Review of the Augmented Reality Systems for Shoulder Rehabilitation

Literature shows an increasing interest for the development of augmented reality (AR) applications in several fields, including rehabilitation. Current studies show the need for new rehabilitation tools for upper extremity, since traditional interventions are less effective than in other body regions. This review aims at: Studying to what extent AR applications are used in shoulder rehabilitation, examining wearable/non-wearable technologies employed, and investigating the evidence supporting AR effectiveness. Nine AR systems were identified and analyzed in terms of: Tracking methods, visualization technologies, integrated feedback, rehabilitation setting, and clinical evaluation. Our findings show that all these systems utilize vision-based registration, mainly with wearable marker-based tracking, and spatial displays. No system uses head-mounted displays, and only one system (11%) integrates a wearable interface (for tactile feedback). Three systems (33%) provide only visual feedback; 66% present visual-audio feedback, and only 33% of these provide visual-audio feedback, 22% visual-audio with biofeedback, and 11% visual-audio with haptic feedback. Moreover, several systems (44%) are designed primarily for home settings. Three systems (33%) have been successfully evaluated in clinical trials with more than 10 patients, showing advantages over traditional rehabilitation methods. Further clinical studies are needed to generalize the obtained findings, supporting the effectiveness of the AR applications

Post-stroke rehabilitation of hand function based on Electromyography biofeedback

L'abstract è presente nell'allegato / the abstract is in the attachmen

Translation of evidence-based Assistive Technologies into stroke rehabilitation: Users' perceptions of the barriers and opportunities

Background: Assistive Technologies (ATs), defined as "electrical or mechanical devices designed to help people recover movement", demonstrate clinical benefits in upper limb stroke rehabilitation; however translation into clinical practice is poor. Uptake is dependent on a complex relationship between all stakeholders. Our aim was to understand patients', carers' (P&Cs) and healthcare professionals' (HCPs) experience and views of upper limb rehabilitation and ATs, to identify barriers and opportunities critical to the effective translation of ATs into clinical practice. This work was conducted in the UK, which has a state funded healthcare system, but the findings have relevance to all healthcare systems. Methods. Two structurally comparable questionnaires, one for P&Cs and one for HCPs, were designed, piloted and completed anonymously. Wide distribution of the questionnaires provided data from HCPs with experience of stroke rehabilitation and P&Cs who had experience of stroke. Questionnaires were designed based on themes identified from four focus groups held with HCPs and P&Cs and piloted with a sample of HCPs (N = 24) and P&Cs (N = 8). Eight of whom (four HCPs and four P&Cs) had been involved in the development. Results: 292 HCPs and 123 P&Cs questionnaires were analysed. 120 (41%) of HCP and 79 (64%) of P&C respondents had never used ATs. Most views were common to both groups, citing lack of information and access to ATs as the main reasons for not using them. Both HCPs (N = 53 [34%]) and P&C (N = 21 [47%]) cited Functional Electrical Stimulation (FES) as the most frequently used AT. Research evidence was rated by HCPs as the most important factor in the design of an ideal technology, yet ATs they used or prescribed were not supported by research evidence. P&Cs rated ease of set-up and comfort more highly. Conclusion: Key barriers to translation of ATs into clinical practice are lack of knowledge, education, awareness and access. Perceptions about arm rehabilitation post-stroke are similar between HCPs and P&Cs. Based on our findings, improvements in AT design, pragmatic clinical evaluation, better knowledge and awareness and improvement in provision of services will contribute to better and cost-effective upper limb stroke rehabilitation. © 2014 Hughes et al.; licensee BioMed Central Ltd

Fatigue-Aware gaming system for motor rehabilitation using biocybernetic loops.

Esta tesis tiene como objetivo proponer una terapia de rehabilitación complementaria basada en paradigmas de interacción humano-computadora (HCI) que exploran i) Técnicas de rehabilitación virtual, integrando tecnologías de realidad virtual (VR) sofisticadas y (hoy en día) accesibles, ii) sensores fisiológicos de bajo costo, a saber, electromiografía de superficie (sEMG) y iii)sistema inteligente, a través de adaptación biocibernética, para proporcionar una nueva técnica de rehabilitación virtual..

