This week, we worked on finalizing our code for the project. Rosina and Saumya worked on expanding the code for the IMU to the z-axis (decoupling x and z and creating separate Kalman filters for each), and flipping the direction of the z-axis. Sarah practiced and presented our final presentation slides and took the lead on making the poster, while Saumya and Rosina helped write the different sections of the software portion.
One interesting problem we solved was the failsafe triggering. We tried to make the failsafe interrupt the IMU to avoid as much drift as possible and “freeze” the mouse in place when the failsafe was let go. No considerable changes have occurred to our design, and we are seemingly on track with the rest of the project. There are no risks to us presenting what we planned to present in the demo.
To answer the ABET question:
Latency: To test our latency requirement, we manually started a timer while at the same time triggered one of the sensors on the glove. In this way, starting the timer would be manual, and the timer would stop when the gesture propagated through. We achieved an average of 40ms for keystrokes.
We noticed a ton of latency (on the order of seconds) when testing our mouse movements, so we tried to reduce the amount of calls to pyautogui and reduce the amount of computation. Specifically, if the mouse position did not change between IMU data arrivals, we didn’t call pyautogui. This improved latency of the mouse to below 1s.
Weight: Unfortunately, we were unable to reach our weight use case requirement, weighing in at ~20g over our budgeted weight. We prioritized battery life as it is more inconvenient for the user to recharge their device multiple times during a session. Additionally, 20g is not very significant in the realm of human detection. Because of this immense increase in battery life, we decided this trade off was well worth it.
Accuracy: To test accuracy, we conducted a user study in which we guided each user through all the available gestures on the glove. Participants were asked to carry out each gesture and we recorded whether this gesture produced the expected shortcut. We were able to achieve a 100% accuracy after many iterations of sensor positioning.
Wireless Range: To test wireless range, we slowly incremented the distance between the glove and the device it was paired to. For each ½ a foot, we triggered one of the sensors and recorded whether the desired gesture was produced. We found that the final distance that triggered a gesture was 3.05 meters (10ft).
Battery Life: To test battery life, we connected our product to a battery of known voltage, current, and battery capacity. We continuously sent out packages over Bluetooth until this battery ran out. Since we are using Bluetooth Low Energy (BLE), we were able to achieve a battery life of over 12 hours for a 5V, 2.1A, 10,000mAh portable charger.