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Computer Science
Medicine

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Using data from the Microsoft Kinect 2 to determine postural stability in healthy subjects: A feasibility trial

The objective of this study was to determine whether kinematic data collected by the Microsoft Kinect 2 (MK2) could be used to quantify postural stability in healthy subjects. Twelve subjects were recruited for the project, and were instructed to perform a sequence of simple postural stability tasks. The movement sequence was performed as subjects were seated on top of a force platform, and the MK2 was positioned in front of them. This sequence of tasks was performed by each subject under three different postural conditions: “both feet on the ground” (1), “One foot off the ground” (2), and “both feet off the ground” (3). We compared force platform and MK2 data to quantify the degree to which the MK2 was returning reliable data across subjects. We then applied a novel machine-learning paradigm to the MK2 data in order to determine the extent to which data from the MK2 could be used to reliably classify different postural conditions. Our initial comparison of force plate and MK2 data showed a strong agreement between the two devices, with strong Pearson correlations between the trunk centroids “Spine_Mid” (0.85 ± 0.06), “Neck” (0.86 ± 0.07) and “Head” (0.87 ± 0.07), and the center of pressure centroid inferred by the force platform. Mean accuracy for the machine learning classifier from MK2 was 97.0%, with a specific classification accuracy breakdown of 90.9%, 100%, and 100% for conditions 1 through 3, respectively. Mean accuracy for the machine learning classifier derived from the force platform data was lower at 84.4%. We conclude that data from the MK2 has sufficient information content to allow us to classify sequences of tasks being performed under different levels of postural stability. Future studies will focus on validating this protocol on large populations of individuals with actual balance impairments in order to create a toolkit that is clinically validated and available to the medical community.

Introduction It is estimated that each year, fall-related injury costs the United States $34 billion in direct costs [1]. For elderly individuals alone, fall-related visits to the emergency department have been estimated to cost an average of $2,823 per visit, with each fall-related hospitalization estimated to cost an average of $25,465 [1]. Methods for stratifying fall risk and focusing preventive measures on those who are at highest risk remains one of the most important management strategies for saving resources and preventing suffering [1][2][3]. However, sensitive and reliable measures for assessing balance and fall-risk are difficult to develop and implement. One widely used balance measure, the Berg Balance Scale (BBS), relies on subjective assessment, and most of its domains require the ability to stand, thereby limiting scoring range and applicability toward non-ambulatory subjects [3][4][5]. By contrast, computerized posturography is highly sensitive, but requires costly specialized equipment only found in institutional settings [6][7][8].
There are an unprecedented number of low cost electronic devices entering the consumer market that are capable of tracking metrics related to human movement [9]. However, despite the potential in this space for healthcare applications, these devices are yet to make their way into clinical practice. One such device, the Microsoft Kinect 2 (MK2; Microsoft Corporation, Redmond, WA) represents significant innovation in the field of accessible and affordable markerless motion capture. The MK2 features a 1080p (for HD video acquisition) camera, an active infrared (IR) camera, and a depth sensor (for 3-Dimensional (3D) skeletal tracking). The resultant technology has the ability to perform 3D tracking of head, trunk, limb, and finger motion by tracking the location of 25 inferred joint centroids across the entire body. The MK2 has a well-developed Application Programming Interface (API) to support the development of custom applications that utilize the raw data collected by the MK2. This innovative technology represents a unique opportunity to create low-cost, automated software systems that can quantify motor behavior and impairment.
The MK2 (and many devices like it) are already being used for human activity recognition, and often utilize state-of-the-art machine learning paradigms to perform their intended roles [10][11][12][13]. Furthermore, the MK2 and Kinect 1 have both been used in the past to make balance assessments using a variety of different techniques [14][15][16][17][18]. However, the existing Machinelearning approaches for the analysis of Kinect data are designed to solve the problem of gesture classification, such as the work described by Li and colleagues [19]. The major focus of this work is to solve a slightly different problem: to classify differences in the relative "quality" of the same gesture being performed in different ways. This is a more challenging problem, and conventional approaches that have previously been used to analyze MK2 data have not addressed this unmet need. The purpose of this study is to investigate the ability of a novel machine learning approach to classify the relative movement quality of different gestures. We hope that this will permit us to make automated determinations about balance ability in our subjects.
There is a critical need for low-cost technology that can quantify balance ability in individuals in a way that is fast, simple, and clinically relevant. It is important that these systems be accessible enough to be rapidly deployed to home environments so that individuals who fall into at-risk populations are capable of safely testing balance at home, rather than facing the financially and logistically challenging prospect of regular clinical check-ups. Here, we propose a modified BBS (mBBS) assessment that can be performed from a seated position. We chose a seated assessment position because it would allow us to test non-ambulatory subjects, and it will allow subjects to perform the assessment safely in an unsupervised environment. We then assess the feasibility of using the MK2 as a tool for balance assessment by comparing data output from the MK2 to data collected from a research-grade force platform. Finally, we investigate whether the data collected from the MK2 can be used to identify differing levels of postural stability during performance of balance assessment tasks.

