2024/01/15 by Ryan Cho, Sunil Patel, Cho, Ryan +5
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Electrical engineering #Hand Gesture Recognition Systems #Machine Learning (cs.LG) #Muscle activation and electromyography studies #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2401.07889
openalex publication_date 2024/01/15 · openalex created_date 2024/01/18 · openalex updated_date 2026/07/28
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.