Flexible Virtual Reality System for Neurorehabilitation and Quality of Life Improvement

As life expectancy is mostly increasing, the incidence of many neurological disorders is also constantly growing. For improving the physical functions affected by a neurological disorder, rehabilitation procedures are mandatory, and they must be performed regularly. Unfortunately, neurorehabilitation procedures have disadvantages in terms of costs, accessibility and a lack of therapists. This paper presents Immersive Neurorehabilitation Exercises Using Virtual Reality (INREX-VR), our innovative immersive neurorehabilitation system using virtual reality. The system is based on a thorough research methodology and is able to capture real-time user movements and evaluate joint mobility for both upper and lower limbs, record training sessions and save electromyography data. The use of the first-person perspective increases immersion, and the joint range of motion is calculated with the help of both the HTC Vive system and inverse kinematics principles applied on skeleton rigs. Tutorial exercises are demonstrated by a virtual therapist, as they were recorded with real-life physicians, and sessions can be monitored and configured through tele-medicine. Complex movements are practiced in gamified settings, encouraging self-improvement and competition. Finally, we proposed a training plan and preliminary tests which show promising results in terms of accuracy and user feedback. As future developments, we plan to improve the system's accuracy and investigate a wireless alternative based on neural networks.Comment: 47 pages, 20 figures, 17 tables (including annexes), part of the MDPI Sesnsors "Special Issue Smart Sensors and Measurements Methods for Quality of Life and Ambient Assisted Living

Assessment of a hand exoskeleton on proximal and distal training in virtual environments for robot mediated upper extremity rehabilitation

Stroke is the leading cause of disability in the United States with approximately 800,000 cases per year. This cerebral vascular accident results in neurological impairments that reduce limb function and limit the daily independence of the individual. Evidence suggests that therapeutic interventions with repetitive motor training can aid in functional recovery of the paretic limb. Robotic rehabilitation may present an exercise intervention that can improve training and induce motor plasticity in individuals with stroke. An active (motorized) hand exoskeleton that provides support for wrist flexion/extension, abduction/adduction, pronation/supination, and finger pinch is integrated with a pre-existing 3-Degree of Freedom (DOF) haptic robot (Haptic Master, FCS Moog) to determine the efficacy of increased DOF during proximal and distal training in Upper Extremity (UE) rehabilitation. Subjects are randomly assigned into four groups to evaluate the significance of increased DOF during virtual training: Haptic Master control group (HM), Haptic Master with Gripper (HMG), Haptic Master with Wrist (HMW), and Haptic Master with Gripper and Wrist (HMWG). Each subject group performs a Pick and Place Task in a virtual environment where the distal hand exoskeleton is mapped to the virtual representation of the hand. Subjects are instructed to transport as many virtual cubes as possible to a specified target in the allotted time period of 120s. Three cube sizes are assessed to determine efficacy of the assistive end-effector. An additional virtual task, Mailbox Task, is performed to determine the effect of training and the ability to transfer skills between virtual settings in an unfamiliar environment. The effects of viewing mediums are also investigated to determine the effect of immersion on performance using an Oculus Rift as an HMD compared to conventional projection displays. It is hypothesized that individuals with both proximal and complete distal hand control (HMWG) will see increased benefit during the Pick and Place Task than individuals without the complete distal attachment, as assisted daily living tasks are often accomplished with coordinated arm and hand movement. The purpose of this study is to investigate the additive effect of increased degrees of freedom at the hand through task-specific training of the upper arm in a virtual environment, validate the ability to transfer skills obtained in a virtual environment to an untrained task, and determine the effects of viewing mediums on performance. A feasibility study is conducted in individuals with stroke to determine if the modular gripper can assist pinch movements. These investigations represent a comprehensive investigation to assess the potential benefits of assistive devices in a virtual reality setting to retrain lost function and increase efficacy in motor control in populations with motor impairments

Low-Cost Sensors and Biological Signals

Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization

A virtual hand assessment system for efficient outcome measures of hand rehabilitation

Previously held under moratorium from 1st December 2016 until 1st December 2021.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control

Design and Validation of an MR Conditional Upper Extremity Evaluation System to Study Brain Activation Patterns after Stroke

Stroke is the third leading cause of death and second most frequent cause of disability in the United States. Stroke rehabilitation methods have been developed to induce the cortical reorganization and motor-relearning that leads to stroke recovery. In this thesis, we designed and developed an MR conditional upper extremity reach and grasp movement evaluation system for the stroke survivors to study their kinematic performances in reach and grasp movement and the relationship between kinematic metrics and the recovery level measured by clinical assessment methods. We also applied the system into the functional MRI experiments to identify the ability to study motor performance with the system inside the scanner and the reach, grasp and reach-to-grasp movements related brain activation patterns. Our experiments demonstrates that ours system is an MR conditional system in the 3.0 Tesla magnetic field. It is able to measure the stroke survivors\u27 reach and grasp movement in terms of grasp aperture and elbow joint angles. We used the Mann Whitney U test to examine the significant metrics in each tasks and principle component analysis to decide the major metrics that are associated with the outcome. Then we discovered better recovery scores are associated with these major kinematic metrics such as larger maximal velocity, larger mean velocity, larger maximal movement angle, and longer time to peak velocity. Additional to these metrics, time to maximal angle, time to target and time to peak velocity could also be used as additional metrics to help predict the recovery and assess robot-assisted therapy and optimize task-oriented rehabilitation strategy. We also identified the movement related brain activations in the motor and sensory areas as well as cerebellum in both normal and stroke survivors

