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This study investigated the use of forearm EMG data for distinguishing eighthand gestures, employing the Neural Network and Random Forest algorithms ondata from ten participants. The Neural Network achieved 97 percent accuracywith 1000-millisecond windows, while the Random Forest achieved 85 percentaccuracy with 200-millisecond windows. Larger window sizes improved gestureclassification due to increased temporal resolution. The Random Forestexhibited faster processing at 92 milliseconds, compared to the NeuralNetwork's 124 milliseconds. In conclusion, the study identified a NeuralNetwork with a 1000-millisecond stream as the most accurate (97 percent), and aRandom Forest with a 200-millisecond stream as the most efficient (85 percent).Future research should focus on increasing sample size, incorporating more handgestures, and exploring different feature extraction methods and modelingalgorithms to enhance system accuracy and efficiency.

Machine Learning Techniques to Identify Hand Gestures amidst Forearm Muscle Signals