Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

A machine learning framework for automatic human activity classification from wearable sensors

Wearable sensors are becoming increasingly common and they permit the capture of physiological data during exercise, recuperation and everyday activities. This work investigated and advanced the current state-of-the-art in machine learning technology for the automatic classification of captured physiological data from wearable sensors. The overall goal of the work presented here is to research and investigate every aspect of the technology and methods involved in this field and to create a framework of technology that can be utilised on low-cost platforms across a wide range of activities. Both rudimentary and advanced techniques were compared, including those that allowed for both real-time processing on an android platform and highly accurate postprocessing on a desktop computer. State-of-the-art feature extraction methods such as Fourier and Wavelet analysis were also researched to ascertain how well they could extract discriminative physiological information. Various classifiers were investigated in terms of their ability to work with different feature extraction methods. Consequently, complex classification fusion models were created to increase the overall accuracy of the activity recognition process. Genetic algorithms were also employed to optimise classifier parameter selection in the multidimensional search space. Large annotated sporting activity datasets were created for a range of sports that allowed different classification models to be compared. This allowed for a machine learning framework to be constructed that could potentially create accurate models when applied to any unknown dataset. This framework was also successfully applied to medical and everyday-activity datasets confirming that the approach could be deployed in different application settings

Development of a hybrid assist-as-need hand exoskeleton for stroke rehabilitation.

Stroke is one of the leading causes of disability globally and can significantly impair a patient’s ability to function on a daily basis. Through physical rehabilitative measures a patient may regain a level of functional independence. However, required therapy dosages are often not met. Rehabilitation is typically implemented through manual one-to-one assistance with a physiotherapist, which quickly becomes labour intensive and costly. Hybrid application of functional electrical stimulation (FES) and robotic support can access the physiological benefits of direct muscle activation while providing controlled and repeatable motion assistance. Furthermore, patient engagement can be heightened through the integration of a volitional intent measure, such as electromyography (EMG). Current hybrid hand-exoskeletons have demonstrated that a balanced hybrid support profile can alleviate FES intensity and motor torque requirements, whilst improving reference tracking errors. However, these support profiles remain fixed and patient fatigue is not addressed. The aim of this thesis was to develop a proof-of-concept assist-as-need hybrid exoskeleton for post-stroke hand rehabilitation, with fatigue monitoring to guide the balance of support modalities. The device required the development and integration of a constant current (CC) stimulator, stimulus-resistant EMG device, and hand-exoskeleton. The hand exoskeleton in this work was formed from a parametric Watt I linkage model that adapts to different finger sizes. Each linkage was optimised with respect to angular precision and compactness using Differential Evolution (DE). The exoskeleton’s output trajectory was shown to be sensitive to parameter variation, potentially caused by finger measurement error and shifts in coupler placement. However, in a set of cylindrical grasping trials it was observed that a range of movement strategies could be employed towards a successful grasp. As there are many possible trajectories that result in a successful grasp, it was deduced that the exoskeleton can still provide functional assistance despite its sensitivity to parameter variation. The CC stimulator developed in this work has a part cost of USD $145 and allows flexible adjustment of waveform parameters through an on-board micro-controller. The device is designed to output current up to ±30mA given a voltage compliance of ±50V. When applied across a 2kΩ load, the device exhibited a linear output transfer function, with a maximum ramp tracking error of 5 The stimulus-resistant EMG device builds on current designs by using a novel Schmitt trigger based artefact detection channel to adaptively blank stimulation artefacts without stimulator synchronisation. The design has a part cost of USD$150 and has been made open-source. The device demonstrated its ability to record EMG over its predominant energy spectrum during stimulation, through the stimulation electrodes or through separate electrodes. Pearson’s correlation coefficients greater than 0.84 were identified be- tween the normalised spectra of volitional EMG (vEMG) estimates during stimulation and of stimulation-free EMG recordings. This spectral similarity permits future research into applications such as spectral-based monitoring of fatigue and muscle coherence, posing an advantage over current same-electrode stimulation and recording systems, which can- not sample the lower end of the EMG spectrum due to elevated high-pass filter cut-off frequencies. The stimulus-resistant EMG device was used to investigate elicited EMG (eEMG)-based fatigue metrics during vEMG-controlled stimulation and hybrid support profiles. During intermittent vEMG-controlled stimulation, the eEMG peak-to-peak amplitude (PTP) index was the median frequency (MDF) had a negative correlation for all subjects with R > 0:62 during stimulation-induced wrist flexion and R > 0:55 during stimulation-induced finger flexion. During hybrid FES-robotic support trials, a 40% reduction in stimulus intensity resulted in an average 21% reduction in MDF gradient magnitudes. This reflects lower levels of fatigue during the hybrid support profile and indicates that the MDF gradient can provide useful information on the progression of muscle fatigue. A hybrid exoskeleton system was formed through the integration of the CC stimulator, stimulus-resistant EMG device, and the hand exoskeleton developed in this work. The system provided assist-as-need functional grasp assistance through stimulation and robotic components, governed by the user’s vEMG. The hybrid support profile demonstrated consistent motion assistance with lowered stimulation intensities, which in-turn lowered the subjects’ perceived levels of fatigue

Development of situation recognition, environment monitoring and patient condition monitoring service modules for hospital robots

An aging society and economic pressure have caused an increase in the patient-to-staff ratio leading to a reduction in healthcare quality. In order to combat the deficiencies in the delivery of patient healthcare, the European Commission in the FP6 scheme approved the financing of a research project for the development of an Intelligent Robot Swarm for Attendance, Recognition, Cleaning and Delivery (iWARD). Each iWARD robot contained a mobile, self-navigating platform and several modules attached to it to perform their specific tasks. As part of the iWARD project, the research described in this thesis is interested to develop hospital robot modules which are able to perform the tasks of surveillance and patient monitoring in a hospital environment for four scenarios: Intruder detection, Patient behavioural analysis, Patient physical condition monitoring, and Environment monitoring. Since the Intruder detection and Patient behavioural analysis scenarios require the same equipment, they can be combined into one common physical module called Situation recognition module. The other two scenarios are to be served by their separate modules: Environment monitoring module and Patient condition monitoring module. The situation recognition module uses non-intrusive machine vision-based concepts. The system includes an RGB video camera and a 3D laser sensor, which monitor the environment in order to detect an intruder, or a patient lying on the floor. The system deals with various image-processing and sensor fusion techniques. The environment monitoring module monitors several parameters of the hospital environment: temperature, humidity and smoke. The patient condition monitoring system remotely measures the following body conditions: body temperature, heart rate, respiratory rate, and others, using sensors attached to the patient’s body. The system algorithm and module software is implemented in C/C++ and uses the OpenCV image analysis and processing library and is successfully tested on Linux (Ubuntu) Platform. The outcome of this research has significant contribution to the robotics application area in the hospital environment