Validity of the Kinect and Myo armband in a serious game for assessing upper limb movement

A cost-effective, easily-accessible neuro-motor rehabilitation solution is proposed that can determine the range of motion and the kinematic ability of participants. A serious game comprising four-scenarios are developed in which the players control an avatar that mirrors the rotations of the upper-limb joints through multi-channel-input devices (Kinect, Myo, FootPedal). Administered functional reach tests (FRT) challenge the player to interact with a 3D-environment while standing or sitting and using the FootPedal which simulates the action of walking whilst body movement is measured concurrently. The FRT’s complexity level is adapted using a Monte Carlo Tree Search algorithm which determines a virtual object’s position based on the proved ability of the user. Twenty-three volunteers were recruited to play the game in 45-minute sessions. The data show that the system has a more positive impact on players performance and is more motivating than formal therapy. The visual representation of the trajectory of the objects is shown to increase the perception of the participants voluntary/involuntary upper extremity movement, and the results show a comparable inter-session reliability (acceptable-good) over two repeated sessions. A high Pearson correlation demonstrates the validity of using Kinect and Myo devices in assessing upper-limb rehabilitation, and the timing and the clinically relevant movement data have a higher accuracy when the devices are paired

ReHabgame: A non-immersive virtual reality rehabilitation system with applications in neuroscience

This paper proposes the use of a non-immersive virtual reality rehabilitation system ”ReHabgame” developed using Microsoft KinectT M and the ThalmicT M Labs Myo gesture control armband. The ReHabgame was developed based on two third-person video games that provide a feasible possibility of assessing postural control and functional reach tests. It accurately quantifies specific postural control mechanisms including timed standing balance, functional reach tests using real-time anatomical landmark orientation, joint velocity, and acceleration while end trajectories were calculated using an inverse kinematics algorithm. The game was designed to help patients with neurological impairment to be subjected to physiotherapy activity and practice postures of daily activities. The subjective experience of the ReHabgame was studied through the development of an Engagement Questionnaire (EQ) for qualitative, quantitative and Rasch model. The Monte-Carlo Tree Search (MCTS) and Random object (ROG) generator algorithms were used to adapt the physical and gameplay intensity in the ReHabgame based on the Motor Assessment Scale (MAS) and Hierarchical Scoring System (HSS). Rasch analysis was conducted to assess the psychometric characteristics of the ReHabgame and to identify if these are any misfitting items in the game. Rasch rating scale model (RSM) was used to assess the engagement of players in the ReHabgame and evaluate the effectiveness and attractiveness of the game. The results showed that the scales assessing the rehabilitation process met Rasch expectations of reliability, and unidimensionality. Infit and outfit mean squares values are in the range of (0.68 − 1.52) for all considered 16 items. The Root Mean Square Residual (RMSR) and the person separation reliability were acceptable. The item/person map showed that the persons and items were clustered symmetrically

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

Machine Learning role in clinical decision-making: Neuro-rehabilitation video game

In this study, we investigated the potential use of Machine Learning algorithms (ML) to predict the outcome of home-based neuro-rehabilitation video game intervention and its advantage in supporting clinical decision-making. We adopted Support Vector Machines (SVM) and K-Nearest Neighbours (KNN) to develop multidimensional functions (multi-variable Kernel functions) since both algorithms were considered significant and active analysis agents for prediction and classification. Supervised SVM and KNN algorithms were trained using the upper extremity (arm, forearm, and hand) joints’ kinematic data and hand gestures of participants while interacting with the developed video games. Data collected from healthy and Multiple sclerosis (MS) participants were compared and used to develop the predictive algorithm. Pre- and post-rehabilitation data of MS subjects were investigated and used to assess the subject’s functional improvements following the program. Bayesian optimization, Sigmoid, polynomial, and Gaussian Radial Basis functions were utilized for training and predicting outcomes. The results showed that the first two kernel regressions had the best performance regarding predictability and cross-validation loss. KNN’s prediction accuracy was exceeded by 91.7% versus SVM, which was 88.0%. The effectiveness of the rehabilitation program was assessed through Spatiotemporal control and motor assessment scale presenting 40% improvement. Our findings suggest that ML has a great potential to be used for decision-making in neuro-rehabilitation programs

Kvantitativna analiza pokreta u rehabilitaciji neuroloških poremećaja korišćenjem vizuelnih i nosivih senzora.

Neuroloska oboljenja, kao sto su Parkinsonova bolest i slog, dovode do ozbiljnih motornih poremecaja, smanjuju kvalitet zivota pacijenata i mogu da uzrokuju smrt. Rana dijagnoza i adekvatno lecenje su krucijalni faktori za drzanje bolesti pod kontrolom, kako bi se omogucio normalan svakodnevni zivot pacijenata. Lecenje neurolo skih bolesti obicno ukljucuje rehabilitacionu terapiju i terapiju lekovima, koje se prilagodavaju u skladu sa stanjem pacijenta tokom vremena. Tradicionalne tehnike evaluacije u dijagnozi i monitoringu neuroloskih bolesti oslanjaju se na klinicke evaluacione alate, tacnije specijalno dizajnirane klinicke testove i skale. Medutim, iako su korisne i najcesce koriscene, klinicke skale su sklone subjektivnim ocenama i nepreciznoj interpretaciji performanse pacijenta...Neurological disorders, such as Parkinson's disease (PD) and stroke, lead to serious motor disabilities, decrease the patients' quality of life and can cause the mortality. Early diagnosis and adequate disease treatment are thus crucial factors towards keeping the disease under control in order to enable the normal every-day life of patients. The treatment of neurological disorders usually includes the rehabilitation therapy and drug treatment, that are adapted based on the evaluation of the patient state over time. Conventional evaluation techniques for diagnosis and monitoring in neurological disorders rely on the clinical assessment tools i.e. specially designed clinical tests and scales. However, although benecial and commonly used, those scales are descriptive (qualitative), primarily intended to be carried out by a trained neurologist, and are prone to subjective rating and imprecise interpretation of patient's performance..

