Contextualising water use in residential settings: a survey of non-intrusive techniques and approaches

Water monitoring in households is important to ensure the sustainability of fresh water reserves on our planet. It provides stakeholders with the statistics required to formulate optimal strategies in residential water management. However, this should not be prohibitive and appliance-level water monitoring cannot practically be achieved by deploying sensors on every faucet or water-consuming device of interest due to the higher hardware costs and complexity, not to mention the risk of accidental leakages that can derive from the extra plumbing needed. Machine learning and data mining techniques are promising techniques to analyse monitored data to obtain non-intrusive water usage disaggregation. This is because they can discern water usage from the aggregated data acquired from a single point of observation. This paper provides an overview of water usage disaggregation systems and related techniques adopted for water event classification. The state-of-the art of algorithms and testbeds used for fixture recognition are reviewed and a discussion on the prominent challenges and future research are also included

Benefits and challenges of using smart meters for advancing residential water demand modeling and management: a review

Over the last two decades, water smart metering programs have been launched in a number of medium to large cities worldwide to nearly continuously monitor water consumption at the single household level. The availability of data at such very high spatial and temporal resolution advanced the ability in characterizing, modeling, and, ultimately, designing user-oriented residential water demand management strategies. Research to date has been focusing on one or more of these aspects but with limited integration between the specialized methodologies developed so far. This manuscript is the first comprehensive review of the literature in this quickly evolving water research domain. The paper contributes a general framework for the classification of residential water demand modeling studies, which allows revising consolidated approaches, describing emerging trends, and identifying potential future developments. In particular, the future challenges posed by growing population demands, constrained sources of water supply and climate change impacts are expected to require more and more integrated procedures for effectively supporting residential water demand modeling and management in several countries across the world

A survey study of steering wheel vibration and sound in automobiles at idle

Copyright @ 2009 Engineering Integrity SocietyThis work is supported by Shell Global Solutions UK for their sponsorship of this research as part of the activities of the EFII3 project

HUMAN ACTIVITY RECOGNITION IN SMART-HOME ENVIRONMENTS FOR HEALTH-CARE APPLICATIONS

With a growing population of elderly people, the number of subjects at risk of cognitive disorders is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Clinicians are interested in monitoring several behavioral aspects for a wide variety of applications: early diagnosis, emergency monitoring, assessment of cognitive disorders, etcetera. Among the several behavioral aspects of interest, anomalous behaviors while performing activities of daily living (ADLs) are of great importance. Indeed, these anomalies can be indicators of serious cognitive diseases like Mild Cognitive Impairment. The recognition of such abnormal behaviors relies on robust and accurate ADLs recognition systems. Moreover, in order to enable unobtrusive and privacy-aware monitoring, environmental sensors in charge of unobtrusively capturing the interaction of the subject with the home infrastructure should be preferred. This thesis presents several contributions on this topic. The major ones are two novel hybrid ADLs recognition algorithms. The former is supervised while the latter is unsupervised. Preliminary results, which still need to be confirmed, show that the recognition rate of the unsupervised method is comparable to the one obtained by the supervised one, with the great advantage of not requiring the acquisition of an annotated dataset. Beyond ADLs recognition, other contributions on smart sensing and anomaly recognition are presented. Regarding unobtrusive sensing, we propose a machine learning technique to detect fine-grained manipulations performed by the inhabitant on household objects instrumented with tiny accelerometer sensors. Finally, a novel rule-based framework for the recognition of fine-grained abnormal behaviors is presented. Experimental results on several datasets show the effectiveness of all the proposed techniques

Augmenting objects at home through programmable sensor tokens: A design journey

End-user development for the home has been gaining momentum in research. Previous works demonstrate feasibility and potential but there is a lack of analysis of the extent of technology needed and its impact on the diversity of activities that can be supported. We present a design exploration with a tangible end-user toolkit for programming smart tokens embedding different sensing technologies. Our system allows to augment physical objects with smart tags and use trigger-action programming with multiple triggers to define smart behaviors. We contribute through a field-oriented study that provided insights on (i) household's activities as emerging from people's lived experience in terms of high-level goals, their ephemerality or recurrence, and the types of triggers, actions and interactions with augmented objects, and (ii) the programmability needed for supporting desired behaviors. We conclude that, while trigger action covers most scenarios, more advanced programming and direct interaction with physical objects spur novel uses.This work was supported by the 2015 UC3M Mobility Grant, the Spanish Ministry of Economy and Competitivity (TIN2014-56534-R, CREAx) and by the Academy of Finland (286440, Evidence)