Robot-Aided Systems for Improving the Assessment of Upper Limb Spasticity: A Systematic Review

This article belongs to the Special Issue Sensors Technology for Medical Robotics.Spasticity is a motor disorder that causes stiffness or tightness of the muscles and can interfere with normal movement, speech, and gait. Traditionally, the spasticity assessment is carried out by clinicians using standardized procedures for objective evaluation. However, these procedures are manually performed and, thereby, they could be influenced by the clinician’s subjectivity or expertise. The automation of such traditional methods for spasticity evaluation is an interesting and emerging field in neurorehabilitation. One of the most promising approaches is the use of robot-aided systems. In this paper, a systematic review of systems focused on the assessment of upper limb (UL) spasticity using robotic technology is presented. A systematic search and review of related articles in the literature were conducted. The chosen works were analyzed according to the morphology of devices, the data acquisition systems, the outcome generation method, and the focus of intervention (assessment and/or training). Finally, a series of guidelines and challenges that must be considered when designing and implementing fully-automated robot-aided systems for the assessment of UL spasticity are summarized

Robotic Rehabilitation and Multimodal Instrumented Assessment of Post-stroke Elbow Motor Functions—A Randomized Controlled Trial Protocol

Background: The reliable assessment, attribution, and alleviation of upper-limb joint stiffness are essential clinical objectives in the early rehabilitation from stroke and other neurological disorders, to prevent the progression of neuromuscular pathology and enable proactive physiotherapy toward functional recovery. However, the current clinical evaluation and treatment of this stiffness (and underlying muscle spasticity) are severely limited by their dependence on subjective evaluation and manual limb mobilization, thus rendering the evaluation imprecise and the treatment insufficiently tailored to the specific pathologies and residual capabilities of individual patients. Methods: To address these needs, the proposed clinical trial will employ the NEUROExos Elbow Module (NEEM), an active robotic exoskeleton, for the passive mobilization and active training of elbow flexion and extension in 60 sub-acute and chronic stroke patients with motor impairments (hemiparesis and/or spasticity) of the right arm. The study protocol is a randomized controlled trial consisting of a 4-week functional rehabilitation program, with both clinical and robotically instrumented assessments to be conducted at baseline and post-treatment. The primary outcome measures will be a set of standard clinical scales for upper limb spasticity and motor function assessment, including the Modified Ashworth Scale and Fugl-Meyer Index, to confirm the safety and evaluate the efficacy of robotic rehabilitation in reducing elbow stiffness and improving function. Secondary outcomes will include biomechanical, muscular activity, and motor performance parameters extracted from instrumented assessments using the NEEM along with synchronous EMG recordings. The study protocol has been registered on clinicaltrials.gov with registration trial number NCT04484571. Conclusions: This randomized controlled trial aims to validate an innovative instrumented methodology for clinical spasticity assessment and functional rehabilitation, relying on the precision and accuracy of an elbow exoskeleton combined with EMG recordings and the expertise of a physiotherapist, thus complementing and maximizing the benefits of both practices

Data-Driven Model for Upper Limb Spasticity Detection

Healthcare providers in the field of physical and rehabilitation medicine play a vital role to help patients suffering spasticity readapting themselves to their normal daily activities. Mathematical modeling of spasticity has the potential to avoid the issue of variability in the assessment of spasticity using the Modified Ashworth scale (MAS). In this work, an existing mathematical model for upper limb spasticity is verified using clinical data sets of upper limb spasticity collected in Malaysia at the level of MAS 1+. The data set consists of torque values measured at each elbow angle as the elbow extends from a full flexion position to a full extension position during slow and fast stretch of the forearm. The aim is to find out the capability of the mathematical model and lay a foundation for the future work on data-driven modeling of upper limb spasticity based on the Modified Ashworth Scale

