Bilateral robots for upper-limb stroke rehabilitation: State of the art and future prospects

Combined robot-assisted therapy virtual reality for upper limb rehabilitation in stroke survivors: a systematic review of randomized controlled trials

BACKGROUND

Upper limb impairments are among the most common consequences following a stroke. Recently, robot-assisted therapy (RT) and virtual reality (VR) have been used to improve upper limb function in stroke survivors.

OBJECTIVES

This review aims to investigate the effects of combined RT and VR on upper limb function in stroke survivors and to provide recommendations for researchers and clinicians in the medical field.

METHODS

We searched PubMed, SCOPUS, REHABDATA, PEDro, EMBASE, and Web of Science from inception to March 28, 2024. Randomized controlled trials (RCTs) involving stroke survivors that compared combined RT and VR interventions with either passive (i.e., sham, rest) or active (i.e., traditional therapy, VR, RT) interventions and assessed outcomes related to upper limb function (e.g., strength, muscle tone, or overall function) were included. The Cochrane Collaboration tool was used to evaluate the methodological quality of the included studies.

RESULTS

Six studies were included in this review. In total, 201 patients with stroke (mean age 57.84 years) were involved in this review. Four studies were considered 'high quality', while two were considered as 'moderate quality' on the Cochrane Collaboration tool. The findings showed inconsistent results for the effects of combined RT and VR interventions on upper limb function poststroke.

CONCLUSION

In conclusion, there are potential effects of combined RT and VR interventions on improving upper limb function, but further research is needed to confirm these findings, understand the underlying mechanisms, and assess the consistency and generalizability of the results.

Restoring of Interhemispheric Symmetry in Patients With Stroke Following Bilateral or Unilateral Robot-Assisted Upper-Limb Rehabilitation: A Pilot Randomized Controlled Trial

Bilateral robotic rehabilitation has proven helpful in the recovery of upper limb motor function in patients with stroke, but its effects on the cortical reorganization mechanisms underlying recovery are still unclear. This pilot Randomized Controlled Trial (RCT) aimed to evaluate the effects on the interhemispheric balance of unilateral or bilateral robotic treatments in patients with subacute stroke, using Quantitative Electroencephalography (qEEG). 19 patients with ischemic stroke underwent a 30-session upper limb neurorehabilitation intervention using a bilateral upper limb exoskeleton. Each patient was randomly assigned to the bilateral (BG, n=10) or unilateral treatment group (UG, n=9). EEG evaluations were performed before (T0) and right after (T [Formula: see text] the first treatment session, after 30 treatment sessions (T1), and at 1-week follow-up (T2), in both eyes open and eyes closed conditions. From the acquired EEG data, the pairwise-derived Brain Symmetry Index (pdBSI) was computed. In addition, clinical evaluation was performed at T0 and T1 with validated clinical scales. After the treatment, a significant improvement in clinical and EEG evaluations was observed for both groups, but only the BG showed reduced pdBSI in delta and theta bands. In the cluster of sensorimotor channels, there was no significant difference between groups. The observed changes were not maintained at follow-up. No significant changes were observed in the pdBSI after a single rehabilitation session. Results suggest that balancing of interhemispheric symmetry comes along with a clinical improvement in the upper extremity and that the pdBSI can be used to investigate the mechanisms of neuronal plasticity involved in robotic rehabilitation after stroke.

A review on EMG/EEG based control scheme of upper limb rehabilitation robots for stroke patients

Stroke is a common worldwide health problem and a crucial contributor to gained disability. The abilities of people, who are subjected to stroke, to live independently are significantly affected since affected upper limbs' functions are essential for our daily life. This review article focuses on emerging trends in BCI-controlled rehabilitation techniques based on EMG, EEG, or EGM + EEG signals in the last few years. Working on developing rehabilitation robotics, is considered a wealthy scientific area for researchers in the last period. There is a significant advantage that the human acquires from the interaction between the machine and his body, rehabilitation for a patient's limb is very important to get the body limb recovery, and this is what is provided mostly by applying robotic devices.

Detecting latent topics and trends of digital twins in healthcare: A structural topic model-based systematic review

Objective

Digital twins (DTs) have received widespread attention recently, providing new ideas and possibilities for future healthcare. This review aims to provide a quantitative review to analyze specific study contents, research focus, and trends of DT in healthcare. Simultaneously, this review intends to expand the connotation of "healthcare" into two directions, namely "Disease treatment" and "Health enhancement" to analyze the content within the "DT + healthcare" field thoroughly.

