A data set with kinematic and ground reaction forces of human balance

Spatial variability and directional shifts in postural control in Parkinson's disease

Individuals with Parkinson's disease exhibit tremors, rigidity, and bradykinesia, disrupting normal movement variability and resulting in postural instability. This comprehensive study aimed to investigate the link between the temporal structure of postural sway variability and Parkinsonism by analyzing multiple datasets from young and older adults, including individuals with Parkinson's disease, across various task conditions. We used the Oriented Fractal Scaling Component Analysis (OFSCA), which identifies minimal and maximal long-range correlations within the center of pressure time series, allowing for detecting directional changes in postural sway variability. The objective was to uncover the primary directions along which individuals exerted control during the posture. The results, as anticipated, revealed that healthy adults predominantly exerted control along two orthogonal directions, closely aligned with the anteroposterior (AP) and mediolateral (ML) axes. In stark contrast, older adults and individuals with Parkinson's disease exhibited control along suborthogonal directions that notably diverged from the AP and ML axes. While older adults and those with Parkinson's disease demonstrated a similar reduction in the angle between these two control directions compared to healthy older adults, their reliance on this suborthogonal angle concerning endogenous fractal correlations exhibited significant differences from the healthy aging cohort. Importantly, individuals with Parkinson's disease did not manifest the sensitivity to destabilizing task settings observed in their healthy counterparts, affirming the distinction between Parkinson's disease and healthy aging.

Older adults and individuals with Parkinson's disease control posture along suborthogonal directions that deviate from the traditional anteroposterior and mediolateral directions

A rich and complex temporal structure of variability in postural sway characterizes healthy and adaptable postural control. However, neurodegenerative disorders such as Parkinson's disease, which often manifest as tremors, rigidity, and bradykinesia, disrupt this healthy variability. This study examined postural sway in young and older adults, including individuals with Parkinson's disease, under different upright standing conditions to investigate the potential connection between the temporal structure of variability in postural sway and Parkinsonism. A novel and innovative method called oriented fractal scaling component analysis was employed. This method involves decomposing the two-dimensional center of pressure (CoP) planar trajectories to pinpoint the directions associated with minimal and maximal temporal correlations in postural sway. As a result, it facilitates a comprehensive assessment of the directional characteristics within the temporal structure of sway variability. The results demonstrated that healthy young adults control posture along two orthogonal directions closely aligned with the traditional anatomical anteroposterior (AP) and mediolateral (ML) axes. In contrast, older adults and individuals with Parkinson's disease controlled posture along suborthogonal directions that significantly deviate from the AP and ML axes. These findings suggest that the altered temporal structure of sway variability is evident in individuals with Parkinson's disease and underlies postural deficits, surpassing what can be explained solely by the natural aging process.

Frequency-dependent behavior of paretic and non-paretic leg force during standing post stroke

Maintaining upright posture in quiet standing is an important skill that is often disrupted by stroke. Despite extensive study of human standing, current understanding is incomplete regarding the muscle coordination strategies that produce the ground-on-foot force (F) that regulates translational and rotational accelerations of the body. Even less is understood about how stroke disrupts that coordination. Humans produce sagittal plane variations in the location (center of pressure, xCP) and orientation (Fx/Fz) of F that, along with the force of gravity, produce sagittal plane body motions. As F changes during quiet standing there is a strong correlation between the xCP and Fx/Fz time-varying signals within narrow frequency bands. The slope of the correlation varies systematically with frequency in non-disabled populations, is sensitive to changes in both environmental and neuromuscular control factors, and emerges from the interaction of body mechanics and neural control. This study characterized the xCP versus Fx/Fz relationship as frequency-dependent Intersection Point (IP) heights for the paretic and non-paretic legs of individuals with history of a stroke (n = 12) as well as in both legs of non-disabled controls (n = 22) to reveal distinguishing motor coordination patterns. No inter-leg difference of IP height was present in the control group. The paretic leg IP height was lower than the non-paretic, and differences from control legs were in opposite directions. These results quantify disrupted coordination that may characterize the paretic leg balance deficit and non-paretic leg compensatory behavior, providing a means of monitoring balance impairment and a target for therapeutic interventions.

