Machine Learning Techniques to Identify Hand Gestures amidst Forearm Muscle Signals
This work addresses gesture recognition for human-computer interaction, but it is incremental as it applies existing methods to a specific dataset.
This study tackled the problem of distinguishing eight hand gestures using forearm EMG data from ten participants, achieving 97% accuracy with a Neural Network using 1000-millisecond windows and 85% accuracy with a Random Forest using 200-millisecond windows.
This study investigated the use of forearm EMG data for distinguishing eight hand gestures, employing the Neural Network and Random Forest algorithms on data from ten participants. The Neural Network achieved 97 percent accuracy with 1000-millisecond windows, while the Random Forest achieved 85 percent accuracy with 200-millisecond windows. Larger window sizes improved gesture classification due to increased temporal resolution. The Random Forest exhibited faster processing at 92 milliseconds, compared to the Neural Network's 124 milliseconds. In conclusion, the study identified a Neural Network with a 1000-millisecond stream as the most accurate (97 percent), and a Random Forest with a 200-millisecond stream as the most efficient (85 percent). Future research should focus on increasing sample size, incorporating more hand gestures, and exploring different feature extraction methods and modeling algorithms to enhance system accuracy and efficiency.