Rehabiliation robotics combined with video game technology provides a means of assisting in the rehabilitation of patients with neuromuscular disorders by performing various facilitation movements. The current work presents ReHabGame, a serious game using a fusion of implemented technologies that can be easily used by patients and therapists to assess and enhance sensorimotor performance and also increase the activities in the daily lives of patients. The game allows a player to control avatar movements through a Kinect Xbox, Myo armband and rudder foot pedal, and involves a series of reach-grasp-collect tasks whose difficulty levels are learnt by a fuzzy interface. The orientation, angular velocity, head and spine tilts and other data generated by the player are monitored and saved, whilst the task completion is calculated by solving an inverse kinematics algorithm which orientates the upper limb joints of the avatar. The different values in upper body quantities of movement provide fuzzy input from which crisp output is determined and used to generate an appropriate subsequent rehabilitation game level. The system can thus provide personalised, autonomously-learnt rehabilitation programmes for patients with neuromuscular disorders with superior predictions to guide the development of improved clinical protocols compared to traditional theraputic activities.
Process re-engineering and optimization in manufacturing industries is a big challenge because of process interdependencies characterized by a high failure rate. Research has shown that over 70% of approaches fail because of complexity as a result of process interdependencies during the implementation phase. This paper investigates data from a manufacturing operation and designs a filtration algorithm to analyze process interdependencies as a new approach for process optimization. The algorithm examines the data from a manufacturing process to identify limitations through cause and effect relationships and implements changes to achieve an optimized result. The proposed cause and effect approach of re-engineering is termed the Khan-Hassan-Butt (KHB) methodology, and it can filter the process interdependencies and use those as key decision-making tools. It provides an improved process optimization framework that incorporates data analysis along with a cause and effect algorithm to filter out the process interdependencies as an approach to increase output and reduce failure factors simultaneously. It also provides a framework for filtering the manufacturing data into smart structured data. Based on the proposed KHB methodology, the study investigated a production line process using the WITNESS Horizon 22 simulation package and analyzed the efficiency of the proposed approach for production optimization. A case study is provided that integrated the KHB methodology with data-driven process re-engineering to analyze the process interdependencies to use them as decision-making tools for production optimization.
The ability to navigate unstructured environments is an essential task for intelligent systems. Autonomous navigation by ground vehicles requires developing an internal representation of space, trained by recognizable landmarks, robust visual processing, computer vision and image processing. A mobile robot needs a platform enabling it to operate in an environment autonomously, recognize the objects, and avoid obstacles in its path. In this study, an open-source ground robot called SROBO was designed to accurately identify its position and navigate certain areas using a deep convolutional neural network and transfer learning. The framework uses an RGB-D MYNTEYE camera, a 2D laser scanner and inertial measurement units (IMU) operating through an embedded system capable of deep learning. The real-time decision-making process and experiments were conducted while the onboard signal processing and image capturing system enabled continuous information analysis. State-of-the-art Real-Time Graph-Based SLAM (RTAB-Map) was adopted to create a map of indoor environments while benefiting from deep convolutional neural network (Deep-CNN) capability. Enforcing Deep-CNN improved the performance quality of the RTAB-Map SLAM. The proposed setting equipped the robot with more insight into its surroundings. The robustness of the SROBO increased by 35% using the proposed system compared to the conventional RTAB-Map SLAM.
This paper presents a system that combines robotic operating system (ROS) and computer vision techniques for fire detection in a mixed reality environment. We have collected video streams from a mini camera on an Unmanned Aerial Vehicle (UAV), where the navigation data relied on state-of-theart Simultaneous Localization and Mapping (SLAM) system. The data collected onboard are communicated to the ground station and processed through the robotic operating system. A robust and efficient re-localisation SLAM was performed to recover from tracking failure and frame lost in the received data. The fire detection algorithm was developed based on the colour, movement attributes, temporal variation of fire intensity and its accumulation around a point. A mixed reality environment was used to visualise and test the proposed system. The observation and data analysis confirmed that the UAV could successfully detect fire and flame, fly towards and hover around it.
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