A Non-Laboratory Gait Dataset of Full Body Kinematics and Egocentric Vision

In this manuscript, we describe a unique dataset of human locomotion captured in a variety of out-of-the-laboratory environments captured using Inertial Measurement Unit (IMU) based wearable motion capture. The data contain full-body kinematics for walking, with and without stops, stair ambulation, obstacle course navigation, dynamic movements intended to test agility, and negotiating common obstacles in public spaces such as chairs. The dataset contains 24.2 total hours of movement data from a college student population with an approximately equal split of males to females. In addition, for one of the activities, we captured the egocentric field of view and gaze of the subjects using an eye tracker. Finally, we provide some examples of applications using the dataset and discuss how it might open possibilities for new studies in human gait analysis.

Predicting UPDRS Motor Symptoms in Individuals with Parkinsons Disease from Force Plates Using Machine Learning

Parkinsons disease (PD) is a neurodegenerative disease that affects motor abilities with increasing severity as the disease progresses. Traditional methods for diagnosing PD include a section where a trained specialist scores qualitative symptoms using the motor subscale of the Unified Parkinsons Disease Rating Scale (UPDRS-III). The aim of this feasibility study was twofold. First, to evaluate quiet standing as an additional, out-of-clinic, objective feature to predict UPDRS-III subscores related to motor symptom severity; and second, to use quiet standing to detect the presence of motor symptoms. Force plate data were collected from 42 PD patients and 43 healthy controls during quiet standing (a task involving standing still with eyes open and closed) as a feasible task in clinics. Predicting each subscore of the UPDRS-III could aid in identifying progression of PD and provide specialists additional tools to make an informed diagnosis. Random Forest feature importance indicated that features correlated with range of center of pressure (i.e. the medial-lateral and anterior-posterior sway) were most useful in the prediction of the top PD prediction subscores of postural stability (r = 0.599; p = 0.014), hand tremor of the left hand (r = 0.650; p = 0.015), and tremor at rest of the left upper extremity (r = 0.703; p = 0.016). Quiet standing can detect body bradykinesia (AUC-ROC = 0.924) and postural stability (AUC-ROC = 0.967) with high predictability. Although there are limited data, these results should be used as a feasibility study that evaluates the predictability of individual UPDRS-III subscores using quiet standing data.

Classification of Older and Fall-Experienced Subjects by Postural Sway Data using Mass Spring Damper Model

The quiet standing test is used to detect diseases of the postural control system. The descriptive statistics of the center of pressure (COP) of older people during the test tend to be larger than those of healthy young people, but they cannot indicate structural problems in postural control. COP trajectories can be mathematically modeled with structural parameters such as viscosity, stiffness, and stochastic terms; however, the classification accuracy of older and fall-experienced people using such parameters has not been sufficiently verified. In this study, six structural parameters of a mass-spring-damper (MSD) model were estimated using two datasets, in which a total of 212 subjects performed quiet standing tests under four conditions. The estimated parameters were used for classification with a random forest algorithm to examine the differences in classification accuracy compared to seven conventional descriptive statistics methods. For the classification of older subjects, the classification accuracy of the MSD parameter method was the highest in foam condition, with positive likelihood ratios approximately 8.0. For the classification of fall-experienced subjects, the positive likelihood ratio of the MSD parameter method was 5.0, which is better than conventional descriptive statistics. Various MSD parameters revealed that aging and changing the floor surface and visual conditions cause oscillations in the COP behavior. While the MSD parameters were confirmed to help classify older subjects more accurately than the conventional descriptive statistics, there was room for further improvement in the classification accuracy of fall-experienced subjects.

Deep Neural Networks for Human's Fall-risk Prediction using Force-Plate Time Series Signal

Early and accurate identification of the balance deficits could reduce falls, in particular for older adults, a prone population. Our work investigates deep neural networks' capacity to identify human balance patterns towards predicting fall-risk. Human balance ability can be characterized based on commonly-used balance metrics, such as those derived from the force-plate time series. We hypothesized that low, moderate, and high risk of falling can be characterized based on balance metrics, derived from the force-plate time series, in conjunction with deep learning algorithms. Further, we predicted that our proposed One-One-One Deep Neural Networks algorithm provides a considerable increase in performance compared to other algorithms. Here, an open source force-plate dataset, which quantified human balance from a wide demographic of human participants (163 females and males aged 18-86) for varied standing conditions (eyes-open firm surface, eyes-closed firm surface, eyes-open foam surface, eyes-closed foam surface) was used. Classification was based on one of the several indicators of fall-risk tied to the fear of falling: the clinically-used Falls Efficacy Scale (FES) assessment. For human fall-risk prediction, the deep learning architecture implemented comprised of: Recurrent Neural Network (RNN), Long-Short Time Memory (LSTM), One Dimensional Convolutional Neural Network (1D-CNN), and a proposed One-One-One Deep Neural Network. Results showed that our One-One-One Deep Neural Networks algorithm outperformed the other aforementioned algorithms and state-of-the-art models on the same dataset. With an accuracy, precision, and sensitivity of 99.9%, 100%, 100%, respectively at the 12th epoch, we found that our proposed One-One-One Deep Neural Network model is the most efficient neural network in predicting human's fall-risk (based on the FES measure) using the force-plate time series signal. This is a novel methodology for an accurate prediction of human risk of fall.

Hypothetical control of postural sway

Quiet standing exhibits strongly intermittent variability that has inspired at least two interpretations. First, variability can be intermittent through the alternating engagement and disengagement of complementary control processes at distinct scales. A second and perhaps deeper way to interpret this intermittency is through the possibility that postural control depends on cascade-like interactions across many timescales at once, suggesting specific non-Gaussian distributional properties at different timescales. Multiscale probability density function (PDF) analysis shows that quiet standing on a stable surface exhibits a crossover from low, increasing non-Gaussianity (consistent with exponential distributions) at shorter timescales, reflecting inertial control, towards higher non-Gaussianity. Feedback-based control at medium to longer timescales yields a linear decrease that is characteristic of cascade dynamics. Destabilizing quiet standing with an unstable surface or closed eyes serves to attenuate inertial control and to elicit more of the feedback-based control over progressively shorter timescales. The result was to strengthen the appearance of the linear decay indicating cascade dynamics. Finally, both linear and nonlinear indices of postural sway also govern the relative strength of crossover or of linear decay, suggesting that tempering of non-Gaussianity across log-timescale is a function of both extrinsic constraints and endogenous postural control. These results provide new evidence that cascading interactions across longer timescales supporting postural corrections can even recruit shorter timescale processes with novel task constraints that can destabilize posture.

Chen B. A Data-Driven Approach to Predict Fatigue in Exercise Based on Motion Data from Wearable Sensors or Force Plate

Fatigue increases the risk of injury during sports training and rehabilitation. Early detection of fatigue during exercises would help adapt the training in order to prevent over-training and injury. This study lays the foundation for a data-driven model to automatically predict the onset of fatigue and quantify consequent fatigue changes using a force plate (FP) or inertial measurement units (IMUs). The force plate and body-worn IMUs were used to capture movements associated with exercises (squats, high knee jacks, and corkscrew toe-touch) to estimate participant-specific fatigue levels in a continuous fashion using random forest (RF) regression and convolutional neural network (CNN) based regression models. Analysis of unseen data showed high correlation (up to 89%, 93%, and 94% for the squat, jack, and corkscrew exercises, respectively) between the predicted fatigue levels and self-reported fatigue levels. Predictions using force plate data achieved similar performance as those with IMU data; the best results in both cases were achieved with a convolutional neural network. The displacement of the center of pressure (COP) was found to be correlated with fatigue compared to other commonly used features of the force plate. Bland-Altman analysis also confirmed that the predicted fatigue levels were close to the true values. These results contribute to the field of human motion recognition by proposing a deep neural network model that can detect fairly small changes of motion data in a continuous process and quantify the movement. Based on the successful findings with three different exercises, the general nature of the methodology is potentially applicable to a variety of other forms of exercises, thereby contributing to the future adaptation of exercise programs and prevention of over-training and injury as a result of excessive fatigue.

