MIT researchers have developed neural networks that can recognize speech patterns that are indicative of depression, writes Anna Powers for Forbes. “Because the model is generalized and does not rely on specific questions to be asked,” explains Powers, “the hope is that this model can be implemented into mobile apps that will allow people to detect depression through natural conversation.”
MIT researchers have developed a model that can help detect depression by analyzing an individual’s speech patterns, reports Kaveh Waddell for Axios. Waddell explains that the researchers, “trained an AI system using 142 recorded conversations to assess whether a person is depressed and, if so, how severely.”
MIT researchers have developed a new system that can detect depression by examining a patient’s speech and writing, reports John Biggs for TechCrunch. Biggs writes that the system could “help real therapists find and isolate issues automatically versus the long process of analysis. It’s a fascinating step forward in mental health.”
Boston Globe reporter Martin Finucane writes that MIT researchers have identified the region of the brain responsible for generating negative emotions. “The findings could help scientists better understand how some of the effects of depression and anxiety arise, and guide development of new treatments,” Finucane explains.
Kate Baggaley writes for NBC News that movement tracking technology developed by MIT researchers can be helpful for monitoring the elderly or sick. The system could be used to monitor an elderly relative and, “receive an instant alert if he or she falls,” or a doctor could use it to monitor the progression of a patient’s disease, explains Baggaley.
CSAIL researchers have created a deep learning system that can isolate individual musical instruments in a video by clicking on the specific instrument, writes Andrew Liszewski for Gizmodo. The researchers suggest the system, “could be a vital tool when it comes to remixing and remastering older performances where the original recordings no longer exist,” explains Liszewski.
BBC Click reporter Gareth Mitchell speaks with postdoc Oggi Rudovic about his work developing a system that allows autism therapy robots to help teach children how to decipher different emotions. Rudovic explains that the technology can “assist the therapist and also to make the whole therapy process engaging for the child.”
Popular Mechanics reporter David Grossman writes that MIT researchers have developed a new system that helps robots used in autism therapy better estimate how engaged a child is during an interaction. Grossman explains that, “using the personalized algorithm, the robot was able to correctly interpret a child's reaction 60 percent of the time.”
By measuring how radio waves bounce off of human bodies, MIT researchers have developed a system that can track movements from behind a wall, writes Andrew Liszewski for Gizmodo. The researchers are working to improve on the current stick figure icons by “generating 3D representations that include subtle and small movements,” writes Liszewski.
Researchers led by Prof. Dina Katabi have developed a system to track people’s movements from behind a wall, writes Kaleigh Rogers of Motherboard. Earlier versions were unable to track precise movements, but the system can now interpret signals bouncing off bodies and “translate it into the movement of 14 different key points on the body, including the head, elbows, and knees.”
CSAIL researchers have developed a new system that uses low-power radio waves to detect and track people behind walls, reports Matt Simon for Wired. The system, which can be used to detect signs of distress in elderly patients, also “distinguishes one person from another in the same way your fingerprint distinguishes you,” explains Prof. Dina Katabi.
CSAIL researchers have created a system that can sense a person’s movements through walls, writes John Biggs for TechCrunch. The system is primarily intended as a healthcare device and could help with “passive monitoring of a subject inside a room without cameras or other intrusions,” and could provide insight into disease progression, Biggs explains.
Fast Company reporter Melissa Locker writes that CSAIL researchers have developed a system that allows wireless devices to sense a person’s movement through walls. Locker explains that the technology was created as a way to help those who are elderly, as it could be used to “monitor diseases like Parkinson’s and multiple sclerosis and provide a better understanding of disease progression.”
MIT researchers have identified the brain circuit required for observational learning, reports Xinhua. According to the study, the area involved in evaluating social information is more active when witnessing an experience and “relays information about the experience” to the region important for processing emotions.
A study led by research scientist Nick Obradovich found that people’s behavior on social media may be influenced by weather conditions. “Positive posts increased as the temperature rose,” reports Robert Preidt in US News & World Report, but “precipitation, humidity levels of 80 percent or higher, and high amounts of cloud cover were associated with a greater number of negative posts.”