tinyML EMEA – Lina Wei: Monitoring of Vital Signs using Embedded AI in wearable devices

Lina Wei
Machine Learning Engineer
7 Sensing Software

Monitoring of Vital Signs using Embedded AI in wearable devices
Vital signs are measurements of the body’s basic functions, such as Respiratory Rate, Heart Rate and Blood Pressure. Monitoring vital signs allows us to assess our wellbeing and detect underlying health issues at an early stage. For example, respiratory rate is an important marker of health; elevated respiratory rate values (27 bpm) have been shown to be
predictive of cardiopulmonary arrest [1]. Respiratory rate is often neglected due to lack of unobtrusive sensors for objective and convenient measurement. Recent improvements of photoplethysmogram (PPG) and growing interest in wearable devices promote the development of digital health. In this presentation, we will show how the combination of ams OSRAM medical and health sensors, and 7 Sensing Software’s embedded AI technology enables the unobtrusive and daily monitoring of Respiratory Rate. The deep learning-based solution has overcome the difficulties caused by data complexity and achieved a performance comparable to that of medical-grade devices. The deep learning model is converted and optimized to be compatible with low-end micro-controllers. The presented solution is currently being deployed to smart watches by OEMs for daily respiratory rate monitoring.


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