A Langevin-Based Model With Moving Posturographic Target to Quantify Postural Control

Falls are a major concern of public health, particularly for older adults, as the consequences of falls include serious injuries and death. Therefore, the understanding and evaluation of postural control is considered key, as its deterioration is an important risk factor predisposing to falls. In this work we introduce a new Langevin-based model, local recall, that integrates the information from both the center of pressure (CoP) and the center of mass (CoM) trajectories, and compare its accuracy to a previously proposed model that only uses the CoP. Nine healthy young participants were studied under quiet bipedal standing conditions with eyes either open or closed, while standing on either a rigid surface or a foam. We show that the local recall model produces significantly more accurate prediction than its counterpart, regardless of the eyes and surface conditions, and we replicate these results using another publicly available human dataset. Additionally, we show that parameters estimated using the local recall model are correlated with the quality of postural control, providing a promising method to evaluate static balance. These results suggest that this approach might be interesting to further extend our understanding of the underlying mechanisms of postural control in quiet stance.

An Evaluation of Symmetries in Ground Reaction Forces during Self-Paced Single- and Dual-Task Treadmill Walking in the Able-Bodied Men

Gait is a complex autonomous activity that has long been viewed as a symmetrical locomotion, even when it adapts to secondary concurrent attention-demanding tasks. This study aimed to evaluate the symmetry of the three ground reaction forces (GRFs) in able-bodied individuals during self-paced treadmill walking with and without concurrent cognitive demands. Twenty-five male participants (age: 34.00 ± 4.44 years) completed two gait assessment sessions, each of whom were familiarized with the walking trials during their first session. Both sessions involved six-minute self-paced treadmill walking under three conditions: single-task walking and walking while concurrently responding to auditory 1-back and 2-back memory tasks. The symmetry of the GRFs was estimated using a nonlinear approach. Changes in the symmetry and walking speed across conditions in both sessions were assessed using inferential statistics. Results demonstrated that the three GRFs deviated from perfect symmetry by ≥10%. Engaging working memory during walking significantly reduced the symmetry of the vertical GRF (p = 0.003), and its detrimental effects on walking speed were significantly reduced in the second session with respect to the first session (p < 0.05). The findings indicate imperfect gait symmetry in able-bodied individuals, suggesting that common perceptions of gait symmetry should be reconsidered to reflect its objective importance in clinical settings.

Validation of an ankle-hip model of balance on a balance board via kinematic frequency-content

BACKGROUND

The ankle-strategy model, where the human body is modeled as a single inverted pendulum hinged at the ankle, has been used for decades to study the dynamics and the stability of the human upright posture (UP). However, the contribution of the hip joints is critical whenever postural disturbances are considered. To account for hip contribution, a double inverted pendulum (DIP) model rotating about the ankle and hip joints has been recently proposed in our previous work but experimental validation efforts are scarce.

METHOD

In the present study, it is investigated whether the DIP model is able to reproduce the experimentally observed frequency spectrum of the ankle and hip joint kinematic for young and elderly subjects balancing on a compliant surface. The DIP model based and experimental kinematics are compared via Fourier analysis to obtain their corresponding amplitude spectrum density (ASD) functions. Quantitative comparisons of the ASD functions are accomplished through Bland-Altman (B&A) analysis, and Pearson correlation coefficient (PCC).

