Development of Rehabilitation System (RehabGame) through Monte-Carlo Tree Search Algorithm using Kinect and Myo Sensor Interface

Artificial and Computational Intelligence in computer games play an important role that could simulate various aspects of real life problems. Development of artificial intelligence techniques in real time decision-making games can provide a platform for the examination of tree search algorithms. In this paper, we present a rehabilitation system known as RehabGame in which the Monte-Carlo Tree Search algorithm is used. The objective of the game is to combat the physical impairment of stroke/ brain injury casualties in order to improve upper limb movement. Through the process of a real-time rehabilitation game, the player decides on paths that could be taken by her/his upper limb in order to reach virtual goal objects. The system has the capability of adjusting the difficulty level to the player0 s ability by learning from the movements made and generating further subsequent objects. The game collects orientation, muscle and joint activity data and utilizes them to make decisions on game progression. Limb movements are stored in the search tree which is used to determine the location of new target virtual fruit objects by accessing the data saved in the background from different game plays. It monitors the enactment of the muscles strain and stress through the Myo armband sensor and provides the next step required for the rehabilitation purpose. The results from two samples show the effectiveness of the MonteCarlo Tree Search in the RehabGame by being able to build a coherent hand motion. It progresses from highly achievable paths to the less achievable ones, thus configuring and personalizing the rehabilitation process

Video Game and Fuzzy Logic to Improve Amblyopia and Convergence Insufficiency

Intuitive learning of visual tasks appears to be an assuring exercise for Amblyopia and Convergence Insufficiency (CI) improvement. Amblyopia is a developmental dysfunction of vision identified by poor monocular visual acuity whereas CI is a common binocular/two-eyed vision disorder in which the eyes do not operate near efficiently. This study proposed the development of virtual reality (VR) video game platform, guided through Fuzzy Logic, targeting casualties with amblyopia and CI condition. The game enables precise control over stimulus parameters, and trains contrast sensitivity with the benefits of motivation and reward that maintain practice over long periods. The dichoptic visual training facilitated through the VR headset and game engine with eye-tracking and stimulus data collection capability. This non-invasive eye-training exercise program aims to train the patients to overcome the common conditions such as lazy eye and exophoria at near. Our vision lab made of 2D and 3D game environments facilitated exercises, which are recommended by opticians\footnote{https://nei.nih.gov/}, through three stimulating video game scenarios. The preliminary results of this study have shown that this program has the potential to be adopted for vision therapy. As such in the future study, a randomized clinical trial with participants 9 to 18 years of age will randomly be assigned to receive 12 weeks of vision lab versus home-based pencil push-ups and vision patching exercise for statistical analysis and validation

Facial Expression and emotion Recognition through Serious game and Kinect device

The human face is considered to be a social stimulus that provides crucial information about a person. Knowing how to read and interpret micro-expressions is an essential part of understanding non-verbal behaviour and reading people. In this study, facial expression features are captured by Kinect Xbox and EMG (electromyography). That are interfaced with the game engine (Unity3D) to read the pose and expressions. Artificial intelligence algorithms are utilized to develop the serious game (SG) that is capable of tracking facial features in a real-time. Procedural Content Generation (PCG) is employed to generate elements of the game’s level to alleviate the work of designing each part of the level with different difficulty

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

Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games

In this paper, an intuitive neuro-rehabilitation video game has been developed employing the fusion of artificial neural networks (ANNs), inverse kinematics (IK), and fuzzy logic (FL) algorithms. The embedded algorithms automatically adjust the game difficulty level based on the player’s interaction with the game. Moreover, it is manifested as an alternative approach for possible movements to improve incorrect positioning through real-time visual feedback on the screen; 52 participants volunteered to engage in the program. Motor assessment scale (MAS) was determined to assess the participants’ functional ability pre- and post-treatments. The system input is received via the Microsoft Kinect, a foot Pedal (Saitek), and the Thalmic Myo armband. The ANN classifier integrates the limb joints orientation, angular velocity, lower arms’ muscle activity, hand gestures, feet sole (plantar) pressure parameters, and the MAS scores to learn from data and predict the improvement following the intervention. The fuzzy input generates a crisp output and provides a personalized rehabilitation program with the potential to be integrated into clinical protocols. Experiments to obtain the input signals and desired outputs were conducted for the learning and validation of the network. The networks pattern recognition, self-organizing map, and non-linear auto-regression analysis performed using feed-forward and Levenberg–Marquardt backpropagation (LMBP) procedure. The results showed the effectiveness of the non-linear auto-regression using the optimized LMBP algorithm to classify and visualize the target categories. Furthermore, the state of the network demonstrates the prediction accuracy exceeding 94%. Clustering algorithm grouped the data based on the similarity. Self-organizing map trained the network to learn the topology of samples with high correlation, presented outputs with high achievement