Methods

A data mining method named Structure Topic Modeling (STM) was used as the analytical tool due to its topic analysis ability and versatility. Google Scholar, Web of Science, and China National Knowledge Infrastructure supplied the material papers in this review.

Results

A total of 94 high-quality papers published between 2018 and 2022 were gathered and categorized into eight topics, collectively covering the transformative impact across a broader spectrum in healthcare. Three main findings have emerged: (1) papers published in healthcare predominantly concentrate on technology development (artificial intelligence, Internet of Things, etc.) and application scenarios(personalized, precise, and real-time health service); (2) the popularity of research topics is influenced by various factors, including policies, COVID-19, and emerging technologies; and (3) the preference for topics is diverse, with a general inclination toward the attribute of "Health enhancement."

Conclusions

This review underscores the significance of real-time capability and accuracy in shaping the future of DT, where algorithms and data transmission methods assume central importance in achieving these goals. Moreover, technological advancements, such as omics and Metaverse, have opened up new possibilities for DT in healthcare. These findings contribute to the existing literature by offering quantitative insights and valuable guidance to keep researchers ahead of the curve.

Design of Wearable Hand Rehabilitation Glove With Bionic Fiber-Reinforced Actuator

Background: The hand motor function is lost and activities in daily life (ADLs) are affected due to some illnesses such as stroke and hemiplegia. As a coping way, we present a wearable rehabilitation glove with the bionic actuator for restoring the hand function and the motor control ability lost by stroke patients. Methods: The soft pneumatic bionic actuator (SPBA) is developed on the basis of the research of human hand bone structure and finger joint characteristics and a series of tests are conducted. Besides, we built the rehabilitation glove system based on the proposed SPBAs to verify the availability due to typical gesture, mirror therapy (MT) and grasping experiment for irregular objects. Result: The bending angle of SPBA can reach 260°. The output force of it can reach 5.1N with 0.25 MPa air pressure input. The maximum variance of the bending angle can be concluded at 5.1° in MT. The grasping experiments of the glove worn on the hand or not shows the proposed glove is flexible, the grip force is large and achieve stable grasping of objects. Conclusion: The designed SPBA is satisfied with the requirements of rehabilitation training and the proposed glove restore the normal hand motion of patients in ADLs.

DEVELOPMENT AND ANALYSIS OF A BILATERAL END-EFFECTER UPPER LIMB REHABILITATION ROBOT

Studies have shown that rehabilitation training with the unaffected side guiding affected side is more consistent with the natural movement pattern of human upper limb compared with unilateral rehabilitation training, which is conducive to improve rehabilitation effect of the affected limb motor function. In this paper, a bilateral end-effector upper limb rehabilitation robot (BEULRR) based on two modern commercial manipulators is developed first, then the kinematics, reachability, and dexterity analysis of BEULRR are performed, respectively. Finally, a bilateral symmetric training protocol with the unaffected side guiding the affected side is proposed and evaluated through healthy human subject experiment testing based on BEULRR. The simulation results show that the developed BEULRR could perform spatial rehabilitation training and its rehabilitation training workspace can fully cover the physiological workspace of human upper limb. The preliminary experiment results from the healthy human subject show that the BEULRR system could provide reliable bilateral symmetric training protocol. These simulation and experiment results demonstrated that the developed BEULRR system could be used in bilateral rehabilitation training application, and also show that the BEULRR system has the potential to be applied to clinical rehabilitation training in the further step. In the close future, the proposed BEULRR and bilateral symmetric training protocol are planned to be applied in elderly volunteers and patients with upper limb motor dysfunction for further evaluating.

Robot-Assisted Therapy for Upper Extremity Motor Impairment After Stroke: A Systematic Review and Meta-Analysis

OBJECTIVE

The purpose of this study was to review the effects of robot-assisted therapy (RT) for improving poststroke upper extremity motor impairment.

METHODS

The PubMed, Embase, Medline, and Web of Science databases were searched from inception to April 8, 2020. Randomized controlled trials that were conducted to evaluate the effects of RT on upper extremity motor impairment poststroke and that used Fugl-Meyer assessment for upper extremity scores as an outcome were included. Two authors independently screened articles, extracted data, and assessed the methodological quality of the included studies using the Physiotherapy Evidence Database (PEDro) scale. A random-effects meta-analysis was performed to pool the effect sizes across the studies.

