Multimodality with Eye tracking and Haptics: A New Horizon for Serious Games?

The goal of this review is to illustrate the emerging use of multimodal virtual reality that can benefit learning-based games. The review begins with an introduction to multimodal virtual reality in serious games and we provide a brief discussion of why cognitive processes involved in learning and training are enhanced under immersive virtual environments. We initially outline studies that have used eye tracking and haptic feedback independently in serious games, and then review some innovative applications that have already combined eye tracking and haptic devices in order to provide applicable multimodal frameworks for learning-based games. Finally, some general conclusions are identified and clarified in order to advance current understanding in multimodal serious game production as well as exploring possible areas for new applications

Development and evaluation of a haptic framework supporting telerehabilitation robotics and group interaction

Telerehabilitation robotics has grown remarkably in the past few years. It can provide intensive training to people with special needs remotely while facilitating therapists to observe the whole process. Telerehabilitation robotics is a promising solution supporting routine care which can help to transform face-to-face and one-on-one treatment sessions that require not only intensive human resource but are also restricted to some specialised care centres to treatments that are technology-based (less human involvement) and easy to access remotely from anywhere. However, there are some limitations such as network latency, jitter, and delay of the internet that can affect negatively user experience and quality of the treatment session. Moreover, the lack of social interaction since all treatments are performed over the internet can reduce motivation of the patients. As a result, these limitations are making it very difficult to deliver an efficient recovery plan. This thesis developed and evaluated a new framework designed to facilitate telerehabilitation robotics. The framework integrates multiple cutting-edge technologies to generate playful activities that involve group interaction with binaural audio, visual, and haptic feedback with robot interaction in a variety of environments. The research questions asked were: 1) Can activity mediated by technology motivate and influence the behaviour of users, so that they engage in the activity and sustain a good level of motivation? 2) Will working as a group enhance users’ motivation and interaction? 3) Can we transfer real life activity involving group interaction to virtual domain and deliver it reliably via the internet? There were three goals in this work: first was to compare people’s behaviours and motivations while doing the task in a group and on their own; second was to determine whether group interaction in virtual and reala environments was different from each other in terms of performance, engagement and strategy to complete the task; finally was to test out the effectiveness of the framework based on the benchmarks generated from socially assistive robotics literature. Three studies have been conducted to achieve the first goal, two with healthy participants and one with seven autistic children. The first study observed how people react in a challenging group task while the other two studies compared group and individual interactions. The results obtained from these studies showed that the group interactions were more enjoyable than individual interactions and most likely had more positive effects in terms of user behaviours. This suggests that the group interaction approach has the potential to motivate individuals to make more movements and be more active and could be applied in the future for more serious therapy. Another study has been conducted to measure group interaction’s performance in virtual and real environments and pointed out which aspect influences users’ strategy for dealing with the task. The results from this study helped to form a better understanding to predict a user’s behaviour in a collaborative task. A simulation has been run to compare the results generated from the predictor and the real data. It has shown that, with an appropriate training method, the predictor can perform very well. This thesis has demonstrated the feasibility of group interaction via the internet using robotic technology which could be beneficial for people who require social interaction (e.g. stroke patients and autistic children) in their treatments without regular visits to the clinical centres

Upper extremity rehabilitation using interactive virtual environments

Stroke affects more than 700,000 people annually in the U.S. It is the leading cause of major disability. Recovery of upper extremity function remains particularly resistant to intervention, with 80% to 95% of persons demonstrating residual upper extremity impairments lasting beyond six months after the stroke. The NJIT Robot Assistive Virtual Rehabilitation (NJIT-RAVR) system has been developed to study optimal strategies for rehabilitation of arm and hand function. Several commercial available devices, such as HapticMaster™, Cyberglove™, trakSTAR™ and Cybergrasp™, have been integrated and 11 simulations were developed to allow users to interact with virtual environments. Visual interfaces used in these simulations were programmed either in Virtools or in C++ using the Open GL library. Stereoscopic glasses were used to enhance depth perception and to present movement targets to the subjects in a 3-dimensional stereo working space. Adaptive online and offline algorithms were developed that provided appropriate task difficulty to optimize the outcomes. A pilot study was done on four stroke patients and two children with cerebral palsy to demonstrate the usability of this robot-assisted VR system. The RAVR system performed well without unexpected glitches during two weeks of training. No subjects experienced side effects such as dizziness, nausea or disorientation while interacting with the virtual environment. Each subject was able to finish the training, either with or without robotic adaptive assistance. To investigate optimal therapeutic approaches, forty stroke subjects were randomly assigned to two groups: Hand and Arm training Together (HAT) and Hand and Arm training Separately (HAS). Each group was trained in similar virtual reality training environments for three hours a day, four days a week for two weeks. In addition, twelve stroke subjects participated as a control group. They received conventional rehabilitation training of similar intensity and duration as the HAS and HAT groups. Clinical outcome measurements included the Jebsen Test of Hand Function, the Wolf Motor Function Test, and the ReachGrasp test. Secondary outcome measurements were calculated from kinematic and kinetic data collected during training in real time at 100 Hz. Both HAS and HAT groups showed significant improvement in clinical and kinematic outcome measurements. Clinical improvement compared favorably to the randomized clinical trials reported in the literature. However, there was no significant improvement difference between the two groups. Subjects from the control group improved in clinical measurements and in the ReachGrasp test. Compared to the control group, the ReachGrasp test showed a larger increase in movement speed during reaching and in the efficiency of lifting an object from the table in the combined HAS and HAT group. The NJIT-RAVR system was further modified to address the needs of children with hemiplegia due to Cerebral Palsy. Thirteen children with cerebral palsy participated in the total of nine sessions of one hour training that lasted for three weeks. Nine of the children were trained using the RAVR system alone, and another four had training with the combined Constraint-Induced Movement therapy and RAVR therapy. As a group, the children demonstrated improved performance across measurements of the Arm Range of Motion (AROM), motor function, kinematics and motor control. While subjects\u27 responses to the games varied, they performed each simulation while maintaining attention sufficient to improve in both robotic task performance and in measures of motor function

