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Fall Prevention Using Linear and Nonlinear Analyses and Perturbation Training Intervention

Description

Injuries and death associated with fall incidences pose a significant burden to society, both in terms of human suffering and economic losses. The main aim of this dissertation is to study approaches that can reduce the risk of falls. One major subset of falls is falls due to neurodegenerative disorders such as Parkinson’s disease (PD). Freezing of gait (FOG) is a major cause of falls in this population. Therefore, a new FOG detection method using wavelet transform technique employing optimal sampling window size, update time, and sensor placements for identification of FOG events is created and validated in this dissertation. Another approach to reduce the risk of falls in PD patients is to correctly diagnose PD motor subtypes. PD can be further divided into two subtypes based on clinical features: tremor dominant (TD), and postural instability and gait difficulty (PIGD). PIGD subtype can place PD patients at a higher risk for falls compared to TD patients and, they have worse postural control in comparison to TD patients. Accordingly, correctly diagnosing subtypes can help caregivers to initiate early amenable interventions to reduce the risk of falls in PIGD patients. As such, a method using the standing center-of-pressure time series data has been developed to identify PD motor subtypes in this dissertation. Finally, an intervention method to improve dynamic stability was tested and validated. Unexpected perturbation-based training (PBT) is an intervention method which has shown promising results in regard to improving balance and reducing falls. Although PBT has shown promising results, the efficacy of such interventions is not well understood and evaluated. In other words, there is paucity of data revealing the effects of PBT on improving dynamic stability of walking and flexible gait adaptability. Therefore, the effects

of three types of perturbation methods on improving dynamics stability was assessed. Treadmill delivered translational perturbations training improved dynamic stability, and adaptability of locomotor system in resisting perturbations while walking.

ContributorsRezvanian, Saba (Author) / Lockhart, Thurmon (Thesis advisor) / Buneo, Christopher (Committee member) / Lieberman, Abraham (Committee member) / Abbas, James (Committee member) / Deep, Aman (Committee member) / Arizona State University (Publisher)

Created2019

The Effects of Perturbation on Dynamic Stability for Fall Risk Analysis

Description

As life expectancy continually rises, many age-related conditions such as deteriorated gait and decreased stability begin to play a larger role in affecting the quality of life for all individuals. Medical expenses associated with falls in the elderly population surpassed $50 Billion in 2015 alone. Understanding fall risk and developing robust metrics and methods of assessment has become more important than ever. While traditional fall risk has looked at classical gait parameters, dynamic stability has gained traction as a more accurate representation of stability during active movement and daily activities. This project seeks to determine the effects on the internal perturbation of gait velocity on dynamic stability represented by the Maximal Lyapunov Exponent (MLE) of multiple acceleration vectors, as well as the efficacy of varying methodology used to assess dynamic stability. Data from 15 healthy, college aged individuals was collected. Significant differences were shown between certain gait velocity trials for one analysis of the three methods explored, while overall trends suggested potential differences between gait velocities with other methodologies warranting further investigation.

ContributorsKreisler, Itai Goeta (Author) / Lockhart, Thurmon (Thesis director) / Rezvanian, Saba (Committee member) / W.P. Carey School of Business (Contributor) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)

Created2018-05

Towards Real-Time Detection of Freezing of Gait Using Wavelet Transform on Wireless Accelerometer Data

Description

Injuries associated with fall incidences continue to pose a significant burden to persons with Parkinson’s disease (PD) both in terms of human suffering and economic loss. Freezing of gait (FOG), which is one of the symptoms of PD, is a common cause of falls in this population. Although a significant amount of work has been performed to characterize/detect FOG using both qualitative and quantitative methods, there remains paucity of data regarding real-time detection of FOG, such as the requirements for minimum sensor nodes, sensor placement locations, and appropriate sampling period and update time. Here, the continuous wavelet transform (CWT) is employed to define an index for correctly identifying FOG. Since the CWT method uses both time and frequency components of a waveform in comparison to other methods utilizing only the frequency component, we hypothesized that using this method could lead to a significant improvement in the accuracy of FOG detection. We tested the proposed index on the data of 10 PD patients who experience FOG. Two hundred and thirty seven (237) FOG events were identified by the physiotherapists. The results show that the index could discriminate FOG in the anterior-posterior axis better than other two axes, and is robust to the update time variability. These results suggest that real time detection of FOG may be realized by using CWT of a single shank sensor with window size of 2 s and update time of 1 s (82.1% and 77.1% for the sensitivity and specificity, respectively). Although implicated, future studies should examine the utility of this method in real-time detection of FOG.

ContributorsRezvanian, Saba (Author) / Lockhart, Thurmon (Author) / Ira A. Fulton Schools of Engineering (Contributor)

Created2016-04-02