Category: Healthcare

Enhancing Indoor Navigation for Elderly with Cognitive Impairments Using iBeacon and Augmented Reality

A new research paper explores the development and application of an indoor navigation system using augmented reality (AR) technology, aimed at helping older people, especially those with Mild Cognitive Impairment (MCI) and Alzheimer’s disease. The use of technology aims to reduce the burden on caregivers and improve patient safety.

One of the key technologies used in the navigation system is iBeacon. This supports the identification of a user’s position, providing real-time navigation guidance by transmitting a unique signal to devices such as smartphones. iBeacons are strategically placed in buildings, allowing a smartphone or tablet to detect their signals and calculate the proximity to the beacon, aiding in determining the user’s location.

The indoor navigation system utilises iBeacon technology by placing beacons in different locations throughout the environment. As users move, their mobile device interacts with these beacons, receiving signals that help update their position on a digital map and provide step-by-step directions. This system also integrates augmented reality to superimpose visual cues, such as directional arrows, on the device screen, guiding users to their destination.

The use of iBeacon allows the indoor navigation system to function with high accuracy (typically within a few meters), making it suitable for complex environments like nursing homes or hospitals, where patients may have difficulty finding their way without assistance. It also works without requiring constant internet connectivity, which can be a major advantage for offline operation in secure environments. This technology is low cost, easy to deploy, and scalable, making it an ideal solution for healthcare settings focused on enhancing the mobility and autonomy of elderly individuals with cognitive impairments.

Improving RSSI Using Relabelling

Researchers from Japan have a new Relabelling Approach to Signal Patterns for Beacon-based Indoor Localization in Nursing Care Facility. Bluetooth beacons were used in a nursing care facility to enhance the tracking and location estimation of caregivers. These beacons were strategically placed throughout the facility, particularly outside patient rooms and in common areas. The caregivers carried smartphones with a mobile application called FonLog installed, which recorded the Received Signal Strength Indicator (RSSI) readings from the beacons and logged location labels.

The beacons were set to a frequency of 10 Hz with a coverage range of up to five meters. The main challenge addressed in this study was the signal loss and limited data, which affected the accuracy of indoor localisation. To improve the data quality, a relabelling approach was applied. This involved observing the signal patterns in different rooms and using these patterns to augment the training data by relabelling RSSI values from one location as samples for another location with low data samples.

This approach aimed to increase the dataset and improve the model’s accuracy in recognising the caregivers’ locations. By doing so, the accuracy of the indoor localisation model improved, achieving an accuracy of 74%, which was a 5% improvement over the original data. The use of Random Forest for location recognition further enhanced the performance, demonstrating the effectiveness of combining relabelling with machine learning techniques for indoor localisation in a healthcare setting.

Enhancing Behavioral Health Monitoring Through Bluetooth Proximity Detection

New research by researchers from Department of Behavioural and Social Sciences Brown University, USA looks into A Bluetooth-Based Smartphone App for Detecting Peer Proximity: Protocol for Evaluating Functionality and Validity.

The study describes a Bluetooth-based smartphone app designed to detect the physical proximity of peers, particularly to monitor health behaviours like alcohol consumption. The app uses Bluetooth beacons and aims to improve upon traditional Ecological Momentary Assessment (EMA) by reducing reliance on participant self-reporting through the passive detection of social interactions.

The primary objective is to develop and validate a system using Bluetooth beacons to passively detect when two or more individuals are in close proximity. The methodology involves 20 participants aged 18-29 years, using a smartphone app to collect data over three weeks. Participants’ influential peers carry Bluetooth beacons, and the app records when beacons come into proximity.

The technology could have significant applications in monitoring and intervening in health behaviours by providing real-time, accurate data on social interactions that influence these behaviours. This could be particularly useful in developing “just-in-time” adaptive interventions targeted at high-risk behaviours as they occur.

Results from the study are expected to be reported by 2025, with potential implications for enhancing the accuracy and efficacy of behavioural health interventions. The technology and methodology developed could be applicable to a broader range of behaviours and settings where social context plays a critical role in health outcomes.

Inovalon Uses Bluetooth iBeacons

Inovalon is a leading provider of cloud-based software solutions focused on data-driven healthcare. Their Inovalon ONE® Platform integrates national-scale connectivity, real-time primary source data access and advanced analytics to improve clinical outcomes and economics across the healthcare ecosystem. It is used by over 20,000 customers, informed by data from more than 78 billion medical events.

The platform uses Bluetooth beacons as part of its healthcare time and attendance management system. These beacons help in accurately tracking employee attendance and location within healthcare facilities, ensuring efficient workforce management. For more details, visit Inovalon’s website.

Indoor Locating Using Beacons in Nursing Care

The new paper Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities by Christina Garcia and Sozo Inoue from the Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Japan, presents a study on enhancing machine learning for indoor localisation in caregiving, specifically in nursing homes, using Bluetooth Low Energy (BLE) technology.

The study addresses the challenge of limited data available for training machine learning models in indoor localisation, which is critical for monitoring staff-to-patient assistance and managing workload in caregiving environments. The authors propose a data augmentation method that repurposes the Received Signal Strength (RSSI) from various beacons by re-labeling them to locations with fewer data samples, thus resolving data imbalances. This method uses standard deviation and Kullback–Leibler divergence to measure signal patterns and find matching beacons for re-labeling. Two variations of re-labeling are implemented: full and partial matching.

