For the past six years, Shri Narayanan has leveraged engineering and quantitative analytics to objectively improve the diagnosis and treatment of autism, a neurological condition that impairs communication, the ability to form relationships and respond appropriately to one’s surroundings.
The Andrew J. Viterbi Professor of Engineering and his interdisciplinary team are at the vanguard of a new field called behavioral signal processing (BSP) and informatics. BSP aims to capture, quantify and interpret human behavior, both overt and covert, through the use of engineering and computing advances. By sifting through massive amounts of data captured by videotape, audio recordings and physiological sensors, they have objectively and consistently evaluated vocal synchronization, word choice, pauses, facial gestures and minute physiological changes in helping to “understand autism in detail and ways that weren't possible before,” he said.
“We want to further empower psychologists, both researchers and clinicians, with scientific data and engineering techniques that would further enhance their work,” Narayanan added.
Recently, Narayanan and his research collaborators Ruth Grossman of Emerson College and Angeliki Metallinou, one of his former USC Viterbi Ph.D. students, have found that children with high functioning autism make facial gestures that lead others to describe them as awkward. The research suggests that such autistic children often display asymmetrical and asynchronous facial movements. For instance, the left and right parts of their faces or the lower and upper parts might fail to move in synchrony when they smile. These children also have jerky and “rough” head motions. Through motion capture technology and the application of statistical modeling, the research team has successfully captured and mathematically quantified their movements.
“Knowing that timing’s off and facial movements are rough and asymmetrical can help with further individualizing diagnosis and treatment plans,” Narayanan said.
High functioning autistic children, the fastest growing subset of kids with autism, possess good cognitive and language skills and the capacity to succeed, said Grossman, an assistant professor at Emerson College’s Department of Communication Sciences and Disorders. However, their awkwardness and people’s reaction to it make it difficult for them to succeed in school, find a job and keep one.
The ability to objectively quantify awkwardness increases the likelihood of “finding an intervention target to reduce some of the awkwardness that comes across,” thereby making it easier for these children to integrate and become contributing members of society, added Grossman, the lead investigator on a recently awarded four-year National Institutes of Health grant that features Narayanan as a collaborator.
Narayanan has also turned his attention to the interaction between autistic children and their therapists. By measuring and analyzing physiological cues such as heart rate variability and electro-dermal activity over time, his collaborators and he believe that researchers and clinicians can peer beneath the surface to gauge whether the therapist or even the type of therapy is a good fit.
This groundbreaking work, which advances the use of physiological signals in the study autism, is part of a new collaboration between renowned autism expert Connie Kasari, a UCLA professor of human development and psychology with a joint appointment in psychiatry; Matthew Goodwin, an assistant professor at Northeastern University; and Narayanan. “Given how complicated the kids are behaviorally, the analytics can help tie it all together,” Kasari said. [Narayanan's doctoral student Theodora Chaspari is also a key member of the team.]
For example, a child might seem perfectly fine during a session, leading the psychologist to conclude he or she is making progress. However, access to wrist sensors might reveal electro-dermal activity or a pulse rate indicating elevated stress or anxiety. That information, Narayanan said, can provide a fuller and more nuanced picture than does observation alone, leading to more effective clinical approaches.
What does successful therapy look like? If the patient and therapist both appear to synergistically interact with one another – and their physiological indicators are also in sync, they might be a good match, Narayanan added.
“I think this is really groundbreaking work," Kasari, added.
Additionally, Narayanan and his team have recently shown how vocal patterns of a psychologist interacting with a child being diagnosed for autism provide valuable insights about the overall diagnosis. Their work, which is to appear in the Journal of Speech, Language, and Hearing Research, underscores the promise of engineering in providing novel insights to experts. Narayanan’s doctoral student Daniel Bone leads the study.
Autism has become the most commonly diagnosed childhood disorder, with autism spectrum disorders (ASDs) now affecting about one in every 88 in the United States, according to a 2012 report by the Centers for Disease Control and Prevention. Diagnoses are increasing at a rate of 10 percent to 17 percent per year, U.S. Department of Education statistics show.
There is no cure for autism yet, although early intervention can have a positive effect on later development.
Given those realities, Narayanan, whose work has received support by the National Science Foundation Expeditions in Computing, among others, said he feels strongly motivated to help decode the puzzle that is autism.
“If technology can be a part of contributing to the health and well-being of people, especially those with cognitive and mental difficulties, what better use of engineering can there be?” he asked.