The effects of hypertension are chronic and continuous; it affects gait, balance, and fall risk. Therefore, it is desirable to assess gait health across hypertensive and nonhypertensive subjects in order to prevent or reduce the risk of falls. Analysis of electromyography (EMG) signals can identify age related changes of neuromuscular activation due to various neuropathies and myopathies, but it is difficult to translate these medical changes to clinical diagnosis. To examine and compare geriatrics patients with these gait-altering diseases, we acquire EMG muscle activation signals, and by use of a timesynchronized mat capable of recording pressure information, we localize the EMG data to the gait cycle, ensuring identical comparison across subjects. Using time-frequency analysis on the EMG signal, in conjunction with several parameters obtained from the time-frequency analyses, we can determine the statistical discrepancy between diseases. We base these parameters on physiological manifestations caused by hypertension, as well as other comorbities that affect the geriatrics community. Using these metrics in a small population, we identify a statistical discrepancy between a control group and subjects with hypertension, neuropathy, diabetes, osteoporosis, arthritis, and several other common diseases which severely affect the geriatrics community.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Information Systems
- Hardware and Architecture
- Electrical and Electronic Engineering