RESULTS

Forty-one randomized controlled trials with 1916 stroke patients were included. Compared with dose-matched conventional rehabilitation, RT significantly improved the Fugl-Meyer assessment for upper extremity scores of the patients with stroke, with a small effect size (Hedges g = 0.25; 95% CI, 0.11-0.38; I2 = 45.9%). The subgroup analysis revealed that the effects of unilateral RT, but not that of bilateral RT, were superior to conventional rehabilitation (Hedges g = 0.32; 95% CI, 0.15-0.50; I2 = 55.9%). Regarding the type of robot devices, the effects of the end effector device (Hedges g = 0.22; 95% CI, 0.09-0.36; I2 = 35.4%), but not the exoskeleton device, were superior to conventional rehabilitation. Regarding the stroke stage, the between-group difference (ie, RT vs convention rehabilitation) was significant only for people with late subacute or chronic stroke (Hedges g = 0.33; 95% CI, 0.16-0.50; I2 = 34.2%).

CONCLUSION

RT might be superior to conventional rehabilitation in improving upper extremity motor impairment in people after stroke with notable upper extremity hemiplegia and limited potential for spontaneous recovery.

Design and testing of a soft parallel robot based on pneumatic artificial muscles for wrist rehabilitation

Wrist rehabilitation is needed to help post-stroke and post-surgery patients recover from wrist fracture or injury. Traditional rehabilitation training is conducted by a therapist in a hospital, which hinders timely treatment due to the corresponding time and space constraints. This paper presents the design and implementation of a soft parallel robot for automated wrist rehabilitation. The presented wrist rehabilitation robot integrates the advantages of both soft robot and parallel robot structures. Unlike traditional rigid-body based rehabilitation robots, this soft parallel robot exhibits a compact structure, which is highly secure, adaptable, and flexible and thus a low-cost solution for personalized treatment. The proposed soft wrist-rehabilitation robot is driven by six evenly distributed linear actuators using pneumatic artificial muscles and one central linear electric motor. The introduced parallel-kinematic mechanism design enables the enhancement of the output stiffness of the soft robot for practical use. An electromyography sensor is adopted to provide feedback signals for evaluating the rehabilitation training process. A kinematic model of the designed robot is derived, and a prototype is fabricated for experimental testing. The results demonstrate that the developed soft rehabilitation robot can assist the wrist to realize all the required training motions, including abduction-adduction, flexion-extension, and supination-pronation. The compact and lightweight structure of this novel robot makes it convenient to use, and suitable rehabilitation training modes can be chosen for tailored rehabilitation at home or in a hospital.

Development and evaluation of a novel music-based therapeutic device for upper extremity movement training: A pre-clinical, single-arm trial

Restoration of upper limb motor function and patient functional independence are crucial treatment targets in neurological rehabilitation. Growing evidence indicates that music-based intervention is a promising therapeutic approach for the restoration of upper extremity functional abilities in neurologic conditions such as cerebral palsy, stroke, and Parkinson's Disease. In this context, music technology may be particularly useful to increase the availability and accessibility of music-based therapy and assist therapists in the implementation and assessment of targeted therapeutic goals. In the present study, we conducted a pre-clinical, single-arm trial to evaluate a novel music-based therapeutic device (SONATA) for upper limb extremity movement training. The device consists of a graphical user interface generated by a single-board computer displayed on a 32" touchscreen with built-in speakers controlled wirelessly by a computer tablet. The system includes two operational modes that allow users to play musical melodies on a virtual keyboard or draw figures/shapes whereby every action input results in controllable sensory feedback. Four motor tasks involving hand/finger movement were performed with 21 healthy individuals (13 males, aged 26.4 ± 3.5 years) to evaluate the device's operational modes and main features. The results of the functional tests suggest that the device is a reliable system to present pre-defined sequences of audiovisual stimuli and shapes and to record response and movement data. This preliminary study also suggests that the device is feasible and adequate for use with healthy individuals. These findings open new avenues for future clinical research to further investigate the feasibility and usability of the SONATA as a tool for upper extremity motor function training in neurological rehabilitation. Directions for future clinical research are discussed.

A Review on Upper Limb Rehabilitation Robots

Rehabilitation is the process of treating post-stroke consequences. Impaired limbs are considered the common outcomes of stroke, which require a professional therapist to rehabilitate the impaired limbs and restore fully or partially its function. Due to the shortage in the number of therapists and other considerations, researchers have been working on developing robots that have the ability to perform the rehabilitation process. During the last two decades, different robots were invented to help in rehabilitation procedures. This paper explains the types of rehabilitation treatments and robot classifications. In addition, a few examples of well-known rehabilitation robots will be explained in terms of their efficiency and controlling mechanisms.