A Framework to Describe, Analyze and Generate Interactive Motor Behaviors

International audienceWhile motor interaction between a robot and a human, or between humans, has important implications for society as well as promising applications, little research has been devoted to its investigation. In particular, it is important to understand the different ways two agents can interact and generate suitable interactive behaviors. Towards this end, this paper introduces a framework for the description and implementation of interactive behaviors of two agents performing a joint motor task. A taxonomy of interactive behaviors is introduced, which can classify tasks and cost functions that represent the way each agent interacts. The role of an agent interacting during a motor task can be directly explained from the cost function this agent is minimizing and the task constraints. The novel framework is used to interpret and classify previous works on human-robot motor interaction. Its implementation power is demonstrated by simulating representative interactions of two humans. It also enables us to interpret and explain the role distribution and switching between roles when performing joint motor tasks

Robotics in health care: Perspectives of robot-aided interventions in clinical practice for rehabilitation of upper limbs

This article belongs to the Special Issue Rehabilitation Robotics: Recent Advancements and New Perspectives about Training and Assessment of Sensorimotor Functions.Robot-aided systems to support the physical rehabilitation of individuals with neurological impairment is one of the fields that has been widely developed in the last few decades. However, the adoption of these systems in clinical practice remains limited. In order to better understanding the causes of this limitation, a systematic review of robot-based systems focused on upper extremity rehabilitation is presented in this paper. A systematic search and review of related articles in the literature were conducted. The chosen works were analyzed according to the type of device, the data analysis capability, the therapy method, the human–robot interaction, the safety strategies, and the focus of treatment. As a conclusion, self-adaptation for personalizing the treatments, safeguarding and enhancing of patient–robot interaction towards training essential factors of movement generation into the same paradigm, or the use of lifelike environments in fully-immersive virtual reality for increasing the assimilation of motor gains could be relevant factors to develop more accepted robot-aided systems in clinical practice.This work was supported in part by the Spanish Ministry of Economy and Competitiveness via the ROBOESPASproject (DPI2017-87562-C2-1-R) and in part by the RoboCity2030-DIH-CMMadrid Robotics Digital Innovation Hub ("Robótica aplicada a la mejora de la calidad de vida de los ciudadanos, Fase IV"; S2018/NMT-4331), which is funded by the Programas de Actividades I+DComunidad de Madrid and cofunded by the Structural Funds of the EU

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

The development of an adaptive upper-limb stroke rehabilitation robotic system

<p>Abstract</p> <p>Background</p> <p>Stroke is the primary cause of adult disability. To support this large population in recovery, robotic technologies are being developed to assist in the delivery of rehabilitation. This paper presents an automated system for a rehabilitation robotic device that guides stroke patients through an upper-limb reaching task. The system uses a decision theoretic model (a partially observable Markov decision process, or POMDP) as its primary engine for decision making. The POMDP allows the system to automatically modify exercise parameters to account for the specific needs and abilities of different individuals, and to use these parameters to take appropriate decisions about stroke rehabilitation exercises.</p> <p>Methods</p> <p>The performance of the system was evaluated by comparing the decisions made by the system with those of a human therapist. A single patient participant was paired up with a therapist participant for the duration of the study, for a total of six sessions. Each session was an hour long and occurred three times a week for two weeks. During each session, three steps were followed: (A) after the system made a decision, the therapist either agreed or disagreed with the decision made; (B) the researcher had the device execute the decision made by the therapist; (C) the patient then performed the reaching exercise. These parts were repeated in the order of A-B-C until the end of the session. Qualitative and quantitative question were asked at the end of each session and at the completion of the study for both participants.</p> <p>Results</p> <p>Overall, the therapist agreed with the system decisions approximately 65% of the time. In general, the therapist thought the system decisions were believable and could envision this system being used in both a clinical and home setting. The patient was satisfied with the system and would use this system as his/her primary method of rehabilitation.</p> <p>Conclusions</p> <p>The data collected in this study can only be used to provide insight into the performance of the system since the sample size was limited. The next stage for this project is to test the system with a larger sample size to obtain significant results.</p

Design and Development of a Low Cost Platform to Facilitate Post-Stroke Rehabilitation of the Elbow/Shoulder Region

For post-stroke rehabilitation of the upper limbs, increased amounts of therapy are directly related to improved rehabilitation outcomes. As such, a low cost therapy platform is proposed suitable for facilitating active therapy and administering activeassist therapy to the shoulder/elbow region of the upper limbs of individuals post-stroke in a local clinic or domestic setting. Enabling a person to undergo intensive rehabilitation therapy outside of a rehabilitation hospital setting permits the amount of therapy administered to be maximised. While studies have shown that technological approaches to post-stroke rehabilitation do not produce better outcomes than equal amounts of traditional therapy in a rehabilitation hospital setting, a technological approach has the potential to have significant benefits when that therapy is being undertaken in a local clinic or domestic setting, where the individual undergoing therapy is relatively unsupervised. These benefits largely relate to a technological approach being more motivational for the person than an equivalent manual approach. However, for such an approach to be economically viable, effective, low cost devices are required. This document presents and critically discusses the design of this proposed low cost therapy platform along with possible routes for its further development