2021/2022 Winning Projects: Student IoT Innovation Capacity Building Challenge
Below are the winning projects from the 2021 and 2022 Student IoT Innovation Capacity Building Challenge. The 2023 Challenge is currently underway and winning projects will be announced here soon.
Intelligent Acoustic Monitoring at the Edge
This project is a low-cost, easy-to-implement application that uses a microphone to learn what a machine typically sounds like and alerts users when anomalous s sounds are identified. The application makes use of unsupervised machine learning to automate all stages of deployment including the determination of machine state, learning the typical sound of the machine, and setting the threshold for classifying new sounds.
StartProto
This project was designed to address the many inherent problems in running a makerspace, including safety assurance, machine time allocation, and data collection. The prototype IoT device provided intelligent access control to tools based on training levels and collected user and machine data through ID scanners and current sensors. StartProto is now a company!
Soft Stethoscope Patch (SSP) for Multi-patient Pulmonary Diagnosis
This project consisted of three soft stethoscope patches, on the chest, back right and left lower lobes, connected to a HIPAA compliant application with integrated machine learning algorithms to classify abnormalities and send real time severe event notifications to a caregiver. This system provides accurate, continuous, and timely cardiopulmonary information on patients.
Elbowroom
The Elbowroom project built a novel Automated Passenger Counter (APC) system to offer transit agencies access to rider occupancy data. The Elbowroom team went on to participate in GT’s CREATE-X LAUNCH and conducted a developmental pilot program with MARTA.
Wearable Stress Monitoring System
This project consisted of a soft sternal patch with optimized mechanics that wirelessly monitored mechanical vibrations on the chest caused by each heartbeat and an android app that conducted real time signal processing and machine learning to identify markers of cognitive stress. This system provides continuous monitoring of objective physiological manifestations of cognitive stress.
Fridge-Freshness
This project was a fridge mounted IoT system to track the shelf-life of stored foods and alert users about expiring items. The prototype consisted of an external device attached to the fridge door and a mobile app operated as a secondary platform for notifying users about expiration dates and food lookup. Users can use an Alexa Skill to add new food items into the system.