Hand gesture recognition from video images is of considerable interest as a means of providing simple and intuitive man-machine interfaces. Possible applications range from replacing the mouse as a pointing device to virtual reality and communication with the deaf. We describe an approach to tracking a hand in an image sequence and recognising, in each video frame, which of five gestures it has adopted. A statistically based Point Distribution Model (PDM) is used to provide a compact parameterised description of the shape of the hand for any of the gestures or the transitions between them. The values of the resulting shape parameters are used in a statistical classifier to identify gestures. The model can be used as a deformable template to track a hand through a video sequence but this proves unreliable. We describe how a set of models, one for each of the five gestures, can be used for tracking with the appropriate model selected automatically. We shown that this results in reliable tracking and gesture recognition for two 'unseen' video sequences in which all the gestures are used.
Assigning sentiment labels to documents is, at first sight, a standard multi-label classification task. Many approaches have been used for this task, but the current state-of-the-art solutions use deep neural networks (DNNs). As such, it seems likely that standard machine learning algorithms, such as these, will provide an effective approach. We describe an alternative approach, involving the use of probabilities to construct a weighted lexicon of sentiment terms, then modifying the lexicon and calculating optimal thresholds for each class. We show that this approach outperforms the use of DNNs and other standard algorithms. We believe that DNNs are not a universal panacea and that paying attention to the nature of the data that you are trying to learn from can be more important than trying out ever more powerful general purpose machine learning algorithms.
Robots are highly incorporated in rehabilitation in the last decade to compensate lost functions in disabled individuals. By controlling the rehabilitation robots from far, many benefits are achieved. These benefits include but not restricted to minimum hospital stays, decreasing cost, and increasing the level of care. The main goal of this work is to have an effective solution to take care of patients from far. Tackling the problem of the remote control of rehabilitation robots is undergoing and highly challenging. In this paper, a remote wrist rehabilitation system is presented. The developed system is a sophisticated robot ensuring the two wrist movements (Flexion /extension and abduction/adduction). Additionally, the proposed system provides a software interface enabling the physiotherapists to control the rehabilitation process remotely. The patient’s safety during the therapy is achieved through the integration of a fuzzy controller in the system control architecture. The fuzzy controller is employed to control the robot action according to the pain felt by the patient. By using fuzzy logic approach, the system can adapt effectively according to the patients’ conditions. The Queue Telemetry Transport Protocol (MQTT) is considered to overcome the latency during the human robot interaction. Based on a Kinect camera, the control technique is made gestural. The physiotherapist gestures are detected and transmitted to the software interface to be processed and be sent to the robot. The acquired measurements are recorded in a database that can be used later to monitor patient progress during the treatment protocol. The obtained experimental results show the effectiveness of the developed remote rehabilitation system.
In recent times, vehicular ad hoc networks (VANET) have become a core part of intelligent transportation systems (ITSs), which aim to achieve continual Internet connectivity among vehicles on the road. The VANET has been used to improve driving safety and construct an ITS in modern cities. However, owing to the wireless characteristics, the message transmitted through the network can be observed, altered, or forged. Since driving safety is a major part of VANET, the security and privacy of these messages must be preserved. Therefore, this paper introduces an efficient privacy-preserving data transmission architecture that makes use of blockchain technology in cluster-based VANET. The cluster-based VANET architecture is used to achieve load balancing and minimize overhead in the network, where the clustering process is performed using the rainfall optimization algorithm (ROA). The ROA-based clustering with blockchain-based data transmission, called a ROAC-B technique, initially clusters the vehicles, and communication takes place via blockchain technology. A sequence of experiments was conducted to ensure the superiority of the ROAC-B technique, and several aspects of the results were considered. The simulation outcome showed that the ROAC-B technique is superior to other techniques in terms of packet delivery ratio (PDR), end to end (ETE) delay, throughput, and cluster size.
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