A Beacon-Based Indoor Positioning System for Location Tracking of Patients in a Hospital

Shipkovenski, Georgi; Kalushkov, Teodor; Petkov, Emiliyan; Angelov, Vladislav

doi:10.1109/hora49412.2020.9152857

Cited by 13 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In indoor positioning, the positions of not only medical devices but also patients and medical staffs are estimated. A positioning system was constructed by attaching a bracelet to the patient's arm [9]. In this system, many BLE beacons are installed in the hospital.…”

Section: Cmdms (Computerized Medical Devicementioning

confidence: 99%

Development of a Monitoring System for the Management of Medical Devices

Kakutani¹,

Ito²,

Shima³

et al. 2023

Computer Networks &Amp; Communications

View full text Add to dashboard Cite

In recent years, according to the sophistication of Medical Devices (MD), many portable MDs have been used and maintained with central management. However, the central management lends hospital staff the MDs only with managing by a ledger, therefore, missing or subletting may be caused. Furthermore, while the demand for the MDs is increasing due to the COVID-19, there is an issue that it is difficult to operate due to the shortage of clinical engineers against management duties of the MDs. In this study, we develop a power strip device which can measure electricity usage of plugged MD and its position and propose a visualization system for position and operation ratio of the MDs. We implemented 75 developed devices in three hospitals and confirmed that the system was effective to evaluate whether the number of the MDs owned by the hospital is appropriate.

show abstract

Section: Cmdms (Computerized Medical Devicementioning

confidence: 99%

Development of a Monitoring System for the Management of Medical Devices

Kakutani¹,

Ito²,

Shima³

et al. 2023

Computer Networks &Amp; Communications

View full text Add to dashboard Cite

show abstract

“…We propose a hospital positioning system based on Bluetooth Low Energy (BLE) beacons [2] using trilateration to provide more precise, real-time positioning information. The system operates by having each patient wear a BLE bracelet, which transmits signals received by the BLE beacons.…”

Section: Introductionmentioning

confidence: 99%

A hospital positioning system based on BLE beacons

Sun

2024

Second International Conference on Informatics, Networking, and Computing (ICINC 2023)

View full text Add to dashboard Cite

This paper presents a BLE beacon-based Hospital Positioning System designed to improve the efficiency and accuracy of patient tracking and positioning in hospital environments. The system uses trilateration to provide real-time, precise positioning information by having each patient wear a BLE bracelet that transmits signals received by the BLE beacons. The signal information is collected and aggregated by the BLE gateway and processed on a cloud platform to determine the patient's specific location. The system also includes optimization algorithms, such as a greedy algorithm to determine the placement and number of BLE beacons, a shifting algorithm to smooth the RSSI values, and a trilateration algorithm to accurately calculate the patient's position. The proposed system has several advantages over traditional manual tracking methods, including improved accuracy, real-time tracking, and reduced workload for hospital staff. The system is scalable, flexible, and easy to deploy, and it can be customized to meet the specific needs of different hospitals. The system has been tested in a real hospital environment, and the results show that it is effective and reliable. This paper provides a new perspective and solution to improve healthcare services, especially patient tracking and positioning in hospital environments.

show abstract

“…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.

View full text Add to dashboard Cite

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.

show abstract

A Beacon-Based Indoor Positioning System for Location Tracking of Patients in a Hospital

Cited by 13 publications

References 8 publications

Development of a Monitoring System for the Management of Medical Devices

Development of a Monitoring System for the Management of Medical Devices

A hospital positioning system based on BLE beacons

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

Contact Info

Product

Resources

About