Data collection
Subjects. Twelve healthy subjects were recruited and data was collected and analyzed with the full approval of the Burke Rehabilitation Hospital Committee for Human Rights in Research (protocol approval number: BRC509). Subjects were recruited from the staff at the Burke Medical Research Institute if they had no history of neurological disorder or serious musculoskeletal injury, displayed healthy upper and lower limb function and were under the age of 65. Because all subjects were healthy individuals engaging in a minimal risk protocol that was designed as a hardware testing protocol for the MK2, the Burke Rehabilitation Hospital Committee for Human Rights in Research deemed it appropriate for data collection to occur without the need for written consent. Verbal consent was obtained from all research participants prior to data collection, and consent was indicated on a Microsoft Excel Spreadsheet checklist. The subject population for this project included 8 females and 4 males (mean age 30.2 ± 6.0 years).
Hardware configuration and experimental setup. Data was collected using an MK2 that was connected to a Microsoft Surface Pro 3 tablet. In order to track joint centroids in real-time with the MK2, subject positioning was of paramount importance. For optimized tracking, subjects were positioned in front of the MK2 sensor that was placed (using a tripod) at a distance of approximately 2.7 meters from the center of the force plate, and 1.15 meters from the floor (Fig 1). This is within the optimal tracking range of sensors that are similar in nature to that of the MK2 [20,21]. The MK2 has a well-documented API that allows users to access much of the raw data that the device collects. For this project, we developed a custom data collection software program in Microsoft Visual Studio that harnessed the video and kinematic data-recording capabilities using the "Body Basics" and "Color Basics" functionalities of the MK2 API. The "Body Basics" system is optimized to skeletally track users while either standing or sitting, whereas "Color Basics" provides a video record of each data collection recording [22][23][24].
In order to assess the reliability of data recorded from the MK2, a Kistler Type 9260AA force plate (KFP) was used as a research-grade comparison. The KFP uses piezoelectric force sensors to accurately measure any forces applied to the surface of the device [25]. The intention of this study was to assess seated balance using the KFP and the MK2. However, the KFP was found to be very sensitive to the type of chair used due to the varying force transfer properties of different materials. In order to create experimental conditions that would ensure stable readings from the force platform, we designed and built a wooden stool (39cm x 49cm x 40cm) for subjects to sit on when completing the protocol. The weight of the subject was transferred to the KFP via two foot-shaped wooden blocks on the base of the box (32cm x 13cm x 2cm) in order to emulate a standing subject while facilitating weight transfer through the left and right ischial tuberosities of each subject. Raw data from the KFP is used to automatically calculate center of pressure (COP) data for each subject, using methods that are detailed completely in online Kistler resources that can be freely accessed at the following urls: http:// bit.ly/2bnL9Lz (Overview of formulae used to derive metrics from the KFP raw data), http:// bit.ly/2cjsOmM (general platform specifications). COP metrics from force platforms have been routinely used to evaluate postural stability in a number of studies, and have been shown to correlate moderately with other established measures of balance ability in both healthy and clinical populations [26][27][28][29].
The wooden box was placed in the center of the force plate. The force plate was positioned against a wall and the MK2 was set up on a tripod 2.7 meters from the center of the box. Prior to each data collection session, each subject sat in the center of the wooden box with both feet on the ground while the force plate calibrated and measured the subject's seated weight. An experimenter also recorded each subject's seated height (from the surface of the force plate to the tops of their heads). It was necessary to calibrate the force plate with a seated subject because subjects remained sitting for the entirety of the data acquisition phase.
Protocol and data acquisition. For this project, each subject was asked to perform a modified version of the Berg Balance Scale (BBS). The BBS is a widely used, gold-standard metric for predicting risk of falling in multiple clinical and non-clinical populations [2,4]. The BBS involves fourteen physical tasks of varying complexity and difficulty that are designed to test an individual's standing balance. However, our intention for this study was to develop a protocol that can test balance ability in individuals who are non-ambulatory, or give ambulatory individuals the ability to safely assess balance from a seated position. In order to test balance in the seated position, we developed a specialized "modified Berg Balance Scale" (mBBS) with six tasks that are closely related to the original BBS items, and also adapted from other work in this field (Table 1; [6]). Each subject was instructed to complete the six tasks of the mBBS protocol under three different conditions (Fig 2) that represented increasing challenges to postural stability:  Comparison of MK2 and KFP data. The goal of this study is to determine whether the MK2 could be reliably used to assess postural stability with comparable accuracy a research-