Displacement Analysis and Design of a (2–RRU)–URR Parallel Mechanism Performing 2R1T Output Motion for Thumb Rehabilitation

The thumb assists other fingers, and any damage in its functionality prevents the human hand from performing dexterous functions. In this paper, the kinematic design of the (2–RRU)–URR parallel mechanism as the application of the thumb rehabilitation device is proposed. This mechanism is an over-constrained mechanism capable of achieving the required mobility with fewer joints. Three degrees of freedom exist—two rotational and one translational mobility—that are related to each thumb movement: adduction–abduction and flexion–extension. Considering the narrow space of the hand, actuators are designed to divide its placement into the surface of the palm. To avoid the collisions between the device and the hand, an offset was adopted. The displacement analysis problem is solved by dividing it into two parts: the planar motion generator (PMG) and orientation generator (OG), according to each functional motion, and the corresponding equations and procedures are presented. To clarify the basic characteristics of this mechanism, the reachable workspace of the PMG and rotational ability and sensitivity of the OG is demonstrated numerically. Because a large input torque difference is dangerous in the rehabilitation mechanism, the effective workspace is determined according to the magnitude of the input torque differences and compared with the measured thumb movements.

Appraisals of robotic locomotor exoskeletons for gait: focus group insights from potential users with spinal cord injuries

Purpose: To describe appraisals of robotic exoskeletons for locomotion by potential users with spinal cord injuries, their perceptions of device benefits and limitations, and recommendations for manufacturers and therapists regarding device use.Materials and methods: We conducted focus groups at three regional rehabilitation hospitals and used thematic analysis to define themes.Results: Across four focus groups, 35 adults participated; they were predominantly middle-aged, male, and diverse in terms of race and ethnicity, well educated, and not working. Participants had been living with SCI an average of two decades. Most participants were aware of exoskeletons. Some were enthusiastic about the usability of the devices while others were more circumspect. They had many questions about device affordability and usability, and were discerning in their appraisal of benefits and suitability to their particular circumstances. They reflected on device cost, the need for caregiver assistance, use of hands, and environmental considerations. They weighed the functional benefits relative to the cost of preferred activities. Their recommendations focused on cost, battery life, and independent use.Conclusions: Potential users' appraisals of mobility technology reflect a nuanced appreciation of device costs; functional, social, and psychological benefits; and limitations. Results provide guidance to therapists and manufacturers regarding device use.Implications for RehabilitationPotential users of robotic locomotor exoskeletons with spinal cord injuries appreciate the functional, social, and psychological benefits that these devices may offer.Their appraisals reflect nuanced consideration of device cost and features, and the suitability of the assistive technology to their circumstances.They recommend that manufacturers focus on reducing cost, extending battery life, and features that allow independent use.

Robot-assisted therapy for arm recovery for stroke patients: state of the art and clinical implication

Introduction: Robot-assisted therapy is an emerging approach that performs highly repetitive, intensive, task oriented and quantifiable neuro-rehabilitation. In the last decades, it has been increasingly used in a wide range of neurological central nervous system conditions implying an upper limb paresis. Results from the studies are controversial, for the many types of robots and their features often not accompanied by specific clinical indications about the target functions, fundamental for the individualized neurorehabilitation program.Areas covered: This article reviews the state of the art and perspectives of robotics in post-stroke rehabilitation for upper limb recovery. Classifications and features of robots have been reported in accordance with technological and clinical contents, together with the definition of determinants specific for each patient, that could modify the efficacy of robotic treatments. The possibility of combining robotic intervention with other therapies has also been discussed.Expert commentary: The recent wide diffusion of robots in neurorehabilitation has generated a confusion due to the commingling of technical and clinical aspects not previously clarified. Our critical review provides a possible hypothesis about how to match a robot with subject's upper limb functional abilities, but also highlights the need of organizing a clinical consensus conference about the robotic therapy.

Low-Cost Robotic Guide Based on a Motor Imagery Brain-Computer Interface for Arm Assisted Rehabilitation

Motor imagery has been suggested as an efficient alternative to improve the rehabilitation process of affected limbs. In this study, a low-cost robotic guide is implemented so that linear position can be controlled via the user’s motor imagination of movement intention. The patient can use this device to move the arm attached to the guide according to their own intentions. The first objective of this study was to check the feasibility and safety of the designed robotic guide controlled via a motor imagery (MI)-based brain–computer interface (MI-BCI) in healthy individuals, with the ultimate aim to apply it to rehabilitation patients. The second objective was to determine which are the most convenient MI strategies to control the different assisted rehabilitation arm movements. The results of this study show a better performance when the BCI task is controlled with an action–action MI strategy versus an action–relaxation one. No statistically significant difference was found between the two action–action MI strategies.

