Augmenting patient therapies with video game technology

PhD ThesisThere is an increasing body of work showing that video games can be used for more than just entertainment, but can also facilitate positive physical and mental changes. For people suffering debilitating side-effects from illnesses such as stroke, there is need to deliver and monitor effective rehabilitative physical therapies; video game technologies could potentially deliver an effective alternative to traditional rehabilitative physical therapy, and alleviate the need for direct therapist oversight. Most existing research into video game therapies has focussed on the use of offthe- shelf games to augment a patient’s ongoing therapy. There has currently been little progress into how best to design bespoke software capable of integrating with traditional therapy, or how to replicate common therapies and medical measurements in software. This thesis investigates the ability for video games to be applied to stroke rehabilitation, using modern gaming peripherals for input. The work presents a quantitative measurement of motion detection quality afforded by such hardware. An extendible game development framework capable of high quality movement data output is also presented, affording detailed analysis of player responsiveness to a video game delivered therapy for acute stroke. Finally, a system by which therapists can interactively create complex physical movements for their patients to replicate in a video game environment is detailed, enabling bespoke therapies to be developed, and providing the means by which rehabilitative games for stroke can provide an assessment of patient ability similar to that afforded by traditional therapies

Gamified Music Learning System with VR Force Feedback for Rehabilitation

Many conditions cause loss of coordination and motor capabilities in the extremities. One such condition is stroke, which affects approximately 15 million people worldwide each year. [1] Many robotic systems have been developed to assist in the physical and neurological rehabilitation of patients who have suffered a stroke. As a result of this project an actuator to be used for hand rehabilitation using visual processing and Bowden cables was designed. This project aims to use the design of the actuator combined with gamification elements to create an interface to be used in future robotic rehabilitation systems as well as address the compliance problem found in rehabilitation

Gamified Music Learning System with VR Force Feedback for

Many conditions cause loss of coordination and motor capabilities in the extremities. One such condition is stroke, which affects approximately 15 million people worldwide each year. Many robotic systems have been developed to assist in the physical and neurological rehabilitation of patients who have suffered a stroke. As a result of this project an actuator, to be used for hand rehabilitation, by means of visual processing and Bowden cables, was designed. This project aims to use the design of the actuator combined with gamification elements to create an interface to be used in future robotic rehabilitation systems as well as address the compliance problem found in rehabilitation

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection and human pose estimation

Embedding smart materials into products to motivate the user: Flexers, a smarter approach to finger splinting

This research study has been supported by the EU-funded FP7 collaborative research project Light.Touch.Matters (LTM), under agreement no. 310311

GIVE-ME: Gamification In Virtual Environments for Multimodal Evaluation - A Framework

In the last few decades, a variety of assistive technologies (AT) have been developed to improve the quality of life of visually impaired people. These include providing an independent means of travel and thus better access to education and places of work. There is, however, no metric for comparing and benchmarking these technologies, especially multimodal systems. In this dissertation, we propose GIVE-ME: Gamification In Virtual Environments for Multimodal Evaluation, a framework which allows for developers and consumers to assess their technologies in a functional and objective manner. This framework is based on three foundations: multimodality, gamification, and virtual reality. It facilitates fuller and more controlled data collection, rapid prototyping and testing of multimodal ATs, benchmarking heterogeneous ATs, and conversion of these evaluation tools into simulation or training tools. Our contributions include: (1) a unified evaluation framework: via developing an evaluative approach for multimodal visual ATs; (2) a sustainable evaluation: by employing virtual environments and gamification techniques to create engaging games for users, while collecting experimental data for analysis; (3) a novel psychophysics evaluation: enabling researchers to conduct psychophysics evaluation despite the experiment being a navigational task; and (4) a novel collaborative environment: enabling developers to rapid prototype and test their ATs with users in an early stakeholder involvement that fosters communication between developers and users. This dissertation first provides a background in assistive technologies and motivation for the framework. This is followed by detailed description of the GIVE-ME Framework, with particular attention to its user interfaces, foundations, and components. Then four applications are presented that describe how the framework is applied. Results and discussions are also presented for each application. Finally, both conclusions and a few directions for future work are presented in the last chapter

Wearables for Movement Analysis in Healthcare

Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Proceedings of the 9th international conference on disability, virtual reality and associated technologies (ICDVRAT 2012)

The proceedings of the conferenc

WiGlove : A Passive Dynamic Orthosis for Home-based Post-stroke Rehabilitation of Hand and Wrist

Stroke survivors often experience varying levels of motor function deficits in their hands affecting their ability to perform activities of daily life. Recovering their hand functions through neurorehabilitation is a significant step in their recovery towards independent living. Home-based rehabilitation using robotic devices allows stroke survivors to train at their convenience independent of factors such as the availability of therapists’ appointments and the need for frequent travel to outpatient clinics. While many robotic solutions have been proposed to address the above concerns, most focus on training only the wrist or the fingers, neglecting the synergy between the two. To address this, the WiGlove was co-designed to allow hemiparetic stroke survivors to train both the wrist and fingers in the comfort of their homes. The central hypothesis of this work is to investigate if a device designed using user-centred methods featuring aspects of usability such as easy donning and doffing and wireless operation, can act as a feasible tool for home-based rehabilitation of the hand and wrist following stroke. In order to aid this investigation, we tackled this task in three stages of usability and feasibility evaluations. Firstly, healthy participants tried the current state of the art, the SCRIPT Passive Orthosis, as well as the WiGlove, in a counterbalanced, within-subject experiment and attested to WiGlove’s improvement in several aspects of usability such as ease of don/doffing, suitability for ADL, unblocked natural degrees of freedom, safety and aesthetic appeal. Subsequently, a heuristic evaluation with six stroke therapists validated these improvements and helped identify issues they perceived to potentially affect the device’s acceptance. Integrating this feedback, the updated WiGlove was subjected to a six-week summative feasibility evaluation with two stroke survivors, with varying levels of impairment, in their homes without supervision from the therapists. Results from this study were overwhelmingly positive on the usability and acceptance of the WiGlove. Furthermore, in the case of the first participant who trained with it for a total of 39 hours, notable improvements were observed in the participant’s hand functions. It showed that even without a prescribed training protocol, both participants were willing to train regularly with the WiGlove and its games, sometimes several times a day. These results demonstrate that WiGlove can be a promising tool for home-based rehabilitation for stroke survivors and serve as evidence for a larger user study with more participants with varying levels of motor impairments due to stroke. The findings of this study also offer preliminary evidence supporting the effectiveness of training with the WiGlove, particularly in the case of the first participant, who exhibited a significant reduction of tone in the hand as a result of increased training intensity. Owing to the participant’s satisfaction with the device, it was requested by him to extend his involvement in the study by using the WiGlove for a longer duration which was facilitated