Modeling individual healthy behavior using home automation sensor data: Results from a field trial

Steen, Enno-Edzard; Frenken, Thomas; Eichelberg, Marco; Frenken, Melina; Hein, Andreas

doi:10.3233/ais-130222

Cited by 19 publications

(4 citation statements)

References 18 publications

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“…Indeed, data from ambient assisted living can also be used to infer the behavior of the person and detect changes in it to find signs of a degradation of the health of the person as soon as possible. There are multiple challenges in this case including the correct segmentation of data in uncontrolled trials [85], an important multimodality using very different kind of data [89], how to adapt the models to the person that we have to monitor [21], the problem to infer behavior or high-level data from the activities that are recognized [21] or the efficiency and capacities of different kind of recognitions [19]. This activity recognition is then crucial in applications related to health in smart homes as it will be the basis to infer the well-being of the person and to link the results to well-known and use scales in geriatrics such as ADLs.…”

Section: Ambient Assisted Livingmentioning

confidence: 99%

Internet of Things for enabling smart environments: A technology-centric perspective

Gómez

Chessa

Fleury

et al. 2019

AIS

122

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The Internet of Things (IoT) is a computing paradigm whereby everyday life objects are augmented with computational and wireless communication capabilities, typically through the incorporation of resource-constrained devices including sensors and actuators, which enable their connection to the Internet. The IoT is seen as the key ingredient for the development of smart environments. Nevertheless, the current IoT ecosystem offers many alternative communication solutions with diverse performance characteristics. This situation presents a major challenge to identifying the most suitable IoT communication solution(s) for a particular smart environment. In this paper we consider the distinct requirements of key smart environments, namely the smart home, smart health, smart cities and smart factories, and relate them to current IoT communication solutions. Specifically, we describe the core characteristics of these smart environments and then proceed to provide a comprehensive survey of relevant IoT communication technologies and architectures. We conclude with our reflections on the crucial features of IoT solutions in this setting and a discussion of challenges that remain open for research.

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Section: Ambient Assisted Livingmentioning

confidence: 99%

Internet of Things for enabling smart environments: A technology-centric perspective

Gómez

Chessa

Fleury

et al. 2019

AIS

122

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“…Some temporal models are specifically tailored to the specific sensor or data source. For example, [21], [22] proposed spatio-temporal models of motion detectors in which an anomaly is seen as a significant deviation from the typical sensor response. Although relatively simple, this approach is very sensitive to potential misplacements or faults of the deployed sensors Alternative activity and temporal models were proposed by [23] using 4D-fluents (i.e.…”

Section: Related Workmentioning

confidence: 99%

Wavelet-based Temporal Models of Human Activity for Anomaly Detection in Smart Robot-assisted Environments

Fernández-Carmona¹,

Bellotto²

2020

Preprint

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This paper presents a novel approach for temporal modelling of long-term human activities based on wavelet transforms. The model is applied to binary smart-home sensors to forecast their signals, which are used then as temporal priors to infer anomalies in office and Active & Assisted Living (AAL) scenarios. Such inference is performed by a new extension of Hybrid Markov Logic Networks (HMLNs) that merges different anomaly indicators, including activity levels detected by sensors, expert rules and the new temporal models. The latter in particular allow the inference system to discover deviations from long-term activity patterns, which cannot by detected by simpler frequency-based models. Two new publicly available datasets were collected using several smart-sensors to evaluate the wavelet-based temporal models and their application to signal forecasting and anomaly detection. The experimental results show the effectiveness of the proposed techniques and their successful application to detect unexpected activities in office and AAL settings.

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“…Work at the University of Amsterdam is examining how best to detect functional health status of older adults using ambient sensing [56] as well as how to visualise such information to ensure it is relevant to caregivers [55]. Steen et al [63] use ambient sensing and models of human behaviour to detect anomalies, or abnormal behaviour, in home environments. A field trial run in two older adults' homes over an 8 month period determined that the models used were capable of describing human behaviour within real world environments, but were not suitable for all rooms in the home, suggesting a varying nature of activities from room to room.…”

Section: Other Research Initiativesmentioning

confidence: 99%

An integrated home-based self-management system to support the wellbeing of older adults

Doyle

Kealy

Loane

et al. 2014

Journal of Ambient Intelligence and Smart Environments

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With an ageing population and the constant need towards improving the quality of life for older people in our society, there comes an urgent challenge to support people where they live in an environment that adapts to their needs as they age. While much research on ubiquitous sensor systems and telehealth devices focuses on this need, many of these solutions operate at less than full capacity, and with little scope at present to assess everyday aspects of wellbeing. They focus on detecting sudden critical physiological and behavioural changes and offer few mechanisms to support preventative actions. The challenge of predicting changes and prompting positive preventative intervention measures, aiding the avoidance of severe physical or mental harm, has not adequately been addressed. This paper discusses our experiences of designing, deploying and testing an integrated home-based ambient assisted living (AAL) system for older adults, consisting of ambient monitoring, behaviour recognition and feedback to support self-management of wellness, in addition to providing feedback on home security and home energy. It offers a complete system overview of an AAL solution in smart environments and discusses our lessons learned with the goal of assisting other researchers in the field in designing and deploying similar environments.

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Modeling individual healthy behavior using home automation sensor data: Results from a field trial

Cited by 19 publications

References 18 publications

Internet of Things for enabling smart environments: A technology-centric perspective

Internet of Things for enabling smart environments: A technology-centric perspective

Wavelet-based Temporal Models of Human Activity for Anomaly Detection in Smart Robot-assisted Environments

An integrated home-based self-management system to support the wellbeing of older adults

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