Testing the accuracy of luminance maps acquired by smart phone cameras

This paper looks at the accuracy of using the built-in camera of smart phones and free software as an economical way to quantify and analyse light exposure by producing luminance maps from High Dynamic Range (HDR) images. HDR images were captured with an Apple iPhone 4S to capture a wide variation of luminance within an indoor and outdoor scene. The HDR images were then processed using Photosphere software (Ward, 2010.) to produce luminance maps, where individual pixel values were compared with calibrated luminance meter readings. This comparison has shown an average luminance error of ~8% between the HDR image pixel values and luminance meter readings, when the range of luminances in the image is limited to approximately 1,500cd/m2.

The next generation health intervention tool - Can smart phone applications help young people track and moderate alcohol use and potential harms?

Whilst alcohol is a common feature of many social gatherings, there are numerous immediate and long-term health and social harms associated with its abuse. Alcohol consumption is the world’s third largest risk factor for disease and disability with almost 4% of all deaths worldwide attributed to alcohol. Not surprisingly, alcohol use and binge drinking by young people is of particular concern with Australian data reporting that 39% of young people (18-19yrs) admitted drinking at least weekly and 32% drank to levels that put them at risk of alcohol-related harm. The growing market penetration and connectivity of smartphones may be an opportunities for innovation in promoting health-related self-management of substance use. However, little is known about how best to harness and optimise this technology for health-related intervention and behaviour change. This paper explores the utility and interface of smartphone technology as a health intervention tool to monitor and moderate alcohol use. A review of the psychological health applications of this technology will be presented along with the findings of a series of focus groups, surveys and behavioural field trials of several drink-monitoring applications. Qualitative and quantitative data will be presented on the perceptions, preferences and utility of the design, usability and functionality of smartphone apps to monitoring and moderate alcohol use. How these findings have shaped the development and evolution of the OnTrack app will be specifically discussed, along with future directions and applications of this technology in health intervention, prevention and promotion.

Multi-field simulations and characterization of CMOS-MEMS high-temperature smart gas sensors based on SOI technology

This paper describes multiple field-coupled simulations and device characterization of fully CMOS-MEMS-compatible smart gas sensors. The sensor structure is designated for gas/vapour detection at high temperatures (>300 °C) with low power consumption, high sensitivity and competent mechanic robustness employing the silicon-on-insulator (SOI) wafer technology, CMOS process and micromachining techniques. The smart gas sensor features micro-heaters using p-type MOSFETs or polysilicon resistors and differentially transducing circuits for in situ temperature measurement. Physical models and 3D electro-thermo-mechanical simulations of the SOI micro-hotplate induced by Joule, self-heating, mechanic stress and piezoresistive effects are provided. The electro-thermal effect initiates and thus affects electronic and mechanical characteristics of the sensor devices at high temperatures. Experiments on variation and characterization of micro-heater resistance, power consumption, thermal imaging, deformation interferometry and dynamic thermal response of the SOI micro-hotplate have been presented and discussed. The full integration of the smart gas sensor with automatically temperature-reading ICs demonstrates the lowest power consumption of 57 mW at 300 °C and fast thermal response of 10 ms. © 2008 IOP Publishing Ltd.

An orientation free adaptive step detection algorithm using a smart phone in physical activity monitoring

In this paper we present an Orientation Free Adaptive Step Detection (OFASD) algorithm for deployment in a smart phone for the purposes of physical activity monitoring. The OFASD algorithm detects individual steps and measures a user’s step counts using the smart phone’s in-built accelerometer. The algorithm considers both the variance of an individual’s walking pattern and the orientation of the smart phone. Experimental validation of the algorithm involved the collection of data from 10 participants using five phones (worn at five different body positions) whilst walking on a treadmill at a controlled speed for periods of 5 min. Results indicated that, for steps detected by the OFASD algorithm, there were no significant differences between where the phones were placed on the body (p > 0.05). The mean step detection accuracies ranged from 93.4 % to 96.4 %. Compared to measurements acquired using existing dedicated commercial devices, the results demonstrated that using a smart phone for monitoring physical activity is promising, as it adds value to an accepted everyday accessory, whilst imposing minimum interaction from the user. The algorithm can be used as the underlying component within an application deployed within a smart phone designed to promote self-management of chronic disease where activity measurement is a significant factor, as it provides a practical solution, with minimal requirements for user intervention and less constraints than current solutions.

Open Source Telecommunications Framework for Time-Series Data from Smart Grid Sensors.

