Portable and Reconfigurable Wrist Robot Improves Hand Function for Post-Stroke Subjects

Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients

Robot-assisted motor training is applied for neurorehabilitation in stroke patients, using motor imagery (MI) as a representative paradigm of brain-computer interfaces to offer real-life assistance to individuals facing movement challenges. However, the effectiveness of training with MI may vary depending on the location of the stroke lesion, which should be considered. This paper introduces a multi-task electroencephalogram-based heterogeneous ensemble learning (MEEG-HEL) specifically designed for cross-subject training. In the proposed framework, common spatial patterns were used for feature extraction, and the features according to stroke lesions are shared and selected through sequential forward floating selection. The heterogeneous ensembles were used as classifiers. Nine patients with chronic ischemic stroke participated, engaging in MI and motor execution (ME) paradigms involving finger tapping. The classification criteria for the multi-task were established in two ways, taking into account the characteristics of stroke patients. In the cross-subject session, the first involved a direction recognition task for two-handed classification, achieving a performance of 0.7419 (±0.0811) in MI and 0.7061 (±0.1270) in ME. The second task focused on motor assessment for lesion location, resulting in a performance of 0.7457 (±0.1317) in MI and 0.6791 (±0.1253) in ME. Comparing the specific-subject session, except for ME on the motor assessment task, performance on both tasks was significantly higher than the cross-subject session. Furthermore, classification performance was similar to or statistically higher in cross-subject sessions compared to baseline models. The proposed MEEG-HEL holds promise in improving the practicality of neurorehabilitation in clinical settings and facilitating the detection of lesions.

Development of portable robotic orthosis and biomechanical validation in people with limited upper limb function after stroke

Stroke has a considerable incidence in the world population and would cause sequelae in the upper limbs. One way to increase the efficiency in the rehabilitation process of patients with these sequelae is through robot-assisted therapy. The present study developed a portable robotic orthosis called Pinotti Portable Robotic Exoskeleton (PPRE) and validated its functioning in clinical tests. The static and dynamic parts of the device modules are described. Design issues, such as heavyweight and engine positioning, have been optimized. The implementation of control was through a smartphone application that communicates with a microcontroller to perform desired movements. Four individuals with motor impairment of the upper limbs due to stroke performed clinical tests to validate the device. Participants did not mention pain, discomfort, tingling, and paresthesia. The robotic device showed the ability to perform the flexion and extension movements of the fingers and elbow. The PPRE was confirmed to be adequate and functional at different levels of motor impairment assessed. The orthosis presented advantages over the currently existing devices, concerning its biomechanical functioning, portability, comfort, and versatility. Thus, the apparatus has the great innovative potential to become a device for home use, serving as an aid to the therapist and facilitating the rehabilitation of patients after an injury. In a larger sample, future studies are needed to assess the effect of a robotic orthosis on the level of rehabilitation in individuals with upper limb impairment.

Global Trends and Hotspots in Research on Rehabilitation Robots: A Bibliometric Analysis From 2010 to 2020

Background: In recent years, with the development of medical science and artificial intelligence, research on rehabilitation robots has gained more and more attention, for nearly 10 years in the Web of Science database by journal of rehabilitation robot-related research literature analysis, to parse and track rehabilitation robot research hotspot and front, and provide some guidance for future research.

Methods: This study employed computer retrieval of rehabilitation robot-related research published in the core data collection of the Web of Science database from 2010 to 2020, using CiteSpace 5.7 visualization software. The hotspots and frontiers of rehabilitation robot research are analyzed from the aspects of high-influence countries or regions, institutions, authors, high-frequency keywords, and emergent words.

Results: A total of 3,194 articles were included. In recent years, the research on rehabilitation robots has been continuously hot, and the annual publication of relevant literature has shown a trend of steady growth. The United States ranked first with 819 papers, and China ranked second with 603 papers. Northwestern University ranked first with 161 publications. R. Riener, a professor at the University of Zurich, Switzerland, ranked as the first author with 48 articles. The Journal of Neural Engineering and Rehabilitation has the most published research, with 211 publications. In the past 10 years, research has focused on intelligent control, task analysis, and the learning, performance, and reliability of rehabilitation robots to realize the natural and precise interaction between humans and machines. Research on neural rehabilitation robots, brain–computer interface, virtual reality, flexible wearables, task analysis, and exoskeletons has attracted more and more attention.

Conclusions: At present, the brain–computer interface, virtual reality, flexible wearables, task analysis, and exoskeleton rehabilitation robots are the research trends and hotspots. Future research should focus on the application of machine learning (ML), dimensionality reduction, and feature engineering technologies in the research and development of rehabilitation robots to improve the speed and accuracy of algorithms. To achieve wide application and commercialization, future rehabilitation robots should also develop toward mass production and low cost. We should pay attention to the functional needs of patients, strengthen multidisciplinary communication and cooperation, and promote rehabilitation robots to better serve the rehabilitation medical field.

