This pilot study aims to develop a deep learning model for predicting seismocardiogram (SCG) signals in the dorsoventral direction from the SCG signals in the right-to-left and head-to-foot directions ($\textrm{SCG}_x$ and $\textrm{SCG}_y$). The dataset used for the training and validation of the model was obtained from 15 healthy adult subjects. The SCG signals were recorded using tri-axial accelerometers placed on the chest of each subject. The signals were then segmented using electrocardiogram R waves, and the segments were downsampled, normalized, and centered around zero. The resulting dataset was used to train and validate a long short-term memory (LSTM) network with two layers and a dropout layer to prevent overfitting. The network took as input 100-time steps of $\textrm{SCG}_x$ and $\textrm{SCG}_y$, representing one cardiac cycle, and outputted a vector that mapped to the target variable being predicted. The results showed that the LSTM model had a mean square error of 0.09 between the predicted and actual SCG segments in the dorsoventral direction. The study demonstrates the potential of deep learning models for reconstructing 3-axis SCG signals using the data obtained from dual-axis accelerometers.

Abstract--Perceptual processes are frequently multi-modal. This is the case of haptic perception. Such data set would be useful to conduct the I. T is well known that human perception is often multisensory where different sources of information accessed This observation motivated us to create a multi-modal through different sensory modalities are merged and integrated data set comprising the signals created when a bare finger by the brain. This integration process is thought to increase the explored varied textured surfaces. The measured signals were robustness of the perception of the properties of objects in the stereoscopic images of the surface, the position and speed of face of uncertainty, to resolve ambiguities, and to contribute the fingertip in images coordinates, the load applied by the to the perceptual stability of sensory scenes [1]-[4].