Researchers from Texas Tech University are collaborating with TI on a research study aimed at the early detection and prevention of falls in elderly people. Two TI parts – the ultra-low-power MSP430™ microcontroller and the CC2500 radio frequency (RF) transceiver – are key components in a fall detection sensor that could significantly reduce falls in the geriatric population in the future.
Dr. Donald Lie, the Keh-Shew Lu Regents Chair Associate Professor of electrical and computer engineering at Texas Tech and Adjunct Associate Professor, Dept. of Surgery, Texas Tech University Health Sciences Center (TTUHSC), recently sat down to answer some questions for us about the study.
How did you come up with the idea for your research project?
Falls are significant risks of mortality and morbidity in the elderly population. We want to create an inexpensive device that will help us understand fall dynamics in different geriatric demographics and ultimately incorporate algorithms that will lead to early warning and even fall prevention.
Tell us a little bit about your fall-detection sensor, and how did you incorporate Texas Instruments technology and components into the device?
The fall detection sensor is composed of accelerometers and gyroscopes, and we use TI's MSP430 microcontroller to digitize all the signals and CC2500 to transmit all the signals. We use the low-power SimpliciTI™ network protocol, TI's proprietary software stack, for transmission. We also use TI's eZ430-RF2500 development tool to send the signals to a nearby PC.
From a collaboration standpoint, what did you learn from working with Texas Instruments?
Texas Tech and the Texas Tech University Health Sciences Center have enjoyed a long and fruitful relationship with Texas Instruments, which has supported the research of many students. Many Texas Tech students also work at TI after they graduate. The collaboration of this project with TI has been wonderful.
What results or information could be implemented as a change agent in society as a result of your research?
Currently, the way to assess whether a person is likely to fall is mostly observational. We are hoping to obtain more specific fall dynamics data sets that can help us derive implementable prevention protocols in the future. Specifically, the goals of the project are to: 1) identify characteristics of a person’s posture and/or gait that will make him/her more at risk for a fall; and 2) identify the pattern of response that is present when an individual falls. This information can help identify those at risk for falls and to identify when a fall has occurred. Since falls that result in an injury are putting a significant strain on health insurance, the knowledge of how to identify those who might be at risk for a fall would be beneficial to society.
Based on your findings, do you see any potential opportunities for further research?
At this stage, even though we have only been able to obtain preliminary data on volunteers, it does appear that we should be able to use the device on varied populations. These populations range from patients with general vestibular disorders and various disorders involving the central nervous system (e.g., Parkinson’s, dementia, etc.).
Is this project going to lead to a consumer product that will help reduce falls in the older population?
We certainly hope and pray that the results of this project will lead to a consumer product that will help reduce falls in the geriatric population. Our final goal of this project is to make a significant difference and practical contribution in the clinical care of the geriatric population, in the U.S. and abroad, with a robust and low-cost consumer product solution.
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