Single access point location tracking for in-home health monitoring

Kelly, Damien P.; McLoone, Seán; Dishongh, Terry; McGrath, Michael J.; Behan, J.

doi:10.1109/wpnc.2008.4510353

Cited by 17 publications

(15 citation statements)

References 9 publications

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“…Nevertheless, a value of 0 indicates the device is moving within the golden range. From the results of the experiment, it was noted that the RSSI contains no less information than LQ [3,4]. In addition, it shows that there is different information encoded between them.…”

Section: Rssimentioning

confidence: 96%

“…It consists of three signal measurements collected by a Bluetooth-based localisation system used in [3,4] from a four-room, denoted as Room1, Room2, Room3 and Room4, setting environment (see Figure 1). The three measurements are link quality (LQ), received signal strength indicators (RSSI) and cellular signal quality (CSQ).…”

Section: Dataset Under Studymentioning

confidence: 99%

“…LQ varies as a function of distance and obstructions between their access point and the Bluetooth device. Nevertheless, Figure 1 Layout of rooms where data were collected (adapted from [4]). …”

Section: Csqmentioning

confidence: 99%

“…Furthermore, within the maximum permitted power, the Bluetooth signals can transmit in the range of 100 meters, which is sufficient for the in-home environment test for most of cases. Kelly et al [3,4] proposed an affordable localization system using a Bluetooth enable mobile device, Blue Radios BR-SC30N class 1, as a single AP connected to a base station computer. The signals received by the base station were used to predict the user's location.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid classification approach to improving location accuracy in a Bluetooth-based room localisation system

Guo

Pan

Wang

et al. 2010

2010 International Conference on Machine Learning and Cybernetics

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It has been well recognised that the use of localisation techniques in home environment are beneficial to the development of health monitoring and activity recognition systems. The Bluetooth devices, as a kind of effective sensor with remarkable characteristics such as low cost, have been widely used in our daily life. Research has been carried out to integrate cellular network signal measurements and Bluetooth link measurements in developing home localisation systems. This paper presents a hybrid classification approach, based on the combination of Bayesian statistics and supported vector machines, to supporting the development of the Bluetooth-based room localisation system. The proposed approach considers the dependency between features and non-linear overlapping of features between rooms. The results show that the prediction accuracy has been improved in comparison to the traditional Naive Bayes classifier and the hidden Markov model used in previous studies.

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Section: Rssimentioning

confidence: 96%

Section: Dataset Under Studymentioning

confidence: 99%

Section: Csqmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hybrid classification approach to improving location accuracy in a Bluetooth-based room localisation system

Guo

Pan

Wang

et al. 2010

2010 International Conference on Machine Learning and Cybernetics

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“…Activities of daily living (Wood et al, 2008) • Safety (Lee et al, 2008) • Location determination (Kelly et al, 2008) • Gait velocity (Hagler et al, 2010, Hayes et al, 2009) • Cognition/dementia (Biswas et al, 2010) • Ambient sensors can also be used to provide inputs into actuators or other forms of integrated systems. Pressure sensors can be used in a bedroom to detect when someone exits their bed.…”

Section: Ambient Sensingmentioning

confidence: 99%

Sensor Deployments for Home and Community Settings

McGrath¹,

Scanaill²

2013

Sensor Technologies

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In this chapter, we will outline the methodologies that have been successfully developed and utilized by Intel and Technology Research for Independent Living (TRIL) Centre researchers in the design, implementation, deployment, management, and analysis of home-and community-based research pilots. Translating research from the confines of the laboratory to real-world environments of community clinics and people's homes is challenging. However, when successful, applying that research correctly can deliver meaningful benefits to both patients and clinicians. Leveraging the expertise of multidisciplinary teams is vital to ensuring that all issues are successfully captured and addressed during the project life cycle. Additionally, the end user must be the center of focus during both the development and evaluation phases. Finally, the trial must generate data and results that are sufficiently robust to withstand rigorous review by clinical and scientific experts. This is vital if any new technology solution is to be successful adopted for clinical use. Note ■The TRIL Centre is a large-scale collaboration between Intel and research teams at University College Dublin (UCD), Trinity College Dublin (TCD), National University of Ireland (NUI) Galway, and St James's Hospital in Dublin. It is a multidisciplinary research center focused on discovering how technology can be used to facilitate older people living independent lives in the location of their choice. TRIL research encompasses identifying, understanding, and accurately measuring clinical variables to predict health, prevent decline, and promote well-being and independence. The driving principles are to facilitate the measurement of critical clinical variables and test technology-enabled clinical assessments and interventions in a nonclinical environment, with validation in both environments. This is combined with a technology design and development process that is person-centered and informed by ethnographic research. Many of the insights presented in this chapter were collected during the course of TRIL research activities. The case studies outlined were carried out collaboratively by Intel and TRIL researchers.You can find additional information at www.trilcentre.org. Healthcare Domain ChallengesTechnology is used during the course of everyday practice in hospital clinics to identify and measure the extent of gait issues, cardiovascular health, sensory impairments, cognitive function, and so on. However, technologies are rarely used in community settings for a variety of reasons, including cost, usability, and the necessity for specialized

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