Circular components in center of pressure signals

Static posturography provides an objective assessment of postural control by characterizing the body sway during upright standing. The Center-of-Pressure (CoP) signal is recorded by a force platform and it is analyzed by means of many different models and techniques. Most of the parameters calculated according to these different approaches are affected by relevant intra- and inter-subject variability and/or do not have a clear physiological interpretation. Traditional approaches decompose the CoP signal into antero-posterior and medio-lateral time series, corresponding to ankle plantar/dorsiflexion and hip adduction/abduction, respectively. In this study we hypothesized that CoP signals show inherent rotational characteristics. To verify our hypothesis we applied the rotary spectra analysis to the 2-dimensional CoP signal to decompose it into clockwise and counter-clockwise rotational components. We demonstrated the presence of rotational components in the CoP signal of healthy subjects, providing a reference data set of the spectral characteristics of these component

Characterization, Classification, and Genesis of Seismocardiographic Signals

Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms. Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features. SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG

Comparing postural stability entropy analyses to differentiate fallers and non-fallers

The health and financial cost of falls has spurred research to differentiate the characteristics of fallers and non-fallers. Postural stability has received much of the attention with recent studies exploring various measures of entropy. This study compared the discriminatory ability of several entropy methods at differentiating two paradigms in the center-of-pressure of elderly individuals: (1) eyes open (EO) vs. eyes closed (EC) and (2) fallers (F) vs. non-fallers (NF). Methods were compared using the area under the curve (AUC) of the receiver-operating characteristic curves developed from logistic regression models. Overall, multiscale entropy (MSE) and composite multiscale entropy (CompMSE) performed the best with AUCs of 0.71 for EO/EC and 0.77 for F/NF. When methods were combined together to maximize the AUC, the entropy classifier had an AUC of for 0.91 the F/NF comparison. These results suggest researchers and clinicians attempting to create clinical tests to identify fallers should consider a combination of every entropy method when creating a classifying test. Additionally, MSE and CompMSE classifiers using polar coordinate data outperformed rectangular coordinate data, encouraging more research into the most appropriate time series for postural stability entropy analysis

Ann Biomed Eng

The health and financial cost of falls has spurred research to differentiate the characteristics of fallers and non-fallers. Postural stability has received much of the attention with recent studies exploring various measures of entropy. This study compared the discriminatory ability of several entropy methods at differentiating two paradigms in the center-of-pressure of elderly individuals: (1) eyes open (EO) vs. eyes closed (EC) and (2) fallers (F) vs. non-fallers (NF). Methods were compared using the area under the curve (AUC) of the receiver-operating characteristic curves developed from logistic regression models. Overall, multiscale entropy (MSE) and composite multiscale entropy (CompMSE) performed the best with AUCs of 0.71 for EO/EC and 0.77 for F/NF. When methods were combined together to maximize the AUC, the entropy classifier had an AUC of for 0.91 the F/NF comparison. These results suggest researchers and clinicians attempting to create clinical tests to identify fallers should consider a combination of every entropy method when creating a classifying test. Additionally, MSE and CompMSE classifiers using polar coordinate data outperformed rectangular coordinate data, encouraging more research into the most appropriate time series for postural stability entropy analysis.L30 AG022963/AG/NIA NIH HHS/United StatesR01 OH009222/OH/NIOSH CDC HHS/United States2017-05-01T00:00:00Z26464267PMC483370

Effects of Sensorimotor Perturbations on Balance Performance and Electrocortical Dynamics

Humans must frequently adapt their posture to prevent loss of balance. Such balance control requires complex, precisely-timed coordination among sensory input, neural processing, and motor output. Despite its importance, our current understanding of cortical involvement during balance control remains limited by traditional neuroimaging methods, which are stationary and have poor time resolution. High-density electroencephalography (EEG), combined with independent component analysis, has become a promising tool for recording cortical dynamics during balance perturbations due to its portability and high temporal resolution. Additionally, recent improvements in immersive virtual reality headsets may provide new rehabilitative paradigms, but the effects of virtual reality on balance and cortical function remain poorly understood. In my first study, I recorded high-density EEG from healthy, young adult subjects as they walked along a beam with and without virtual reality high heights exposure. While virtual high heights did induce stress, the use of virtual reality during the task increased performance errors and EEG measures of cognitive loading compared to real-world viewing without a headset. In my second study, I collected high-density EEG from healthy young adults as they walked along a treadmill-mounted balance beam to determine the effect of a transient visual perturbation on training in virtual reality. Subjects in the perturbations group improved comparably to those that trained without virtual reality, indicating that the perturbation helped subjects overcome the negative effects of virtual reality on motor learning. The perturbation primarily elicited a cognitive change. In my third study, healthy, young adult EEG was recorded during physical pull and visual rotation perturbations to tandem walking and tandem standing. I found similar electrocortical patterns for both perturbation types, but different cortical areas were involved for each. In my fourth study, I used a phantom head to validate EEG connectivity methods based on Granger causality in a real-world environment. In general, connectivity measures could determine the underlying connections, but many were susceptible to high-frequency false positives. Using data from my third study, my fifth study analyzed corticomuscular connectivity patterns following sensorimotor balance perturbations. I found strong occipito-parietal connections regardless of perturbation type, along with evidence of direct muscular control from the supplementary motor area during the standing perturbation response. Taken together, the work presented in this dissertation greatly expands upon the current knowledge of cortical processing during sensorimotor balance perturbations and the effect of such perturbations on short-term motor learning, providing multiple avenues for future exploration.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147615/1/stepeter_1.pd

