Proceedings of the 3rd IUI Workshop on Interacting with Smart Objects

These are the Proceedings of the 3rd IUI Workshop on Interacting with Smart Objects. Objects that we use in our everyday life are expanding their restricted interaction capabilities and provide functionalities that go far beyond their original functionality. They feature computing capabilities and are thus able to capture information, process and store it and interact with their environments, turning them into smart objects

Gesture Recognition and Control Part 1 - Basics, Literature Review & Different Techniques

This Exploratory paper series reveals the technological aspects of Gesture Controlled User Interface (GCUI), and identifies trends in technology, application and usability. It is found that GCUI now affords realistic opportunities for specific application are as, and especially for use rs who are uncomfortable with more commonly used input devices. It further explored collated chronograph research information on which covers the past research work in Literature Review . Researchers investigated different types of gestures, its uses, applic ations, technology, issues and results from existing research

An Overview of Self-Adaptive Technologies Within Virtual Reality Training

This overview presents the current state-of-the-art of self-adaptive technologies within virtual reality (VR) training. Virtual reality training and assessment is increasingly used for five key areas: medical, industrial & commercial training, serious games, rehabilitation and remote training such as Massive Open Online Courses (MOOCs). Adaptation can be applied to five core technologies of VR including haptic devices, stereo graphics, adaptive content, assessment and autonomous agents. Automation of VR training can contribute to automation of actual procedures including remote and robotic assisted surgery which reduces injury and improves accuracy of the procedure. Automated haptic interaction can enable tele-presence and virtual artefact tactile interaction from either remote or simulated environments. Automation, machine learning and data driven features play an important role in providing trainee-specific individual adaptive training content. Data from trainee assessment can form an input to autonomous systems for customised training and automated difficulty levels to match individual requirements. Self-adaptive technology has been developed previously within individual technologies of VR training. One of the conclusions of this research is that while it does not exist, an enhanced portable framework is needed and it would be beneficial to combine automation of core technologies, producing a reusable automation framework for VR training

Wearable exoskeleton systems based-on pneumatic soft actuators and controlled by parallel processing

Human assistance innovation is essential in an increasingly aging society and one technology that may be applicable is exoskeletons. However, traditional rigid exoskeletons have many drawbacks. This research includes the design and implementation of upper-limb power assist and rehabilitation exoskeletons based on pneumatic soft actuators. A novel extensor-contractor pneumatic muscle has been designed and constructed. This new actuator has bidirectional action, allowing it to both extend and contract, as well as create force in both directions. A mathematical model has been developed for the new novel actuator which depicts the output force of the actuator. Another new design has been used to create a novel bending pneumatic muscle, based on an extending McKibben muscle and modelled mathematically according to its geometric parameters. This novel bending muscle design has been used to create two versions of power augmentation gloves. These exoskeletons are controlled by adaptive controllers using human intention. For finger rehabilitation a glove has been developed to bend the fingers (full bending) by using our novel bending muscles. Inspired by the zero position (straight fingers) problem for post-stroke patients, a new controllable stiffness bending actuator has been developed with a novel prototype. To control this new rehabilitation exoskeleton, online and offline controller systems have been designed for the hand exoskeleton and the results have been assessed experimentally. Another new design of variable stiffness actuator, which controls the bending segment, has been developed to create a new version of hand exoskeletons in order to achieve more rehabilitation movements in the same single glove. For Forearm rehabilitation, a rehabilitation exoskeleton has been developed for pronation and supination movements by using the novel extensor-contractor pneumatic muscle. For the Elbow rehabilitation an elbow rehabilitation exoskeleton was designed which relies on novel two-directional bending actuators with online and offline feedback controllers. Lastly for upper-limb joint is the wrist, we designed a novel all-directional bending actuator by using the moulding bladder to develop the wrist rehabilitation exoskeleton by a single all-directional bending muscle. Finally, a totally portable, power assistive and rehabilitative prototype has been developed using a parallel processing intelligent control chip

Hybrid wheelchair controller for handicapped and quadriplegic patients

In this dissertation, a hybrid wheelchair controller for handicapped and quadriplegic patient is proposed. The system has two sub-controllers which are the voice controller and the head tilt controller. The system aims to help quadriplegic, handicapped, elderly and paralyzed patients to control a robotic wheelchair using voice commands and head movements instead of a traditional joystick controller. The multi-input design makes the system more flexible to adapt to the available body signals. The low-cost design is taken into consideration as it allows more patients to use this system

Towards fog-driven IoT eHealth:Promises and challenges of IoT in medicine and healthcare

Internet of Things (IoT) offers a seamless platform to connect people and objects to one another for enriching and making our lives easier. This vision carries us from compute-based centralized schemes to a more distributed environment offering a vast amount of applications such as smart wearables, smart home, smart mobility, and smart cities. In this paper we discuss applicability of IoT in healthcare and medicine by presenting a holistic architecture of IoT eHealth ecosystem. Healthcare is becoming increasingly difficult to manage due to insufficient and less effective healthcare services to meet the increasing demands of rising aging population with chronic diseases. We propose that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other. This patient-centric IoT eHealth ecosystem needs a multi-layer architecture: (1) device, (2) fog computing and (3) cloud to empower handling of complex data in terms of its variety, speed, and latency. This fog-driven IoT architecture is followed by various case examples of services and applications that are implemented on those layers. Those examples range from mobile health, assisted living, e-medicine, implants, early warning systems, to population monitoring in smart cities. We then finally address the challenges of IoT eHealth such as data management, scalability, regulations, interoperability, device–network–human interfaces, security, and privacy

Machine learning methods for sign language recognition: a critical review and analysis.

Sign language is an essential tool to bridge the communication gap between normal and hearing-impaired people. However, the diversity of over 7000 present-day sign languages with variability in motion position, hand shape, and position of body parts making automatic sign language recognition (ASLR) a complex system. In order to overcome such complexity, researchers are investigating better ways of developing ASLR systems to seek intelligent solutions and have demonstrated remarkable success. This paper aims to analyse the research published on intelligent systems in sign language recognition over the past two decades. A total of 649 publications related to decision support and intelligent systems on sign language recognition (SLR) are extracted from the Scopus database and analysed. The extracted publications are analysed using bibliometric VOSViewer software to (1) obtain the publications temporal and regional distributions, (2) create the cooperation networks between affiliations and authors and identify productive institutions in this context. Moreover, reviews of techniques for vision-based sign language recognition are presented. Various features extraction and classification techniques used in SLR to achieve good results are discussed. The literature review presented in this paper shows the importance of incorporating intelligent solutions into the sign language recognition systems and reveals that perfect intelligent systems for sign language recognition are still an open problem. Overall, it is expected that this study will facilitate knowledge accumulation and creation of intelligent-based SLR and provide readers, researchers, and practitioners a roadmap to guide future direction