More than 2.8 million older Americans visit emergency rooms for fall-related injuries each year, but 麻豆原创 Assistant Professor Helen Huang hopes those numbers can be reduced with the help of a new $1.5 million research grant she received to find new approaches for predicting fall risk and creating balance-training programs.<\/p>\n
Huang secured the National Institutes of Health R01 Award in her second year as an assistant professor in 麻豆原创\u2019s Department of Mechanical & Aerospace Engineering. The five-year grant awarded in August by the National Institute on Aging will look at “Adaptation of brain and body responses to perturbations during gait in young and older adults.”<\/p>\n
To work towards brain-based gait rehabilitation and fall interventions, researchers must first determine the brain processes involved in balance control during walking, and recovery from losses of balance in young and older adults. Huang\u2019s research involves collecting brain-wave and muscle-activity data to understand how people maintain their balance and adapt their movement patterns to disruptions during walking and exercise.<\/p>\n
In addition to preventing the debilitation of hip fractures and head injuries, the findings would help reduce the\u00a0economic burden of falls among older adults. The medical costs associated with falls in the United States total about $31 billion each year, according to the National Center for Injury Prevention and Control.<\/p>\n
Tests will be carried out on a specially fitted treadmill that has two belts to walk on, one for each leg. The belt speeds can be run independently so one belt can go faster than the other.<\/p>\n
\u201cWe use this feature to suddenly slow down or speed up a belt for a fraction of a second to create a small slip backwards or trip forwards,\u201d Huang said. \u201cThe treadmill can also shift side to side. With this feature, we can create a slip in the side-to-side direction.\u201d<\/p>\n
By applying changes on the treadmill, researchers can see how the subjects react and adapt their walking pattern.<\/p>\n
The treadmill\u2019s incline\/decline also can be adjusted as people walk to create a rolling terrain and it also has a self-paced mode, which allows the machine to change its speed to match the person\u2019s walking speed.<\/p>\n
While walking on the treadmill, the subjects\u2019 brain waves will be recorded using electroencephalography with electrodes placed on the scalp to measure electrical activity generated by neurons in the brain. The system has 128 electrodes and will process the data to identify the brain areas that are the primary sources of the electrical activity during walking and responding to the perturbations.<\/p>\n
\u201cWe expect to find that both young and older adults can adapt to these perturbations but that older adults will adapt less,\u201d Huang said. \u201cWe expect that subjects will use a combination of anticipating and reacting to the perturbations to maintain their balance.<\/p>\n
\u201cIf we could identify who is more likely to fall, then we could develop preventative balance-training programs to help reduce their fall risk. Additionally, we hope to be able to use brain dynamics to help customize fall-training programs and interventions for each individual.\u201d<\/p>\n
Her\u00a0co-investigator on the grant is\u00a0Professor Carolynn Patten at the University of Florida. Also helping with the project will be Assistant Professor Ladda Thiamwong in the 麻豆原创 College of Nursing and\u00a0Assistant Professor Hsin-Hsiung Huang in the Statistics department, who were added co-investigators once the project began.<\/p>\n
Huang, who has a Ph.D. in biomedical engineering from the University of Michigan, said this research meshes with her lifetime interest in movement and sports.<\/p>\n
\u201cI\u2019ve played many sports throughout my life, and having a good sense of balance is beneficial. Unfortunately, I have really bad balance from repeated ankle sprains,\u201d she said. \u201cI seem to trip over imaginary lines and sprain my ankles on pea-sized pebbles.\u201d<\/p>\n