An Internet-of-Things (IoT) system development and implementation for bathroom safety enhancement

Statistics show that a bathroom is one of the most hazardous places especially for older people. Older people typically have greater difficulties with mobility and balance, making them more vulnerable to fall and slip injuries in a bathroom and causing serious health issues related to short and long-term well-being. Various components in a bathroom including shower, tub, floor, and toilet have been re-designed, and independently upgraded their ergonomics and safety aspects; however, the number of bathroom injuries remains consistently high in general. Internet-of-Things (IoT) is a new concept applicable to almost everywhere and man-made objects. Wireless sensors detect abnormalities and send data through the network. A large amount of data can be collected from multiple IoT systems and it can be utilized for a big data analysis. The big data may reveal a hidden positive outcome beyond the initially intended purposes. A few commercial IoT applications such as wearable health monitoring and intelligent transportation systems are available. Nevertheless, An IoT application for a bathroom is not currently known. Unlike other applications, bathrooms have some unique aspects such as privacy and wet environment. This paper presents a holistic conceptual approach of an Internet-of-Things (IoT) system development and implementation to enhance bathroom safety. The concept focuses on the application in a large nursing care facility as a pilot testing bed. Authors propose 1) sensor selection and application, 2) integration of a wireless sensor local network system, 3) design concept for IoT implementation, and 4) a big data analysis system model in this paper

Intelligent Energy Optimization for User Intelligible Goals in Smart Home Environments

Intelligent management of energy consumption is one of the key issues for future energy distribution systems, smart buildings, and consumer appliances. The problem can be tackled both from the point of view of the utility provider, with the intelligence embedded in the smart grid, or from the point of view of the consumer, thanks to suitable local energy management systems (EMS). Conserving energy, however, should respect the user requirements regarding the desired state of the environment, therefore an EMS should constantly and intelligently find the balance between user requirements and energy saving. The paper proposes a solution to this problem, based on explicit high-level modeling of user intentions and automatic control of device states through the solution and optimization of a constrained Boolean satisfiability problem. The proposed approach has been integrated into a smart environment framework, and promising preliminary results are reporte

SAT based Enforcement of Domotic Effects in Smart Environments

The emergence of economically viable and efficient sensor technology provided impetus to the development of smart devices (or appliances). Modern smart environments are equipped with a multitude of smart devices and sensors, aimed at delivering intelligent services to the users of smart environments. The presence of these diverse smart devices has raised a major problem of managing environments. A rising solution to the problem is the modeling of user goals and intentions, and then interacting with the environments using user defined goals. `Domotic Effects' is a user goal modeling framework, which provides Ambient Intelligence (AmI) designers and integrators with an abstract layer that enables the definition of generic goals in a smart environment, in a declarative way, which can be used to design and develop intelligent applications. The high-level nature of domotic effects also allows the residents to program their personal space as they see fit: they can define different achievement criteria for a particular generic goal, e.g., by defining a combination of devices having some particular states, by using domain-specific custom operators. This paper describes an approach for the automatic enforcement of domotic effects in case of the Boolean application domain, suitable for intelligent monitoring and control in domotic environments. Effect enforcement is the ability to determine device configurations that can achieve a set of generic goals (domotic effects). The paper also presents an architecture to implement the enforcement of Boolean domotic effects, and results obtained from carried out experiments prove the feasibility of the proposed approach and highlight the responsiveness of the implemented effect enforcement architectur

‘Future Bathroom’, What to make? Or How to Make? Challenges in meeting sustainable needs.

This paper is a case study that describes a design research programme, ‘the future bathroom’, undertaken by the authors which illuminates both challenges and solutions for inclusive and sustainable design. A co-design research methodology was adopted and engaged older users and community lay researchers to help overcome the barriers of developing a comprehensive understanding of the issues related to highly personal, private and intimate activities. We adopt the term co-design to describe an approach to design that encourages both user involvement and interdisciplinary design. Our challenge has been to provide an environment where an exchange of ideas between stakeholders could take place and to foster what Manzini (1) has referred to as a ‘creative community’. From the project emerged both insight and understanding of age related disability and bathroom use and potential design solutions to support these needs. Adopting an inclusive approach to design research we have developed flexible, durable and sustainable solutions that meet the diverse and changing needs of bathroom usage The paper discusses how sustainability in the context of inclusive design might need to consider more ‘what we should make’ rather than ‘how we should make’

