Panoramic Digital Health has developed a solution which comprises: 

• A wearable bracelet
• Optional "tags" that provide environmental context
• A back-end that provides study set-up and oversight
• An app to initiate the device for an individual subject
• A home hub to transfer data.

The device contains sensors that can measure activity and movement, and from which more sophisticated measures can be derived. Sensors include:

• 3 axis accelerometer
• 3 axis gyroscope
• Magnetometer
• Pressure sensor
• Temperature sensor (to detect non-wear)

Summary data is synced on a defined schedule (e.g. hourly or daily) via a home hub. 
1GByte of memory is included for raw data storage, and recovery using a USB hub during the next hospital visit. 

The 9 minute video below provides a more detailed introduction to the technology (a shorter 3 min version is available on the home page. 

X-Ray image of the Panoramic Bracelet.

Why Choose the Panoramic Platform?

• Configurable data acquisition mode
• A single device that can be flexibility configured to connect only the data required
• Enable intensive data collection over a few days, or long term concise data collection over months without charging.
• Scheduling
• Make your study adaptive: collect the data you want when you want
• Optimize battery life and avoid a deluge of unwanted data
• Low energy activity detection with Machine Learning Core
• Smart detection of activities of interest in real-time.
• Streamlines data processing and lengthens battery life
• Label the data with context 
• Control variability in your data by automatically labelling it with the context of data collection
• Battery powered beacons can be placed around the home, to provide local context
• A Bluetooth module in each beacon transmits a signal once per second that can be detected by a nearby wearable  
• Bluetooth signal strength provides and estimate of distance between wearable and each beacon.
• Room temperature, light level and noise level is transmitted by beacons to the wearable, to be stored alongside the wearable sensor data. 

More about Configurability

• The Panoramic Wearable platform contains multiple sensors
  • Accelerometer, Gyroscope, magnetometer, air pressure
  • Each sensor can be individually programmed (eg: output data rate, signal filtering)
• The panoramic wearable supports pre-defined “Data acquisition modes”, which allow you to configure which sensors are on, and how they are collecting data on a per-study basis

More about Scheduling

• The panoramic wearable lets you dynamically change the data acquisition mode based on a project-specific time schedule. A few examples: 
  • Step count all the time, but high resolution accelerometer on at night
  • 26Hz accelerometer all the time, but for 2 hour each morning, collect data with all sensors operating
• Data acquisition mode can also be altered by user interaction with the wearable – by tapping it in a particular way. 

More about the Machine Learning Core

• The Panoramic wearable can detect activities of interest from sensor data while the wearable is in low-power “sleep mode”.
• The innovative STMicroelectronics machine learning core (MLC) can be configured to continuously look out for combinations of sensor values that characterize an activity of interest
• When an activity is detected, the MLC wakes up the wearable, to store the time and duration of the detected event, and optionally the associated raw sensor data.
• The Panoramic Wearable can be can configured to detect new activities of interest by collecting training data, doing the machine learning off-line, then adding the new activity to the configuration of the wearable for a particular study.  

Adding environmental context to the sensor data

• To transform the interpretation of the collected sensor data, the panoramic platform can label the sensor measures with context from tags positioned in the home:
  • Which room is the wearer in
  • Are they with a family member or carer?
  • Which chair are they getting up from?
  • What are ambient conditions (temperature, luminosity, noise)?