iBeacon, Eddystone, Bluetooth, IoT sensor beacons, apps, platforms
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.
This uses MQTT, Node-Red and a database.
The Future of Personal Health has an article on Innovate Oncology Operations With RTLS Patient Flow Technology.
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.
There’s recent research on Active Aging in Place Supported by a Caregiver-Centered Modular Low-Cost Platform (pdf) by João Paulo Rangel Marques Capinha of Nova School Of Science And Technology, Portugal. Aging in place is where the elderly reside in their own homes rather than being taken into care.
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.
Due to the pandemic, hospitals and care facilities have been experiencing greater patient numbers leading to pressures to accelerate digital transformation to increase efficiency. At BeaconZone, these are the main reasons customers have been using locating systems:
However, there are many more areas suitable for increasing efficiency and safety:
Time saved improving the above activities leads to more time being spent with patients and hence potentially saved lives.
Here are some considerations if you are comparing solutions:
One final tip. It’s our experience that healthcare providers under-estimate the human element in attempting to implement new systems. There are often internal problems as to who will be responsible for a) purchasing, b) installing and c) running new systems. Work these out and agree up-front before embarking on these transformative changes so as to prevent your project becoming blocked.
Read about PrecisionRTLS™
There’s recent research by Roche Pharma Research and Early Development (pRED), Switzerland on a Beacon-Based Remote Measurement of Social Behaviour in ASD Clinical Trials: A Technical Feasibility Assessment.
Beacons were used to determine the location of participants in an observational Autism Spectrum Disorder (ASD) clinical trial designed to assess social behaviour. Beacons were placed by the participants or caregivers in separate rooms in the household and a smartwatch used to detect the beacons as the participant moved from room to room. A smartphone app was used to map each beacon with each room.
A key aspect of the study is that it was conducted with no participant training and without the supervision of a technical person.
The study also provides a comparison with prior work and a comparison of locating technologies:
The researchers provide some good practice guidelines for using beacons for indoor locating:
The study achieved an accuracy of 97.2% proving that beacons have the potential to provide deep insights into in-home behaviour. This provides more objective data than would be the case with commonly used questionnaire-based studies.
James Bayliss, a final year industrial design student at Loughborough University, has designed a smart mobility aid that uses beacons. It’s allows people with dementia to live safely in their own home for longer.
The system, called ‘AIDE’, comprises of a walking stick that works with Bluetooth beacons situated around the home.
It tracks the person’s movement and uses machine learning software to detect behaviours and actions that are out of the ordinary. The system also provides reminders to the person to help re-orient them if they have a confused episode.
There’s recent research into using Bluetooth beacons to measure human gait speed. The ability to walk can be used as a core indicator of health in aging and disease. For example, it can enable early detection of cognitive diseases such as dementia or Alzheimer’s disease.
Researchers at Universitat Jaume I and University of Extremadura, Spain, have created a new dataset. In their paper BLE-GSpeed: A New BLE-Based Dataset to Estimate User Gait Speed (pdf) they describe how they collected the data.
The database is freely available and includes:
It database contains RSSI measurements from different wearable devices and different BLE beacons, corresponding to 382 walks performed by 13 actors. The open source code used is available on GitHub.
There’s interesting new research from University of Oulu, Finland, on Wake-up radio enabled BLE wearables: empirical and analytical evaluation of energy efficiency.
Wake Up Radio (WUR) uses a very low power device that senses a radio signal to switch other devices, in this case a Bluetooth LE transmitter. A AS3930 WUR senses a signal in the range 110-150 kHz and switches a Texas Instruments Bluetooth CC2640R2 LaunchPad board.
The idea is that usually Bluetooth LE advertises every say 100ms to 1000ms and this is wasteful on battery power if the advertising is only needed for short periods of time. The paper assesses the feasibility of using WUR to turn advertising on and off to save battery power. While this is in in the context of wearables, the authors don’t mention much more regarding what might switch the beacons to advertise, other than:
The transmitter of this wake-up signal, which is usually a less restricted device, might be integrated with the communication infrastructure or deployed as an independent system element
The authors later mention healthcare so perhaps wearable beacons might only transmit when needed in particular areas.
It’s also mentioned that WUR can mitigate against the problem of interference when many Bluetooth devices advertise at the same time. This problem is rare and requires a very large number of devices. The authors later mention healthcare but this is unlikely to be a problem. A warehouse with thousands of assets might be a more realistic scenario. In this case, you could envisage wanting a Bluetooth beacon only transmitting when invited to do so.
The paper has some useful charts showing usual Bluetooth power use over time (without WUR):
You can see the periodic advertising which isn’t regular due to the 10ms long pseudo-random delay between advertisements. This is the part of the Bluetooth standard that helps ensure two device that collide usually don’t do so the next time they advertise. In between advertising, the power use a very low 0.3 µW.
The paper shows that energy consumption of the system as a function of the number of wake-ups in a period of time and the maximum application-level latency:
The paper concludes that the WUR approach can be more energy efficient when the desired latency for data delivery is below 2.11s. Even though the consumption of the WUR is low, it unfortunately exceeds the level of a BLE only system sleep mode by almost two orders of magnitude.
In our opinion the researchers are trying to improve on something that is already very low power. In between advertising, power use is extremely low. A CR2477 battery in a Bluetooth wearable can advertise periodically for up to 3 years. Also, for the wearable scenario, it’s more normal to use a low power accelerometer to only have the wearable transmit when moving. This way the battery lasts an extremely long time that’s limited more by the physical lifetime of the battery (5 to 10 years) rather than battery consumption.