Hand Gestures Recognition for Human-Machine Interfaces: A Low-Power Bio-Inspired Armband

Hand gesture recognition has recently increased its popularity as Human-Machine Interface (HMI) in the biomedical field. Indeed, it can be performed involving many different non-invasive techniques, e.g., surface ElectroMyoGraphy (sEMG) or PhotoPlethysmoGraphy (PPG). In the last few years, the interest demonstrated by both academia and industry brought to a continuous spawning of commercial and custom wearable devices, which tried to address different challenges in many application fields, from tele-rehabilitation to sign language recognition. In this work, we propose a novel 7-channel sEMG armband, which can be employed as HMI for both serious gaming control and rehabilitation support. In particular, we designed the prototype focusing on the capability of our device to compute the Average Threshold Crossing (ATC) parameter, which is evaluated by counting how many times the sEMG signal crosses a threshold during a fixed time duration (i.e., 130 ms), directly on the wearable device. Exploiting the event-driven characteristic of the ATC, our armband is able to accomplish the on-board prediction of common hand gestures requiring less power w.r.t. state of the art devices. At the end of an acquisition campaign that involved the participation of 26 people, we obtained an average classifier accuracy of 91.9% when aiming to recognize in real time 8 active hand gestures plus the idle state. Furthermore, with 2.92mA of current absorption during active functioning and 1.34mA prediction latency, this prototype confirmed our expectations and can be an appealing solution for long-term (up to 60 h) medical and consumer applications

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

Une étude sur la prise en compte simultanée de deux modalités pour la reconnaissance de gestes de SoundPainting

National audienceNowadays, gestures are being adopted as a new modality in the field of Human-Computer Interaction (HMI), where the physical movements of the whole body can perform unlimited actions. Soundpainting is a language of artistic composition used for more than forty years. However, the work on the recognition of SoundPainting gestures is limited and they do not take into account the movements of the fingers and the hand in the gestures which constitute an essential part of SoundPainting. In this context, we con- ducted a study to explore the combination of 3D postures and muscle activity for the recognition of SoundPainting gestures. In order to carry out this study, we created a Sound- Painting database of 17 gestures with data from two sensors (Kinect and Myo). We formulated four hypotheses concerning the accuracy of recognition. The results allowed to characterize the best sensor according to the typology of the gesture, to show that a "simple" combination of the two sensors does not necessarily improves the recognition, that a combination of features is not necessarily more efficient than taking into account a single well-chosen feature, finally, that changing the frequency of the data acquisition provided by these sensors does not have a significant impact on the recognition of gestures.Actuellement, les gestes sont adoptés comme une nouvelle modalité dans le domaine de l'interaction homme-machine, où les mouvements physiques de tout le corps peuvent effectuer des actions quasi-illimitées. Le Soundpainting est un langage de composition artistique utilisé depuis plus de quarante ans. Pourtant, les travaux sur la reconnaissance des gestes SoundPainting sont limités et ils ne prennent pas en compte les mouvements des doigts et de la main dans les gestes qui constituent une partie essentielle de SoundPainting. Dans ce contexte, nous avons réalisé une étude pour explorer la combinaison de postures 3D et de l'activité musculaire pour la reconnaissance des gestes SoundPainting. Pour réaliser cette étude, nous avons créé une base de données SoundPainting de 17 gestes avec les données provenant de deux capteurs (Kinect et Myo). Nous avons formulé quatre hypothèses portant sur la précision de la reconnaissance. Les résultats ont permis de caractériser le meilleur capteur en fonction de la typologie du geste, de montrer qu'une "simple" combinaison des deux capteurs n'entraîne pas forcément une amélioration de la reconnaissance, de même une combinaisons de caractéristiques n'est pas forcément plus performante que la prise en compte d'une seule caractéristique bien choisie, enfin, que le changement de la cadence d'acquisition des données fournies par ces capteurs n'a pas un impact significatif sur la reconnaissance des gestes

Human Health Engineering Volume II

In this Special Issue on “Human Health Engineering Volume II”, we invited submissions exploring recent contributions to the field of human health engineering, i.e., technology for monitoring the physical or mental health status of individuals in a variety of applications. Contributions could focus on sensors, wearable hardware, algorithms, or integrated monitoring systems. We organized the different papers according to their contributions to the main parts of the monitoring and control engineering scheme applied to human health applications, namely papers focusing on measuring/sensing physiological variables, papers highlighting health-monitoring applications, and examples of control and process management applications for human health. In comparison to biomedical engineering, we envision that the field of human health engineering will also cover applications for healthy humans (e.g., sports, sleep, and stress), and thus not only contribute to the development of technology for curing patients or supporting chronically ill people, but also to more general disease prevention and optimization of human well-being