Dr. Manuel Hernandez is a researcher and Associate Professor at the University of Illinois at Urbana-Champaign, focused on the interaction of movement, cognition, and physiological signals in aging and neurological disease. He told us how his lab leverages novel technologies and data analytics techniques to study mobility and advance earlier intervention across a range of health conditions.
Many of us don’t think twice about walking across a room. We talk, check our phones, navigate furniture, all without a second thought. But for millions of people living with neurological conditions like multiple sclerosis or Parkinson’s disease, those same actions demand intense, conscious effort.
Understanding exactly why is the life’s work of Dr. Manuel Hernandez, an associate professor in the Department of Biomedical and Translational Sciences at the Carle Illinois College of Medicine, and director of the Mobility and Fall Prevention Research Laboratory, which operates at the intersection of neuroscience, biomechanics, and data science.
The early stages of Hernandez’s career began at Cornell University, where he studied mechanical engineering as an undergraduate student. “I was in my second year when I realized that I didn’t want to spend the rest of my life just sitting in front of a desk,” he says.
Hernandez was looking for something more. Thanks to a faculty mentor, he found it. “During my undergraduate degree, I had the opportunity to work with a faculty member who introduced me to the concept of biomechanics and how we can apply the principles of physics to better understand human movement,” he says.
Hernandez’s mentor opened his eyes to the possibility of pursuing a graduate education to answer bigger research questions. “I still remember one of his biggest pieces of advice: to go through a stack of scientific journals and pick a topic to pursue that spoke to me,” he says. “Whole-body biomechanics was one of them.”
The study of whole-body biomechanics examines how the body’s muscles and joints coordinate movement during everyday activities like walking, balancing, or recovering from a stumble. Specifically, Hernandez was interested in how whole-body biomechanics related to aging and falls.
Over the next few years, Hernandez pursued several opportunities that steadily deepened that interest, from a PhD in biomedical engineering at the University of Michigan – Ann Arbor to a postdoc in a neuroscience lab at the University of California – San Diego.
When a position opened at the University of Illinois focused specifically on fall prevention, it seemed like the stars had aligned. “I felt like I could finally bring my backgrounds in neuroscience and biomechanics together to answer some larger questions,” he recalls.
In 2014, Hernandez joined the Illinois Department of Kinesiology and Community Health as an assistant professor. Soon after, Illinois established the Carle Illinois College of Medicine.
“The College of Medicine allowed for the integration of engineering into medical applications and training of medical students,” says Hernandez. Over the following decade, it grew into an interdisciplinary program that brought health professionals, engineers, and data scientists into the same research conversations.
That collaborative environment would become foundational to the work taking place in Hernandez’s lab.
The Lab and its Mission
The Mobility and Fall Prevention Research Laboratory is true to its name, championing research initiatives that have a focus on aging, mobility, and fall risk in older adults and individuals with neurological disorders.
“The aging process serves as a really great baseline as we start to get a better understanding of the impact of concurrent neurological conditions on the body,” says Hernandez.
Specifically, much of the research at the lab has centered on multiple sclerosis and Parkinson’s disease. Both are neurological conditions that erode the brain’s ability to regulate movement over time.
By studying these populations alongside healthy older adults, Manuel and his team can begin to assess which changes are part of normal aging and which are specific to disease progression.
Many of these changes become most visible through mobility. “We focus a lot on balance and gait, particularly because they serve as a really important precursor to other functional activities where falls might be more likely to occur,” explains Hernandez. “Walking may seem automatic, but it depends heavily on other cognitive processes.”
When those demands compete, such as in busy or unpredictable environments, gait can become less stable. This, Hernandez notes, makes balance and gait a critical point of intervention in older adults to prevent falls before they occur.
Assessing the Body in Motion
To study these processes more closely, Hernandez and team utilize a plethora of biomechanical tools to capture whole-body movement, heart activity, and brain activity in real time. “A key question for us is getting a better sense of what role the brain plays in different processes and our ability to control everyday activity,” says Hernandez.
Mobile brain imaging is one of such tools. Specifically, Hernandez’s lab frequently utilizes functional near-infrared spectroscopy (fNIRS), a brain imaging technology which measures brain activity by detecting changes in blood flow to different brain regions using light.
When neurons in a brain region become more active, blood flow to that area increases. By tracking these shifts, Hernandez and his team can collect valuable data on the neurological effort it might take to execute these different activities.