Task Instruction
Task 1 With arms crossed, sit unsupported for 10 seconds.
Task 2 With arms crossed and eyes closed, sit unsupported for 10 seconds.
Task 3 Arms at your sides, lean back against the wall for 10 seconds. Without using your hands, sit up unsupported for 10 seconds. Repeat once.
Task 4 Arms crossed, look over your left shoulder for 10 seconds. Face front then turn to look over your right shoulder for 10 seconds. Return to face front.
Task 5 Pick up an object from the floor with your left hand, and return to sit upright, unsupported. Lean over to place the object back on the floor. Repeat using right hand. End in upright seated position.
Task 6 Start with arms crossed, sitting unsupported. Raise one arm 90˚out from your side. Hold for 10 seconds, then return to start position for 10 seconds. Repeat with other arm. grade measurement tool (the KFP). Data from the two devices are of similar natures (multivariate time-series) and therefore can be handled using the same data processing pipeline. The comparison will be done on 3 levels: 1. Benchmarking the MK2. First we must ensure the quality of the data coming from the MK2. Observing the Pearson's correlation and root mean squared error (RMSE) value between the signals from the two devices will act as a measure of the similarity between time-series.
2. Classification across tasks. In this setup, we are interested in predicting the task of a given trial. This is a gesture recognition problem where the MK2 is particularly adapted.
3. Classification across conditions. In this setup, we are looking to predict the postural condition under which the 6 tasks were performed. While the KFP is supposed to perform relatively well at this task, the ability of the MK2 to discriminate between the conditions remains the main question of this study.
Structure of raw motion capture data (MK2). The MK2 quantifies body motion by tracking the coordinates of 25 anatomical joint centroids in 3-dimensional space ( Fig 3A). It records at a sampling frequency of 30Hz. Since variation in balance was emulated by asking the subject to perform the task under different leg postures (Fig 2), we were concerned that using data collected from the lower body joint centroids would lead to classification based on the posture itself, rather than postural stability. Therefore, for the purposes of this study, we only used 16 out of the 25 joint centroids that characterized motion of the upper part of the body: starting from the Spine Mid centroid and above ( Fig 3B). We also chose to independently examine the four centroids that make up the spinal column: Spine Mid, Spine Shoulder, Neck, and Head ( Fig 3C).
Positional X, Y, and Z coordinates were recorded for each of our 16 centroids. Let x MK2 (t) 2 R 48 be a data recorded at the time-point t, we have: Where (J1 x (t),J1 y (t),J1 z (t)) are the x, y, z coordinates of the joint #1 ("Spine_Mid") of the MK2. As each subject was directed to perform the mBBS, a new data file was recorded for each of the 6 tasks of the mBBS, and data files were sorted according to task and condition. Therefore, each task of index i 2 {1: K} is represented as a matrix X MK2,i 2 R 48×Nt such as: where Nt is the number of frames in each recording and T is the transpose operator. Structure of the raw force platform data (KFP). The KFP provides 2-dimensional coordinates of the subject's center of pressure in the horizontal plane. The sampling frequency is 600 Hz. Therefore, data from the KFP can be expressed in the same way as the MK2 data. Let x KFP (t) denote the two dimensional time series recorded at the time-point t by the KFP, we have: where (COP x (t),COP y (t)) are the x, y coordinates of the center of pressure of the subject. Following the same notation as in Eq (2), we can express the data corresponding to the task of index i by a matrix X KFP,i 2 R 2×Nt such as: Data analysis-Classification protocol Covariance matrices and feature vectors. Covariance matrices are essential elements in the majority of algorithms and methodologies dedicated to the analysis of multivariate time series. They define all the second-order statistics of the data, which in regards to our two devices, can be seen as a measure of quantity and amplitude of movement. Specifically, in the context of the MK2 data, each task performed by the subject produces distinctive covariance structures, due to the constraints that link the joints of the skeleton. In addition, covariance estimation produces matrices of equal shape regardless the duration of the task, which varies from a subject to another. Covariance matrices estimation is also unaffected by irregular sample rate, which can be observed on low cost motion capture device such as the MK2. Finally, since postural instability is associated with compensatory movements, we expect to see changes in the covariance structure of the signal when the subject's balance is challenged (as is the case in our three postural conditions). Lastly, the spatial covariance estimation (see Eq 5), by removing the first order moment of the signal, will be independent of the initial posture of the subject, insuring that the classification results will not be biased by this information. Therefore, we selected covariance matrices as the most appropriate and informative way to represent the kinematic features of the raw data originating from the MK2 and KFP. These matrices will be used as input of the classification pipeline. This approach has been successfully used to categorize human activity with a high degree of accuracy in recent works [30,31]. For the sake of simplicity, in the following equations we will use X i for denoting a trial of a given task, regardless the acquisition device. Let C i 2 R N×N denote the covariance matrix of the trial X i and N the number of time series of the corresponding device, the covariance is estimated by: where T is the transpose operator and " X i the average across the Nt time samples of the time series.
Covariance matrices are symmetric and positive definite (SPD) matrices. Their manipulation must take this property into account in order to avoid loss or distortion of meaningful information embedded in the covariance structure. Riemannian geometry is a branch of differential geometry that studies Riemannian manifolds and can be used to manipulate SPD matrices [32]. In this framework, SPD matrices are seen as points of a Riemannian manifold with a canonical metric that allows for their manipulation while keeping their inner structure intact. In the past decade, this approach has been successfully used in numerous fields, such as EEG signal processing for Brain Computer Interface applications [33,34], radar signal processing [35], or image processing for pedestrian detection from video data [36]. Given two covariance matrices C 1 and C 2 , the Riemannian distance is defined by [37]: where λ i are the real eigenvalues of C À 1 1 C 2 . This metric is invariant by affine transformation of the data, i.e. the distance between two covariance matrices is unaffected by any transformation that can be represented by an invertible matrix V 2 R N×N : This includes any scaling or rotation of the data. In the context of MK2 data, this property makes the classification robust to small variations in starting position of the MK2 with respect to subject location, as well as differences in height and body structure of different subjects.
While Riemannian metrics have not previously been used for gesture recognition, other covariance-based distances (euclidean or log-euclidean) have been successfully used to classify different types of gestures with a nearest neighbor's approach [30]. Performance of distancebased classifiers is highly dependent on the sensitivity of the metric used to measure similarity between points. With respect to other metrics, the Riemannian metric is more suited to measure differences between covariance matrices. However, the distance defined in Eq (6) integrates both noise and signal equally, and therefore can lack sensitivity in the most difficult cases i.e. when the classes are overlapping in most of the dimensions of the feature space. Our approach aims to classify variations in quality of the same gesture. As a consequence, classes are less separable, and distance-based classifiers failed to produce consistent and accurate results. To overcome this limitation, we relied on tangent space mapping. Tangent space mapping is a local projection that maps the elements of the manifold into an Euclidean space while keeping their distance relationships intact. Therefore, once projected into the tangent space, covariance matrices can be vectorized and fed into any state-of-the-art classifier, giving the algorithm the opportunity to produce a more complex decision function and in doing so, separate signal from noise. The use of tangent space for classification has been first introduced for image classification in [36], and showed superior accuracy over distance-based classification in other contexts [33,34]. A tangent space is defined locally (i.e. around a reference point that sets the origin of the tangent space). Let S i denote a square symmetric matrix, projecting the covariance matrix C i into the tangent space, we have: where C is a covariance matrix called "reference point," and Log(.) is the matrix logarithm. For any given point of the tangent space, we have equivalence between its Euclidean distance (Frobenius norm) and the Riemannian distance of the corresponding point to the reference point in the original manifold: Therefore, the matrix S i represents the position, with respect to the reference point, of the corresponding covariance matrix in the Riemannian manifold. The choice of the reference point can be seen as a parameter of the algorithm and can influence, although moderately, performance of the classification. In previous works [33,34,36], the Riemannian mean [32,38] of all the training data has been used as reference point for the tangent space mapping. In this work, we will use the log-euclidean mean as a computationally effective approximation of the Riemannian mean [39]: The matrix S i , being a square symmetric matrix with N Ã (N+1)/2 unique elements, can be reduced and vectorized in order to produce a vector that can be fed into a standard classification algorithm. Let s i 2 R N Ã (N+1)/2 denotes such a vector, we have: where the upper(.) operator keeps the upper triangular part of a symmetric matrix and vectorizes it by applying unity weight for diagonal elements and p 2 weight for out-of-diagonal elements [36]. The purpose of this weight is to keep the norm equivalence between the matrix and its vectorized representation.
Algorithm 1 summarizes the process by which feature vectors can be generated from raw data. An implementation of this algorithm is available online as part of a Python (Python Software Foundation, https://www.python.org) toolbox called 'pyRiemann' (https://github.com/ alexandrebarachant/pyRiem ann). In addition, the full dataset and code utilized in this manuscript can be accessed on Dryad (doi:10.5061/dryad.fk4cb) and Github (https://github.com/ alexandrebarachant/WMFT_JBHI).

Algorithm 1: Feature vectors estimation
Input: a set of K MK2 data matrix X i Output: a set of K tangent vectors s i 1. Estimate covariance matrices C i -Eq (5) 2. Compute reference point C-Eq (10) 3. Compute tangent matrices S i -Eq (8) 4. Vectorize S i to get the tangent vectors s i -Eq (11) 5. Return s i Classification across tasks. Predicting which task the user is performing is a typical gesture recognition problem. In this setup, classes (i.e. the 6 tasks) are well separated in their feature representation domain. In this context, feature vectors will be directly classified by a simple Logistic Regression, regardless of the condition under which they have been recorded, as illustrated in Fig 4. Classification across conditions. Classification across condition represents a more challenging problem. First, differences between conditions are of a smaller order of magnitude than differences between tasks, and therefore, classes are less separable. Second, the presence of six different tasks creates clusters in which each of the three conditions are nested. Therefore, it is very difficult to find a single model to separate each condition regardless of the task performed. In this context, we will train a classification model for each of the six tasks independently, and will rely on ensembling techniques in order to combine outputs of these six models into one single prediction. This pipeline is consists in applying the pipeline described Fig 4 independently on the data from each task. Ensembling of the six models is simply achieved by summation of the output probabilities of each individual logistic regression. Thus, the final prediction is given by the condition with the highest sum of probabilities.

Study participants
Twelve subjects were recruited for the study and all subjects successfully completed the data collection protocol. We obtained 216 simultaneous recordings from both the KFP and the MK2 of each subject completing the 6 tasks of the mBBS under the three different postural conditions. The performance of each task was carefully timed, in order to ensure no differences arose due to timing discrepancies between subjects. Each task typically took no longer than 10 seconds (300 frames) to complete.