A comparative analysis of haptic and EEG devices for evaluation and training of post-stroke patients within a virtual environment

Virtual Rehabilitation benefits from the usage of interfaces other than the mouse and keyboard, but also possess disadvantages: haptic peripherals can utilize the subject\u27s hand to provide position information or joint angles, and allow direct training for specific movements; but can also place unneeded strain on the limbs; brain-machine interfaces (BMI) can provide direct connections from the user to external hardware or software, but are currently inaccurate for the full diversity of user movements in daily life and require invasive surgery to implement. A compromise between these two extremes is a BMI that can be adapted to specific users, can function with a wide range of hardware and software, and is both noninvasive and convenient to wear for extended periods of time. A suitable BMI using Electroencephalography (EEG) input, known as the Emotiv EPOC™ by Emotiv Systems was evaluated using multiple input specializations and tested with an external robotic arm to determine if it was suitable for control of peripherals. Users were given a preset periodicity to follow in order to evaluate their ability to translate specific facial movements into commands as well as their responsiveness to change the robot arm\u27s direction. Within 2 weeks of training, they maintained or improved axial control of the robot arm, and reduced their overall performance time. Although the EPOC™ does require further testing and development, its adaptability to multiple software programs, users and peripherals allows it to serve both Virtual Rehabilitation and device control in the immediate future

Recent developments in biofeedback for neuromotor rehabilitation

The original use of biofeedback to train single muscle activity in static positions or movement unrelated to function did not correlate well to motor function improvements in patients with central nervous system injuries. The concept of task-oriented repetitive training suggests that biofeedback therapy should be delivered during functionally related dynamic movement to optimize motor function improvement. Current, advanced technologies facilitate the design of novel biofeedback systems that possess diverse parameters, advanced cue display, and sophisticated control systems for use in task-oriented biofeedback. In light of these advancements, this article: (1) reviews early biofeedback studies and their conclusions; (2) presents recent developments in biofeedback technologies and their applications to task-oriented biofeedback interventions; and (3) discusses considerations regarding the therapeutic system design and the clinical application of task-oriented biofeedback therapy. This review should provide a framework to further broaden the application of task-oriented biofeedback therapy in neuromotor rehabilitation

Facial Electromyography-based Adaptive Virtual Reality Gaming for Cognitive Training

Cognitive training has shown promising results for delivering improvements in human cognition related to attention, problem solving, reading comprehension and information retrieval. However, two frequently cited problems in cognitive training literature are a lack of user engagement with the training programme, and a failure of developed skills to generalise to daily life. This paper introduces a new cognitive training (CT) paradigm designed to address these two limitations by combining the benefits of gamification, virtual reality (VR), and affective adaptation in the development of an engaging, ecologically valid, CT task. Additionally, it incorporates facial electromyography (EMG) as a means of determining user affect while engaged in the CT task. This information is then utilised to dynamically adjust the game's difficulty in real-time as users play, with the aim of leading them into a state of flow. Affect recognition rates of 64.1% and 76.2%, for valence and arousal respectively, were achieved by classifying a DWT-Haar approximation of the input signal using kNN. The affect-aware VR cognitive training intervention was then evaluated with a control group of older adults. The results obtained substantiate the notion that adaptation techniques can lead to greater feelings of competence and a more appropriate challenge of the user's skills

A non-invasive human-machine interfacing framework for investigating dexterous control of hand muscles