A qualitative study on user acceptance of a home-based stroke telerehabilitation system

Objective: This paper reports a qualitative study of a home-based stroke telerehabilitation system. The telerehabilitation system delivers treatment sessions in the form of daily guided rehabilitation games, exercises, and stroke education in the patient's home. The aims of the current report are to investigate patient perceived benefits of and barriers to using the telerehabilitation system at home.Methods: We used a qualitative study design that involved in-depth semi-structured interviews with 13 participants who were patients in the subacute phase after stroke and had completed a six-week intervention using the home-based telerehabilitation system. Thematic analysis was conducted to analyze the data.Results: Participants mostly reported positive experiences with the telerehabilitation system. Benefits included observed improvements in limb functions, cognitive abilities, and emotional well-being. They also perceived the system easy to use due to the engaging experience and the convenience of conducting sessions at home. Meanwhile, participants pointed out the importance of considering technical support and physical environment at home. Further, family members' support helped them sustain in their rehabilitation. Finally, adjusting difficulty levels and visualizing patients' rehabilitation progress might help them in continued use of the telerehabilitation system.Conclusion: Telerehabilitation systems can be used as an efficient and user-friendly tool to deliver home-based stroke rehabilitation that enhance patients' physical recovery and mental and social-emotional wellbeing. Such systems need to be designed to offer engaging experience, display of recovery progress, and flexibility of schedule and location, with consideration of facilitating and social factors.

Speed-Interactive Pedaling Training Using Smartphone Virtual Reality Application for Stroke Patients: Single-Blinded, Randomized Clinical Trial

This study aimed to investigate the effects of speed-interactive pedaling training (SIPT) using a smartphone virtual reality application to improve lower limb motor function, trunk sitting balance, and gait in stroke patients. Forty-two patients who had previously experienced a stroke and could sit independently participated in the study. The subjects were assigned to the SIPT group (n = 21) and the control group (n = 21). The SIPT group had cycle training with SIPT for 40 min a day, five days a week, in a six-week period, in addition to conventional therapy. The control group had cycle training without SIPT and conventional therapy. The Fugl–Meyer Assessment, postural sway, modified functional reach test, trunk impairment scale, and spatiotemporal parameters of gait were used to assess the changes in lower extremity function, the static balance of sitting, the dynamic balance of sitting, and gait ability after the intervention. The Fugl–Meyer Assessment, postural sway, modified functional reach test, trunk impairment scale, and gait ability in the SIPT group were significantly better compared to that of the control group (p < 0.05). Based on this result, we propose that SIPT, which improves function, balance, and gait, could be used as an effective training method to improve patients’ functional activities in the clinical setting. The results of this study suggest that SIPT could be used as an effective training method to restore a patient’s function by improving trunk balance and motor function.

Exoskeleton and End-Effector Robots for Upper and Lower Limbs Rehabilitation: Narrative Review

Recovery of upper and lower limbs function is essential to reach independence in daily activities in patients with upper motor neuron syndrome (UMNS). Rehabilitation can provide a guide for motor recovery influencing the neurobiology of neuronal plasticity providing controlled, repetitive, and variable patterns. Increasing therapy dosage, intensity, number of repetition, execution of task-oriented exercises, and combining top-down and bottom-up approaches can promote plasticity and functional recovery. Robotic exoskeletons for upper and lower limbs, based on the principle of motor learning, have been introduced in neurorehabilitation. In this narrative review, we provide an overview of literature published on exoskeleton devices for upper and lower limb rehabilitation in patients with UMNS; we summarized the available current research evidence and outlined the new challenges that neurorehabilitation and bioengineering will have to face in the upcoming years. Robotic treatment should be considered a rehabilitation tool useful to generate a more complex, controlled multisensory stimulation of the patient and useful to modify the plasticity of neural connections through the experience of movement. Efficacy and efficiency of robotic treatment should be defined starting from intensity, complexity, and specificity of the robotic exercise, that are related to human-robot interaction in terms of motion, emotion, motivation, meaning of the task, feedback from the exoskeleton, and fine motion assistance. Duration of a single session, global period of the treatment, and the timing for beginning of robotic treatment are still open questions. There is the need to evaluate and individualize the treatment according to patient's characteristics. Robotic devices for upper and lower limbs open a window to define therapeutic modalities as possible beneficial drug, able to boost biological, neurobiological, and epigenetic changes in central nervous system. We need to implement large and innovative research programs to answer these issues in the near future.

