Technology use in everyday life: Implications for designing for older users

This study examines the experience and attitudes of older adults towards technology and how they compare with younger age groups. Two hundred and thirty seven participants completed an extensive questionnaire exploring their daily lifestyle, use of technology, attitudes towards technology, and perceived difficulty of household devices. The main findings from the study were; (1) there was a strong motivation to learn or to continue learning to use computers by the older group; (2) social connectedness influenced how the older group used or would like to use technology and also why some preferred not to use it; and finally (3) there was an age-related increase in perceived difficulty for many household devices, however some devices maintained intergenerational usability. These finding can be used to inform the design of future intergenerational household technologies

Engineering ambient visual sensors

Visual sensors are an indispensable prerequisite for those AmI environments that require a surveillance component. One practical issue concerns maximizing the operational longevity of such sensors as the operational lifetime of an AmI environment itself is dependent on that of its constituent components. In this paper, the intelligent agent paradigm is considered as a basis for managing a camera collective such that the conflicting demands of power usage optimization and system performance are reconciled

Views from the coalface: chemo-sensors, sensor networks and the semantic sensor web

Currently millions of sensors are being deployed in sensor networks across the world. These networks generate vast quantities of heterogeneous data across various levels of spatial and temporal granularity. Sensors range from single-point in situ sensors to remote satellite sensors which can cover the globe. The semantic sensor web in principle should allow for the unification of the web with the real-word. In this position paper, we discuss the major challenges to this unification from the perspective of sensor developers (especially chemo-sensors) and integrating sensors data in real-world deployments. These challenges include: (1) identifying the quality of the data; (2) heterogeneity of data sources and data transport methods; (3) integrating data streams from different sources and modalities (esp. contextual information), and (4) pushing intelligence to the sensor level

Intelligent middleware for adaptive sensing of tennis coaching sessions

In professional tennis training matches, the coach needs to be able to view play from the most appropriate angle in order to monitor players activities. In this paper, we present a system which can adapt the operation of a series of cameras in order to maintain optimal system performance based on a set of wireless sensors. This setup is used as a testbed for an agent based intelligent middleware that can correlate data from many different wired and wireless sensors and provide effective in-situ decision making. The proposed solution is flexible enough to allow the addition of new sensors and actuators. Within this setup we also provide details of a case study for the embedded control of cameras through the use of Ubisense data

The CLARITY modular ambient health and wellness measurement platform

Emerging healthcare applications can benefit enormously from recent advances in pervasive technology and computing. This paper introduces the CLARITY Modular Ambient Health and Wellness Measurement Platform:, which is a heterogeneous and robust pervasive healthcare solution currently under development at the CLARITY Center for Sensor Web Technologies. This intelligent and context-aware platform comprises the Tyndall Wireless Sensor Network prototyping system, augmented with an agent-based middleware and frontend computing architecture. The key contribution of this work is to highlight how interoperability, expandability, reusability and robustness can be manifested in the modular design of the constituent nodes and the inherently distributed nature of the controlling software architecture.Emerging healthcare applications can benefit enormously from recent advances in pervasive technology and computing. This paper introduces the CLARITY Modular Ambient Health and Wellness Measurement Platform:, which is a heterogeneous and robust pervasive healthcare solution currently under development at the CLARITY Center for Sensor Web Technologies. This intelligent and context-aware platform comprises the Tyndall Wireless Sensor Network prototyping system, augmented with an agent-based middleware and frontend computing architecture. The key contribution of this work is to highlight how interoperability, expandability, reusability and robustness can be manifested in the modular design of the constituent nodes and the inherently distributed nature of the controlling software architecture

