GumPack: A Personal Health Assistant with Reconfigurable Surface Components

Li, Kejia; Warren, Steven F.

doi:10.1260/2040-2295.4.1.145

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…A personalized assistive technology for persons with dementia can become part of the living environment and a routine daily life to support independence and improve quality of life (Kerssens et al , 2015; Hein et al , 2015; Lazarou et al , 2016). The method of personalization through personal health assistance using mobile technology equipped with multi-sensors was developed for healthcare monitoring purposes (Li and Warren, 2013; Chen et al , 2005). Multiple sensors have been used to remotely monitor the changes of physiological data (Fanucci et al , 2013; Palumbo et al , 2014; Malhi et al , 2012).…”

Section: Introductionmentioning

confidence: 99%

Personalized adaptive system for elderly care in smart home using fuzzy inference system

Kurnianingsih

Nugroho

Widyawan

et al. 2018

IJPCC

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Purpose The decline of the motoric and cognitive functions of the elderly and the high risk of changes in their vital signs lead to some disabilities that inconvenience them. This paper aims to assist the elderly in their daily lives through personalized and seamless technologies. Design/methodology/approach The authors developed a personalized adaptive system for elderly care in a smart home using a fuzzy inference system (FIS), which consists of a predictive positioning system, reflexive alert system and adaptive conditioning system. Reflexive sensing is obtained from a body sensor and environmental sensor networks. Three methods comprising the FIS generation algorithm – fuzzy subtractive clustering (FSC), grid partitioning and fuzzy c-means clustering (FCM) – were compared to obtain the best prediction accuracy. Findings The results of the experiment showed that FSC produced the best F1-score (96 per cent positioning accuracy, 94 per cent reflexive alert accuracy, 96 per cent air conditioning accuracy and 95 per cent lighting conditioning accuracy), whereas others failed to predict some classes and had lower validation accuracy results. Therefore, it is concluded that FSC is the best FIS generation method for our proposed system. Social implications Personalized and seamless technologies for elderly implies life-share awareness, stakeholder awareness and community awareness. Originality/value This paper presents a model of personalized adaptive system based on their preferences and medical reference, which consists of a predictive positioning system, reflexive alert system and adaptive conditioning system.

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Section: Introductionmentioning

confidence: 99%

Personalized adaptive system for elderly care in smart home using fuzzy inference system

Kurnianingsih

Nugroho

Widyawan

et al. 2018

IJPCC

View full text Add to dashboard Cite

show abstract

“…Technology advances have greatly extended capabilities in engineering of healthcare-related systems such as body area networks (BAN) [1], personalized health monitoring (PHM) [2], and personal health assistants (PHA) [3], to name a few. Multiple medical instrumentations are integrated within those systems and are able to ensure an interruptible remote monitoring of the patient's health through Internet-based communication.…”

Section: Introductionmentioning

confidence: 99%

“…This technological leap opens new possibilities in creating more advanced medical devices as well as medical applications. On the other hand, due to a growing proportion of senior citizens (e.g., in developed societies), there is an evident demand for the more advanced and personalized medical systems to suit user-specific needs [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A Model-Driven Framework to Develop Personalized Health Monitoring

et al. 2016

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Both distributed healthcare systems and the Internet of Things (IoT) are currently hot topics. The latter is a new computing paradigm to enable advanced capabilities in engineering various applications, including those for healthcare. For such systems, the core social requirement is the privacy/security of the patient information along with the technical requirements (e.g., energy consumption) and capabilities for adaptability and personalization. Typically, the functionality of the systems is predefined by the patient's data collected using sensor networks along with medical instrumentation; then, the data is transferred through the Internet for treatment and decision-making. Therefore, systems creation is indeed challenging. In this paper, we propose a model-driven framework to develop the IoT-based prototype and its reference architecture for personalized health monitoring (PHM) applications. The framework contains a multi-layered structure with feature-based modeling and feature model transformations at the top and the application software generation at the bottom. We have validated the framework using available tools and developed an experimental PHM to test some aspects of the functionality of the reference architecture in real time. The main contribution of the paper is the development of the model-driven computational framework with emphasis on the synergistic effect of security and energy issues.

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BRAINsens: Body-Worn Reconfigurable Architecture of Integrated Network Sensors

2018

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Body sensor network (BSN) is a promising human-centric technology to monitor neurophysiological data. We propose a fully-reconfigurable architecture that addresses the major challenges of a heterogenous BSN, such as scalabiliy, modularity and flexibility in deployment. Existing BSNs especially with Electroencephalogarm (EEG) have these limitations mainly due to the use of driven-right-leg (DRL) circuit. We address these limitations by custom-designing DRL-less EEG smart sensing nodes (SSN) for modular and spatially distributed systems. Each single-channel EEG SSN with a input-referred noise of 0.82 μV and CMRR of 70 dB (at 60 Hz), samples brain signals at 512 sps. SSNs in the network can be configured at the time of deployment and can process information locally to significantly reduce data payload of the network. A Control Command Node (CCN) initializes, synchronizes, periodically scans for the available SSNs in the network, aggregates their data and sends it wirelessly to a paired device at a baud rate of 115.2 kbps. At the given settings of the IC bus speed of 100 kbps, CCN can configure up to 39 EEG SSNs in a lego-like platform. The temporal and frequency-domain performance of the designed "DRL-less" EEG SSNs is evaluated against a research-grade Neuroscan and consumer-grade Emotiv EPOC EEG. The results show that the proposed network system with wearable EEG can be deployed in situ for continuous brain signal recording in real-life scenarios. The proposed system can also seamlessly incorporate other physiological SSNs for ECG, HRV, temperature etc. along with EEG within the same topology.

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GumPack: A Personal Health Assistant with Reconfigurable Surface Components

Cited by 5 publications

References 14 publications

Personalized adaptive system for elderly care in smart home using fuzzy inference system

Personalized adaptive system for elderly care in smart home using fuzzy inference system

A Model-Driven Framework to Develop Personalized Health Monitoring

BRAINsens: Body-Worn Reconfigurable Architecture of Integrated Network Sensors

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