Phase Dynamics in Human Visuomotor Control - Health & Disease

In this thesis, comprised of four publications, I investigated phase dynamics of visuomotor control in humans during upright stance in response to an oscillatory visual drive. For this purpose, I applied different versions of a ‘moving room’ paradigm in virtual reality while stimulating human participants with anterior-posterior motion of their visual surround and analyzed their bodily responses. Human balance control constitutes a complex interplay of interdependent processes. The main sensory contributors include vision, vestibular input, and proprioception, with a dominant role attributed to vision. The purpose of the balance control system is to keep the body’s center of mass (COM) within a certain spatial range around the current base of support. Ever-changing environmental circumstances along with sensory noise cause the body to permanently sway around its point of equilibrium. Considering this sway, the human body can be modelled as a (multi-link) inverted pendulum. To maintain balance while being exposed to perturbations of the visual environment, humans adjust their sway to counteract the perceived motion of their bodies. Neurodegenerative diseases like Parkinson’s impair balance control and thus are likely to affect these mechanisms. Hence, investigation of bodily responses to a visual drive gives insight into visuomotor control in health and disease. In my first study, I introduced inter-trial phase coherence (ITPC) as a novel method to investigate postural responses to periodical visual stimulation. I found that human participants phase-locked the motion of their center of pressure (COP) to a 3-D dot cloud which oscillated in the anterior-posterior direction. This effect was equally strong for a low frequency of visual stimulation at 0.2 Hz and a high frequency of 1.5 Hz, the latter exceeding the previously assumed frequency range associated with coherent postural sway responses to periodical oscillations of the visual environment (moving room). Moreover, I was able to show that ITPC reliably captured responses in almost all participants, thereby addressing the common problem of inter-subject variability in body sway research. Based on the results of my first study, I concluded phase locking to be an essential feature in human postural control. For the second study, I introduced a mobile and cost-effective setup to apply a visual paradigm consisting of a virtual tunnel which stretched in the anterior-posterior direction and oscillated back and forth at three distinct frequencies (0.2 Hz, 0.8 Hz, and 1.2 Hz). Because tracking of the COP alone neglects crucial information about how COM shifts are arranged across the body, I included additional full-body motion tracking here to evaluate sway of individual body segments. Using a modified measure of phase locking, the phase locking value (PLV), allowed me to find participants phase-locking not only their COP, but also additional segments of their body to the visual drive. While their COP exhibited a strong phase locking to all frequencies of visual stimulation, distribution of phase locking across the body underwent a shift as the frequency of the visual stimulation increased. For the lowest frequency of 0.2 Hz, participants phase-locked almost their entire body to the stimulus. At higher frequencies, this phase locking shifted towards the lower torso and hip, with subjects almost exclusively phase-locking their hip to the visual drive at the highest frequency of 1.2 Hz. Having introduced a novel and reliable measurement along with a mobile setup, these results allowed me to empirically confirm shifts in postural strategies previously proposed in the literature. In the third study, a collaboration with the neurology department of the Universitätsklinikum Gießen und Marburg (UKGM), I used the same setup and paradigm as in the previous study and additionally derived the trajectory of the COM from a weighted combination of certain body segments. The aim was to investigate phase locking of body sway in a group of patients suffering from Parkinson’s disease (PD) to find potential means for an early diagnosis of the illness. For this purpose, I recruited a group of PD patients, an age-matched control group, and a group of young healthy adults. Even though the sway amplitude of PD patients was significantly larger than that of both other groups, they phase-locked their COP and COM in a similar manner as the control groups. However, considering individual body segments, the shift in PLV distribution differed between groups. While young healthy adults, analogous to the participants in the second study, exhibited a shift towards exclusive phase locking of their hips as frequency of the stimulation increased, both PD patients and age-matched controls maintained a rather homogeneous phase locking across their body. This suggested increased body stiffness, although being an effect of age rather than disease. Overall, I concluded that patients of early-to-mid stage PD exhibit impaired motor control, reflected in their increased sway amplitude, but intact visuomotor processing, indicated by their ability to phase-lock the motion of their body to a visual drive. The fourth study, to which I contributed as second author, used experimental data collected from an additional visual condition in the course of the third study. This condition consisted of unpredictable back and forward motion of the simulated tunnel. Here, we investigated the velocity profiles of the COP and COM in response to the unpredictable visual motion and a baseline condition at which the tunnel remained static. We found PD patients to exhibit larger velocities of their COP and COM under both conditions when compared to the control groups. When examining the net increase that unpredictable motion had on the velocity of both parameters, we found a significantly higher increase in COP velocity for both PD patients and age-matched controls, but no increase in COM velocity in any of the groups. These results suggested that all groups successfully maintained their balance under unpredictable visual perturbations, but that PD patients and older adults required more effort to accomplish this task, as reflected by the increased velocity of their COP. Again, these results indicated an effect of age rather than disease on the observed postural responses. In summary, using innovative phase-locking techniques and simultaneously tracking multiple body sway parameters, I was able to provide novel insight into visuomotor control in humans. First, I overcame previous issues of inconsistent sway parameters in groups of participants; Second, I found phase-locking to be an essential feature of visuomotor processing, which also allowed me to empirically confirm previously established theories of postural control; Third, through studies in collaboration with the neurology department of the UKGM, I was able to uncover new aspects of visuomotor processing in Parkinson’s, contributing to a better understanding of the sensorimotor aspects of the disease

Principal Component Analysis

This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction