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.

研究使用前臂肌电数据来区分八种手势，采用神经网络和随机森林算法对来自十个参与者的数据进行分析。神经网络在 1000 毫秒窗口下达到了 97% 的准确率，而随机森林在 200 毫秒窗口下达到了 85% 的准确率。更大的窗口大小提高了手势分类的准确性，由于时间分辨率的增加。随机森林的处理速度为 92 毫秒，比神经网络的 124 毫秒更快。研究得出结论，使用 1000 毫秒的神经网络为最准确的（97%），使用 200 毫秒的随机森林为最高效的（85%）。未来的研究应该着重增加样本数量，添加更多手势，以及探索不同的特征提取方法和建模算法来提高系统的准确性和效率。