Development and Evaluation of BLE‐Based Room‐Level Localization to Improve Hand Hygiene Performance Estimation

Hadian, Kimia; Fernie, Geoff; Fekr, Atena Roshan

doi:10.1155/2023/4258362

Cited by 3 publications

(3 citation statements)

References 37 publications

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“…BLE offers a balance between Wi-Fi and RFID, which is promising for reliable indoor-location tracking due to its wider wireless coverage than RFID and relatively lower power consumption than Wi-Fi, which is practical for clinical applications ( Givehchian et al, 2022 ; Shang and Wang, 2022 ). There have been several reports on location-identification systems using BLE in hospitals ( Rozum et al, 2019 ; Shipkovenski et al, 2020 ; Wichmann, 2022 ; Hadian et al, 2023 ), and in particular, systems that record patient locations covering their entire life space in hospitals in real-world settings have been developed ( Abubeker and Baskar, 2023 ; Samama and Patarot, 2023 ). However, neither the automated recording of the duration of inpatients staying in their bedrooms nor its prediction has been demonstrated.…”

Section: Introductionmentioning

confidence: 99%

Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

Ogasawara,

Mukaino,

Matsunaga

et al. 2024

Front. Bioeng. Biotechnol.

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Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patients staying in their bedrooms is possible with reduced cost. We assessed using machine learning to estimate bedroom-stay duration using activity data recorded with wearable devices.Method: We recruited 99 stroke hemiparesis inpatients and conducted 343 measurements. Data on electrocardiograms and chest acceleration were measured using a wearable device, and the location name of the access point that detected the signal of the device was recorded. We first investigated the correlation between bedroom-stay duration measured from the access point as the objective variable and activity data measured with a wearable device and demographic information as explanatory variables. To evaluate the duration predictability, we then compared machine-learning models commonly used in medical studies.Results: We conducted 228 measurements that surpassed a 90% data-acquisition rate using Bluetooth Low Energy. Among the explanatory variables, the period spent reclining and sitting/standing were correlated with bedroom-stay duration (Spearman’s rank correlation coefficient (R) of 0.56 and −0.52, p < 0.001). Interestingly, the sum of the motor and cognitive categories of the functional independence measure, clinical indicators of the abilities of stroke patients, lacked correlation. The correlation between the actual bedroom-stay duration and predicted one using machine-learning models resulted in an R of 0.72 and p < 0.001, suggesting the possibility of predicting bedroom-stay duration from activity data and demographics.Conclusion: Wearable devices, coupled with machine learning, can predict the duration of patients staying in their bedrooms. Once trained, the machine-learning model can predict without continuously tracking the actual location, enabling more cost-effective and privacy-centric future measurements.

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Section: Introductionmentioning

confidence: 99%

Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

Ogasawara,

Mukaino,

Matsunaga

et al. 2024

Front. Bioeng. Biotechnol.

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“…For large indoor spaces with complex structures, like hospitals, BLE solutions have been primarily used as a room-level localization technique, limiting the understanding of detailed behaviors of individuals [8]. Conventionally, the received signal strength indicator (RSSI) is used for BLE-based localization as it is a function of the distance between the receiver and the BLE beacons/transmitters [9].…”

Section: Introductionmentioning

confidence: 99%

Graph Trilateration for Indoor Localization in Sparsely Distributed Edge Computing Devices in Complex Environments Using Bluetooth Technology

Kiarashi,

Saghafi,

Das

et al. 2023

Sensors

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Spatial navigation patterns in indoor space usage can reveal important cues about the cognitive health of participants. In this work, we present a low-cost, scalable, open-source edge computing system using Bluetooth low energy (BLE) beacons for tracking indoor movements in a large, 1700 m2 facility used to carry out therapeutic activities for participants with mild cognitive impairment (MCI). The facility is instrumented with 39 edge computing systems, along with an on-premise fog server. The participants carry a BLE beacon, in which BLE signals are received and analyzed by the edge computing systems. Edge computing systems are sparsely distributed in the wide, complex indoor space, challenging the standard trilateration technique for localizing subjects, which assumes a dense installation of BLE beacons. We propose a graph trilateration approach that considers the temporal density of hits from the BLE beacon to surrounding edge devices to handle the inconsistent coverage of edge devices. This proposed method helps us tackle the varying signal strength, which leads to intermittent detection of beacons. The proposed method can pinpoint the positions of multiple participants with an average error of 4.4 m and over 85% accuracy in region-level localization across the entire study area. Our experimental results, evaluated in a clinical environment, suggest that an ordinary medical facility can be transformed into a smart space that enables automatic assessment of individuals’ movements, which may reflect health status or response to treatment.

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“…Many RF-based localization systems have a centimeterlevel resolution and can locate a variety of objects. Many technologies, such as those in references [1]- [7], are used in a wide range of civilian, military, and smart environment applications [8]- [17]. The main objective of locationsensing systems is to identify and estimate the physical location of the objects of interest in each space [18]- [26].…”

Section: Introductionmentioning

confidence: 99%

2D-Localization Based on Tracking Antenna Using Artificial Intelligence Mapping (AIM) Approach

Madany

2023

IEEE Access

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Many recent low-range applications require only two-dimensional localization. It is more dependable and simpler to use in real-time low-power and range applications because it requires significantly fewer calculations than three dimensions. As antennas can function as sensors in this system, they are regarded as crucial components in the development of localization systems. To avoid a relatively difficult object detection and achieve 360° azimuth plane coverage, the antenna must be as small as possible. The purpose of this study is to design an effective algorithm for the 2D-localization of low-range object positioning using the proposed Artificial Intelligence Mapping (AIM) approach. The proposed AIM approach's modelling and simulation of object localization based on a tracking antenna and the proposed Modified Triangulation Method (MTM) are introduced and analyzed. The design and implementation of the object-localization system were completed. To fulfil the specific aim and suggested AIM approach, the practical 2D-Localization Object System (2D-LOS) uses a commercial identification system. Several trace scenarios for the tracking antenna were illustrated, tested, and compared with samples from other methods in the literature to demonstrate the effectiveness of the proposed localization method for different low-range applications. The proposed AIM approach, which is based on real-world data, has a relatively low resolution and a small error difference.

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Development and Evaluation of BLE‐Based Room‐Level Localization to Improve Hand Hygiene Performance Estimation

Cited by 3 publications

References 37 publications

Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

Graph Trilateration for Indoor Localization in Sparsely Distributed Edge Computing Devices in Complex Environments Using Bluetooth Technology

2D-Localization Based on Tracking Antenna Using Artificial Intelligence Mapping (AIM) Approach

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