Academic Review and Perspectives on Robotic Exoskeletons

Since the first robotic exoskeleton was developed in 1960, this research field has attracted much interest from both the academic and industrial communities resulting in scientific publications, prototype developments and commercialized products. In this article, to document the progress in and current status of this field, we performed a bibliometric analysis. This analysis evaluated the publications in the field of robotic exoskeletons from 1990 to July 2019 that were retrieved from the Science Citation Index Expanded database. The bibliometric analyses were presented in terms of author keywords, year, country, institution, journal, author, and the citation. Results show that currently the United States has taken the leading position in this field and has built the largest collaborative network with other countries. The Massachusetts Institute of Technology (MIT) made the greatest contribution to the field of robotic exoskeleton investigations in terms of the number of publications, average citations per publication and the h-index. In addition, the Journal of NeuroEngineering and Rehabilitation ranks first among the top 20 academic journals in terms of the number of publications related to robotic exoskeletons during the period investigated. Author keyword analysis indicates that most research has focused on rehabilitation robotics. Biomedical engineering, rehabilitation and the neurosciences are the most common disciplines conducting research in this area according to the Web of Science (WoS). Our study comprehensively assesses the current research status and collaboration network of robotic exoskeletons, thus helping researchers steer their projects or locate potential collaborators.

Development of a biological signal-based evaluator for robot-assisted upper-limb rehabilitation: a pilot study

Bio-signal based assessment for upper-limb functions is an attractive technology for rehabilitation. In this work, an upper-limb function evaluator is developed based on biological signals, which could be used for selecting different robotic training protocols. Interaction force (IF) and participation level (PL, processed surface electromyography (sEMG) signals) are used as the key bio-signal inputs for the evaluator. Accordingly, a robot-based standardized performance testing (SPT) is developed to measure these key bio-signal data. Moreover, fuzzy logic is used to regulate biological signals, and a rules-based selector is then developed to select different training protocols. To the authors' knowledge, studies focused on biological signal-based evaluator for selecting robotic training protocols, especially for robot-based bilateral rehabilitation, has not yet been reported in literature. The implementation of SPT and fuzzy logic to measure and process key bio-signal data with a rehabilitation robot system is the first of its kind. Five healthy participants were then recruited to test the performance of the SPT, fuzzy logic and evaluator in three different conditions (tasks). The results show: (1) the developed SPT has an ability to measure precise bio-signal data from participants; (2) the utilized fuzzy logic has an ability to process the measured data with the accuracy of 86.7% and 100% for the IF and PL respectively; and (3) the proposed evaluator has an ability to distinguish the intensity of biological signals and thus to select different robotic training protocols. The results from the proposed evaluator, and biological signals measured from healthy people could also be used to standardize the criteria to assess the results of stroke patients later.

Human Action Recognition Based on Inertial Sensors and Complexity Classification

In this article, a human action recognition technique based on complexity classification is proposed. Considering the features of human actions such as continuity, individuality, variety randomness, the demands for recognition of different types of actions are different, the problem of action recognition can be classified into simple action recognition and complex action recognition -- the classification criterions are given respectively. Meanwhile, the hardware design of data acquisition device is introduced and the angle variation is chosen to represent the user's body state changes. For simple actions, a real-time recognition algorithm based on template matching performed well on cost control, and a method based on BLSTM-RNN is used for complex motion recognition to improve the accuracy of identification.

Alterations in muscle activation patterns during robot-assisted bilateral training: A pilot study

Robot-assisted bilateral training is being developed as a new rehabilitation approach for stroke patients. However, there is still a lack of understanding of muscle functions when performing robot-assisted synchronous movements. The aim of this work is to explore the muscle activation patterns and the voluntary effort of participants during different robot-assisted bilateral training protocols. To this end, 10 healthy participants were recruited to take part in a 60-minute experiment. The experiment included two different bilateral exercises, and each exercise contained four different training protocols. Trajectories of the robots, interaction force and surface electromyogram signals were recorded during training. The results show that the robots do affect the muscle activation patterns during different training protocols and exercises rather than the controller. Specifically, the activity of muscles is reduced in robot-assisted training but is increased in active force involved robot-assisted training when compared to robot-unassisted training. Meanwhile, the voluntary effort of participants can be presented by the adjusted trajectories via the controller. In addition, the results also suggest that the activations for the same muscle groups in the left and right arms are highly correlated with each other in both exercises. Furthermore, the training protocols and methods developed in this work could be further extended in future clinical trials to investigate therapeutic outcomes for patients as well as to better understand bilateral recovery processes.