Benchmarking KFP data against the MK2
Our first goal was to benchmark the data collected by the MK2 against that collected by the KFP in order to determine the quality of MK2 data as compared to a well-validated researchgrade measuring device. Raw data was collected from both devices, and saved as.csv (KFP) and.bin (MK2) files that could be easily loaded into MATLAB (Mathworks, Natick MA) and Python for visualization (Fig 5). The first step in evaluating data from the MK2 in relation to a research-grade technology was to compare the variance of the COP metric reported by the KFP with the variance of the spinal centroid data recorded from the MK2.
Using t-SNE dimensionality reduction [36] in combination of the Riemannian metric, we are able to visualize our high dimensional dataset in a comprehensive fashion and have a first glance at the quality of the information being recorded by both devices (Fig 6). As seen in Fig  6, data form clusters that correspond to the different tasks. With the exception of the task 1 and 2: the subject being seated with eyes open (task 1) and eyes closed (task 2), all other tasks clearly lie in different regions of the Riemannian manifold. Given that we were working with healthy subjects, it should be stated that we did not expect to see a significant difference between data collected during task 1 and task 2. Despite tracking less information about body motion than the MK2, the force plate data are clearly separable. However, for tasks 3-6 the  MK2 can actually better distinguish which task is being performed than the force plate. Additionally, for each cluster, we can find the difference between each of the postural 3 conditions. While the class separation ability is of a lower degree of magnitude, we can identify trends that can be attributed to change in postural stability. In order to perform an initial, cursory examination of the similarities of the feature space between data from the KFP and MK2, we ran Pearson correlations between the variances in KFP COP data and the MK2 spinal centroid data. The highest correlations were seen in the Spine Mid, Neck, and Head centroids. The correlations for each task and both movement directions of KFP data were averaged across all subjects and presented in Table 2. All correlation coefficients were significant, with a p-value of less than 0.0001. The strong correlations observed between the variance in KFP and MK2 data output indicated to us that the MK2 data was of sufficient accuracy and sensitivity to be used for reliable classification analysis.

Classification of conditions from MK2 and KFP data
Using the logistic regression classification method detailed previously, we input data from the MK2 to see how well we could classify the postural condition under which each mBBS task was performed. Performances are evaluated by mean of categorical accuracy. In order to represent a realist use-case of an unknown subject using the system for the first time, the evaluation is done with a leave-one-subject-out cross-validation procedure. Each subject is alternatively held out of the total training set, and used as a test subject. We repeated this procedure for each Visualization of raw data that was recorded during one subject's performance of mBBS Task 5. We have plotted motion data from the KFP, the "Spine Mid" MK2 centroid, the "Neck" MK2 centroid and the "Head" MK2 centroid on the three axis. Note similarities in "x" and "y" directional data from the KFP and MK2 centroids. Each time series is normalized by its standard deviation for comparison. doi:10.1371/journal.pone.0170890.g005

Fig 6. t-SNE representation of the dataset for the MK2 (left) and the KFP (right).
A clear separation between tasks can be observed for both devices, but the MK2 data are more separable. Each cluster is formed by the points of the 3 conditions performed under the corresponding task. A zoom over the cluster of task 5 highlights a lower separation of the conditions. doi:10.1371/journal.pone.0170890.g006 Table 2. Mean (± standard deviation) Pearson Rhos (r) showing the degree of variance correlation between selected spinal MK2 centroids and the KFP COP across all tasks. "X" and "Y" directions were named in reference to data coordinate system used by the KFP. of our twelve subjects and the average accuracy is reported. The specific results of classifier performance are presented in Fig 8. The values in these confusion matrices represent the probability of our algorithm to correctly identify each condition. When analyzing all 16 of the selected MK2 upper-body centroids, average accuracy across all conditions was 90.9% (Fig 7A). We ran another analysis to find out how accurately our classifier could classify conditions using the KFP output, but this revealed an average accuracy of only 84.8% (Fig 7C). Furthermore, we found that when we used the classifier on the data from only the four spinal centroids, our classifier's average accuracy increases to 97% (Fig 7B). This means that our classifier is 97% accurate when identifying postural motion using only the centroids that define the trunk. Indeed, 4 of the 6 tasks were performed while the subject had their arms crossed over their chests. Therefore, centroid corresponding to the arms and shoulders were not providing additional information. Moreover, the MK2 has difficulties in reliably locating upper limb joint centroids in this position, introducing more noise than information into the system when the arms are crossed.

Classification of tasks from MK2 and KFP data
Our classifier also proved quite accurate at determining which task was being performed based upon MK2 and KFP data from all 12 subjects. Average accuracy of task classification from upper body MK2 and KFP COP data was 87.8% and 66.2%, respectively. Our classifier was most accurate at identifying each task when it used the 16-centroid data set from the MK2 (Fig 3B). Classifier performance was less accurate for the first two tasks, with both tasks only reaching 66.7% and 75.8% accuracy, respectively (Fig 8A). This is an expected result, considering the first and second task only differ in whether the subject's eyes are open or closed. The remaining four tasks were kinematically unique from one another, allowing for much more robust classification. The KFP on the other hand, was far less accurate at discriminating the individual tasks. The force plate data did not allow for reliable classification of task 4, which was identified correctly only 42.4% of the time ( Fig 8C). As before, we also evaluated the spinal centroids ( Fig 3C) that carry the most information pertaining to postural stability in the seated position. The collective average accuracy of the prediction based on spinal centroids is 77.8% ( Fig 8B). While it is advantageous to look only at the spine centroids when differentiating conditions, data from the 16 centroids of the upper body proved more helpful in distinguishing kinematically unique tasks.

Accuracy of the KFP and MK2 as sample size increases
As is typically the case, when using machine-learning concepts to analyze kinematic data, there is reason to believe that classification accuracy will increase as the size of the tested population increases. While this maxim holds for the MK2 data, the KFP shows no such trend (Fig  9). The KFP prediction accuracy plateaus after three to four subjects, whereas the MK2 classification accuracy continues to grow as the sample size increases. This can be explained by the fact that the KFP only provides data from a lower dimensional space (one 2D centroid compared with sixteen 3D centroids from the MK2), which limits the power of the classification technique when using data from the KFP. We predict that as a classification algorithm is used more and more frequently in clinical practice, its detection abilities will become more and more precise.

