Hi, which neural network can I use for hand gesture recognition, with a dataset of data acquired with mediapipe? the data is standardized and saved as an array, and for each one its class is associated
Electromyography (EMG) data is typically a time-series data that records the electrical activity of muscles. In my opinion, as RNNs are designed to handle time-series data, they are suitable for EMG data analysis and can capture the temporal dependencies in the signal. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) are popular RNN architectures that have been used in EMG data analysis.
@@mitkosokolov9382 Yes, RNNs can also be used for the analysis of EKG and EEG data, which are also time-series data. EKG records the electrical activity of the heart, while EEG records the electrical activity of the brain. Like EMG data, EKG and EEG data are sequential data and require the analysis of temporal dependencies. RNNs, such as LSTM and GRU, can be used to model these dependencies and make predictions based on past observations. Regarding medical images, such as ultrasound and X-ray, RNNs are not typically used as these are not sequential data. Instead, convolutional neural networks (CNNs) are commonly used for medical image analysis. CNNs are well-suited for this type of data because they are able to detect patterns in the spatial structure of the image. CNNs have been used successfully for tasks such as medical image segmentation, detection, and classification. Hope this helps!