Home-based technologies for stroke rehabilitation: A systematic review

BACKGROUND

Many forms of home-based technology targeting stroke rehabilitation have been devised, and a number of human factors are important to their application, suggesting the need to examine this information in a comprehensive review.

OBJECTIVE

The systematic review aims to synthesize the current knowledge of technologies and human factors in home-based technologies for stroke rehabilitation.

METHODS

We conducted a systematic literature search in three electronic databases (IEEE, ACM, PubMed), including secondary citations from the literature search. We included articles that used technological means to help stroke patients conduct rehabilitation at home, reported empirical studies that evaluated the technologies with patients in the home environment, and were published in English. Three authors independently conducted the content analysis of searched articles using a list of interactively defined factors.

RESULTS

The search yielded 832 potentially relevant articles, leading to 31 articles that were included for in-depth analysis. The types of technology of reviewed articles included games, telerehabilitation, robotic devices, virtual reality devices, sensors, and tablets. We present the merits and limitations of each type of technology. We then derive two main human factors in designing home-based technologies for stroke rehabilitation: designing for engagement (including external and internal motivation) and designing for the home environment (including understanding the social context, practical challenges, and technical proficiency).

CONCLUSION

This systematic review presents an overview of key technologies and human factors for designing home-based technologies for stroke rehabilitation.

Rehabilitation robots for the treatment of sensorimotor deficits: a neurophysiological perspective

The past decades have seen rapid and vast developments of robots for the rehabilitation of sensorimotor deficits after damage to the central nervous system (CNS). Many of these innovations were technology-driven, limiting their clinical application and impact. Yet, rehabilitation robots should be designed on the basis of neurophysiological insights underlying normal and impaired sensorimotor functions, which requires interdisciplinary collaboration and background knowledge.

Recovery of sensorimotor function after CNS damage is based on the exploitation of neuroplasticity, with a focus on the rehabilitation of movements needed for self-independence. This requires a physiological limb muscle activation that can be achieved through functional arm/hand and leg movement exercises and the activation of appropriate peripheral receptors. Such considerations have already led to the development of innovative rehabilitation robots with advanced interaction control schemes and the use of integrated sensors to continuously monitor and adapt the support to the actual state of patients, but many challenges remain. For a positive impact on outcome of function, rehabilitation approaches should be based on neurophysiological and clinical insights, keeping in mind that recovery of function is limited. Consequently, the design of rehabilitation robots requires a combination of specialized engineering and neurophysiological knowledge. When appropriately applied, robot-assisted therapy can provide a number of advantages over conventional approaches, including a standardized training environment, adaptable support and the ability to increase therapy intensity and dose, while reducing the physical burden on therapists. Rehabilitation robots are thus an ideal means to complement conventional therapy in the clinic, and bear great potential for continued therapy and assistance at home using simpler devices.

This review summarizes the evolution of the field of rehabilitation robotics, as well as the current state of clinical evidence. It highlights fundamental neurophysiological factors influencing the recovery of sensorimotor function after a stroke or spinal cord injury, and discusses their implications for the development of effective rehabilitation robots. It thus provides insights on essential neurophysiological mechanisms to be considered for a successful development and clinical inclusion of robots in rehabilitation.

Myoelectric Pattern Recognition for Controlling a Robotic Hand: A Feasibility Study in Stroke

OBJECTIVE

Myoelectric pattern recognition has been successfully applied as a human-machine interface to control robotic devices such as prostheses and exoskeletons, significantly improving the dexterity of myoelectric control. This study investigates the feasibility of applying myoelectric pattern recognition for controlling a robotic hand in stroke patients.

METHODS

Myoelectric pattern recognition of six hand motion patterns was performed using forearm electromyogram signals in paretic side of eight stroke subjects. Both the random cross validation (RCV) and the chronological handout validation (CHV) were applied to assess the offline myoelectric pattern recognition performance. Experiments on real-time myoelectric pattern recognition control of an exoskeleton robotic hand were also performed.

RESULTS

An average classification accuracy of 84.1% (the mean value from two different classifiers) and individual subject differences were observed in the offline myoelectric pattern recognition analysis using the RCV, while the accuracy decreased to 65.7% when the CHV was used. The stroke subjects achieved an average accuracy of 61.3 ± 20.9% for controlling the robotic hand. However, our study did not reveal a clear correlation between the real-time control accuracy and the offline myoelectric pattern recognition performance, or any specific characteristics of the stroke subjects.