Discussion
The MK2 is leading the field of affordable, whole-body markerless motion capture technology that is appropriate for home use. Several studies have utilized the MK1 or the MK2 for balance training and assessment and found reliability between both high-end motion capture and clinical scales [40][41][42][43], establishing that data from the MK1 and the MK2 is viable for such an approach. Here, we have proven that the MK2 can also make inferences about an individual's postural stability. Building on this initial work, we will create a platform that can perform computerized balance assessments to identify individuals who are at risk of falling. Stratification of fall risk is related to a decrease in falls, and subsequently, a decrease in fall-related injuries [44]. An automated and computerized fall risk assessment that can be completed safely in a home environment will significantly aid attempts to stratify each user's fall risk, and devote resources appropriately to fall prevention. This work is a necessary first step in developing such a platform. While the Kinect is known to have high agreement with research-grade, markered motioncapture systems, its reliability as compared to a force plate has not been investigated as thoroughly. High-end force platforms that can perform detailed posturography are accurate, research-grade measuring tools in biomechanical studies of balance and postural stability [6,45]. Our study benchmarked the MK2 against the KFP, and found that the MK2 spinal centroid data showed a high level of agreement with the COP centroid from the KFP ( Table 2). Previous work has also shown strong agreement between the MK2 and force-plate measurements of postural sway [41], and our study confirms these findings. Unlike research-grade devices such as the KFP, if is a financially viable and realistic option to deploy commodity devices such as the MK2 into home and clinical environments to assist in the stratification of fall-risk and objectively assess response to therapy. However, to date, no groups have developed and shared an analytics platform that can use data collected from the MK2 to accurately classify postural stability during task performance. This study presents an innovative methodology for meeting this need in the rehabilitation community.
We have determined that the data recorded by the MK2 is sufficient to classify three distinct states of postural stability in a healthy individual performing the mBBS. Previous studies that have attempted to use the MK 1 or 2 to quantify balance have shown encouraging results, but have not presented a methodology for analytics that could eventually allow for unsupervised data collection and classification. The goal of this work was to develop, demonstrate and release software that is capable of fulfilling these goals. In this study, we demonstrated a machine-learning algorithm with the ability to successfully differentiate between three distinct states of postural stability in a seated position with up to 97% accuracy based upon raw data collected by the MK2. The modified balance assessment that we developed allows for data collection to occur in a seated position, and to only involve assessment items that did not put the subject at any risk of falling, which is a concern for existing fall assessments such as the BBS. Because of the safety and speed with which our protocol may be performed, we think our assessment could feasibly be performed in the home, making it a strong candidate for many telemedicine applications.
Furthermore, our data analysis protocols revealed that we could use data from the MK2 to robustly determine which of the six tasks were being performed. Our classifier showed 87.8% accuracy in predicting which of the tasks were being performed, and was only largely inaccurate in differentiating between the first two tasks: sitting with eyes open (task one) and sitting with eyes closed (task two). These analyses are important to pursue, because they provide a framework for creating robust protocols for future home-use applications, by allowing researchers to perform remote evaluation of data quality for each task. For instance, if ability to classify the difference between task 3 and 4 drops dramatically in a subject's balance assessment data, a researcher would be able to infer that something is going wrong with the data collection protocol: perhaps the MK2 sensor is performing poorly, or the task is being performed incorrectly. This would be an indication that researchers should check in with a research subject, rather than continue to collect low-quality data and only know.

Limitations and considerations
The main limitation of our study was the use of healthy subjects. While this is a necessary step in the benchmarking and machine learning process, we are unable to draw any conclusions about how the classifier may perform in patient populations. Another factor for consideration is that our experimental data was collected under controlled conditions: all data was collected in a lab environment with controlled light, background and seating conditions. Since certain environmental conditions can degrade the signal-to-noise ratio of the raw Kinect data, we must also conduct experiments to measure the impact that variable environmental conditions can have on data quality, and therefore classification performance. In-home studies would have to be conducted in order to predict the extent and effect of the noise in question.

Conclusions and future directions
We have presented novel methodology that can repurpose a widely available consumer hardware product with a custom software platform that has the potential to detect individuals who may be at risk of falling. From a telemedicine perspective, establishing our motion capture and analysis algorithms in a healthy population is an exciting first step into a larger sphere of innovative possibilities: • Remote physical therapy assessments • Continuous kinematic feedback to individual users during home exercise completion • Alerts sent to the therapist if a patient is losing function between clinic visits • An improved ability to conduct longitudinal clinical trials with multiple, quantitative observations taken in the home environment rather than requiring multiple clinical visits.
In order to come closer to these goals, a future focus of this work will be centered on developing a user interface that is simple and usable, such that it will allow quick and easy data capture-even when it is being used by individuals with little to no previous experience with using computers. The potential of this work cannot be understated: the MK2 is a device that can be rapidly and cost-effectively deployed to homes and clinics globally. The methods presented here for classifying motor behavior can be expanded for multiple conditions, allowing individuals with motor impairments to be monitored more closely and treated more safely and effectively.
Another future direction for this work is to begin data collection on a large number of individuals who are at risk of falling, including, but not limited to, elderly individuals, people with spinal cord injury, people with Parkinson's Disease and stroke survivors. Our protocol and resulting data will allow us to test the classifier in different clinical and non-clinical populations, and allow us to correlate our MK2 data with clinical gold standards such as the Berg Balance Scale.
Finally, our long-term vision for this work is to truly revolutionize the nature of clinical outcome assessment itself: currently, clinical researchers must select from a myriad of clinical outcome measures, each with varying degrees of applicability, sensitivity, and validation for different study populations. Automated visual analysis would extract more salient information regarding the individual's natural function in his or her own home environment. This approach, which has never been implemented before, has the potential to replace subjectively assessed clinical testing protocols. This would drastically improve data interoperability across different clinical trials, achieving one of the main goals of the National Institutes of Health's 'Common Data Elements' initiative.


s2fieldsofstudy: Biology Psychology

text:
Paired Associative Stimulation Targeting the Tibialis Anterior Muscle using either Mono or Biphasic Transcranial Magnetic Stimulation

Paired associative stimulation (PAS) protocols induce plastic changes within the motor cortex. The objectives of this study were to investigate PAS effects targeting the tibialis anterior (TA) muscle using a biphasic transcranial magnetic stimulation (TMS) pulse form and, to determine whether a reduced intensity of this pulse would lead to significant changes as has been reported for hand muscles using a monophasic TMS pulse. Three interventions were investigated: (1) suprathreshold PAbi-PAS (n = 11); (2) suprathreshold PAmono-PAS (n = 11) where PAS was applied using a biphasic or monophasic pulse form at 120% resting motor threshold (RMT); (3) subthreshold PAbi-PAS (n = 10) where PAS was applied as for (1) at 95% active motor threshold (AMT). The peak-to-peak motor evoked potentials (MEPs) were quantified prior to, immediately following, and 30 min after the cessation of the intervention. TA MEP size increased significantly for all interventions immediately post (61% for suprathreshold PAbi-PAS, 83% for suprathreshold PAmono-PAS, 55% for subthreshold PAbi-PAS) and 30 min after the cessation of the intervention (123% for suprathreshold PAbi-PAS, 105% for suprathreshold PAmono-PAS, 80% for subthreshold PAbi-PAS. PAS using a biphasic pulse form at subthreshold intensities induces similar effects to conventional PAS.