Semantic-based decision support for remote care of dementia patients

This paper investigates the challenges in developing a semantic-based Dementia Care Decision Support System based on the non-intrusive monitoring of the patient's behaviour. Semantic-based approaches are well suited for modelling context-aware scenarios similar to Dementia care systems, where the patient's dynamic behaviour observations (occupants movement, equipment use) need to be analysed against the semantic knowledge about the patient's condition (illness history, medical advice, known symptoms) in an integrated knowledgebase. However, our research findings establish that the ability of semantic technologies to reason upon the complex interrelated events emanating from the behaviour monitoring sensors to infer knowledge assisting medical advice represents a major challenge. We attempt to address this problem by introducing a new approach that relies on propositional calculus modelling to segregate complex events that are amenable for semantic reasoning from events that require pre-processing outside the semantic engine before they can be reasoned upon. The event pre-processing activity also controls the timing of triggering the reasoning process in order to further improve the efficiency of the inference process. Using regression analysis, we evaluate the response-time as the number of monitored patients increases and conclude that the incurred overhead on the response time of the prototype decision support systems remains tolerable

Single-occupancy simulator for ambient intelligent environment

In this paper, the simulation of an occupant’s behaviour in a single-occupant ambient intelligent environment is addressed. The algorithm of the simulator is designed flexible enough to accept different environmental profiles including the number of areas and the connections between them along with different occupant’s profiles including expected daily occupancy pattern of him/her and the uncertainty of his/her behaviour to follow this occupancy pattern. The generated occupancy signal by the simulator represents the occupancy of areas by assuming a signal level for the occupancy of each area in a single-occupant environment with the resolution of one minute in a whole day activity of the occupant in the environment. The validity of the simulator will be verified by tuning the simulator’s parameters to occupancy data collected by sensory agents from a real equivalent environment. By applying the generated data from this simulator to the data mining techniques, the ability of different techniques will be investigated

Gait Velocity Estimation using time interleaved between Consecutive Passive IR Sensor Activations

Gait velocity has been consistently shown to be an important indicator and predictor of health status, especially in older adults. It is often assessed clinically, but the assessments occur infrequently and do not allow optimal detection of key health changes when they occur. In this paper, we show that the time gap between activations of a pair of Passive Infrared (PIR) motion sensors installed in the consecutively visited room pair carry rich latent information about a person's gait velocity. We name this time gap transition time and show that despite a six second refractory period of the PIR sensors, transition time can be used to obtain an accurate representation of gait velocity. Using a Support Vector Regression (SVR) approach to model the relationship between transition time and gait velocity, we show that gait velocity can be estimated with an average error less than 2.5 cm/sec. This is demonstrated with data collected over a 5 year period from 74 older adults monitored in their own homes. This method is simple and cost effective and has advantages over competing approaches such as: obtaining 20 to 100x more gait velocity measurements per day and offering the fusion of location-specific information with time stamped gait estimates. These advantages allow stable estimates of gait parameters (maximum or average speed, variability) at shorter time scales than current approaches. This also provides a pervasive in-home method for context-aware gait velocity sensing that allows for monitoring of gait trajectories in space and time

The bathroom of the future - prospects for information and control

Across sectors, innovative data collection at a device level and command and control appliances, are providing an opportunity for improved resource efficiencies, facilities management and user experiences. The applications of intelligent technologies and localised networks are growing rapidly. This discussion paper demonstrates the value and potential applications of smart water management technologies specifically focused on commercial bathroom products. The work was commissioned by the GWA Bathrooms and Kitchens Group. The paper has been developed using available knowledge, with a literature scan of fixture driven innovations, innovations in collecting and using data from fixtures and other monitoring devices

Robust energy disaggregation using appliance-specific temporal contextual information

An extension of the baseline non-intrusive load monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail, the proposed approach uses a two-stage disaggregation methodology with appliance-specific temporal contextual information in order to capture time-varying power consumption patterns in low-frequency datasets. The proposed methodology was evaluated using datasets of different sampling frequency, number and type of appliances. When employing appliance-specific temporal contextual information, an improvement of 1.5% up to 7.3% was observed. With the two-stage disaggregation architecture and using appliance-specific temporal contextual information, the overall energy disaggregation accuracy was further improved across all evaluated datasets with the maximum observed improvement, in terms of absolute increase of accuracy, being equal to 6.8%, thus resulting in a maximum total energy disaggregation accuracy improvement equal to 10.0%.Peer reviewedFinal Published versio