An Extensible System for Sleep Activity Pattern Monitoring

Biswas, Jit; Jayachandran, M.; Li, Shue; Xiao, Wendong; Yap, Philip

doi:10.1109/issnip.2007.4496904

Cited by 10 publications

(7 citation statements)

References 6 publications

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“…From these loosely coupled services, applications are created. In this paper, we present three such applications, namely Indoor/Outdoor Sleep Activity Pattern Monitoring [4], Continence Management in Smart Nursing Home [5], and Automated Recognition of Meal Preparation and Eating [6]. The first two are more applied and have been incorporated into trials involving people with dementia.…”

Section: The Reminder Service (Rs)mentioning

confidence: 99%

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Health and wellness monitoring through wearable and ambient sensors: exemplars from home-based care of elderly with mild dementia

et al. 2010

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Monitoring and timely intervention are extremely important in the continuous management of health and wellness among all segments of the population, but particularly among those with mild dementia. In relation to this, we prescribe three design principles for the construction of services and applications. These are ambient intelligence, service continuity, and micro-context. In this paper, we provide three exemplars from our research and development activities that illustrate the use of these design principles in the construction of services and applications. All the applications are drawn from the field of care for mild dementia patients in their living quarters.

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Section: The Reminder Service (Rs)mentioning

confidence: 99%

“…Mild cognitive decline Mild working memory deficits (attention, concentration) 4 Moderate cognitive decline Episodic memory deficits (memory of recent events) 5 Moderately severe cognitive decline Explicit memory deficits (ability to accomplish usual tasks) 6 Severe cognitive decline Severe memory deficits (which cause delusion) 7…”

Section: Introductionmentioning

confidence: 99%

Health and wellness monitoring through wearable and ambient sensors: exemplars from home-based care of elderly with mild dementia

et al. 2010

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“…In other ongoing work [7] we are developing service oriented architectures for monitoring patient information such as accelerometer data about their movements which allow us to gauge their sleep activity patterns. In another ongoing project [5] we are dealing with the problem of continence management among the bed-ridden elderly or patients with advanced stages of dementia, through the use of a smart monitoring system that can detect the wetness level of a patient's soiled diaper and forward an appropriate message to the care-giver.…”

Section: Eating Activity Detectionmentioning

confidence: 99%

Model and algorithmic framework for detection and correction of cognitive errors

Feki

Biswas

Tolstikov

2009

THC

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This paper outlines an approach that we are taking for elder-care applications in the smart home, involving cognitive errors and their compensation. Our approach involves high level modeling of daily activities of the elderly by breaking down these activities into smaller units, which can then be automatically recognized at a low level by collections of sensors placed in the homes of the elderly. This separation allows us to employ plan recognition algorithms and systems at a high level, while developing stand-alone activity recognition algorithms and systems at a low level. It also allows the mixing and matching of multi-modality sensors of various kinds that go to support the same high level requirement.Currently our plan recognition algorithms are still at a conceptual stage, whereas a number of low level activity recognition algorithms and systems have been developed. Herein we present our model for plan recognition, providing a brief survey of the background literature. We also present some concrete results that we have achieved for activity recognition, emphasizing how these results are incorporated into the overall plan recognition system.

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“…Let us refer to [2], [5], [25], [27], [31], [32], [33], [35], [36] for examples of recent results, background information and further references.…”

Section: Introductionmentioning

confidence: 99%

Empirical Study of Decision Trees and Ensemble Classifiers for Monitoring of Diabetes Patients in Pervasive Healthcare

Kelarev

Stranieri

Yearwood

et al. 2012

2012 15th International Conference on Network-Based Information Systems

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Diabetes is a condition requiring continuous everyday monitoring of health related tests. To monitor specific clinical complications one has to find a small set of features to be collected from the sensors and efficient resource-aware algorithms for their processing. This article is concerned with the detection and monitoring of cardiovascular autonomic neuropathy, CAN, in diabetes patients. Using a small set of features identified previously, we carry out an empirical investigation and comparison of several ensemble methods based on decision trees for a novel application of the processing of sensor data from diabetes patients for pervasive health monitoring of CAN. Our experiments relied on an extensive database collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University and concentrated on the particular task of the detection and monitoring of cardiovascular autonomic neuropathy. Most of the features in the database can now be collected using wearable sensors. Our experiments included several essential ensemble methods, a few more advanced and recent techniques, and a novel consensus function. The results show that our novel application of the decision trees in ensemble classifiers for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the outcomes obtained previously in the literature.

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An Extensible System for Sleep Activity Pattern Monitoring

Cited by 10 publications

References 6 publications

Health and wellness monitoring through wearable and ambient sensors: exemplars from home-based care of elderly with mild dementia

Health and wellness monitoring through wearable and ambient sensors: exemplars from home-based care of elderly with mild dementia

Model and algorithmic framework for detection and correction of cognitive errors

Empirical Study of Decision Trees and Ensemble Classifiers for Monitoring of Diabetes Patients in Pervasive Healthcare

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