Smart Homes offer potential solutions for various forms of independent living for the elderly. The assistive and protective environment afforded by smart homes offer a safe, relatively inexpensive, dependable and viable alternative to vulnerable inhabitants. Nevertheless, the success of a smart home rests upon the quality of information its decision support system receives and this in turn places great importance on the issue of correct sensor deployment. In this article we present a software tool that has been developed to address the elusive issue of sensor distribution within smart homes. Details of the tool will be presented and it will be shown how it can be used to emulate any real world environment whereby virtual sensor distributions can be rapidly implemented and assessed without the requirement for physical deployment for evaluation. As such, this approach offers the potential of tailoring sensor distributions to the specific needs of a patient in a non-evasive manner. The heuristics based tool presented here has been developed as the first part of a three stage project.
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http://dx.doi.org/10.3233/THC-2009-0550 | DOI Listing |
Sensors (Basel)
January 2025
Institute of Theoretical & Applied Informatics, Polish Academy of Sciences (IITiS-PAN), 44-100 Gliwice, Poland.
Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Center for Generic Aerospace Technology, Huanjiang Laboratory, Zhuji 311816, China.
This paper introduces Re-DQN, a deep reinforcement learning-based algorithm for comprehensive coverage path planning in lawn mowing robots. In the fields of smart homes and agricultural automation, lawn mowing robots are rapidly gaining popularity to reduce the demand for manual labor. The algorithm introduces a new exploration mechanism, combined with an intrinsic reward function based on state novelty and a dynamic input structure, effectively enhancing the robot's adaptability and path optimization capabilities in dynamic environments.
View Article and Find Full Text PDFMicromachines (Basel)
January 2025
School of Instrument and Electronics, North University of China, Taiyuan 030051, China.
As an innovative branch of electronics, intelligent electronic textiles (e-textiles) have broad prospects in applications such as e-skin, human-computer interaction, and smart homes. However, it is still a challenge to distinguish multiple stimuli in the same e-textile. Herein, we propose a dual-parameter smart e-textile that can detect human pulse and body temperature in real time, with high performance and no signal interference.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Medicine, University of Chicago, Chicago, IL, USA.
Inadequate information exists regarding physiological changes post-COVID-19 infection. We used smart beds to record biometric data following COVID-19 infection in nonhospitalized patients. Recordings of daily biometric signals over 14 weeks in 59 COVID-positive participants' homes in 2020 were compared with the same participants' data from 2019.
View Article and Find Full Text PDFFront Psychol
January 2025
PaCT Lab, Northumbria University, Newcastle upon Tyne, United Kingdom.
Despite a growing number of studies describing the digital ecosystems of the home, few have explored the human component of this ecosystem and fewer have accounted for household and relationship diversity. We asked the inhabitants of nine households to share images of their digital devices and then interviewed them about how the technology was distributed and used, what roles they adopted in relation to the different devices and what boundaries or rules they set up to manage joint use. Following a thematic analysis, we describe (i) the digital components of the ecosystem and their use; (ii) the humans in the ecosystem and their relationships with technology and with each other, and (iii) interconnectedness in terms of joint use and self- or other-imposed restrictions.
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