Optimal exoskeleton design and effective human-in-the-loop control frameworks for rehabilitation robotics

Attention, since they decrease the cost of repetitive movement therapies, enable quantitative measurement of the patient progress and promise development of more e ective rehabilitation protocols. The goal of this dissertation is to provide systematic frameworks for optimal design of rehabilitation robots and e ective delivery of therapeutic exercises. The design framework is built upon identification and categorization of the design requirements, and satisfaction of them through several design stages. In particular, type selection is performed to ensure imperative design requirements of safety, ergonomy and wearability, optimal dimensional synthesis is undertaken to maximize global kinematic and dynamic performance defined over the singularity-free workspace volume, while workspace optimization is performed to utilize maximum singularity-free device workspace computed via Grassmann line theory. Then, humanin- the-loop controllers that ensure coupled stability of the human-robot system are implemented in the robot task space using appropriate error metrics. The design framework is demonstrated on a forearm-wrist exoskeleton, since forearm and wrist rotations are critical in performing activities of daily living and recovery of these joints is essential for achieving functional independence of patients. In particular, a non-symmetric 3RPS-R mechanism is selected as the underlying kinematics type and the performance improvements due to workspace and multi-criteria optimizations are experimentally characterized as 27 % larger workspace volume, 32 % higher position control bandwidth and 17 % increase in kinematic isotropy when compared to a similar device in the literature. The exoskeleton is also shown to feature high passive back-driveability and accurate sti ness rendering capability, even under open-loop impedance control. Local controllers to accommodate for each stage of rehabilitation therapies are designed for the forearm-wrist exoskeleton in SO(3): trajectory tracking controllers are designed for early stages of rehabilitation when severely injured patients are kept passive, impedance controllers are designed to render virtual tunnels implementing forbidden regions in the device workspace and allowing for haptic interactions with virtual environments, and passive contour tracking controllers are implemented to allow for rehabilitation exercises that emphasize coordination and synchronization of multi degrees-of-freedom movements, while leaving the exact timing along the desired contour to the patient. These local controllers are incorporated into a multi-lateral shared controller architecture, which allows for patients to train with online virtual dynamic tasks in collaboration with a therapist. Utilizing this control architecture not only enables the shift of control authority of each agent so that therapists can guide or evaluate movements of patients or share the control with them, but also enables the implementation of remote and group therapies, as well as remote assessments. The proposed control framework to deliver e ective robotic therapies can ensure active involvement of patients through online modification of the task parameters, while simultaneously guaranteeing their safety. In particular, utilizing passive velocity field control and extending it with a method for online generation of velocity fields for parametric curves, temporal, spatial and assistive aspects of a desired task can be seamlessly modified online, while ensuring passivity with respect to externally applied forces. Through human subject experiments, this control framework is shown to be e ective in delivering evidence-based rehabilitation therapies, providing assistance as-needed, preventing slacking behavior of patients, and delivering repetitive therapies without exact repetition. Lastly, to guide design of e ective rehabilitation treatment protocols, a set of healthy human subject experiments are conducted in order to identify underlying principles of adaptation mechanism of human motor control system. In these catch-trial based experiments, equivalent transfer functions are utilized during execution of rhythmic dynamic tasks. Statistical evidence suggests that i) force feedback is the dominant factor that guides human adaptation while performing fast rhythmic dynamic tasks rather than the visual feedback and ii) as the e ort required to perform the task increases, the rate of adaptation decreases; indicating a fundamental trade-o between task performance and level of force feedback provided

