Capacitive sensor to detect fallen humans in conditions of low visibility

This paper examines the potential for a capacitive sensor to be used as part of a system to detect fallen humans at very close range. Previous research suggests that a robotic system incorporating a low cost capacitive sensor could potentially distinguish between different materials. The work reported in this paper stemmed from an attempt to determine the true extent to which such a system might reliably differentiate between fallen humans and other objects. The work is motivated by the fact that there are several different emergency circumstances in which such a system might save lives if it could reliably detect immobile humans. These scenarios include situations where older people have fallen and are unable to move or raise an alert, and circumstances where people have been overcome by smoke in a burning building. Current sensing systems are typically unsuitable in conditions of low visibility such as smoke filled rooms. This analysis focused specifically on the potential for a robot equipped with a capacitive sensing system to identify an immobile human in a low visibility emergency scenario. It is concluded that further work would be required to determine whether this type of capacitive sensing system is genuinely suitable for this task

Knowledge transfer model for the development of software equirements analysis CASE tools to be used in cross time-zone projects

This article describes work undertaken to evaluate an approach for developing collaborative requirementsanalysis CASE tools that are specifically designed to address the needs of cross-time-zone development teams, that is, teams spread across different geographical locations around the world. Few of the software requirements analysis computer assisted software environment (CASE) tools readily available are designed specifically for cross-time-zone development activities. We propose a specifically tailored data and knowledge-transfer model, and investigate its suitability for the development of a cross-time-zone oriented CASE tool. The approach was used to develop a working prototype. The approach and prototype will be further evaluated in a collaborative undertaking involving the Wroclaw University of Technology, the University of Technology Sydney and the University of Arizona (UA)

Fall detection using a Gaussian distribution of clustered knowledge, augmented radial basis neural-network, and multilayer perceptron

The rapidly increasing population of elderly people has posed a big challenge to research in fall prevention and detection. Substantial amounts of injuries, disabilities, traumas and deaths among elderly people due to falls have been reported worldwide. There is therefore a need for a reliable, simple, and affordable automatic fall detection system. This paper proposes a reliable fall detection algorithm using minimal information from a single waist worn wireless tri-axial accelerometer. The method proposed is to approach fall detection using digital signal processing and neural networks. This method includes the application of Discrete Wavelet Transform (DWT), Regrouping Particle Swarm Optimization (RegPSO), a proposed method called Gaussian Distribution of Clustered Knowledge (GCK), and an Ensemble of Classifiers using two different classifiers: Multilayer Perceptron Neural Network (MLP) and Augmented Radial Basis Neural Networks (ARBF). The proposed method has been tested on 8 healthy individuals in a home environment and yields promising result of up to 100% sensitivity on ingroup, 97.65% sensitivity on outgroup, and 99.56% specificity on Activities of Daily Living (ADL) data. © 2011 IEEE

A measurement model to analyze the effect of agile enterprise architecture on geographically distributed agile development.

Abstract Efficient and effective communication (active communication) among stakeholders is thought to be central to agile development. However, in geographically distributed agile development (GDAD) environments, it can be difficult to achieve active communication among distributed teams due to challenges such as differences in proximity and time. To date, there is little empirical evidence about how active communication can be established to enhance GDAD performance. To address this knowledge gap, we develop and evaluate a measurement model to quantitatively analyze the impact of agile enterprise architecture (AEA) on GDAD communication and GDAD performance. The measurement model was developed and evaluated through developing the AEA driven GDAD model and associated measurement model based on the extensive literature review, model pre-testing, pilot testing, item screening, and empirical evaluation through a web-based quantitative questionnaire that contained 26 different weighted questions related to the model constructs (AEA, GDAD active communication, and GDAD performance). The measurement model evaluation resulted in validated research model and 26 measures: 7 formative items for AEA, 5 reflective items for communication efficiency, 4 reflective items for communication effectiveness, 2 reflective items for each on-time and on-budget completion, and 3 reflective items for each software functionality and quality. The results indicate the appropriateness and applicability of the proposed measurement model to quantitatively analyze the impact of AEA on GDAD communication and performance

Voice Operated Guidance Systems for Vision Impaired People: Investigating a User-Centered Open Source Model

People who have impaired vision regularly use white canes and/or guide dogs to assist in obstacle avoidance. Guide dogs can also be of limited assistance for finding the way to a remote location, known as ??wayfinding?? Several electronic devices are currently available for providing guidance to a remote location, but these tend to be expensive, or make use of a Braille interface. This project investigated the suitability of a user centered client server approach for the development of a talking GPS system intended to fill a niche for outdoor wayfinding. The work resulted in a working prototype proof-of-concept system that uses a speech-recognition speech-synthesis interface. The prototype solution includes a custom web application which accesses the Google maps API. The system is intended to be scalable and extensible with additional features such as sensors for obstacle avoidance and access to web-based information such as weather, train or bus timetable information. The client server approach was found to be suitable for the development of this type of application

Method for increasing the energy efficiency of wirelessly networked ambulatory health monitoring devices

In-home healthcare applications that use wearable devices ordinarily have strict power constraints due to the small size of the battery in the device. The power constraints are a key driver of research to develop new methods for improving the energy efficiency of ambulatory health monitoring devices. The radio-communication components typically consume a large proportion of the available energy in systems such as these. Given that radio transmissions use far more power than on-board processing, it is proposed that energy can be conserved by performing fall detection at the node. The proposed algorithm is intended to be performed at the node and provide a suitable balance between power consumption and detection accuracy. The research and prototype system described in this article focuses on wearable fall detection devices to be used elderly people who are living in non-hospital settings, and discusses considerations arising from the development of a prototype system. The outcomes of the system design and development process are discussed, and conclusions are drawn concerning the potential of the method to improve the energy efficiency of fall detection systems