INTRODUCTION
Paired Associative Stimulation (PAS), introduced by Stefan et al. (2000), consists of the repetitive pairing of a peripheral electrical and a central magnetic stimulus at low frequency. Typically, the first stimulus is a single electrical pulse applied to the peripheral nerve innervating the target muscle, followed by a second stimulus applied using transcranial magnetic stimulation (TMS) over that area of the motor cortex that has direct corticospinal projections to the target muscle. Depending on the relative timing between these two stimuli, the direction of the synaptic change is either one of potentiation or depression (Wolters et al., 2003). PAS is based on studies of associative long term potentiation (LTP) and depression (LTD) in animal models (Bi and Poo, 1998) where the activation of presynaptic and postsynaptic neurons correlated in time, is artificially induced and the continued pairing of these two events leads to a strengthening of the synapse that outlasts the period of stimulation. Like LTP, PAS effects are dependent on the activation of NMDA-receptors and involvement of (L-type voltage-gated) Ca 2+ channels (Stefan et al., 2002;Wolters et al., 2003). In this way, Stefan et al. (2002) were able to demonstrate that indeed many components of PAS resemble those of LTP. LTP is one mechanism for inducing synaptic plasticity thought to underlie processes of memory storage and learning (Letzkus et al., 2007).
Since these initial reports, PAS has been applied to numerous target muscles located in the hand (Ridding and Taylor, 2001;Ridding and Uy, 2003;Fratello et al., 2006;Kujirai et al., 2006;Quartarone et al., 2006;Ridding and Flavel, 2006;Rosenkranz and Rothwell, 2006;Roy et al., 2007), to those in the lower limb (Stinear and Hornby, 2005;Prior and Stinear, 2006;Mrachacz-Kersting et al., 2007;Roy et al., 2007;Kumpulainen et al., 2012Kumpulainen et al., , 2015Mrachacz-Kersting, 2013) and in a variety of patient populations (Quartarone et al., 2003;Uy et al., 2003;Bagnato et al., 2006;Weise et al., 2006;Castle-Lacanal et al., 2007). However, different protocols make direct comparisons between these studies difficult. For example, while in hand muscles, PAS can have an effect in the relaxed muscle and with a standard interstimulus interval (ISI) between the peripheral and central stimulus across participants (Stefan et al., 2000). PAS applied to lower limb muscles such as the tibialis anterior (TA) requires either a pre-activated muscle or an individualized ISI to have a significant effect (Mrachacz-Kersting et al., 2007;Kumpulainen et al., 2012). In 2008, the first consensus article on motor cortex plasticity protocols was published (Ziemann et al., 2008) that highlighted the importance of investigating the effects following PAS in providing further information on mechanisms of memory formation and learning in the intact human. At this time only two studies with lower limb muscles as the target of PAS were cited (Mrachacz-Kersting et al., 2007;Roy et al., 2007). It is well known that the organization of neural pathways of lower limb muscles differs from that of the upper limb muscles. For example, motor evoked potentials (MEPs) in hand muscles are transiently inhibited by an afferent volley arriving at the sensory cortex following median nerve stimulation (Tokimura et al., 2000) while the afferent volley from the tibial nerve facilitates both the TA and Soleus MEPs (Roy and Gorassini, 2008). Furthermore, the I waves which are thought to be involved in the effects following PAS are predominantly I3 waves (Kujirai et al., 2006). These are readily elicited in upper limb muscles at TMS intensities around motor threshold when the current induced in the brain flows from anterior to posterior (Sakai et al., 1997). For lower limb muscles however it is not possible to preferentially recruit I3 waves (Di Lazzaro et al., 2001b).
The representation of the TA on the motor cortex is buried in the interhemispheric fissures, whereas that of hand muscles lie closer to the surface (Rothwell, 2003). A stronger magnetic field is required to induce an electric current to stimulate neurons in the cortical representation of TA. Evidence suggests that the stimulus efficacy is higher, both in terms of the threshold for excitation as well as response size, for a biphasic pulse waveform when TMS is applied (Maccabee et al., 1998). Further, a biphasic current will activate neurons within the cortex that are orientated in the AP as well as the PA direction and thus may not activate the same neural elements as a monophasic pulse form. It has also been argued that the biphasic pulse form may activate different sets of neurons with different thresholds (Kammer et al., 2001;Sommer et al., 2006), compared to the monophasic pulse form. It may thus be speculated that biphasic TMS might activate a more diverse set of cortical neurons than monophasic TMS. Indeed, Arai et al. (2005Arai et al. ( , 2007 suggested that during biphasic repetitive TMS (rTMS), several different populations of neurons are activated as compared to monophasic rTMS which activates only one population of neurons oriented in one direction. Whether this also leads to more effective alterations in excitability is not known.
There were two aims of this study: first, to establish whether PAS using either a suprathreshold monophasic or biphasic TMS stimulus will result in similar changes in the excitability of the cortical projections to the human TA muscle when applied at rest. Second, to determine whether it is possible to induce similar effects with PAS using a subthreshold biphasic TMS compared to a suprathreshold biphasic TMS pulse.

Participants
All participants in Experiment 1 and 2 provided written and informed consent in accordance with the Declaration of Helsinki to participate in this study. Approval was given by the Scientific Ethics Committee of Northern Jutland (Reference number: VN-20070015). All participants were classified as right side dominant according to the Edinburgh handedness inventory questionnaire (Oldfield, 1971) with a mean laterality quotient of 0.95 (range: 0.56-1). At the time of the study, all participants were free of any known physical or neurological disorders.

Apparatus and Instrumentation
Surface electrodes (20 mm Blue Sensor Ag/AgCl, AMBU A/S, Denmark) were used to record the electromyographic (EMG) activity of TA and soleus (SOL) of the right leg for all aspects of the experiments. The electrodes were placed in accordance with recommendations of Cram and Kasman (1998). All data were sampled at a frequency of 4 kHz. Post-acquisition, the EMG signals were amplified and band pass filtered at 20 Hz-2 kHz offline.

Stimulation
Depending on the experimental protocol, either a Magstim 200 or a MagstimRapid 2 (Magstim Company, Dyfed, UK) with a focal figure of eight double-cone coil (110 mm diameter) was used to apply single pulses to elicit a MEP in the muscle of primary interest which was the right TA muscle. The direction of the current flow across the motor cortex was directed from posterior to anterior.
Stimulation of the right common peroneal nerve (CPN) was applied using a NoxiTest isolated peripheral stimulator (IES 230). Stimulating electrodes (32 mm, PALS Platinum, Patented Conductive Neurostimulation Electrodes, Axelgaard Manufacturing Co., Ltd., Fallbrook, CA, USA) were placed on the skin overlying the deep branch of the right CPN (L4 and L5) with the cathode proximal. A suitable position for stimulation, defined as the site where a maximal M-wave was produced in the TA with no activity from the synergistic peroneal muscles and no activity from the antagonist SOL, was located. Palpation of SOL and peroneal muscles was performed during stimulation trials to ensure that this was occurring. This site corresponded to a point just anterior to the level of the caput fibulae. The pulse width was set to 1 ms and the intensity to 1× motor threshold. Motor threshold was defined as that intensity of stimulation where an M-wave first became visible in the EMG traces. Pilot studies revealed that these pulse settings produced the most consistent changes when combined with TMS (Mrachacz-Kersting et al., 2007).

Somatosensory Evoked Potentials
One week prior to the main experiment, the cortical potentials evoked by the imposed stimulation of the right CPN were recorded with surface disc electrodes (E21-9 Disk Electrode-Standard 9 mm tin cup, Cephalon, DK) placed on the scalp, according to the International 10-20 system (Yamada, 2000). Somatosensory evoked potentials (SEPs) were recorded with a vertex electrode placed over CP z and one placed over CP 2 (band pass, 0.05-1000 Hz; sampling rate, 10 kHz, referenced to F z ). A minimum of 1000 (maximum 3000) traces were recorded and ensemble averaged online. The characteristics of the pulse were the same as those used during the application of PAS (width of 1 ms, intensity of 1× motor threshold). The arrival of the evoked potential was measured as the time of occurrence of the first negative peak as has been done previously (Mrachacz-Kersting et al., 2007).

