Real-Time Location Systems for Asset Management in Nursing Homes: An Explorative Study of Ethical Aspects

Hoof, Joost van; Verboor, Joey; Weernink, Corinne; Sponselee, Anne-mie; Sturm, Janienke; Kazak, Jan; Govers, Gérard; Zaalen, Yvonne van

doi:10.3390/info9040080

Cited by 16 publications

(18 citation statements)

References 37 publications

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“…Because lagging accounts for part of the error in predicting trajectories, we defined a delaycorrected accuracy to exclude the effect of delay, so we could measure the other part of the error when delay is not the major concern. To do this, considering that every zone change has a different delay, we followed Equation (5) to calculate the delay in predicting zone change, but we confined to a range of [-10,10] around every ground truth zone change, meaning we considered the delay to be limited to within 10 seconds; delay over 10 seconds will still be included in the delaycorrected error. Then, we compared the predicting and the delay-adjusted ground truth trajectories to obtain the delay-corrected accuracy.…”

Section: Resultsmentioning

confidence: 99%

Development of a real‐time indoor location system using bluetooth low energy technology and deep learning to facilitate clinical applications

et al. 2020

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An indoor, real-time location system (RTLS) can benefit both hospitals and patients by improving clinical efficiency through data-driven optimization of procedures. Bluetooth-based RTLS systems are cost-effective but lack accuracy and robustness because Bluetooth signal strength is subject to fluctuation. We developed a machine learning-based solution using a Long Short-Term Memory (LSTM) network followed by a Multilayer Perceptron classifier and a posterior constraint algorithm to improve RTLS performance. Training and validation datasets showed that most machine learning models perform well in classifying individual location zones, although LSTM was most reliable. However, when faced with data indicating cross-zone trajectories, all models showed erratic zone switching. Thus, we implemented a history-based posterior constraint algorithm to reduce the variability in exchange for a slight decrease in responsiveness. This network increases robustness at the expense of latency. When latency is less of a concern, we computed the latency-corrected accuracy which is 100% for our testing data, significantly improved from LSTM without constraint which is 96.2%. The balance between robustness and responsiveness can be considered and adjusted on a case-by-case basis, according to the specific needs of downstream clinical applications. This system was deployed and validated in an academic medical center. Industry best practices enabled system scaling without substantial compromises to performance or cost.In EROC at UTSW, we deployed a fleet of 142 Raspberry Pis to host the Bluetooth sensors. Raspberry Pis are small, robust, low-cost ($35), and simple-to-install computers that run a Linux distribution. They are powered and connected by four 24V routing switches using power-over-Ethernet with a voltage transformer for each Pi. Thus, they require no additional 110V electric outlets, which eliminates a common safety concern for hospitals. Additionally, because we use a wired connection to communicate with the Raspberry Pis, data transmission is more reliable than with Wi-Fi, and there is no increased burden for the Wi-Fi that might affect patient and staff internet usage. The Raspberry Pis are installed in the ceiling, invisible from the ground, so they do not affect the building's appearance.Many RTLS implementations use fixed parts in the infrastructure, such as Wi-Fi routers, as transmitters and moving parts, such as smart phones, as receivers. While this scheme is convenient for brief applications, it is limited when it comes to mass deployment. For example, the battery life of the moving parts is extremely low (e.g., 1-2 days for smart phones) because of an excessive workload that includes continuous BLE signal scanning and communication with clouds, which makes tracking equipment infeasible. Moreover, smart phones or tags with communication functions are usually more expensive than simple active BLE beacons, and their communication with clouds is not as robust or efficient as wired devices. Thus, considering battery life...

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Section: Resultsmentioning

confidence: 99%

Development of a real‐time indoor location system using bluetooth low energy technology and deep learning to facilitate clinical applications

et al. 2020

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“…Focus groups are useful when exploring subjective meanings and understandings from the research participants’ perspective, and the similarities and differences in participants’ views and experiences [42,43,44,45]. The focus group methodology has been used in housing and technology implementation studies previously [46,47,48,49].…”

Section: Methodsmentioning

confidence: 99%

The Living Environment and Thermal Behaviours of Older South Australians: A Multi-Focus Group Study

Hoof

Bennetts

Hansen

et al. 2019

IJERPH

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Ageing brings about physiological changes that affect people’s thermal sensitivity and thermoregulation. The majority of older Australians prefer to age in place and modifications to the home environment are often required to accommodate the occupants as they age and possibly become frail. However, modifications to aid thermal comfort are not always considered. Using a qualitative approach this study aims to understand the thermal qualities of the existing living environment of older South Australians, their strategies for keeping cool in hot weather and warm in cold weather and to identify existing problems related to planning and house design, and the use of heating and cooling. Data were gathered via seven focus group sessions with 49 older people living in three climate zones in South Australia. The sessions yielded four main themes, namely ‘personal factors’, ‘feeling’, ‘knowing’ and ‘doing’. These themes can be used as a basis to develop information and guidelines for older people in dealing with hot and cold weather.

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“…The privacy of older adults must be ensured in order to prevent unauthorised identification and tracking. From this perspective, the higher the level of autonomy and intelligence of IoT items, the more challenges concerning the protection of identities and privacy would come to the fore [31,32]. Oude Weernink et al [33] further distinguish between the settings in which such technologies are used, for instance, the nursing home or hospital environment versus the own home or on the street, and whether technologies are used to track or monitor older people, or even their belongings, as part of a maintenance protocol.…”

Section: Horizontal Componentmentioning

confidence: 99%

Quality of Life Framework for Personalised Ageing: A Systematic Review of ICT Solutions

Baraković

Husić

Hoof

et al. 2020

IJERPH

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Given the growing number of older people, society as a whole should ideally provide a higher quality of life (QoL) for its ageing citizens through the concept of personalised ageing. Information and communication technologies (ICT) are subject to constant and rapid development, and can contribute to the goal of an improved QoL for older adults. In order to utilise future ICT solutions as a part of an age-friendly smart environment that helps achieve personalised ageing with an increased QoL, one must first determine whether the existing ICT solutions are satisfying the needs of older people. In order to accomplish that, this study contributes in three ways. First, it proposes a framework for the QoL of older adults, in order to provide a systematic review of the state-of-the-art literature and patents in this field. The second contribution is the finding that selected ICT solutions covered by articles and patents are intended for older adults and are validated by them. The third contribution of the study are the six recommendations that are derived from the review of the literature and the patents which would help move the agenda concerning the QoL of older people and personalised ageing with the use of ICT solutions forward.

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Real-Time Location Systems for Asset Management in Nursing Homes: An Explorative Study of Ethical Aspects

Cited by 16 publications

References 37 publications

Development of a real‐time indoor location system using bluetooth low energy technology and deep learning to facilitate clinical applications

Development of a real‐time indoor location system using bluetooth low energy technology and deep learning to facilitate clinical applications

The Living Environment and Thermal Behaviours of Older South Australians: A Multi-Focus Group Study

Quality of Life Framework for Personalised Ageing: A Systematic Review of ICT Solutions

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