Just as importantly, fNIRS is non-invasive. “This is a wearable technology,” says Hernandez. “You’re going to be able to walk, you’re going to be able to talk.”
The system is worn like a lightweight headband fitted with sensors, allowing participants to move freely during experiments.
For Hernandez, that wearability is key. “Examining what happens in the brain when you’re doing concurrent activities might be a better analog for how our brain functions on an everyday basis,” he says. “So we do a lot of examinations using dual tasks, like walking and talking. The unifying factor is looking at what happens in the brain when we challenge ourselves.”
The brain imaging technology has given Hernandez important insights into how significantly neurological conditions can alter the cognitive demand of routine movements.
“We had the chance to bring in adults with multiple sclerosis and take a look at their brain activity while they performed a walking-pace task. Something that for other people might be automatic took a very significant amount of attention to perform,” recalls Hernandez. “It helps explain why this particular population tends to be so fatigued by the end of the day. It’s like they are always using their maximum capacity in terms of attentional effort.”
“This used to be very time-intensive, because you would need a trained researcher to do a visual inspection of the data themselves,” Hernandez says. “Now we can use pre-trained models that fast-track this process, which makes a real difference in terms of increasing access to this kind of analysis.”
Searching for a Needle in a Haystack
Another tool frequently used in the Mobility and Fall Prevention Research lab is electroencephalography (EEG), which measures the brain’s electrical activity through sensors placed on the scalp. Unlike fNIRS, which tracks changes in blood flow, EEG captures rapid shifts in neural activity in real time.
By analyzing patterns in these signals, Hernandez and his team can examine how processes like stress and anxiety change during certain tasks. The challenge, however, is the quantity of the data.
A single EEG session can generate thousands of recordings per second across dozens of sensors. “It can be like dealing with a needle in a haystack,” Hernandez quips. “This is high-dimensional data, and there can be large sources of noise.”
Making sense of the data requires a healthy mix of domain expertise and computational tools.
“Having a good idea of where to look and when to look is very important,” explains Hernandez. “Different parts of the brain are associated with different processes, so if you know what task someone is performing, you can narrow in on the signals that are most relevant.”
That expertise is increasingly paired with machine learning approaches that can rapidly process large datasets, helping filter out noise from meaningful signals. “This used to be very time-intensive, because you would need a trained researcher to do a visual inspection of the data themselves,” Hernandez says. “Now we can use pre-trained models that fast-track this process, which makes a real difference in terms of increasing access to this kind of analysis.”
Hernandez’s lab is working to combine smaller datasets from controlled experiments with real-world data collected through these wearable devices. The goal is to identify recognizable patterns within an individual’s baseline and use them to interpret physiological state in context.
Wearable Technologies and Real World Applications
The data collected in Hernandez’s lab goes far beyond headsets for brain monitoring. From wristbands to “smart shirts,” the lab is increasingly exploring a variety of wearable technologies that can capture physiological changes like heart rate, temperature, and respiratory activity.
Hernandez and his team have used these signals to explore everything from mental health markers to early indicators of change in cardiac function.
“What we’re finding more recently is that there are a lot of challenges because what you might expect person to person can be quite different,” says Hernandez. “My physiological baseline may be very different from yours.”
Because of that, the lab is working to combine smaller datasets from controlled experiments with real-world data collected through these wearable devices. The goal is to identify recognizable patterns within an individual’s baseline and use them to interpret physiological state in context.
“The hope is that if someone wears the shirt over a few weeks and we start to see a concerning pattern in their physiological behavior, we can flag it early,” he says.
Manuel hopes that this will be able to provide important information that could previously only be found via a traditional clinical assessment, a resource that may not be accessible to certain groups.
“A lot of these tools I see being applicable to rural healthcare and different communities that might not have the same access to medical help.”
Manuel notes that shortages in medical staffing confound this problem even more. “There’s just not enough medical professionals going into the pipeline,” he says.
As wait times for medical appointments grow longer and longer, Manuel’s research is oriented towards a different model — one that provides a thorough, more accessible level of preventative care. “We tend to only seek care when something has gone wrong. This technology could change that.”
Learn More and Get Involved
Learn more about Dr. Hernandez’s work at the Mobility and Fall Prevention Research Laboratory website.
Contact the Office of Data Science Research to tell us about other people or resources we could feature here. ODSR is a campuswide convening organization that facilitates collaborations, resource sharing, and public engagement focused on data science research activities at the University of Illinois.