RESULTS

The DIP model can reproduce part of the experimental frequency spectrum of the ankle and hip joint angle position and velocity, especially for frequencies larger than 0.35 Hz. Moreover, the model captures the decaying behavior of the experimental ASD functions as frequency increases. With respect to joint angle velocities, the highest PCC between model-based and experimental ASD functions is found for the hip joint of elderly subjects. The B&A analysis shows that the zero-difference between model-based and experimental ASD functions lies between the 95 % confidence interval, especially for the joint angle position results. These suggest that the DIP model reproduces part of the experimentally observed frequency spectrum, which validates the model to study the dynamics and stability of the human upright posture.

Direct estimation of the parameters of a delayed, intermittent activation feedback model of postural sway during quiet standing

Human postural sway during quiet standing has been characterized as a proportional-integral-derivative controller with intermittent activation. In the model, patterns of sway result from both instantaneous, passive, mechanical resistance and delayed, intermittent resistance signaled by the central nervous system. A Kalman-Filter framework was designed to directly estimate from experimental data the parameters of the model’s stochastic delay differential equations with discrete dynamic switching conditions. Simulations showed that all parameters could be estimated over a variety of possible data-generating configurations with varying degrees of bias and variance depending on their empirical identification. Applications to experimental data reveal distributions of each parameter that correspond well to previous findings, suggesting that many useful, physiological measures may be extracted from sway data. Individuals varied in degree and type of deviation from theoretical expectations, ranging from harmonic oscillation to non-equilibrium Langevin dynamics.

Estimating Center of Mass Trajectory in Quiet Standing: a Review

As falls prevalence increases with the aging of the population, early detection of balance degradation is of great importance for efficient prevention and treatment. This work compares a wide range of state-of-the-art methods to estimate the trajectory of the center of mass - a key aspect of postural control quantification. This comparison is done through multiple complementary metrics over a large dataset, highlighting the pros and cons of each method as well as the key influence of the data preprocessing. Additionally, we introduce several improvements and parameter tunings for these methods that increase their accuracy or reduce contextual information requirement.

Acute effects of contract-relax proprioceptive neuromuscular facilitation stretching of hip abductors and adductors on dynamic balance

Background

Balance control has been shown to play a fundamental role both in everyday life and many athletic activities. An important component of balance control is the somatosensory information gained from muscle spindles and Golgi tendon organs. The changes in the muscle-tendon unit stiffness could alter the ability to detect and respond promptly to changes of an unstable environment. One of the procedures affecting muscle stiffness is stretching, and contract-relax PNF stretching (CRS) is considered as one of the safest and most effective techniques. So far, there are no studies on the impact of CRS of hip adductor and abductor muscles on body balance. These muscle groups are responsible for maintaining mediolateral balance which is of particular interest, since it is more affected by ageing and disease and since its deterioration has been associated with an increased risk of falling. In light of the above, the aim of the present study was to investigate the effects of a single dose of contract-relax proprioceptive neuromuscular facilitation stretching of hip adductors and abductors on mediolateral dynamic balance.

Methods

The study involved 45 healthy individuals (age 19–23 years) assigned to the intervention group (IG) or the control group (CG). Balance testing was carried out before (Pre) and immediately after CRS in the intervention group or after 5-minute rest in the control group (Post). There were performed three repetitions of the CRS targeting the adductor and abductor muscles of the hip.

Results

Statistically significant differences between Pre and Post condition were observed only in the intervention group. The values of all measured variables defining the body’s dynamic balance were significantly lower immediately after the applied CRS, which indicates an improved body balance: Global Index (p = 0.0001), total area of sway (p = 0.0001), external area of sway (p = 0.00004), external time (p = 0.0004) and reaction time (p = 0.0003).

Conclusions

A single dose of contract-relax proprioceptive neuromuscular facilitation stretching of the hip adductor and abductor muscles improved mediolateral dynamic balance.