Navigational needs and requirements of hospital staff: Geneva University hospitals case study

Anagnostopoulos, Grigorios G.; Deriaz, Michel; Gaspoz, Jean‐Michel; Konstantas, Dimitri; Guessous, Idris

doi:10.1109/ipin.2017.8115958

Cited by 17 publications

(27 citation statements)

References 15 publications

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“…There are already algorithms that minimize patient waiting times and doctor idle times [16]. Optimizing buildings for navigation may also reduce the unnecessary amount of time that patients and visitors spend in hospitals [17]. However, an additional objective to optimize against is to minimize the number of patients coming simultaneously at the hospital to maintain a level of social distancing.…”

Section: Related Workmentioning

confidence: 99%

A Queue Management Approach for Social Distancing and Contact Tracing

Kyritsis

Deriaz

2020

2020 Third International Conference on Artificial Intelligence for Industries (AI4I)

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Social distancing is necessary to prevent the rapid spread of a highly contagious disease, such as COVID-19, at least until a vaccine is found and mass-produced. By reducing the probability of an uninfected person coming close or in physical contact with an infected one, the disease transmission in the community can be suppressed. Although social distancing is simple to comprehend, it is not always easy to implement, mainly because not all public spaces are designed with this requirement in mind. In this paper, we present a queue management tool that can be used to allow people that wait for a service practice social distancing. In our approach, people are asked to join a virtual queue, in order to avoid crowds in physical waiting rooms or long waiting queues. Machine learning is used to predict the estimated waiting time of queuers, so they are called just in time to get served. We use past data and machine learning to predict how busy a location will be so that customers can pick the best time to visit the service. Finally, we present the method we use to monitor people taking a service at any time and implement contact tracing in a privacy-preserving manner.

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Section: Related Workmentioning

confidence: 99%

A Queue Management Approach for Social Distancing and Contact Tracing

Kyritsis

Deriaz

2020

2020 Third International Conference on Artificial Intelligence for Industries (AI4I)

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“…As social distancing is known to prevent infections [30] and the navigation in hospitals is often analogue (e.g. by painted lines on the wall and floor [3]), this aggravates the possibilities to keep distance to other individuals since everyone will use the same lines. Here, Indoor Navigation/Indoor Localization (IN/IL) systems could contribute to social distancing, as every individual would receive their own navigational route depending on the target, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…by investigating the usability of location based solutions in commercial contexts by surveying frequent IN/IL system users, exists [5]. The only study available in the hospital context was conducted for hospital staff, showing that there is a demand for an IN/IL system in the Geneva University Hospital [3]. However, patients (or visitors) reasons for using IN/IL systems in hospitals have not yet been investigated despite an urgent need to navigate these groups that are unfamiliar with the environment.…”

Section: Introductionmentioning

confidence: 99%

Why do Visitors intend to use Indoor Navigation and Indoor Localization Systems in Hospitals? A Quantitative Survey from Germany

Wichmann

Leyer

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Hygiene is a very important topic in hospitals. Indoor Navigation/Indoor Localization (IN/IL) approaches are an effective way to minimize unplanned interactions and thus infections in hospitals. As hospitals are a relatively new area for the implementation of IN/IL systems, this research contributes to the field as it investigates the reasons for its acceptance by hospital visitors as an important target group. We surveyed 323 visitors in Germany concerning their reasons and intention to use an IN/IL system in a hospital. The results show that intention to use is quite high with attitude being the main predictor, perceived norms having some influence and behavioral control not being relevant at all. Thus, we highlight that the reasoned action approach is suitable for the analysis and crystallize the relevant factors influencing usage intention. The results contribute to our understanding how to convince visitors in hospitals to use IN/IL systems.

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“…Recently, the need for indoor positioning systems is rapidly growing due to the emerging indoor commercial application market, including asset tracking, personal security and entertainment (Holman, 2012) with ILBS, fueled by the proliferation of using personal smart devices. In general, the typical requirements of indoor positioning techniques using smart devices are: low cost, high accuracy and availability in a large variety of scenarios; e.g., large-scale environments (Anagnostopoulos et al, 2017). Since GPS devices generally work poorly in indoor environment, various radio-frequency (RF) based alternative approaches with different signals and sensors, such as Wi-Fi, Bluetooth Low Energy (BLE) beacons, Radio Frequency Identification (RFID), Ultra-wideband (UWB), etc., have been proposed for indoor positioning (Yassin et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A 3d Map Aided Deep Learning Based Indoor Localization System for Smart Devices

Yang

Toth

Brzezinska

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Indoor positioning technologies represent a fast developing field of research due to the rapidly increasing need for indoor location-based services (ILBS); in particular, for applications using personal smart devices. Recently, progress in indoor mapping, including 3D modeling and semantic labeling started to offer benefits to indoor positioning algorithms; mainly, in terms of accuracy. This work presents a method for efficient and robust indoor localization, allowing to support applications in large-scale environments. To achieve high performance, the proposed concept integrates two main indoor localization techniques: Wi-Fi fingerprinting and deep learning-based visual localization using 3D map. The robustness and efficiency of technique is demonstrated with real-world experiences.

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Navigational needs and requirements of hospital staff: Geneva University hospitals case study

Cited by 17 publications

References 15 publications

A Queue Management Approach for Social Distancing and Contact Tracing

A Queue Management Approach for Social Distancing and Contact Tracing

Why do Visitors intend to use Indoor Navigation and Indoor Localization Systems in Hospitals? A Quantitative Survey from Germany

A 3d Map Aided Deep Learning Based Indoor Localization System for Smart Devices

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