The recent fast development of virtual reality and robotic assistive devices enables to augment the capabilities of able-body individuals as well as to overcome the motor missing functions of neurologically impaired or amputee individuals. To control these devices, movement intentions can be captured from biological structures involved in the process of motor planning and execution, such as the central nervous system (CNS), the peripheral nervous system (in particular the spinal motor neurons) and the musculoskeletal system. Thus, human-machine interfaces (HMI) enable to transfer neural information from the neuro-muscular system to machines. To prevent any risks due to surgical operations or tissue damage in implementing these HMIs, a non-invasive approach is proposed in this thesis. In the last five decades, surface electromyography (sEMG) has been extensively explored as a non-invasive source of neural information. EMG signals are constituted by the mixed electrical activity of several recruited motor units, the fundamental components of muscle contraction. High-density sEMG (HD-sEMG) with the use of blind source separation methods enabled to identify the discharge patterns of many of these active motor units. From these decomposed discharge patterns, the net common synaptic input (CSI) to the corresponding spinal motor neurons was quantified with cross-correlation in the time and frequency domain or with principal component analysis (PCA) on one or few muscles. It has been hypothesised that this CSI would result from the contribution of spinal descending commands sent by supra-spinal structures and afferences integrated by spinal interneurons. Another motor strategy implying the integration of descending commands at the spinal level is the one regarding the coordination of many muscles to control a large number of articular joints. This neurophysiological mechanism was investigated by measuring a single EMG amplitude per muscle, thus without the use of HD-sEMG and decomposition. In this case, the aim was to understand how the central nervous system (CNS) could control a large set of muscles actuating a vast set of combinations of degrees of freedom in a modular way. Thus, time-invariant patterns of muscle coordination, i.e. muscle synergies , were found in animals and humans from EMG amplitude of many muscles, modulated by time-varying commands to be combined to fulfil complex movements. In this thesis, for the first time, we present a non-invasive framework for human-machine interfaces based on both spinal motor neuron recruitment strategy and muscle synergistic control for unifying the understanding of these two motor control strategies and producing control signals correlated to biomechanical quantities. This implies recording both from many muscles and using HD-sEMG for each muscle. We investigated 14 muscles of the hand, 6 extrinsic and 8 intrinsic. The first two studies, (in Chapters 2 and 3, respectively) present the framework for CSI quantification by PCA and the extraction of the synergistic organisation of spinal motor neurons innervating the 14 investigated muscles. For the latter analysis, in Chapter 3, we proposed the existence of what we named as motor neuron synergies extracted with non-negative matrix factorisation (NMF) from the identified motor neurons. In these first two studies, we considered 7 subjects and 7 grip types involving differently all the four fingers in opposition with the thumb. In the first study, we found that the variance explained by the CSI among all motor neuron spike trains was (53.0 ± 10.9) % and its cross-correlation with force was 0.67 ± 0.10, remarkably high with respect to previous findings. In the second study, 4 motor neuron synergies were identified and associated with the actuation of one finger in opposition with the thumb, finding even higher correlation values with force (over 0.8) for each synergy associated with a finger during the actuation of the relative finger. In Chapter 4, we then extended the set of analysed movements in a vast repertoire of gestures and repeated the analysis of Chapter 3 by finding a different synergistic organisation during the execution of tens of tasks. We divided the contribution among extrinsic and intrinsic muscles and we found that intrinsic better enable single-finger spatial discrimination, while no difference was found in regression of joint angles by dividing the two groups of muscles. Finally, in Chapter 5 we proposed the techniques of the previous chapters for cases of impairment due both to amputation and stroke. We analysed one case of pre and post rehabilitation sessions of a trans-humeral amputee, the case of a post-stroke trans-radial amputee and three cases of acute stroke, i.e. less than one month from the stroke event. We present future perspectives (Chapter 6) aimed to design and implement a platform for both rehabilitation monitoring and myoelectric control. Thus, this thesis provides a bridge between two extensively studied motor control mechanisms, i.e. motor neuron recruitment and muscle synergies, and proposes this framework as suitable for rehabilitation monitoring and control of assistive devices.Open Acces

