Extracting and understanding human emotions from complex, high-dimensional and heterogeneous biomedical data remains a key challenge on our journey throughout the much evolving healthcare and wellness domains.
Traditional data mining and statistical learning approaches must perform preliminary steps in order to obtain effective and more robust features from the data, and then build prediction or clustering models on top of it.
There are lots of challenges on both steps in a scenario of complicated data and lacking sufficient domain knowledge.
The unsupervised pre-training breakthrough, new methods to prevent overfitting, the use of general-purpose graphic processing units to speed-up computations and the development of high-level modules to easily build neural networks allowed deep models to establish as state-of-the-art solutions for various tasks. However, with that being said, there are still many challenges that exists when facing biometric data.
address various challenges that still remain unsolved when facing physiological data by using deep architectures with new and improved methods and tools that enable deep learning to interface with physiological information. Key issues such as:
The latest advances of deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data.
Deep learning approaches are the vehicle for translating big biomedical data into an improved healthy life and better productivity in both our work environment and personal life.
Given its demonstrated performance in different domains and the rapid progresses of methodological improvements, deep learning introduce exciting new opportunities that Kenko capitalizes.
Machine learning is a general-purpose method of artificial intelligence that can learn relationships from data without the need to define them a priori. Conventional techniques are composed of a single, often linear, transformation of the input space and are limited in their ability to process natural data in their raw form.
Deep learning is different from traditional machine learning in how representations are learned from the raw data. In fact, deep learning allows computational models that are composed of multiple processing layers based on neural networks to learn representations of data with multiple levels of abstraction. The major differences between deep learning and traditional artificial neural networks (ANNs) are the number of hidden layers, their connections and the capability to learn meaningful abstractions of the inputs.
The key aspect of deep learning is that the layers of features are not designed by human engineers, but they are learned from data using a general purpose learning procedure, and it is one of the keys for translation physiological data in emotions and mental states (figure 1 illustrates the differences at a high level).
Figure 1. An example for the difference between an ANN model and Deep Learning model
Figure 1. An example for the difference between
an ANN model and Deep Learning model
The way we process and analyze data has evolved significantly over the past few years. Nowadays, the usage of complex deep learning networks is bringing us closer to understanding our emotions and how they affect our body. Still, there are many obstacles to overcome.