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

Wearable and IoT technologies application for physical rehabilitation

This research consists in the development an IoT Physical Rehabilitation solution based on wearable devices, combining a set of smart gloves and smart headband for use in natural interactions with a set of VR therapeutic serious games developed on the Unity 3D gaming platform. The system permits to perform training sessions for hands and fingers motor rehabilitation. Data acquisition is performed by Arduino Nano Microcontroller computation platform with ADC connected to the analog measurement channels materialized by piezo-resistive force sensors and connected to an IMU module via I2C. Data communication is performed using the Bluetooth wireless communication protocol. The smart headband, designed to be used as a first- person-controller in game scenes, will be responsible for collecting the patient's head rotation value, this parameter will be used as the player's avatar head rotation value, approaching the user and the virtual environment in a semi-immersive way. The acquired data are stored and processed on a remote server, which will help the physiotherapist to evaluate the patients' performance around the different physical activities during a rehabilitation session, using a Mobile Application developed for the configuration of games and visualization of results. The use of serious games allows a patient with motor impairments to perform exercises in a highly interactive and non-intrusive way, based on different scenarios of Virtual Reality, contributing to increase the motivation during the rehabilitation process. The system allows to perform an unlimited number of training sessions, making possible to visualize historical values and compare the results of the different performed sessions, for objective evolution of rehabilitation outcome. Some metrics associated with upper limb exercises were also considered to characterize the patient’s movement during the session.Este trabalho de pesquisa consiste no desenvolvimento de uma solução de Reabilitação Física IoT baseada em dispositivos de vestuário, combinando um conjunto de luvas inteligentes e uma fita-de-cabeça inteligente para utilização em interações naturais com um conjunto de jogos terapêuticos sérios de Realidade Virtual desenvolvidos na plataforma de jogos Unity 3D. O sistema permite realizar sessões de treino para reabilitação motora de mãos e dedos. A aquisição de dados é realizada pela plataforma de computação Arduino utilizando um Microcontrolador Nano com ADC (Conversor Analógico-Digital) conectado aos canais de medição analógicos materializados por sensores de força piezo-resistivos e a um módulo IMU por I2C. A comunicação de dados é realizada usando o protocolo de comunicação sem fio Bluetooth. A fita-de-cabeça inteligente, projetada para ser usada como controlador de primeira pessoa nos cenários de jogo, será responsável por coletar o valor de rotação da cabeça do paciente, esse parâmetro será usado como valor de rotação da cabeça do avatar do jogador, aproximando o utilizador e o ambiente virtual de forma semi-imersiva. Os dados adquiridos são armazenados e processados num servidor remoto, o que ajudará o fisioterapeuta a avaliar o desempenho dos pacientes em diferentes atividades físicas durante uma sessão de reabilitação, utilizando uma Aplicação Móvel desenvolvido para configuração de jogos e visualização de resultados. A utilização de jogos sérios permite que um paciente com deficiências motoras realize exercícios de forma altamente interativa e não intrusiva, com base em diferentes cenários de Realidade Virtual, contribuindo para aumentar a motivação durante o processo de reabilitação. O sistema permite realizar um número ilimitado de sessões de treinamento, possibilitando visualizar valores históricos e comparar os resultados das diferentes sessões realizadas, para a evolução objetiva do resultado da reabilitação. Algumas métricas associadas aos exercícios dos membros superiores também foram consideradas para caracterizar o movimento do paciente durante a sessão

Multimodal series elastic actuator for human-machine interaction with applications in robot-aided rehabilitation

Series elastic actuators (SEAs) are becoming an elemental building block in collaborative robotic systems. They introduce an elastic element between the mechanical drive and the end-effector, making otherwise rigid structures compliant when in contact with humans. Topologically, SEAs are more amenable to accurate force control than classical actuation techniques, as the elastic element may be used to provide a direct force estimate. The compliant nature of SEAs provides the potential to be applied in robot-aided rehabilitation. This thesis proposes the design of a novel SEA to be used in robot-aided musculoskeletal rehabilitation. An active disturbance rejection controller is derived and experimentally validated and multiobjective optimization is executed to tune the controller for best performance in human-machine interaction. This thesis also evaluates the constrained workspaces for individuals experiencing upper-limb musculoskeletal disorders. This evaluation can be used as a tool to determine the kinematic structure of devices centred around the novel SEA

Sensor based systems for quantification of sensorimotor function and rehabilitation of the upper limb

The thesis presents targeted sensor-based devices and methods for the training and assessment of upper extremity. These systems are all passive (non-actuated) thus intrinsically safe for (semi) independent use. An isometric assessment system is first presented, which uses a handle fixed on a force/torque sensor to investigate the force signal parameters and their relation to functional disability scales. The results from multiple sclerosis and healthy populations establish relation of isometric control and strength measures, its dependence on direction and how they are related to functional scales. The dissertation then introduces the novel platform MIMATE, Multimodal Interactive Motor Assessment and Training Environment, which is a wireless embedded platform for designing systems for training and assessing sensorimotor behaviour. MIMATE’s potential for designing clinically useful neurorehabilitation systems was demonstrated in a rehabilitation technology course. Based on MIMATE, intelligent objects (IObjects) are presented, which can measure position and force during training and assessing of manipulation tasks relevant to activities of daily living. A preliminary study with an IObject exhibits potential metrics and techniques that can be used to assess motor performance during fine manipulation tasks. The IObjects are part of the SITAR system, which is a novel sensor-based platform based on a force sensitive touchscreen and IObjects. It is used for training and assessment of sensorimotor deficits by focusing on meaningful functional tasks. Pilot assessment study with SITAR indicated a significant difference in performance of stroke and healthy populations during different sensorimotor tasks. Finally the thesis presents LOBSTER, a low cost, portable, bimanual self-trainer for exercising hand opening/closing, wrist flexion/extension or pronation/supination. The major novelty of the system relies on exploiting the movement of the unaffected limb to train the affected limb, making it safe for independent use. Study with LOBSTER will determine its usability for home based use.Open Acces