This paper presents a framework for a telecommunications interface which allows data from sensors embedded in Smart Grid applications to reliably archive data in an appropriate time-series database. The challenge in doing so is two-fold, firstly the various formats in which sensor data is represented, secondly the problems of telecoms reliability. A prototype of the authors' framework is detailed which showcases the main features of the framework in a case study featuring Phasor Measurement Units (PMU) as the application. Useful analysis of PMU data is achieved whenever data from multiple locations can be compared on a common time axis. The prototype developed highlights its reliability, extensibility and adoptability; features which are largely deferred from industry standards for data representation to proprietary database solutions. The open source framework presented provides link reliability for any type of Smart Grid sensor and is interoperable with existing proprietary database systems, and open database systems. The features of the authors' framework allow for researchers and developers to focus on the core of their real-time or historical analysis applications, rather than having to spend time interfacing with complex protocols.

Building a person home behavior model based on data from smart house sensors 2015

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Smart-phone based electrocardiogram wavelet decomposition and neural network classification

This paper discusses ECG classification after parametrizing the ECG waveforms in the wavelet domain. The aim of the work is to develop an accurate classification algorithm that can be used to diagnose cardiac beat abnormalities detected using a mobile platform such as smart-phones. Continuous time recurrent neural network classifiers are considered for this task. Records from the European ST-T Database are decomposed in the wavelet domain using discrete wavelet transform (DWT) filter banks and the resulting DWT coefficients are filtered and used as inputs for training the neural network classifier. Advantages of the proposed methodology are the reduced memory requirement for the signals which is of relevance to mobile applications as well as an improvement in the ability of the neural network in its generalization ability due to the more parsimonious representation of the signal to its inputs.

Increasing Utilization of US Electric Grids via Smart Technologies: Integration of Load Management, Real Time Pricing, Renewables, EVs and Smart Grid Sensors

Electric power grids throughout the world suffer from serious inefficiencies associated with under-utilization due to demand patterns, engineering design and load following approaches in use today. These grids consume much of the world’s energy and represent a large carbon footprint. From material utilization perspectives significant hardware is manufactured and installed for this infrastructure often to be used at less than 20-40% of its operational capacity for most of its lifetime. These inefficiencies lead engineers to require additional grid support and conventional generation capacity additions when renewable technologies (such as solar and wind) and electric vehicles are to be added to the utility demand/supply mix. Using actual data from the PJM [PJM 2009] the work shows that consumer load management, real time price signals, sensors and intelligent demand/supply control offer a compelling path forward to increase the efficient utilization and carbon footprint reduction of the world’s grids. Underutilization factors from many distribution companies indicate that distribution feeders are often operated at only 70-80% of their peak capacity for a few hours per year, and on average are loaded to less than 30-40% of their capability. By creating strong societal connections between consumers and energy providers technology can radically change this situation. Intelligent deployment of smart sensors, smart electric vehicles, consumer-based load management technology very high saturations of intermittent renewable energy supplies can be effectively controlled and dispatched to increase the levels of utilization of existing utility distribution, substation, transmission, and generation equipment. The strengthening of these technology, society and consumer relationships requires rapid dissemination of knowledge (real time prices, costs & benefit sharing, demand response requirements) in order to incentivize behaviors that can increase the effective use of technological equipment that represents one of the largest capital assets modern society has created.

Associations of self-compassion and global self-esteem with positive and negative affect and stress reactivity in daily life: Findings from a smart phone study

The present study examined trait self-compassion and trait self-esteem in relation to positive (PA) and negative affect (NA), as well as their associations with stress reactivity in daily life. One hundred and one subjects completed questionnaires on perceived stress and affect twice a day for 14 consecutive days on smart phones. Results indicated that self-compassion and global self-esteem were positively related to PA and negatively to NA. After controlling for self-esteem, self-compassion remained significantly associated with PA and NA, whereas self-esteem was no longer associated with PA and NA after controlling for self-compassion. Furthermore, results indicated that self-compassion buffered the effect of stress on NA, whereas this was not the case for global self-esteem. Neither self-compassion nor self-esteem moderated the relation of stress on PA in separate models. The results of the present study add to the growing literature regarding beneficial relations of self-compassion and psychological well-being and further emphasize the distinction of self-compassion and global self-esteem.

Smart structure sensors based on embedded fibre Bragg grating arrays in aluminium alloy matrix by ultrasonic consolidation

Smart structure sensors based on embedded fibre Bragg grating (FBG) arrays in aluminium alloy matrix by ultrasonic consolidation (UC) technique have been proposed and demonstrated successfully. The temperature, loading and bending responses of the embedded FBG arrays have been systematically characterized. The embedded FBGs exhibit an average temperature sensitivity of ~36 pm °C-1, which is three times higher than that of normal FBGs, a bending sensitivity of 0.73 nm/m-1 and a loading responsivity of ~0.1 nm kg-1 within the dynamic range from 0 kg to 3 kg. These initial experimental results clearly demonstrate that the UC produced metal matrix structures can be embedded with FBG sensor arrays to become smart structures with capabilities to monitor the structure operation and health conditions in applications.