L3Harris: Military Hand Signal Interpreter
Team: Colin Bernhardt, Jeremy Croft, Dylan Hammond, Ryan Palmer, and Michael Van Shaar
Sponsor: L3Harris
Project Description
In military and operations, silent communication is essential. Our glove interprets military hand signs and wirelessly transmits them, enabling discreet long-range communication even when out of sight.
The glove uses onboard sensors and algorithms to recognize gestures and can also serve as a controller for drones or RC vehicles in the field. Designed for durability and simplicity, it’s user-friendly, and optimized for use in harsh environments.


Design Description
The glove uses various sensors combined with a machine learning algorithm to interpret hand signals:
- 5 Flex deflection sensors (1 for each finger)
- MPU 6050 for hand gesture data
- Custom PCB with ESP-NOW Antenna
- Easy-access switches to turn glove on/off and toggle between control of drones
- RC drone cars with ESP-NOW
- Button to start hand signal
- RGB LED to indicate glove status

Performance Review
Testing was done to ensure glove meets all design requirements including:
- Signal Interpretation accuracy
- Transmission range test
- Battery life test
- Drone control functionality
Transmission Range Test Results (681 ft.)
| Requirements/ Constraints | Target | Threshold | Actual Performance |
|---|---|---|---|
| Military Hand Signals Interpreted | 25 signals, with capacity to add more | 10 signals | 23 signals |
| Glove Weight | 0.25 lb. | 1 lbs. | 0.304 lb. |
| Drone Control | Toggle control of multiple drones | Controls 1 drone | Toggle control of multiple drones |
| Transmission Range | 720 ft, signal can travel through obstacles | 100 ft | 681 ft. |
| Battery Life | 12+ hours, rechargeable | 6+ hours , rechargeable | 12.3 hours |
| Glove Cost | Under $200 | Under $800 | $185.36 |
Conclusion
Performance
- The glove is functional and meets all threshold requirements and most target requirements
- The glove successfully interprets 23 hand signals with an average 95% accuracy
Lessons Learned
- Prototype design iterations push the project forward
- Better understanding of electronic part capabilities and limitations leads to more efficient prototyping
Future Work
- Further design iteration of PCB/holder box for more compact fit
- Further battery/weight optimization with weatherproofing considerations
- Optimize machine learning algorithm for higher accuracy