Radio sleep mode optimization in wireless sensor networks

Energy efficiency is a central challenge in sensor networks, and the radio is a major contributor to overall energy node consumption. Current energy-efficient MAC protocols for sensor networks use a fixed low-power radio mode for putting the radio to sleep. Fixed low-power modes involve an inherent trade-off: deep sleep modes have low current draw and high energy cost and latency for switching the radio to active mode, while light sleep modes have quick and inexpensive switching to active mode with a higher current draw. This paper proposes adaptive radio low-power sleep modes based on current traffic conditions in the network. It first introduces a comprehensive node energy model, which includes energy components for radio switching, transmission, reception, listening, and sleeping, as well as the often disregarded microcontroller energy component for determining the optimal sleep mode and MAC protocol to use for given traffic scenarios. The model is then used for evaluating the energy-related performance of our recently proposed RFIDImpulse protocol enhanced with adaptive low-power modes, and comparing it against BMAC and IEEE 802.15.4, for both MicaZ and TelosB platforms under varying data rates. The comparative analysis confirms that RFIDImpulse with adaptive low-power modes provides up to 20 times lower energy consumption than IEEE 802.15.4 in low traffic scenario. The evaluation also yields the optimal settings of low-power modes on the basis of data rates for each node platform, and provides guidelines and a simple algorithm for the selection of appropriate MAC protocol, low-power mode, and node platform for a given set of traffic requirements of a sensor network application. © 2010 IEEE

Remote Electricity Actuation and Monitoring mote

This work presents the design and evaluation of the REAM (Remote Electricity Actuation and Monitoring) node based around the modular Tyndall Mote platform. The REAM node enables the user to remotely actuate power to a mains power extension board while sampling the current, voltage, power and power factor of the attached load. The node contains a current transformer interfaced to an Energy Metering IC which continuously samples current and voltage. These values are periodically read from the part by a PIC24 microcontroller, which calculates the RMS current and voltage, power factor and overall power. The resultant values can then be queried wirelessly employing the Tyndall 802.15.4 compliant wireless module

Bacteriophages from faecal contamination are an important reservoir for AMR in aquatic environments

Bacteriophages have been shown to play an important role in harbouring and propagating antibiotic resistance genes (ARGs). Faecal matter contains high levels of phages, suggesting that faecal contamination of water bodies may lead to increased antimicrobial resistance (AMR) levels due to increased phage loading in aquatic environments. In this study, we assessed whether faecal pollution of three rivers (Rivers Liffey, Tolka, and Dodder) was responsible for increased levels of ARGs in phage particles using established phage-faecal markers, focusing on four ARGs (blaTEM, tet(O), qnrS, and sul1). We observed all four ARGs in phage fractions in all three rivers, with ARGs more frequently observed in agricultural and urban sampling sites compared to their source. These findings highlight the role of faecal pollution in environmental AMR and the impact of agricultural and urban activities on water quality. Furthermore, our results suggest the importance of including phages as indicators when assessing environmental AMR, as they serve as significant reservoirs of resistance genes in aquatic environments. This study provides important insights into the role of faecal pollution and phages in the prevalence of AMR in the environment and the need for their inclusion in future studies to provide a comprehensive understanding of environmental AMR.</p

Land use as a critical determinant of faecal and antimicrobial resistance gene pollution in riverine systems

The WHO recognises antimicrobial resistance (AMR) as a global health threat. The environment can act as a reservoir, facilitating the exchange and the physical movement of resistance. Aquatic environments are at particular risk of pollution, with large rivers subject to pollution from nearby human, industrial or agricultural activities. The land uses associated with these activities can influence the type of pollution. One type of pollution and a likely contributor to AMR pollution that lowers water quality is faecal pollution. Both pose an acute health risk and could have implications for resistance circulating in communities. The effects of land use are typically studied using physiochemical parameters or in isolation of one another. However, this study aimed to investigate the impact of different land uses on riverine systems. We explored whether differences in sources of faecal contamination are reflected in AMR gene concentrations across agricultural and urban areas. Water quality from three rivers impacted by different land uses was assessed over one year by quantifying faecal indicator bacteria (FIB), microbial source tracking markers (MST) and AMR genes. In addition, a multiparametric analysis of AMR gene pollution was carried out to understand whether agricultural and urban areas are similarly impacted. Faecal indicators varied greatly, with the highest levels of FIB and the human MST marker observed in urban regions. In addition, these faecal markers correlated with AMR genes. Similarly, significant correlations between the ruminant MST marker and AMR gene levels in agriculture areas were observed. Overall, applying multiparametric analyses to include AMR gene levels, separation and clustering of sites were seen based on land use characterisation. This study suggests that differences in prescription of antimicrobials used in animal and human healthcare may influence environmental resistomes across agricultural and urban areas. In addition, public health risks due to exposure to faecal contamination and AMR genes are highlighted.</p