RTLS in Oncology Operations

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.

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Aging in Place Assisted by Bluetooth Beacons

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.

Probabilistic vs Neural Network iBeacon Positioning

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.

Real Time Location Systems (RTLS) in Healthcare

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:

  • To save time searching for equipment, particularly highly mobile equipment such as wheelchairs
  • To monitor the location and temperature of medicines
  • To monitor the location of hospital porters
  • To track the location of vulnerable patients
  • To audit the visiting of care givers to patients

However, there are many more areas suitable for increasing efficiency and safety:

  • Tracking expensive assets such as beds and medical devices
  • Tracking rental/borrowed equipment to ensure they are returned on time to avoid unintended costs
  • Staff distress SOS for increased safety
  • Hygiene management, for example, on hand washing stations
  • Inventory counts and stock checks
  • Analysis of workflows to detect choke points and streamline processes
  • Production of key metrics such as time being spent with patients, patient throughput and wait times

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:

  • Tag costs – Prefer commodity rather than proprietary hardware to reduce costs and allow 2nd sourcing to reduce future risk
  • Real time – Prefer systems that detect continuously over those that rely on error-prone manual scanning
  • Scalable – Prefer software systems that will scale financially, particularly in large hospitals
  • Ongoing costs – Prefer systems that have known future system costs – ideally with a one-off licence rather than varying subscription.

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.

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Using Beacons in Clinical Trials

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:

  • Set the beacons to have the same transmission power to allow the signals to be comparable
  • Beacons should be placed in an open area in each room that is close to the activity centre of the room to minimize interference
  • Beacons should ideally have line of sight and face toward the participant and not considerably higher than the receiving smartwatch

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.

A Beacon-Based Mobility Aid for People with Dementia

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.

Using Bluetooth Beacons to Measure Gait Speed

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:

  • mac: The MAC address of the detected beacon.
  • rssi: The RSSI value obtained for the beacon.
  • device: A four-character descriptor for the smartwatch that performed the scan.
  • timestamp: The time stamp at which the scan was received.
  • user: The id of the user that was performing the experiment.
  • direction: A number (0 or 1) indicating the direction of the walk.
  • walk_id: A number that identifies each walk.
  • speed: The actual speed of the user, in $m/s$.

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.

Combining Wake Up Radio (WUR) and Bluetooth LE

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.

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Dementia Anti-Wandering Using Beacons

The Hong Kong Multimedia Technology Research Center (MTREC) has an interesting project that implements a dementia anti-wandering system using iBeacons.

A paper (pdf) explains how it uses a novel multi-hop system to track targets using mobile sensors. The multi-hop approach extends the sensing area and reduces the deployment cost.

iBeacon Cooperative Tracking

The system uses a particle filter which analyses the temporal and spatial information of the targets to achieve 4.37m and 9.46m tracking error in a campus and a shopping mall respectively.

Read about Beacons in Life Sciences

Bluetooth in Healthcare

The Bluetooth blog has a recent post on 4 Reasons to Use Bluetooth in Your Healthcare Facility. It explains some advantages of Bluetooth and mentions some uses within healthcare.

Bluetooth can be used as a way of connecting wearables and equipment to other devices. When equipment and people are Bluetooth-enabled, asset tracking and wayfinding become possible. Staff can quickly locate valuable hospital assets and patients in need for urgent care.

Another reason for using Bluetooth is reliability. The article mentions Bluetooth’s adaptive frequency hopping (AFH) that makes communication more reliable in noisy wireless environments. You can read more about the technical aspects in our post on Bluetooth LE on the Factory Floor.

A further reason for using Bluetooth, particularly Bluetooth LE, is low power. Stand-alone devices can work on coin-cell batteries for many years.

The final reason given for using Bluetooth is the ability to create larger site-wide networks using Bluetooth mesh. Mesh can be used for control, monitoring and automation systems without the need for WiFi that can be unreliable and congested in hospitals.

For a further look at usecases, see the post on RTLS in Healthcare.