Browsing by Author "Soangra, Rahul"

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    Dynamical Properties of Postural Control in Obese Community-Dwelling Older Adults
    Frames, Christopher W.; Soangra, Rahul; Lockhart, Thurmon E.; Lach, John; Ha, Dong Sam; Roberto, Karen A.; Lieberman, Abraham (MDPI, 2018-05-24)
    Postural control is a key aspect in preventing falls. The aim of this study was to determine if obesity affected balance in community-dwelling older adults and serve as an indicator of fall risk. The participants were randomly assigned to receive a comprehensive geriatric assessment followed by a longitudinal assessment of their fall history. The standing postural balance was measured for 98 participants with a Body Mass Index (BMI) ranging from 18 to 63 kg/m2, using a force plate and an inertial measurement unit affixed at the sternum. Participants’ fall history was recorded over 2 years and participants with at least one fall in the prior year were classified as fallers. The results suggest that body weight/BMI is an additional risk factor for falling in elderly persons and may be an important marker for fall risk. The linear variables of postural analysis suggest that the obese fallers have significantly higher sway area and sway ranges, along with higher root mean square and standard deviation of time series. Additionally, it was found that obese fallers have lower complexity of anterior-posterior center of pressure time series. Future studies should examine more closely the combined effect of aging and obesity on dynamic balance.
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    Prediction of fall risk among community-dwelling older adults using a wearable system
    Lockhart, Thurmon E.; Soangra, Rahul; Yoon, Hyunsoo; Wu, Teresa; Frames, Christopher W.; Weaver, Raven; Roberto, Karen A. (2021-10-25)
    Falls are among the most common cause of decreased mobility and independence in older adults and rank as one of the most severe public health problems with frequent fatal consequences. In the present study, gait characteristics from 171 community-dwelling older adults were evaluated to determine their predictive ability for future falls using a wearable system. Participants wore a wearable sensor (inertial measurement unit, IMU) affixed to the sternum and performed a 10-m walking test. Measures of gait variability, complexity, and smoothness were extracted from each participant, and prospective fall incidence was evaluated over the following 6-months. Gait parameters were refined to better represent features for a random forest classifier for the fall-risk classification utilizing three experiments. The results show that the best-trained model for faller classification used both linear and nonlinear gait parameters and achieved an overall 81.6 +/- 0.7% accuracy, 86.7 +/- 0.5% sensitivity, 80.3 +/- 0.2% specificity in the blind test. These findings augment the wearable sensor's potential as an ambulatory fall risk identification tool in community-dwelling settings. Furthermore, they highlight the importance of gait features that rely less on event detection methods, and more on time series analysis techniques. Fall prevention is a critical component in older individuals' healthcare, and simple models based on gait-related tasks and a wearable IMU sensor can determine the risk of future falls.
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    Understanding Variability in Older adults using Inertial Sensors
    Soangra, Rahul (Virginia Tech, 2014-06-30)
    Falls are the most frequent cause of unintentional injuries among older adults; afflicting 30 percent of persons aged 65 and older and more than 50 percent of persons aged 85 and older. There is a serious need for strategies to prevent falls in elderly individuals, but an important challenge in fall prevention is the paucity of objective evidence regarding the mechanisms that lead directly to falls. There exists no mechanisms about how to predict and manage elderly falls, which has multifactorial risk factors associated with its occurrence in the elderly. As the U.S. population continues to age, both the number of falls as well as the cost of treatment of fall injuries will continue to grow. Decades of research in fall prevention has not led to a decrease in the fall incidence; thus new strategies need to be introduced to understand and prevent falls. Aging reduces the adaptability of various physical and environmental stressors that hinder stability and balance maintenance and may therefore result in a fall. Movement variability in an individual's task performance can be used to assess the limitations of the movement control system. Maintaining variation in movement engenders flexible and adaptable modalities for elderly individuals to prevent falls in an unpredictable and ever changing external environment. Conversely, excessive variability of movement may drive the control system closer to its stability limits during balance and walking tasks. Accordingly, inertial sensors are an emerging wearable technology that can facilitate noninvasive monitoring of fall prone individuals in clinical settings. This research examined the potential of inertial sensors for use in clinical settings, and evaluated their effectiveness in comparison to mature laboratory systems (i.e., force platform and camera system). Study findings showed a relationship between movement variability and fall risk among healthy young and older adults. Further, the outcomes of this work translates to the clinical environment to better understand the health status (leading to frailty) of cardiac patients; reflected by the underlying adaptability of the control system, but requires further improvements if to be used as robust clinical tool. This research provides the groundwork for rapid clinical assessments in which its validity and robustness should be investigated in future efforts.