Experimental Procedures
Participants were seated in a fixed chair (Hip 90 • , Knee 130 • ) with their right foot resting on a moveable footplate. Initially, the stimulation intensity for the TMS was set at approximately 50% of maximum stimulator output (MSO) to find the optimal site for evoking a MEP in the right TA. The hot-spot was taken as the coordinate where the peak-to-peak amplitudes of the MEPs was greater in the target muscle than amplitudes of adjacent coordinates for a given stimulus intensity. For all participants, this site was approximately 2-3 cm anterior to the vertex and a stimulation applied to this area also evoked a response in the SOL. Once the hot-spot was identified, it was stored in Brainsight (Brainsight TM version 1.5. Rouge Research Inc., Montreal, QC, Canada). This program was used throughout the experiment to ensure the coil position was maintained so that the stimulation was always applied over the same area of the motor cortex.
Subsequently, the resting motor threshold (RMT), defined according to the recommendations of the IFCN Committee (Chen et al., 2008) as the highest stimulus intensity that produced no more than 5 of 10 consecutive TA MEPs with an amplitude of ∼50 µV while the muscle was at rest, was identified. Unless otherwise stated, all subsequent stimuli were delivered at 120% of RMT to ensure TA peak-to-peak MEP amplitude of approximately 0.5 mV. However, due to the location of the TA representation on the motor cortex, it was not possible to attain an MEP amplitude of 0.5 mV in all participants even when the intensity of the TMS pulse was increased. As a consequence, the amplitude value varied from 0.1 mV to 0.9 mV across all participants. For Experiment 2 (see below), the active motor threshold (AMT) was identified. This was defined as the highest stimulus intensity that produced no more than 5 out of 10 consecutive TA MEPs with an amplitude of ∼200 µV while the muscle was contracting at 5% of its maximum voluntary contraction (MVC; Rossini et al., 1994;Chen et al., 2008). In all experiments, the participants were initially asked to perform a MVC of the TA to determine the maximum force which the participants was able to voluntarily exert at the plate. Participants were initially asked to contract their TA as hard as possible. Participants were instructed to pull their toes upwards as powerfully as possible on the word ''go'', and to maintain this position until instructed to relax after 2-3 s. They were then allowed to relax for 1-3 min prior to the next trial. The best of a total of three collected trials was deemed the participants' MVC. The root mean square (RMS) value of the rectified TA EMG for the MVC over a 1 s period was calculated. Subsequently, the participants were provided with visual feedback via a computer screen displaying a horizontal marking set at 5% MVC and a vertical bar displaying the participants' current level of TA activation. Participants were asked to maintain the bar at the horizontal marking while AMT was identified.

Paired Associative Stimulation (PAS) Protocol
PAS consisted of a single electrical stimulation of the CPN delivered at motor threshold, followed by a single TMS pulse delivered to the motor cortex. Depending on the experiment (i.e., the PAS protocol), the intensity of this was either 120% RMT or 95% AMT. A total of 360 pairs of stimuli were applied at a rate of 0.2 Hz (Figure 1).

Experiment 1a: The Effect of Suprathreshold Monophasic Compared to Suprathreshold Biphasic TMS on PAS Induced Changes
To address the first aim, a total of 11 participants took part in Experiment 1 (9 males, 2 females, age 27 ± 5 years), which consisted of two interventions being applied to the participants as outlined in Figure 1. One week elapsed between each intervention. The TMS pulse used during the intervention was always set at 120% RMT and the current across the motor cortex directed from posterior to anterior (PA), however, the pulse type was either monophasic or biphasic. For convenience, these two interventions will from now on be referred to as suprathreshold PA mono -PAS and suprathreshold PA bi -PAS respectively. Sixteen MEPs were elicited every 5-7 s at an intensity of 120% RMT pre, immediately post, and 30 min post the intervention. The mean peak-topeak amplitude was later extracted and used as an indication of excitability changes in the corticospinal projections to the TA.

Experiment 1b: Assessment of Spinal Excitability
To investigate whether any changes occurred at the spinal level following the suprathreshold PA bi -PAS intervention, stretch reflexes were elicited prior to and following the intervention in nine participants (6 males, 3 females, age 26 ± 3 years). The right leg was affixed to a servo-controlled hydraulic actuator (MTS-systems Corporation, 215.35;Voigt et al., 1999), such that the anatomical ankle axis of rotation was closely aligned with the fulcrum of the actuator. The foot segment of the right leg of the participant was firmly strapped to a custom made plate that extended from the actuator, thus producing a tight interface between the arm of the motor and the foot of the participant, ensuring that the movement of the actuator was transmitted solely to the ankle joint. The angular position of the actuator was monitored by an angular displacement transducer (Transtek, DC ADT series 600). The participants were asked to maintain a 5% MVC in their right TA while the perturbations were applied. The EMG level was displayed on a computer screen placed in front of the subject. The instructions to the subjects at all times were to maintain the 5% MVC EMG level without interfering with the imposed plantarflexion perturbation.
Thirty stretches were randomly applied at intervals ranging from 5 s to 7 s (velocity: 100 • s −1 to 200 • s −1 ; amplitude: 4 • to 6 • ; hold-time: 460 ms). The angular velocity and the amplitude of the imposed perturbations were adjusted for each subject so that the amplitude of the three response peaks observed in the TA EMG trace were approximately the same and also similar to the amplitude of the TA MEP prior to the intervention.
The latencies of the first and third response peaks (termed short-latency reflex (SLR) and long-latency reflex (LLR) or alternatively M1 and M3 in the literature) were extracted from the data both prior to and immediately following the intervention. The RMS value of a window extending 10 ms on either side of SLR and LLR (thus 20 ms for each window in total) was calculated and used as an indication of the size of each of these components of the TA stretch reflex. In this way it was ensured that neither the SLR nor the LLR responses were contaminated by the second response which is observed in some participants following an imposed perturbations as applied here.

Experiment 2: The Effect of Subthreshold Compared to Suprathreshold Biphasic TMS on PAS Induced Changes
To address the second aim of whether PAS using a subthreshold biphasic TMS was as effective as PAS using a suprathreshold biphasic TMS pulse, 10 participants (7 males, 3 females, age 25 ± 3 years) received two interventions spaced at least 1 week apart. Suprathreshold PA bi -PAS was administered as for Experiment 1a. Subthreshold PA bi -PAS differed from suprathreshold PA bi -PAS in that the TMS pulse intensity used during the intervention was 95% of AMT. The order in which the two interventions were administered was randomized. As for Experiment 1a, the mean peak-to-peak amplitude was assessed both prior to and immediately following each intervention.

