QuasaR™ is the most accurate optical biometric sensor in the the market
Our wearable device uses the QuasaR™ sensor, and can track the maximum number of health and fitness parameters in the marketplace
Sensor - Our patent pending technology measures Heart Rate, Heart Rate Variability, Respiratory Rate and Blood Oxygen Saturation with medical grade accuracy.
Device - Contains the QuasaR™ Sensor, temperature sensor, 9DoF motion sensor, GPS and Bluetooth.
AI powered haptic engine - (custom firmware) - Builds cardiac endurance, by making the user run at the lowest speed where they achieve their highest heart rate.
Strap - Patented strap system for wearing QuasaR™ Device.
Apparel - Apparel with the partner’s branding fits on the top of the QuasaR™ strap system.
SDK - Helps our partner’s platform get a continuous live stream of biometric data and movement from each user.
App - Helps users to visualise and actively monitor their own biometric data, and track endurance. Also their coaches/doctors can visualise aggregated data.
Dashboard - Helps our partners actively track users’ engagement.
Algorithms for Monitoring Heart Rate and Respiratory Rate From the Video of a User’s Face
IEEE Journal of Translational Engineering in Health and Medicine, 2018
Smartphone cameras can measure heart rate (HR) by detecting pulsatile photoplethysmographic (iPPG) signals from post-processing the video of a subject's face. The iPPG signal is often derived from variations in the intensity of the green channel as shown by Poh et. al. and Verkruysse et. al.. In this pilot study, we have introduced a novel iPPG method where by measuring variations in color of reflected light, i.e., Hue, and can therefore measure both HR and respiratory rate (RR) from the video of a subject's face. This paper was performed on 25 healthy individuals (Ages 20-30, 15 males and 10 females, and skin color was Fitzpatrick scale 1-6). For each subject we took two 20 second video of the subject's face with minimal movement, one with flash ON and one with flash OFF. While recording the videos we simultaneously measuring HR using a Biosync B-50DL Finger Heart Rate Monitor, and RR using self-reporting. This paper shows that our proposed approach of measuring iPPG using Hue (range 0-0.1) gives more accurate readings than the Green channel. HR/Hue (range 0-0.1) (r = 0.9201,p-value = 4.1617, and RMSE = 0.8887) is more accurate compared with HR/Green (r = 0.4916,p-value = 11.60172, and RMSE = 0.9068). RR/Hue (range 0-0.1) (r = 0.6575, p-value = 0.2885, and RMSE = 3.8884) is more accurate compared with RR/Green (r = 0.3352, p-value = 0.5608, and RMSE = 5.6885). We hope that this hardware agnostic approach for detection of vital signals will have a huge potential impact in telemedicine, and can be used to tackle challenges, such as continuous non-contact monitoring of neo-natal and elderly patients. An implementation of the algorithm can be found at https://pulser.thinkbiosolution.com