EchoTrace: Sensors
Enabling data collection




Screen Time

By understanding usage behaviors and patterns of participants, time events and durations can be mapped to these behavior classifiers, e.g. a participants mood and/or lifestyle changes.


Screen Time and Mobile Usage

Nearby devices can deliver insights into a participants approximate location. When considered across participants, insights into their social behaviors can potentially be inferred.


Wireless devices and Proximity

Identifying location can be classified to location type, such as a pub or library, and can have varying levels of accuracy to balance battery of the participants with the accuracy needs of the study.


Location and Classification

When individuals move, there are multiple degrees of freedom that can be observed to determine if they are climbing stairs, jogging, and generally moving that would suggest a change in position.


Accelerometer and Movement

What other sensors do we support?


If you want additional information about the sensors we can connect with,
please reach out by email or schedule a demo call

How can machine learning be applied

Data is accessible through several reporting options using Vibrent's secure cloud-hosted servers.  Researchers can download and retrieve information offline for and individual and a specific sensors or all individuals and all sensors. Alternately, for a real-time in-lab experiment and analysis, computer scientists can evaluate data through our APIs for real-time experiments and machine learning models on the exchange of data using a common set of research tools.

Remotely configuring sensors

Researchers can log into their research portal for their study and adjust associated participant data streams as a group or at an individual level.  This information can be used to trigger other sensors when enabling opportunities around machine learning and internet-of-things (IoT) applications.

By remotely configuring sensors, you decide when data should be collected, at what precision or sampling rate, and whether to turn off and on sensors programmatically to save power, bandwidth and cost of data transactions between participants and the intended use cases you have for your study. You're in control.