BabyZen - a flexible sensor BoosterPack

Please note: This article is only available in English.
We outline our submission for the European Design Contest organized by Texas Instruments.

BabyZen is an application to monitor important quantities defining the environment of a baby with an advanced general purpose sensor board in form of a flexible Texas Instruments BoosterPack, which is fully compliant with the official design guidelines. Our manually etched and soldered PCB comprises high precision sensors to measure temperature, humidity, ambient light, acceleration and barometric pressure. It also features an ADC, which supports up to four additional external analog sensors.

The ultra low-power BoosterPack is stacked onto the CC3200 LaunchPad, which preprocesses the sensor data and sends it to a server via HTTPS. Subsequently the data is stored in a database and analyzed by a machine learning framework to detect correlations between environmental conditions and the baby's well-being. An integrated mobile application provides the parents with visualizations and recommendations on how to their baby's life.

The hardware is a combination of a custom design and parts from Texas Instruments. Instead of placing a CC3200 on a custom board, we decided to create a BoosterPack, which is a nice addition for the LaunchPad. The LaunchPad is ideal for rapid prototyping and allows us to further investigate the possibilities of the BabyZen application. We do not have to spend lots of money on creating prototypes, but can easily scale up in design by producing a BoosterPack.

The software side is completely managed by Microsoft Azure. We use Mobile Services to provide an API which can be used by anyone. A standard web app is hosted on Azure Websites. Here the deployment is staged via git. Internally the data is processed by Azure Stream Analytics, after Event Hubs received and aggregated the incoming sensor data. Small SignalR clients make real-time evaluation possible without introducing too much dependencies. Finally recommendation systems are exposed via Azure Machine Learning. Here the API is called via web jobs, which are hosted and run from the web API.

Download ti-report.pdf (2743 kB)

Created .


Sharing is caring!