Fire detection of Unmanned Aerial Vehicle in a Mixed Reality-based System

This paper proposes the employment of a low-cost Micro-electro-mechanical system including; inertial measurement unit (IMU), a consumer-grade digital camera and a fire detection algorithm with a nano unmanned aerial vehicle for inspection application. The video stream (monocular camera) and navigation data (IMU) rely on state-of-the-art indoor/outdoor navigation system. The system combines robotic operating system and computer vision techniques to render metric scale of monocular vision and gravity observable to provide robust, accurate and novel inter-frame motion estimates. The collected onboard data are communicated to the ground station and processed using a Simultaneous Localisation and Mapping (SLAM) system. A robust and efficient re-localisation SLAM was performed to recover from tracking failure, motion blur and frame lost in the received data. The fire detection algorithm was deployed based on the colour, movement attributes, temporal variation of fire's intensity and its accumulation around a point. A cumulative time derivative matrix was used to detect areas with fire's high-frequency luminance flicker (random characteristic) to analyse the frame-by-frame changes. We considered colour, surface coarseness, boundary roughness and skewness features while the quadrotor flies autonomously within clutter and congested areas. Mixed Reality system was adopted to visualise and test the proposed system in a physical/virtual environment. The results showed that the UAV could successfully detect fire and flame, fly towards and hover around it, communicate with the ground station and generate SLAM system

Hybrid Manufacturing and Experimental Testing of Glass Fiber Enhanced Thermoplastic Composites

Additive Manufacturing (AM) is gaining enormous attention from academic and industrial sectors for product development using different materials. Fused Deposition Modelling (FDM) is a popular AM method that works with thermoplastics. This process offers benefits of customisation both in terms of hardware and software in the case of desktop-based FDM systems. Enhancement of mechanical properties for the traditional thermoplastic material is a widely researched area and various materials have been added to achieve this goal. This paper focuses on the manufacture of glass fiber reinforced plastic (GFRP) composites using Hybrid Fused Deposition Modelling (HFDM). Commonly available polylactic acid or polylactide (PLA) material was inter-laced with 0.03 mm thick glass fiber sheets to manufacture GFRP products followed by tensile testing. This was done to investigate whether adding more layers increases the tensile strength of the GFRP products or not. Furthermore, the maximum number of glass fiber layers that can be added to the 4 mm thick specimen was also identified. This was done to demonstrate that there is an optimal number of glass fiber layers that can be added as after this optimal number, the tensile strength start to deteriorate. Microstructural analysis was undertaken after tensile testing followed by ultrasonic testing to assess the uniformity of the GFRP composites

Design, development and numerical analysis of honeycomb core with variable crushing strength

A honeycomb core with half-circular cut-away sections at the spine (the adjoining cell walls) is designed and developed and numerically tested under axial dynamic load condition. The parametric study is invoked to identify the effect of various circular cut-away dimensions. In one embodiment a half-circular shaped cuts are removed from the top of the cell where the cell is impacted and its radius decreases toward the trailing edge of the cell. Numerical (FE) analysis was performed using explicit ANSYS/LS-DYNA and LS-DYNA codes to investigate the crushing performance, where impact angles 30° and 90° was combined with velocity of 5:3 m/sec. The crushing strength and internal energy absorption of the modified honeycomb cores with cut-away sections are then monitored to define the design parameters. The representative Y-section (axisymmetric model) is used for numerical analysis which simulates the honeycomb crushing performance. The numerical results of these innovative models show cyclic buckling effect in which crushing strength increases linearly as the rigid wall passes through. The FE results are validated with corresponding published experiments of the original unmodified honeycomb core (without cut-away)