Our Technology

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The SENSAURA API lets you retrieve the user’s emotion from any supported wearable device, empowering you to create a new breed of smarter and more personalized app. Use our API to build real-time emotion based applications leveraging a variety of sensor data including the user’s location, date and time, biometric sensor data and much more!

CONNECT WITH YOUR USER

SUPPORTED DEVICES

We extract physiological information, such as heart rate and skin conductivity, which is transmitted from the sensors inside a variety of wearable devices.

SENSAURA PROCESS

We take that input and perform advanced signal processing techniques and regression machine learning to map the outputs in an emotion matrix.

EMOTION OUTPUTS

We provide you with an emotional output and a confidence level according to the quality signal of the input data.

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Over Five Years of Scientific Research

Our five year scientific research combine with our highly qualified Technical Advisors helped us offering you state-of-the-art emotion recognition technology  for your mobile applications by communicating with wearable devices and map the biosignals to the appropriate emotion response on a continuous basis.

The Research Behind the Technology

HRV Analysis Technique

The heart rate involves emotional cues. Generally, heart rate information is useful to differentiate between positive and negative emotions. Heart rate variability (HRV) refers to the oscillation of heart rate and has been used as an indication of mental effort and stress in adults. We also extract emotional cues from time-frquency analysis of HRV.

GSR Analysis Technique

Galvanic skin response (GSR) measures the electrical resistance changes of the skin due to variations in perspiration rate. Therefore, GSR signal involves information of sweat glands that are controlled by the sympathetic nervous system,  and  hence  the  signal  contains  information  about  the emotion of the users. Particularly, GSR values decrease due to an increase of perspiration, which usually occurs when the user is experiencing stress or surprise. For affect analysis, we extract linear and nonlinear information from time-frequency analysis of the signal over low (0-0.2 Hz) frequency component.

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Explore the Possibilities!

5 Years Worth of Research at Your Fingertips!

Five years of state-of-the-art emotion recognition technology available for your application to track daily user emotional trends and statistics.