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.
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.
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.
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.
We now supply the Minew B7 wearable wristband beacon.
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.
We have the new W7 security beacon in stock, suitable for use in places such as hospitals and prisons. It’s fitted with a security screwdriver and advertises an alert if the wristband is removed or cut off.
The W7 advertises iBeacon and Eddystone as well as acceleration (x y z) and body temperature. It’s waterproof to IP67 and is rechargeable via magnetic USB cable. The battery lasts up to a year on one charge, depending on settings.
There’s new research into a home people tracking system to detect people who are isolated at home. The context is home isolation due to Covid but this could equally be used for people with limited mobility who need to stay indoors.
The idea is to use Bluetooth rather than visual, camera-based monitoring. Smart bracelets are used that can also monitor position, blood oxygen and heart rate.
The system can also send early warning signals to organisations or relatives through instant messaging software.
The system is implemented using ESP32 single board computers and a Raspberry Pi for data collection.
The article explains how 75% of cancer program management cited workflow inefficiencies as the most concerning bottleneck to patient care delivery. There are problems with patient flow that stresses care teams and ultimately jeopardises the safety of patients.
RTLS can be used to know and optimise how long patients have been waiting, their stage of care, who has seen them and who they need to see next. This reduces both patient and staff frustration. The article claims it is possible to increase increase capacity by 10% without adding physical space.
While mentioned in an oncology setting, this is just as applicable to other health settings where patients are waiting.
A platform is proposed that supports aging in place with a focus on Ambient Assisted Living (AAL), the use of Information and Communication Technologies (ICT) to stimulate the elderly to remain active for longer, remain in society and live independently.
The paper describes beacon advertising protocols, received signal strength (RSSI), real time location systems (RTLS), trilateration and fingerprinting. It lists similar projects such as CarePredict, SANITAG, DOMO, 2PCS, CARU, LIFEPOD.
Knowing the routine of daily activities allows detection of activities, critical situations and vocal calls for assistance.
The system uses Bluetooth beacons, Bluetooth temperature/humidity sensors, ESP32-based gateways and Bluetooth wearables. It uses machine learning techniques to identify situations of potential risk, triggering triage processes and consequently any necessary actions so that a caregiver can intervene in a timely manner.
A receiver within Bluetooth bracelets detects beacons in rooms. When in a room, sensors in the room are triggered by the platform through the gateway located in the room.
There’s new research by ITMO University, Russia on the Implementation of Indoor Positioning Methods: Virtual Hospital Case. The paper describes how positioning can be used to discover typical pathways, queues and bottlenecks in healthcare scenarios. The researchers implemented and compared two ways to mitigate noise in Bluetooth beacon RSSI data.
The probabilistic and neural network methods both use past recorded data to compare with new data. This is known as fingerprinting. The neural network method is less complex when there’s need to scale to locating many objects. The researchers tested the methods at the outpatient department of the cardio medical unit of Almazov National Medical Research Centre.
Comparison of the methods showed they give approximately the same error of between 0.96m and 2.11m. However, the neural network-based approach significantly increased performance.