Ambient Assisted Living system for in-home monitoring of healthy independent elders

Botía, Juan A.; Villa, Ana; Palma, José

doi:10.1016/j.eswa.2012.01.153

Cited by 114 publications

(46 citation statements)

References 20 publications

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“…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).…”

Section: Related Workmentioning

confidence: 97%

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

Soulas

Lenca

Thépaut

2015

Engineering Applications of Artificial Intelligence

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Section: Related Workmentioning

confidence: 97%

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

Soulas

Lenca

Thépaut

2015

Engineering Applications of Artificial Intelligence

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“…Examples of emergencies are falls, leading to immobilization, cardiac arrest, or helplessness: when unnoticed for hours, they may lead to severe follow-up complications. Age-related chronic diseases, such as dementia, depression, cardiac insufficiency, or arthritis, can be faced in a proactive and preventive way in order to let patients take advantage of more adequate assistance services [4,5].…”

Section: Introductionmentioning

confidence: 99%

Monitoring elderly behavior via indoor position-based stigmergy

Barsocchi

Cimino

Ferro

et al. 2015

Pervasive and Mobile Computing

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“…That is, it is possible to generate additional knowledge about the resident from the data collected and sensed (Fayyad et al 1996). Also, deviations in the behavior of the elderly have been analyzed to detect health risks (Botia et al 2012) and situations that may require attention (Shin et al 2011). Using sensors, it is possible to infer elders' health, by monitoring the level of physical activity performed (Kaluža et al 2012), as an indicator of their frailty (Bravata et al 2007).…”

Section: Related Workmentioning

confidence: 99%

Design of a Predictive Scheduling System to Improve Assisted Living Services for Elders

Soto-Mendoza

García-Macías

Chávez

et al. 2015

ACM Trans. Intell. Syst. Technol.

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As the number of older adults increases, and with it the demand for dedicated care, geriatric residences face a shortage of caregivers, who themselves experience work overload, stress and burden. We conducted a long-term field study in three geriatric residences to understand the work conditions of caregivers with the aim of developing technologies to assist them in their work and help them deal with their burden. From this study we obtained relevant requirements and insights of design that were used to design, implement and evaluate two prototypes for supporting caregivers' tasks (e.g. electronic recording and automatic notifications), in order to validate the feasibility of their implementation in-situ and the technical requirements. The evaluation in-situ of the prototypes was conducted for a period of four weeks. The results of the evaluation, together with the data collected from six months of use, motivated the design of a predictive schedule. Such design was iteratively improved and evaluated in participative sessions with caregivers. PRESENCE, the predictive schedule we propose, triggers real-time alerts of risky situations (e.g. falls, entering off-limits areas such as the infirmary or the kitchen) and, informs caregivers of routine tasks that need to be performed (e.g. medication administration, diaper change, etc.). Moreover, PRESENCE helps caregivers to record caring tasks (such as diaper changes or medication) and wellbeing assessments (such as the mood), which are difficult to automatize. This facilitates caregiver's shift handover, and can help to train new caregivers by suggesting routine tasks and by sending reminders and timely information about the residents. It can be seen as a tool to reduce the workload of caregivers and medical staff. Instead of trying to substitute the caregiver with an automatic caring system, as proposed by others, we propose the design of our predictive schedule system that blends caregiver's assessments and measurements from sensors. We show the feasibility of predicting caregiver's tasks and a formative evaluation with caregivers that provides preliminary evidence of its utility.

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Ambient Assisted Living system for in-home monitoring of healthy independent elders

Cited by 114 publications

References 20 publications

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

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

Monitoring elderly behavior via indoor position-based stigmergy

Design of a Predictive Scheduling System to Improve Assisted Living Services for Elders

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