Monitoring health in smart homes using simple sensors.

We consider use of an ambient sensor network, installed in Smart Homes, to identify low level events taking place which can then be analysed to generate a resident's profile of activities of daily living (ADLs). These ADL profiles are compared to both the resident's typical profile and to known 'risky' profiles to support evidence-based interventions. Human activity recognition to identify ADLs from sensor data is a key challenge, a windowbased representation is compared on four existing datasets. We find that windowing works well, giving consistent performance. We also introduce FITsense, which is building a Smart Home environment to specifically identify increased risk of falls to allow interventions before falls occurs

Employing multi-modal sensors for personalised smart home health monitoring.

As the prevalence of IoT sensor equipment in smart homes continues to rise, long term monitoring for personalised and more representative health tracking has become more accessible. The estimation of physiological health factors such as gait and heart rate can be captured using a range of diverse sensor equipment, while behavioural changes are now being monitored using simple binary sensors through activity classification and profiling. Combining both physiological and behavioural monitoring in fixed layout properties has already allowed us to effectively consider fall risk. However, expanding application of the system to new layouts and conditions requires consideration of differing retro fit home layouts and sensor configurations. A wider selection of sensors in varying configurations could potentially allow for the identification of other health conditions such as heart disease and stroke

Activity recognition in smart homes using UWB radars

In the last decade, smart homes have transitioned from a potential solution for aging-in-place to a real set of technologies being deployed in the real-world. This technological transfer has been mostly supported by simple, commercially available sensors such as passive infrared and electromagnetic contacts. On the other hand, many teams of research claim that the sensing capabilities are still too low to offer accurate, robust health-related monitoring and services. In this paper, we investigate the possibility of using Ultra-wideband (UWB) Doppler radars for the purpose of recognizing the ongoing ADLs in smart homes. Our team found out that with simple configuration and classical features engineering, a small set of UWB radars could reasonably be used to recognize ADLs in a realistic home environment. A dataset was built from 10 persons performing 15 different ADLs in a 40 square meters apartment with movement on the other side of the wall. Random Forest was able to attain 80% accuracy with an F1-Score of 79%, and a Kappa of 77%. Those results indicate the use of Doppler radars can be a good research avenue for smart homes

RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

Fusion of footsteps and face biometrics on an unsupervised and uncontrolled environment

Ruben Vera-Rodriguez ; Pedro Tome ; Julian Fierrez ; Javier Ortega-Garcia, "Fusion of footsteps and face biometrics on an unsupervised and uncontrolled environment", Sensing Technologies for Global Health, Military Medicine, Disaster Response, and Environmental Monitoring II; and Biometric Technology for Human Identification IX, Proc. SPIE 8371 (May 1, 2012); doi:10.1117/12.918550. Copyright 2012 Society of Photo‑Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.Proceedings of the II Sensing Technologies for Global Health, Military Medicine, Disaster Response, and Environmental Monitoring; and IX Biometric Technology for Human Identification (Baltimore, Maryland, USA)This paper reports for the first time experiments on the fusion of footsteps and face on an unsupervised and not controlled environment for person authentication. Footstep recognition is a relatively new biometric based on signals extracted from people walking over floor sensors. The idea of the fusion between footsteps and face starts from the premise that in an area where footstep sensors are installed it is very simple to place a camera to capture also the face of the person that walks over the sensors. This setup may find application in scenarios like ambient assisted living, smart homes, eldercare, or security access. The paper reports a comparative assessment of both biometrics using the same database and experimental protocols. In the experimental work we consider two different applications: smart homes (small group of users with a large set of training data) and security access (larger group of users with a small set of training data) obtaining results of 0.9% and 5.8% EER respectively for the fusion of both modalities. This is a significant performance improvement compared with the results obtained by the individual systems.This work has been partially supported by projects Contexts (S2009/TIC-1485), Bio-Challenge (TEC2009-11186) and ”Catedra UAM-Telefonica”

The Role of Web Services at Home

The increase in computational power and the networking abilities of home appliances are revolutionizing the way we interact with our homes. This trend is growing stronger and opening a number of technological challenges. From the point of view of distributed systems, there is a need to design architectures for enhancing the comfort and safety of the home, which deal with issues of heterogeneity, scalability and openness. By considering the evolution of domotic research and projects, we advocate a role for web services in the domestic network, and propose an infrastructure based on web services. As a case study, we present an implementation for monitoring the health of an elder adult using multiple sensors and clients

Experiences of in-home evaluation of independent living technologies for older adults

Evaluating home-based independent living technologies for older adults is essential. Whilst older adults are a diverse group with a range of computing experiences, it is likely that many of this user group may have little experience with technology and may be challenged with age-related impairments that can further impact upon their interaction with technology. However, the evaluation life cycle of independent living technologies does not only involve usability testing of such technologies in the home. It must also consider the evaluation of the older adult’s living space to ensure technologies can be easily integrated into their homes and daily routines. Assessing the impact of these technologies on older adults is equally critical as they can only be successful if older adults are willing to accept and adopt them. In this paper we present three case studies that illustrate the evaluation life cycle of independent living technologies within TRIL, which include ethnographic assessment of participant attitudes and expectations, evaluation of the living space prior to the deployment of any technology, to the final evaluation of usability and participant perspectives