SHORT DESCRIPTION: Students will create a low-cost embedded product with camera that is able to learn and recognize a custom and reconfigurable set of 5-10 basic hand signals. For example, this could be used as an alternative method to control digital devices from a far. A mobile application should allow users to 1) configure the device to connect to WiFi/Bluetooth, 2) perform real-time machine learning and training for new hand signals, 3) recognize and effectively identify signals when present. The real constraint of this project is the embedded hardware cost and size, meaning that most computation will need to be forwarded to a cloud server for processing.

DESIGN COST CONSTRAINTS < $5/unit

DESIGN TIME CONSTRAINTS < 6 months for development and testing

OTHER QUANTITATIVE DESIGN CONSTRAINTS (at least 3):

1. Prediction Accuracy > 75%

2. False Alarm Rate < 5%

3. Prediction Speed < 1 second

MAXIMUM LEVEL DESIGN: Choose One of Two Below

System-Level: meaning that students can use any components they see fit, with advisor approval.

Summary

Students will create a low-cost embedded product with camera that is able to learn and recognize a custom and reconfigurable set of 5-10 basic hand signals. For example, this could be used as an alternative method to control digital devices from a far.

Faculty Advisor

• Dr. Anthony Deese

Industry Advisor

Joseph Jesson

Other Quantitative Design/Evaluation Criteria (at least 3)

DESIGN COST CONSTRAINTS < $5/unit
DESIGN TIME CONSTRAINTS < 6 months for development and testing
OTHER QUANTITATIVE DESIGN CONSTRAINTS (at least 3):
1. Prediction Accuracy > 75%
2. False Alarm Rate < 5%
3. Prediction Speed < 1 second