The performance of this method is evaluated using a real-world dataset collected over five days in a nursing care facility equipped with 25 Bluetooth beacons.

Overall, the study highlights the effectiveness of the proposed re-labelling method in enhancing indoor localisation accuracy in nursing care facilities, providing a valuable contribution to the field of caregiving and workload management.

Location System Anchor Optimisation

Researchers from Department of Computer Science, University of Jaén, Spain have a new paper on OBLEA: A New Methodology to Optimise Bluetooth Low Energy Anchors in Multi-occupancy Location Systems.

This paper introduces a new methodology called OBLEA, which aims to optimise BLE anchor configurations in indoor settings. It takes into account various BLE variables to enhance flexibility and applicability to different environments. The method uses a data-driven approach, aiming to obtain the best configuration with as few anchors as possible.

The OBLEA method offers a flexible framework for indoor spaces where the occupants are fitted with wrist activity bracelets (beacons) and BLE anchors are set up. The anchors then collect and aggregate data, sending it to a central point (fog node) via MQTT.

A dataset was generated with the maximum number of anchors in the indoor environment, and different configurations were then trained and tested based on this dataset. The best balance between fewer anchors and high accuracy was chosen as the optimal configuration.

This methodology was tested and optimised in a real-world scenario, in a Spanish nursing home in Alcaudete, Jaén. The experiment involved seven inhabitants in four shared double rooms. As a result of this optimisation, the inhabitants could be located in real time with an accuracy of 99.82%, using a method called the K-Nearest-Neighbour algorithm and collating the signal strength (RSSIs) in 30-second time windows.

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Using Bluetooth Beacons for Medical Equipment Tracking

There’s recent research from Faculty of Sciences and Technology, Bansomdejchaopraya Rajabhat University, Bangkok on Development of Prototypical Indoor Real-time Location System for Medical Equipment Management Based on BLE Devices.


The research proposes a prototype for an indoor real-time location system (RTLS) using Bluetooth Low-Energy devices for tracking medical equipment. The ESP32 microcontroller acts as a receiver node in each room, collecting the MAC address from the HM-10 beacon attached to the equipment and sending this data to a web server.

To calculate the distance between the node and the beacon, the Received Signal Strength value is filtered to reduce noise. Tests show that the average distance error between the beacon and node is roughly 3 metres and and the maximum time to update the location from node to node is less than 15 seconds.

This solution offers precise timestamps and location information based on distance, range, duration or direction.

Indoor Tracking of Individuals with Mild Cognitive Impairment

There’s new research from the USA on Indoor Localization using Bluetooth and Inertial Motion Sensors in Distributed Edge and Cloud Computing Environment (PDF). The paper describes a low-cost, scalable, edge computing system for tracking indoor movements in a large indoor facility. The system uses Bluetooth Low Energy (BLE) and Inertial Measurement Unit sensors (IMU) and is designed to facilitate therapeutic activities for individuals with Mild Cognitive Impairment.


The implementation involved instrumenting a facility with 39 edge computing systems and an on-premise fog server. Subjects carried BLE beacon and IMU sensors on-body. The researchers developed an adaptive trilateration approach that considered the temporal density of hits from the BLE beacon to surrounding edge devices to handle inconsistent coverage of edge devices in large spaces with varying signal strength. They also integrated IMU-based tracking methods using a dead-reckoning technique to improve the system’s accuracy.


The conclusions of the study showed that the proposed system could robustly localise the position of multiple people with an average error of 4 meters across the entire study space, also showing 87% accuracy for room-level localisations. The integration of IMU-based dead-reckoning with Bluetooth-based localisation further enhanced the system’s accuracy.

Using Beacons to Mitigate Staff Duress

Staff duress, also known as employee duress or worker duress, is where employees may feel threatened, intimidated, or unsafe while performing their job duties. This can occur in a variety of industries, including healthcare, education, retail, hospitality, and security.

Problems associated with staff duress include:

  • Employee safety: If employees feel threatened or unsafe, it can have a negative impact on their well-being, job satisfaction, and productivity.
  • Employer liability: Employers have a legal obligation to provide a safe working environment for their employees. Failure to do so can result in legal action and financial penalties.
  • Costly incidents: If an employee is injured due to a safety issue, it can result in costly workers’ compensation claims, lawsuits, and reputational damage to the employer.

Beacons with buttons, used with real time locating systems, can help mitigate staff duress by providing a quick and effective way for employees to signal for help in an emergency situation. These devices have a wearable or handheld button that employees can press to trigger an alert. The alert is then sent to a designated response team, who can quickly assess the situation and provide assistance as needed.

Beacons with buttons can be especially useful in industries where employees work alone or in remote locations. They can also be helpful in schools and universities, where teachers and staff members may be at risk of violence or other safety threats.

Beacons with buttons

Minew B7 In Stock

We now supply the Minew B7 wearable wristband beacon.

B7

This waterproof (IP67) beacon offers the usual iBeacon and Eddystone advertising as well as acceleration sensing. This can be via x y z in the advertising or for motion triggered broadcast. This beacon is also one of the few that also has an NFC chip for additional RFiD-based sensing. The button can be used for on/off as well as button triggered broadcasting in situations such as lone working or SOS.

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