Neural Network to Recognize Depression

In subsequent testing, artificial intelligence managed to recognize depression in 77% of cases
06 September 2018   270

Researchers from the Computational Science and Artificial Intelligence Laboratory (CSAIL) of the Massachusetts Institute of Technology (MIT) have developed a neural network that allows to determine the level of depression of a patient. Artificial intelligence is able to establish an oppressed psychological state, without relying on context and without asking specific questions. To obtain the test result, it is enough to record a patient interview in video or audio format.

By training the neural network, CSAIL scientists used 142 interview records from the Distress Analysis Interview Corpus, a compilation intended for the diagnosis of mental illness. Artificial intelligence analyzed speech of patients, revealing sound and text patterns. Patterns have become, including word-markers, such as "sad", "low", combined with long pauses and a monotonous voice. The oppressed condition of each patient was assessed on a scale from 0 to 27. Depression was considered to be a level of 15 and above.

In subsequent testing, artificial intelligence managed to recognize depression in 77% of cases. According to the developers, this result is one of the best among all available.

The new technology is considered as a tool that allows to simplify the work of the therapist and specify certain markers, which should be noted in the diagnosis.

AI to be Used to Create 3D Motion Sculptures

The system developed by the MIT and Berkeley scientists is called MoSculp and is based on artificial inteligence
21 September 2018   110

MoSculp, the joint work of MIT scientists and the University of California at Berkeley, is built on the basis of a neural network. The development analyzes the video recording of a moving person and generates what the creators called "interactive visualization of form and time." According to the lead specialist of the project Xiuming Zhang, software will be useful for athletes for detailed analysis of movements.

At the first stage, the system scans the video frame-by-frame and determines the position of key points of the object's body, such as elbows, knees, ankles. For this, scientists decided to resort to the OpenPose library, developed by the Carnegie Mellon University. Based on the received data, the neural network compiles a 3D model of the person in each frame, and calculates the trajectory of the motion, obtaining a "motion sculpture".

At this stage, the image, according to the developers, suffers from a lack of textures and details, so the application integrates the "sculpture" in the original video. To avoid overlapping, MoSculp calculates a depth map for the original object and the 3D model.

MoSculp 3D Model
MoSculp 3D Model

The operator can adjust the image during the processing, select the "sculpture" material, color, lighting, and also what parts of the body will be tracked. The system is able to print the result using a 3D printer.

The team of researchers announced plans to further develop the MoSculp technology. Developers want to achieve from the processing system more than one object on the video, which is currently impossible. The creators of the technology believe that the program will be used to study group dynamics, social disorders and interpersonal interactions.

The principle of creating a 3D model based on human movements has been used before. For example, in August 2018, scientists at the same University of California at Berkeley demonstrated an algorithm that transfers the movements of one person to another.