Exploring Stroke Rehabilitation in Malaysia: Are Robots Better than Humans for Stroke Recuperation?

Ranked as the second leading cause of death and the primary factor to adult disability worldwide, stroke has become a global epidemic problem and burden. As a developing country, Malaysia still faces challenges in providing ideal rehabilitation services to individuals with physical disabilities including stroke survivors. Conventional post-stroke care is often delivered in a team-based approach and involves several disciplines, such as physical therapy, occupational therapy, speech and language therapy, depending on the nature and severity of the deficits. Robots are potential tools for stroke rehabilitation as they can enhance existing conventional therapy by delivering a precise and consistent therapy of highly repetitive movements. In addition, robot-assisted physiotherapy could facilitate the effectiveness of unsupervised rehabilitation and thus, may reduce the cost and duration of therapist-assisted rehabilitation. Research on robot-assisted physiotherapy for stroke in Malaysia is slowly coming into the limelight in the past two decades. This review explores the effectiveness of robot-assisted physiotherapy particularly in improving motor functions of stroke survivors in Malaysia.

Degree of muscle-and-tendon tonus effects on kinesthetic illusion in wrist joints toward advanced rehabilitation robotics

Due to increasing demand for rehabilitation and therapy for cerebrovascular diseases, patients require advanced development of medical rehabilitation robots. In our laboratory, we focus on the formation capability of the substitute neural path caused by brain plasticity using the kinesthetic illusion (KI), which is effective for therapies using robots. In KI, people perceive an illusionary limb movement without an actual movement when a vibration stimulus is applied to a limb’s tendons. In previous research, the optimal frequency that induces the maximum KI has a correlation factor of about 0.5 with the tendon’s natural frequency when a human subject is in a state of laxity. However, we do not know whether the above finding can be applied to actual rehabilitation because muscles and tendons are sometimes in tonus during rehabilitation, a state that varies the natural frequency. In this study, we investigate the correlation between the optimal and natural frequencies of tendon by systematically changing their tension to clarify the effects on the illusion induced by the muscle and the tendon when they are in tonus. We identified a negative correlation between the optimal and natural frequencies when they are in tonus, although a positive correlation appeared when they are in laxity. This result suggests that KI’s optimal frequency should be changed based on the degree of the tendon and muscle tonus. Therefore, our present findings provide a suitable vibration frequency that induces KI due to the degree of the tendon and muscle tonus during robot therapies.

Smart Skin: Vision-Based Soft Pressure Sensing System for In-Home Hand Rehabilitation

We introduce a novel in-home hand rehabilitation system for monitoring hand motions and assessing grip forces of stroke patients. The overall system is composed of a sensing device and a computer vision system. The sensing device is a lightweight cylindrical object for easy grip and manipulation, which is covered by a passive sensing layer called "Smart Skin." The Smart Skin is fabricated using soft silicone elastomer, which contains embedded microchannels partially filled with colored fluid. When the Smart Skin is compressed by grip forces, the colored fluid rises and fills in the top surface display area. Then, the computer vision system captures the image of the display area through a red-green-blue camera, detects the length change of the liquid through image processing, and eventually maps the liquid length to the calibrated force for estimating the gripping force. The passive sensing mechanism of the proposed Smart Skin device works in conjunction with a single camera setup, making the system simple and easy to use, while also requiring minimum maintenance effort. Our system, on one hand, aims to support home-based rehabilitation therapy with minimal or no supervision by recording the training process and the force data, which can be automatically conveyed to physical therapists. In contrast, the therapists can also remotely instruct the patients with their training prescriptions through online videos. This study first describes the design, fabrication, and calibration of the Smart Skin, and the algorithm for image processing, and then presents experimental results from the integrated system. The Smart Skin prototype shows a relatively linear relationship between the applied force and the length change of the liquid in the range of 0-35 N. The computer vision system shows the estimation error <4% and a relatively high stability in estimation under different hand motions.

Robot-assisted rehabilitation of hand function after stroke: Development of prediction models for reference to therapy

BACKGROUND

Recovery of hand function after stroke represents the hardest target for clinicians. Robot-assisted therapy has been proved to be effective for hand recovery. Nevertheless, studies aimed to refer patients to the best therapy are missing.

METHODS

With the aim to identify which clinical features are predictive for referring to robot-assisted hand therapy, 174 stroke patients were assessed with: Fugl-Meyer Assessment (FMA), Functional Independence Measure (FIM), Reaching Performance Scale (RPS), Box and Block Test (BBT), Modified Ashworth Scale (MAS), Nine Hole Pegboard Test (NHPT). Moreover, patients ability to control the robot with residual force and surface EMG (sEMG) independently, was checked. ROC curves were calculated to determine which of the measures were the predictors of the event.

RESULTS

sEMG control (AUC = 0.925) was significantly determined by FMA upper extremity (FMUE) (>24/66) and sensation (>23/24) sections, MAS at Flexor Carpi (<3/4) and total MAS (>4/20). Force control (AUC = 0.928) was correlated only with FMUE (>24/66).

