We recently came across RightHear, an app that assists people with orientation difficulties or vision impairments. It provides navigation information in indoor and outdoor settings.
The app acts as a virtual directory for users, directing them through locations with audio cues (such as ‘reception is 20 feet ahead to the left’ or ‘exit is 50 feet ahead’). Users can point their phone in a specific direction to learn what’s in front of them.
For companies, the app improves accessibility compliance, aids corporate responsibility and improves a brand’s narrative regarding inclusion. It works using Bluetooth beacons that are picked up by the app. The app creates auditory descriptions and notifications. There’s also a dashboard for companies/admin to control and track the solution.
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.
Our BeaconRTLS™ and PrecisionRTLS™ produce a lot of historical data. How this data is used varies considerably from project to project. One use of the data is for determining human behaviour. For example, consumer behaviour, workplace safety behaviour, developmental child behaviour or other health-based analysis.
There’s recent research into Indoor Location Data for Tracking Human Behaviours: A Scoping Review that’s meta research in that it’s an analysis of past RTLS-based human behaviour research. The Canadian researchers looked into the varied ways behaviour can be extracted from RTLS data and the features that can extracted. They examined 79 studies using RTLS data to describe aspects of human behaviour. The most common use was to monitor health status, followed by analysing consumer behaviours, increasing safety, operational efficiency and investigating developmental child behaviours.
The main behaviour features were found to be dwell time, trajectory and proximity. While many papers were able to detect features and hence behaviours, few continued to clinically validate their findings. Beyond activity recognition, few took the opportunity to create models for use in their respective fields, for example, “detecting abnormal behaviours in older adults”. Such models might be used to provide useable baselines for behaviour and health monitoring.
The paper mentions using different locating technologies for different granularity. More specifically, RFID and IR technologies provide too low a level of granularity in location tracking that can prevent extraction of behaviours or continuous movement patterns. Conversely, UWB needs constant battery changing or recharging that can make data collection difficult.
The researchers conclude that while RTLS technologies provide a valuable tool to analyse patterns of human behaviours, future studies should use more complex feature analysis methods to make more of the richness of location-based data.
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.
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.
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.
Solutions usually detect and store contact events between Bluetooth devices that has poor interoperability when applied to smartphones. Adoption rates are also low due to privacy concerns and resultant systems depend on subsequent manual contact tracing.
Instead, a new architecture is used that comprises standard beacons carried by users and detectors placed in strategic locations where infection clusters are most likely to originate. [This is similar to the architecture used for IoT sensing using gateways.]
The system helps control disease spread at lower adoption rates. It also provides significantly higher sensitivity and specificity than existing app-based systems.