M-002 · Miscellaneous
Smart Bassinet
Final Group Project for SMRTTECH 4ID3: IoT Devices and Networks
2024 · ESP32 · Sensor integration · MQTT
Overview
The smart bassinet proof of concept was submitted as the group term project for the SMRTTECH 4ID3 course at McMaster University. The project involved analyzing a stakeholder pain point and developing an IoT proof of concept based on the material learned in the course.
Stakeholder & Pain Point
Our group looked at new parents and tried to tackle the pain point of Sudden Infant Death Syndrome (SIDS). Every year 110 Canadian infants die in their sleep. Accidental suffocation occurred in 27% of cases in 2020, related to improper sleeping position or sleep surface. Overheating is another leading cause of SIDS. Cradlewise and Snoo are current commercial options but come at a high price and lack a full feature set including baby monitor, sound machine, sleep rocker, and temperature controls.
Our Design
We prototyped an array of perception-action instruments to monitor and aid the infant using MQTT and Node-Red. The concept had two networked ESP32 boards publishing and subscribing to a cloud server monitoring the baby's condition. The full block diagram can be seen in Figure 2. Our prototype demonstrated the following features:
Baby monitoring - an Arducam Mini Module is used to view the baby through a cloud connection and parents can talk to their infant through the Gikfun speaker
Wake-up detection and soothing - using the KY-038 audio sensor crying can be detected and the baby will be rocked back to sleep with a DC motor. Using the speaker lullabies can be played
Climate control - dual DHT-11 sensors monitor the room and baby temperature, starting a fan if a threshold is met
Diaper change detection - an MQ135 air quality sensor monitors the room conditions notifying parents of changes in the room or when the baby needs a diaper change.
Results
The project was a success with the group receiving an A+ grade. It was interesting to learn about SIDS and the current commercial monitoring ecosystem for infants. The most rewarding part of the whole project was being able to fully see all of our ideas work, having all the sensors publish data to the cloud and being able to apply basic commands to act on that data.