CONCLUSION

The findings suggest that it is feasible to apply myoelectric pattern recognition to control the robotic hand in some but not all of the stroke patients. Each stroke subject should be individually online tested for the feasibility of applying myoelectric pattern recognition control for robot-assisted rehabilitation.

New Design of a Soft Robotics Wearable Elbow Exoskeleton Based on Shape Memory Alloy Wire Actuators

The elbow joint is a complex articulation composed of the humeroulnar and humeroradial joints (for flexion-extension movement) and the proximal radioulnar articulation (for pronation-supination movement). During the flexion-extension movement of the elbow joint, the rotation center changes and this articulation cannot be truly represented as a simple hinge joint. The main goal of this project is to design and assemble a medical rehabilitation exoskeleton for the elbow with one degree of freedom for flexion-extension, using the rotation center for proper patient elbow joint articulation. Compared with the current solutions, which align the exoskeleton axis with the elbow axis, this offers an ergonomic physical human-robot interface with a comfortable interaction. The exoskeleton is actuated with shape memory alloy wire-based actuators having minimum rigid parts, for guiding the actuators. Thanks to this unusual actuation system, the proposed exoskeleton is lightweight and has low noise in operation with a simple design 3D-printed structure. Using this exoskeleton, these advantages will improve the medical rehabilitation process of patients that suffered stroke and will influence how their lifestyle will change to recover from these diseases and improve their ability with activities of daily living, thanks to brain plasticity. The exoskeleton can also be used to evaluate the real status of a patient, with stroke and even spinal cord injury, thanks to an elbow movement analysis.

Interaction force and motion estimators facilitating impedance control of the upper limb rehabilitation robot

In order to enhance the performance of rehabilitation robots, it is imperative to know both force and motion caused by the interaction between user and robot. However, common direct measurement of both signals through force and motion sensors not only increases the complexity of the system but also impedes affordability of the system. As an alternative of the direct measurement, in this work, we present new force and motion estimators for the proper control of the upper-limb rehabilitation Universal Haptic Pantograph (UHP) robot. The estimators are based on the kinematic and dynamic model of the UHP and the use of signals measured by means of common low-cost sensors. In order to demonstrate the effectiveness of the estimators, several experimental tests were carried out. The force and impedance control of the UHP was implemented first by directly measuring the interaction force using accurate extra sensors and the robot performance was compared to the case where the proposed estimators replace the direct measured values. The experimental results reveal that the controller based on the estimators has similar performance to that using direct measurement (less than 1 N difference in root mean square error between two cases), indicating that the proposed force and motion estimators can facilitate implementation of interactive controller for the UHP in robotmediated rehabilitation trainings.

Real-Time Control of an Exoskeleton Hand Robot with Myoelectric Pattern Recognition

Robot-assisted training provides an effective approach to neurological injury rehabilitation. To meet the challenge of hand rehabilitation after neurological injuries, this study presents an advanced myoelectric pattern recognition scheme for real-time intention-driven control of a hand exoskeleton. The developed scheme detects and recognizes user’s intention of six different hand motions using four channels of surface electromyography (EMG) signals acquired from the forearm and hand muscles, and then drives the exoskeleton to assist the user accomplish the intended motion. The system was tested with eight neurologically intact subjects and two individuals with spinal cord injury (SCI). The overall control accuracy was [Formula: see text] for the neurologically intact subjects and [Formula: see text] for the SCI subjects. The total lag of the system was approximately 250[Formula: see text]ms including data acquisition, transmission and processing. One SCI subject also participated in training sessions in his second and third visits. Both the control accuracy and efficiency tended to improve. These results show great potential for applying the advanced myoelectric pattern recognition control of the wearable robotic hand system toward improving hand function after neurological injuries.

The Present and Future of Robotic Technology in Rehabilitation

Robotic technology designed to assist rehabilitation can potentially increase the efficiency of and accessibility to therapy by assisting therapists to provide consistent training for extended periods of time, and collecting data to assess progress. Automatization of therapy may enable many patients to be treated simultaneously and possibly even remotely, in the comfort of their own homes, through telerehabilitation. The data collected can be used to objectively assess performance and document compliance as well as progress. All of these characteristics can make therapists more efficient in treating larger numbers of patients. Most importantly for the patient, it can increase access to therapy which is often in high demand and rationed severely in today's fiscal climate. In recent years, many consumer grade low-cost and off-the-shelf devices have been adopted for use in therapy sessions and methods for increasing motivation and engagement have been integrated with them. This review paper outlines the effort devoted to the development and integration of robotic technology for rehabilitation.