  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.
