Ambient assisted living system with capacitive occupancy sensor

Abstract

Alert intelligent device is an ambient assisted living (AAL) system that allows the evaluation of potentially dangerous situations for elderly people living alone at home. This evaluation is obtained by an ad hoc network of sensor nodes, working in conjunction with an ambient intelligence layer embedded in a personal computer that learns from user behaviour patterns and warns when a detected pattern differs significantly from previously acquired normal patterns. Each new datum read from sensors is processed in the ambient intelligence layer through three processing levels: shallow, intermediate and deep. The shallow processing level focuses on physical data and sensory features. The intermediate level covers information interpretation and its translation into the form required by the third level: the reasoning processing or deep level. In alert intelligent device, energy is a critical issue, so that sensor devices need to be properly designed and managed to achieve significant energy saving. The use of bed/chair occupancy sensors is mandatory for this kind of ubiquitous computing system. A first way to approach this problem relied on the use of pressure mats, but several environmental drawbacks showed them inappropriate as an efficient and reliable solution for large volume deployments. Moreover, solutions based on force‐to‐resistor transducers entail power consumption budgets that keep them from being integrated on wireless sensor nodes. In this paper, a force‐capacitive transducer based sensor has been proposed, implemented and tested. This sensor is based on electromechanical film (EMFi) transducers, which are able to detect force variations in a quasi‐passive way. This kind of transducer behaves as capacitors with variable capacitance depending on the force exerted on its surface. We have developed a new technique to carry out the characterization of these transducers, where the detection of a force change is used to trigger an active mechanism that allows us to measure the weight by means of a novel modelling approach. A low‐power wireless sensor node prototype that includes this new transducer has been assembled and tested on a wide range of weights. Occupancy detection achieved by this technology has proven to be successful, increasing the total power consumption of the node by less than 15%, which makes it suitable for implementation.

A Review on Applications of Ambient Assisted Living

“…Ruiz et al [22] proposed a scheme which is based on sensors where force-capacitive transducer based sensors are used. It is an electromechanical film (EMFi) transducer which is able to detect force variations in a quasi-passive way.…”

Unsupervised discovery of activities of daily living characterized by their periodicity and variability

“…Thus, they cover a large number of topics directly related to daily living, like fall detection (Botía et al, 2012;Mirchevska et al, 2013), recognition of the visits from family members or professional caregivers (Aicha et al, 2013), sleep pattern recognition (Ni et al, 2012), identification and prediction of abnormal behaviors (Jakkula et al, 2009;Roy et al, 2011;Lotfi et al, 2012;Meffre et al, 2014). SHAAL systems may also aim to fight against isolation (Bothorel et al, 2011), consider user acceptance, privacy, accuracy and cost issues (Chernbumroong et al, 2013), and make efforts to propose efficient designs of sensor networks for the reduction of energy consumption (Francisco et al, 2014).…”

Intelligent monitoring for people assistance and safety

“…In this paper, topic models are employed to learn the latent structure and the dynamics of sensor network data in order, efficiently to analyse data with the aim of learning and discovering what is happening in the monitored environment. Lastly, the paper ‘Ambient Assisted Living System with Capacitive Occupancy Sensor’ (Fernandez‐Luque et al ., ) introduces an Ambient Assisted Living system that allows inferring a potential dangerous action of an elderly person living alone at home. This inference is obtained by a specific sensitisation with sensor nodes and a reasoning layer embedded in a personal computer that learns of the users' behaviour patterns and advices when the current one differs significantly of the normal patterns.…”