CONCLUSIONS

FMUE and MAS were the best predictors of preserved ability to control the device by two different modalities. This finding opens the possibility to plan specific therapies aimed at maximizing the highest functional outcome achievable after stroke.

Connected Elbow Exoskeleton System for Rehabilitation Training Based on Virtual Reality and Context-Aware

Traditional physiotherapy rehabilitation systems are evolving into more advanced systems based on exoskeleton systems and Virtual Reality (VR) environments that enhance and improve rehabilitation techniques and physical exercise. In addition, due to current connected systems and paradigms such as the Internet of Things (IoT) or Ambient Intelligent (AmI) systems, it is possible to design and develop advanced, effective, and low-cost medical tools that patients may have in their homes. This article presents a low-cost exoskeleton for the elbow that is connected to a Context-Aware architecture and thanks to a VR system the patient can perform rehabilitation exercises in an interactive way. The integration of virtual reality technology in rehabilitation exercises provides an intensive, repetitive and task-oriented capacity to improve patient motivation and reduce work on medical professionals. One of the system highlights is the intelligent ability to generate new exercises, monitor the exercises performed by users in search of progress or possible problems and the dynamic modification of the exercises characteristics. The platform also allows the incorporation of commercial medical sensors capable of collecting valuable information for greater accuracy in the diagnosis and evolution of patients. A case study with real patients with promising results has been carried out.

Exoskeletons With Virtual Reality, Augmented Reality, and Gamification for Stroke Patients' Rehabilitation: Systematic Review

BACKGROUND

Robot-assisted therapy has become a promising technology in the field of rehabilitation for poststroke patients with motor disorders. Motivation during the rehabilitation process is a top priority for most stroke survivors. With current advancements in technology there has been the introduction of virtual reality (VR), augmented reality (AR), customizable games, or a combination thereof, that aid robotic therapy in retaining, or increasing the interests of, patients so they keep performing their exercises. However, there are gaps in the evidence regarding the transition from clinical rehabilitation to home-based therapy which calls for an updated synthesis of the literature that showcases this trend. The present review proposes a categorization of these studies according to technologies used, and details research in both upper limb and lower limb applications.

OBJECTIVE

The goal of this work was to review the practices and technologies implemented in the rehabilitation of poststroke patients. It aims to assess the effectiveness of exoskeleton robotics in conjunction with any of the three technologies (VR, AR, or gamification) in improving activity and participation in poststroke survivors.

METHODS

A systematic search of the literature on exoskeleton robotics applied with any of the three technologies of interest (VR, AR, or gamification) was performed in the following databases: MEDLINE, EMBASE, Science Direct & The Cochrane Library. Exoskeleton-based studies that did not include any VR, AR or gamification elements were excluded, but publications from the years 2010 to 2017 were included. Results in the form of improvements in the patients' condition were also recorded and taken into consideration in determining the effectiveness of any of the therapies on the patients.

RESULTS

Thirty studies were identified based on the inclusion criteria, and this included randomized controlled trials as well as exploratory research pieces. There were a total of about 385 participants across the various studies. The use of technologies such as VR-, AR-, or gamification-based exoskeletons could fill the transition from the clinic to a home-based setting. Our analysis showed that there were general improvements in the motor function of patients using the novel interfacing techniques with exoskeletons. This categorization of studies helps with understanding the scope of rehabilitation therapies that can be successfully arranged for home-based rehabilitation.

CONCLUSIONS

Future studies are necessary to explore various types of customizable games required to retain or increase the motivation of patients going through the individual therapies.

Development, Dynamic Modeling, and Multi-Modal Control of a Therapeutic Exoskeleton for Upper Limb Rehabilitation Training

Robot-assisted training is a promising technology in clinical rehabilitation providing effective treatment to the patients with motor disability. In this paper, a multi-modal control strategy for a therapeutic upper limb exoskeleton is proposed to assist the disabled persons perform patient-passive training and patient-cooperative training. A comprehensive overview of the exoskeleton with seven actuated degrees of freedom is introduced. The dynamic modeling and parameters identification strategies of the human-robot interaction system are analyzed. Moreover, an adaptive sliding mode controller with disturbance observer (ASMCDO) is developed to ensure the position control accuracy in patient-passive training. A cascade-proportional-integral-derivative (CPID)-based impedance controller with graphical game-like interface is designed to improve interaction compliance and motivate the active participation of patients in patient-cooperative training. Three typical experiments are conducted to verify the feasibility of the proposed control strategy, including the trajectory tracking experiments, the trajectory tracking experiments with impedance adjustment, and the intention-based training experiments. The experimental results suggest that the tracking error of ASMCDO controller is smaller than that of terminal sliding mode controller. By optimally changing the impedance parameters of CPID-based impedance controller, the training intensity can be adjusted to meet the requirement of different patients.