Indoor positioning systems in hospitals: A scoping review

Wichmann, Johannes

doi:10.1177/20552076221081696

Cited by 19 publications

(26 citation statements)

References 59 publications

(151 reference statements)

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“…In addition, the shift toward patient-centered care has been facilitated by technologies that assess and support daily activities and physical functions [ 11 , 34 ]. The integration of machine learning and sensor data fusion has further enhanced the ability to monitor and analyze patient movement in real time, contributing to preventive health care and improved treatment outcomes [ 12 , 35 ]. Researchers in health care and computer science have discussed the possibility of using motion tracking.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, scholars in computing have claimed that high-quality data would enhance data-driven AI processing [ 38 , 39 ]. IoT, with motion tracking techniques (eg, inertial measurement unit, smartphone, smartwatch, Mocap system, etc) [ 40 , 41 ] and AI processing (eg, human activity recognition, positioning system, etc) [ 35 , 42 ], have been widely used. It is anticipated that AI processing using high-quality motion tracking data would provide accurate and timely health information.…”

Section: Introductionmentioning

confidence: 99%

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Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers’ Perspective

Wei,

Wang

2024

JMIR Mhealth Uhealth

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Background Motion tracking technologies serve as crucial links between physical activities and health care insights, facilitating data acquisition essential for analyzing and intervening in physical activity. Yet, systematic methodologies for evaluating motion tracking data, especially concerning user activity recognition in health care applications, remain underreported. Objective This study aims to systematically review motion tracking in daily living and physical activities, emphasizing the critical interaction among devices, users, and environments from a design perspective, and to analyze the process involved in health care application research. It intends to delineate the design and application intricacies in health care contexts, focusing on enhancing motion tracking data’s accuracy and applicability for health monitoring and intervention strategies. Methods Using a systematic review, this research scrutinized motion tracking data and their application in health care and wellness, examining studies from Scopus, Web of Science, EBSCO, and PubMed databases. The review used actor network theory and data-enabled design to understand the complex interplay between humans, devices, and environments within these applications. Results Out of 1501 initially identified studies, 54 (3.66%) were included for in-depth analysis. These articles predominantly used accelerometer and gyroscope sensors (n=43, 80%) to monitor and analyze motion, demonstrating a strong preference for these technologies in capturing both dynamic and static activities. While incorporating portable devices (n=11, 20%) and multisensor configurations (n=16, 30%), the application of sensors across the body (n=15, 28%) and within physical spaces (n=17, 31%) highlights the diverse applications of motion tracking technologies in health care research. This diversity reflects the application’s alignment with activity types ranging from daily movements to specialized scenarios. The results also reveal a diverse participant pool, including the general public, athletes, and specialized groups, with a focus on healthy individuals (n=31, 57%) and athletes (n=14, 26%). Despite this extensive application range, the focus primarily on laboratory-based studies (n=39, 72%) aimed at professional uses, such as precise activity identification and joint functionality assessment, emphasizes a significant challenge in translating findings from controlled environments to the dynamic conditions of everyday physical activities. Conclusions This study’s comprehensive investigation of motion tracking technology in health care research reveals a significant gap between the methods used for data collection and their practical application in real-world scenarios. It proposes an innovative approach that includes designers in the research process, emphasizing the importance of incorporating data-enabled design framework. This ensures that motion data collection is aligned with the dynamic and varied nature of daily living and physical activities. Such integration is crucial for developing health applications that are accessible, intuitive, and tailored to meet diverse user needs. By leveraging a multidisciplinary approach that combines design, engineering, and health sciences, the research opens new pathways for enhancing the usability and effectiveness of health technologies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers’ Perspective

Wei,

Wang

2024

JMIR Mhealth Uhealth

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“…Because of their widespread applications, location-based computing and services have received more attention as the Internet of Things (IoT) has improved. As a result, Information about the target location is critical in localization systems [1,2]. Localization methods are used in developing existing systems using various technologies and methods based on the application.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Indoor Localization Based on Deep Attention Learning Model

Abozeid¹,

Taloba²,

El-Aziz³

et al. 2023

Computer Systems Science and Engineering

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Indoor localization methods can help many sectors, such as healthcare centers, smart homes, museums, warehouses, and retail malls, improve their service areas. As a result, it is crucial to look for low-cost methods that can provide exact localization in indoor locations. In this context, imagebased localization methods can play an important role in estimating both the position and the orientation of cameras regarding an object. Image-based localization faces many issues, such as image scale and rotation variance. Also, image-based localization's accuracy and speed (latency) are two critical factors. This paper proposes an efficient 6-DoF deep-learning model for image-based localization. This model incorporates the channel attention module and the Scale Pyramid Module (SPM). It not only enhances accuracy but also ensures the model's real-time performance. In complex scenes, a channel attention module is employed to distinguish between the textures of the foregrounds and backgrounds. Our model adapted an SPM, a feature pyramid module for dealing with image scale and rotation variance issues. Furthermore, the proposed model employs two regressions (two fully connected layers), one for position and the other for orientation, which increases outcome accuracy. Experiments on standard indoor and outdoor datasets show that the proposed model has a significantly lower Mean Squared Error (MSE) for both position and orientation. On the indoor 7-Scenes dataset, the MSE for the position is reduced to 0.19 m and 6.25°for the orientation. Furthermore, on the outdoor Cambridge landmarks dataset, the MSE for the position is reduced to 0.63 m and 2.03°for the orientation. According to the findings, the proposed approach is superior and more successful than the baseline methods.

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“…Recent studies [ 11 , 12 ] show that there is no current clear prevalent technology or method for IPS. The variety of environments and applications makes it difficult to find a general solution applicable to most situations, even though it emerges that WiFi and Bluetooth Low Energy (BLE) fingerprinting are the most popular methods for indoor positioning.…”

Section: Introductionmentioning

confidence: 99%

Designing and developing a mobile application for indoor real-time positioning and navigation in healthcare facilities

Luschi

Villa

Gherardelli

et al. 2022

THC

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BACKGROUND: Navigation portable applications have largely grown during the last years. However, the majority of them works just for outdoor positioning and routing, due to their architecture based upon Global Positioning System signals. Real-Time Positioning System intended to provide position estimation inside buildings is known as Indoor Positioning System (IPS). OBJECTIVE: This paper presents an IPS implemented as a mobile application that can guide patients and visitors throughout a healthcare premise. METHODS: The proposed system exploits the geolocation capabilities offered by existing navigation frameworks for determining and displaying the user’s position. A hybrid mobile application architecture has been adopted because it allows to deploy the code to multiple platforms, simplifying maintenance and upgrading. RESULTS: The developed application features two different working modes for on-site and off-site navigation, which offer both the possibility of actual navigation within the hospital, or planning a route from a list of available starting points to the desired target, without being within the navigable area. Tests have been conducted to evaluate the performance and the accuracy of the system. CONCLUSION: The proposed application aims to overcome the limitations of Global Navigation Satellite System by using magnetic fingerprinting in combination with sensor fusion simultaneously. This prevents to rely on a single technology, reducing possible system failures and increasing the scalability.

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Indoor positioning systems in hospitals: A scoping review

Cited by 19 publications

References 59 publications

Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers’ Perspective

Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers’ Perspective

An Efficient Indoor Localization Based on Deep Attention Learning Model

Designing and developing a mobile application for indoor real-time positioning and navigation in healthcare facilities

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