Interventions for improving upper limb function after stroke

Background: Improving upper limb function is a core element of stroke rehabilitation needed to maximise patient outcomes and reduce disability. Evidence about effects of individual treatment techniques and modalities is synthesised within many reviews. For selection of effective rehabilitation treatment, the relative effectiveness of interventions must be known. However, a comprehensive overview of systematic reviews in this area is currently lacking. Objectives: To carry out a Cochrane overview by synthesising systematic reviews of interventions provided to improve upper limb function after stroke. Methods: Search methods: We comprehensively searched the Cochrane Database of Systematic Reviews; the Database of Reviews of Effects; and PROSPERO (an international prospective register of systematic reviews) (June 2013). We also contacted review authors in an effort to identify further relevant reviews. Selection criteria: We included Cochrane and non‐Cochrane reviews of randomised controlled trials (RCTs) of patients with stroke comparing upper limb interventions with no treatment, usual care or alternative treatments. Our primary outcome of interest was upper limb function; secondary outcomes included motor impairment and performance of activities of daily living. When we identified overlapping reviews, we systematically identified the most up‐to‐date and comprehensive review and excluded reviews that overlapped with this. Data collection and analysis: Two overview authors independently applied the selection criteria, excluding reviews that were superseded by more up‐to‐date reviews including the same (or similar) studies. Two overview authors independently assessed the methodological quality of reviews (using a modified version of the AMSTAR tool) and extracted data. Quality of evidence within each comparison in each review was determined using objective criteria (based on numbers of participants, risk of bias, heterogeneity and review quality) to apply GRADE (Grades of Recommendation, Assessment, Development and Evaluation) levels of evidence. We resolved disagreements through discussion. We systematically tabulated the effects of interventions and used quality of evidence to determine implications for clinical practice and to make recommendations for future research. Main results: Our searches identified 1840 records, from which we included 40 completed reviews (19 Cochrane; 21 non‐Cochrane), covering 18 individual interventions and dose and setting of interventions. The 40 reviews contain 503 studies (18,078 participants). We extracted pooled data from 31 reviews related to 127 comparisons. We judged the quality of evidence to be high for 1/127 comparisons (transcranial direct current stimulation (tDCS) demonstrating no benefit for outcomes of activities of daily living (ADLs)); moderate for 49/127 comparisons (covering seven individual interventions) and low or very low for 77/127 comparisons. Moderate‐quality evidence showed a beneficial effect of constraint‐induced movement therapy (CIMT), mental practice, mirror therapy, interventions for sensory impairment, virtual reality and a relatively high dose of repetitive task practice, suggesting that these may be effective interventions; moderate‐quality evidence also indicated that unilateral arm training may be more effective than bilateral arm training. Information was insufficient to reveal the relative effectiveness of different interventions. Moderate‐quality evidence from subgroup analyses comparing greater and lesser doses of mental practice, repetitive task training and virtual reality demonstrates a beneficial effect for the group given the greater dose, although not for the group given the smaller dose; however tests for subgroup differences do not suggest a statistically significant difference between these groups. Future research related to dose is essential. Specific recommendations for future research are derived from current evidence. These recommendations include but are not limited to adequately powered, high‐quality RCTs to confirm the benefit of CIMT, mental practice, mirror therapy, virtual reality and a relatively high dose of repetitive task practice; high‐quality RCTs to explore the effects of repetitive transcranial magnetic stimulation (rTMS), tDCS, hands‐on therapy, music therapy, pharmacological interventions and interventions for sensory impairment; and up‐to‐date reviews related to biofeedback, Bobath therapy, electrical stimulation, reach‐to‐grasp exercise, repetitive task training, strength training and stretching and positioning. Authors' conclusions: Large numbers of overlapping reviews related to interventions to improve upper limb function following stroke have been identified, and this overview serves to signpost clinicians and policy makers toward relevant systematic reviews to support clinical decisions, providing one accessible, comprehensive document, which should support clinicians and policy makers in clinical decision making for stroke rehabilitation. Currently, no high‐quality evidence can be found for any interventions that are currently used as part of routine practice, and evidence is insufficient to enable comparison of the relative effectiveness of interventions. Effective collaboration is urgently needed to support large, robust RCTs of interventions currently used routinely within clinical practice. Evidence related to dose of interventions is particularly needed, as this information has widespread clinical and research implications

Functional Electrical Stimulation mediated by Iterative Learning Control and 3D robotics reduces motor impairment in chronic stroke

Background: Novel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients’ voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort. Methods: Five hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants’ arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression. Results: From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced. Conclusions: The concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this