to the therapist. Also, a VR-based system for the automated assessment of motor function based on Fugl-Meyer Assessment Test (FMA) is addressed. The proposed system attempts to be an useful therapeutic tool for tele-rehabilitation in order to reduce the number of patients, time spent and cost to
hospitals.

I. Introduction

Biomechanical analysis is an important feature during the evaluation and clinical diagnosis of motor deficits caused by traumas or neurological diseases. For that reason Motion capture (MoCap) systems are widely used in biomechanical studies, in order to collect position data from anatomical landmarks with high accuracy. Their results are used to estimate joint movements, positions, and muscle forces. These quantitative results improve the tracking of changes in motor functions over time, being more accurately than clinical ratings [1]. For clinical applications, these results are usually transformed into clinically meaningful and interpretable parameters, such as gait speed, motion range of joints and body balance.

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[Conference paper] The Role of Virtual Reality and Biomechanical Technologies in Stroke Rehabilitation

Abstract

The aim of this paper is to present a spectrum of virtual reality and biomechanical technologies that can be potentially used in supporting the rehabilitation of people after stroke, in both clinical and home conditions. The methodology was based on a systematic review of up-to-date, published research works available in Elsevier Science Direct database including peer-reviewed journal articles. As a result, trends, possible promising solutions and gaps in the area of innovative rehabilitation tools for post-stroke patients were recognized and discussed. Particularly, the new knowledge and good practices focused on the applicability of biomechanical systems and Virtual Reality (VR) technologies in stroke treatment were searched, which is the subject of an educational and international Erasmus+ project entitled “Development of innovative training contents based on the applicability of virtual reality in the field of stroke rehabilitation- Brain4Train”. The training content, which is one of the project outcomes, will be provided to all interested professionals engaged in post-stroke patients’ rehabilitation, in order to make them capable to develop customized rehabilitation programs based on techno-innovative rehabilitation models.

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via The Role of Virtual Reality and Biomechanical Technologies in Stroke Rehabilitation | SpringerLink

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[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] Biomechatronics design of a robotic arm for rehabilitation – IEEE Conference Publication

Abstract

Rehabilitation is an important process to restore muscle strength and joint’s range of motion. This paper proposes a biomechatronic design of a robotic arm that is able to mimic the natural movement of the human shoulder, elbow and wrist joint. In a preliminary experiment, a subject was asked to perform four different arm movements using the developed robotic arm for a period of two weeks. The experimental results were recorded and can be plotted into graphical results using Matlab. Based on the results, the robotic arm shows encouraging effect by increasing the performance of rehabilitation process. This is proven when the result in degree value are accurate when being compared with the flexion of both shoulder and elbow joints. This project can give advantages on research if the input parameter needed in the flexion of elbow and wrist.

I. Introduction

According to the United Nations (UN), by 2030 the number of people over 60 years will increase by 56 per cent, from 901 million to more than 1.4 billion worldwide [1]. As the number of older persons is expected to grow, it is imperative that government and private health care providers prepare adequate and modern facilities that can provide quality services for the needs of older persons especially in rehabilitation centers. Implementation of robotic technology in rehabilitation process is a modern method and definitely can contribute in this policy and capable in promoting early recovery and motor learning [2]. Furthermore, systematic application of robotic technology can produce significant clinical results in motor recovery of post-traumatic central nervous system injury by assisting in physical exercise based on voluntary movement in rehabilitation [3].

via Biomechatronics design of a robotic arm for rehabilitation – IEEE Conference Publication

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[BOOK Chapter] Biomechanics of the Upper Limb – Google Books

The BOOK —> Atlas of Orthoses and Assistive Devices E-Book – Joseph Webster, Douglas Murphy – Google Books

 Go to Chapter 11: Biomechanics of the Upper Limb

 

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