Remote Biofeedback Method for Biomedical Data Analysis

In recent years, the introduction of methods supported by technology has positively modified the traditional paradigm of rehabilitation. Interactive systems have been developed to facilitate patient involvement and to help therapist in patient\u2019s management. ReMoVES (REmote MOnitoring Validation Engineering System) platform addresses the problem of continuity of care in a smart and economical way. It can help patients with neurological, post-stroke and orthopedic impairments in recovering physical, psychological and social functions; such system will not only improve the quality of life and accelerate the recovery process for patients, but also aims at rationalizing and help the manpower required monitoring and coaching individual patients at rehabilitation centers. In order to help and support therapist work, the Remote Biofeedback Method is proposed as an instrument to understand how the patient has executed the rehabilitation exercises without seeing him directly. Therefore, the purpose of this method is to demonstrate that through the joint observation of data from simple sensors, it is possible to determine: time and method of execution of the exercises, performance and improvements during the rehabilitation session, pertinence of exercise and plan of care. The system, during the rehabilitation session, automatically transmits patient\u2019s biofeedback through three different channels: movement, physiological signals and a questionnaire. The therapist uses patient\u2019s data to determine whether the plan of care assigned is appropriate for the recovery of lost functionalities. He will then return a remote feedback to the patient who will not see any kind of graphical or verbal output, but you will see lighter rehabilitative session if it was too difficult or more intense if one assigned was too simple. The rehabilitation protocol proposed consists of the performance of different exercises, which begins with a breathing activity, designed to relax the patient before the \u201ceffective\u201d rehabilitation session. To make the subject comfortable, and to bring again the heartbeat to a basal value, before the rehabilitation session, the patient, in a sitting position, is leading to breathing with a regular rhythm by following a \u201cbreath ball\u201d. From the results obtained in the breathing exercise, it can be concluded that the negative trend of the regression line that approximates the heartbeat signal is an index of relaxation, principal goal for which the exercise was designed. The proposed activities include execution of reaching and grasping, balance and control posture functional exercises, masked through serious games to simulate some of the most common gestures of daily life. In some exercises, a cognitive component will also be involved in achieving the goal required by the activity. For each activity, heart rate, gameplay scores, and different motion parameters were captured and analyzed depending on the type of exercise performed. The heart rate was used as an indicator of motivation and involvement during the execution of several rehabilitative exercises. Others parameters analyzed are the score obtained during the execution of the task, and the time interval between the execution of one exercise and the following one. In addition to the analysis of the individual signals, a preliminary analysis of the correlation between the trend of the heart rate and the performance of the score was also carried out. The results showed that heartbeat in conjunction with score and inter-exercise time could be a high-quality indicator of a patient\u2019s status. The indicators extracted, in fact, in most cases, correspond to the information reported from the therapist who observed the patients during the rehabilitation session. A deep analysis of movement signal was carried on, with the extraction of several indicators for the different body segments involved in rehabilitation, such as the upper limb, the hand, the lower limbs and the posture, included the detection of compensation strategies to reach the targets proposed by the exercise. The results have been extracted by comparing the patient performance to a model extracted by a healthy subjects group. Of particular importance is the spatial map for patients with neglect, an innovative tool that traces the positions where the movement was performed and also provides the therapist with the spatial coordinates where the targets were proposed. Another innovative aspect is the analysis of Center of Pressure (CoP) without the use of a specific footboard, but only through the processing of data from the motion sensor. The results obtained by the application of the Remote Biofeedback Methods to the signals acquired during ReMoVES testing phase show interesting applications of the method to the clinical practice. In fact, the indicators extracted show a realistic correspondence between the disabilities affected the patients and the performance obtained during the execution of the exercises. From the study of the different exercises it can be concluded that the analysis of the signals and the parameters extracted individually, do not provide enough information to outline how the rehabilitation exercise has been executed. By combining the different indicators, it is possible to outline an accurate picture that allows the therapist to make decisions about the assigned plan of care. In conclusion, the Remote Biofeedback Method proposed is now ready to be tested on a wider dataset in order to be consolidated on a larger number of athologies and to associate, if necessary, particular indicators to a particular disease. The future steps will be, a creation of a model starting from patients signals, in order to have a better comparison term, and a testing phase on a larger number of patients, following a clinical protocol, subdividing subject by disease

Multimodal adaptive interfaces for 3D robot-mediated upper limb neuro-rehabilitation: An overview of bio-cooperative systems

n/