Statistical Analysis
For Experiment 1a, a two-way within-subjects analysis of variance (ANOVA) was employed to determine the effect of the intervention (suprathreshold PA bi -PAS and suprathreshold PA mono -PAS) across time (pre, post, and 30 min post) on the TA MEP amplitude. In Experiment 1b, paired t-tests (2-tailed) were employed to evaluate the effect of the suprathreshold PAS interventions on the TA SLR and LLR. For Experiment 2, a two-way within-subjects ANOVA was employed to assess changes in TA MEP amplitude, with intervention (suprathreshold PA bi -PAS and subthreshold PA bi -PAS) and time (pre, post, and post 30 min) as the within-subjects factors. The significance level was set at p < 0.05. Bonferroni corrections were applied to multiple post hoc comparisons to determine the locus of the differences. The adjusted alpha level for post hoc comparisons following a significant main effect of time was therefore set at p < 0.0167. If not otherwise stated, all data are given as mean ± standard deviation.

Somatosensory Evoked Potentials and the Interstimulus Interval during PAS
Across all participants, the afferent volley arrived at the somatosensory cortex at on average 46 ms (range: 41-50 ms) following the CPN stimulation. Based on our previous study (Mrachacz-Kersting et al., 2007), a central processing of 6 ms was added. Depending on the participant, the TMS was therefore triggered between 47 ms and 56 ms following the CPN stimulus during the PAS intervention. In Figure 2A, the averaged (16 sweeps) raw TA MEP data prior to and after suprathreshold PA bi -PAS are presented for one participant. The raw MEP values were 0.24 mV and 0.51 mV, respectively. Across all participants, the mean pre-intervention TA MEP amplitude was 0.32 ± 0.23 mV, 0.43 ± 0.24 mV immediately post intervention, and 0.60 ± 0.33 mV 30 min after the intervention (Figure 2B). Immediately following the intervention, one participant failed to show an increase in the TA MEP amplitude, though 30 min post all participants exhibited an increase. For the suprathreshold PA mono -PAS, the TA MEP amplitudes were 0.28 ± 0.14 mV pre-intervention, 0.44 ± 0.22 mV immediately post intervention, and 0.49 ± 0.18 mV 30 min post intervention ( Figure 2B). The two-way within-subjects ANOVA revealed a significant main effect of time across both suprathreshold PAS interventions (F (2,20) = 18.12, p < 0.001). Post hoc analyses revealed that the TA MEPs were significantly larger immediately following (0.44 ± 0.20 mV; p = 0.018) and 30 min after the interventions (0.50 ± 0.17 mV; p < 0.001) compared with pre-intervention values (0.27 ± 0.11 mV), and that the TA MEPs were not significantly different 30 min after the interventions compared with immediately post-intervention (p = 0.156). There was no significant main effect of intervention (F (1,10) < 0.00, p = 0.99) nor a significant time by intervention interaction (F (2,20) = 0.19, p = 0.83), indicating that both suprathreshold PAS interventions were effective at increasing the TA MEP amplitude. Figure 2C contains the normalized TA MEP amplitude immediately following and 30 min after the cessation for both interventions expressed as a percent change from pre-intervention values (a value of zero indicates no change from pre) for all participants. On average, the TA MEP amplitude increased by 74% (range: 9%-250%) immediately following and by 117% (range: 4%-300%) 30 min following the cessation of the suprathreshold PA bi -PAS intervention and by 83% (range: 9%-335%) and 105% (range: 35%-265%) for the suprathreshold PA mono -PAS intervention.

Experiment 1b: Assessment of Spinal Excitability
The change in ankle angle as well as the EMG recording of the TA both prior to and following the suprathreshold PA bi PAS intervention is shown for one participant in Figures 3A,B, respectively. Each trace is the mean of 30 imposed rotations. The TA responds with three discernible peaks as seen in the EMG trace ( Figure 3B). Care was taken to ensure that the amplitude of all three components was reasonably similar prior to the intervention and similar to the size of the TA MEP evoked in the pre-intervention measures.
Across all participants, the TA MEP amplitude increased following suprathreshold PA bi PAS without a significant increase in the SLR (t (8) = 0.93, p = 0.37) component of the TA stretch reflex. The LLR, which is at least in part of cortical origin , increased on average by 117% across all participants, however this increase was not statistically significant (t (8) = −0.66, p = 0.52). The background level of activation during the imposed plantar flexion perturbations did not differ significantly pre and post the intervention (t (8) = 1.21, p = 0.25).

Experiment 2: The Effect of Subthreshold Compared to Suprathreshold Biphasic TMS on PAS Induced Changes
Across all participants, the RMT was 49 ± 8% S.O. and the AMT 41 ± 8% S.O. The averaged (16 sweeps) raw TA MEP data prior to and following subthreshold PA bi -PAS for one participant is displayed in Figure 4A. The raw TA MEP was 0.20 mV prior to and 0.65 mV following the intervention.
Across all participants, the pre-intervention mean TA MEP amplitude was 0.37 ± 0.26 mV for suprathreshold PA bi -PAS and 0.39 ± 0.29 mV for subthreshold PA bi -PAS. Immediately following the intervention, these values were 0.46 ± 0.22 mV and 0.50 ± 0.39 mV for suprathreshold PA bi -PAS and subthreshold PA bi -PAS, respectively, while 30 min post-intervention they were 0.62 ± 0.37 mV and 0.60 ± 0.47 mV (Figure 4B). On average, the TA MEP amplitude increased by 53% and 55% immediately following and by 95% and 80% 30 min following the cessation of the suprathreshold PA bi -PAS and subthreshold PA bi -PAS interventions, respectively, compared to the pre-intervention values ( Figure 4C).
The two-way within-subjects ANOVA revealed that there was a significant main effect across the factor time (F (2,18) = 14.21, p < 0.001). Post hoc analyses revealed that the TA MEPs were significantly larger immediately following (0.50 ± 0.08 mV; p = 0.028) and 30 min after the intervention (0.62 ± 0.11 mV; p = 0.002) compared with pre-intervention values (0.37 ± 0.08 mV). The TA MEPs were not significantly different immediately following compared with 30 min after the interventions (p = 0.13). There was no significant difference between the two interventions (F (1,9) = 0.08, p = 0.79) or a significant interaction between intervention and time (F (2,18) = 0.28, p = 0.76), indicating that both the suprathreshold PA bi -PAS and subthreshold PA bi -PAS interventions were effective at increasing the TA MEP amplitude.
Immediately following the intervention, two participants failed to show an increase in the TA MEP amplitude when suprathreshold PA bi -PAS was applied and one when subthreshold PA bi -PAS was applied. At 30 min post intervention, all participants exhibited an increase for suprathreshold PA bi -PAS while the MEP amplitude of the one participant remained unchanged following the subthreshold PA bi -PAS.

Effects of Experimental Paradigms on the Antagonist SOL
As a stimulation applied to the area of the motor cortex associated with the TA also evoked a response in the SOL, we chose to monitor this muscle throughout the experimental sessions for possible changes. However, as in our previous study (Mrachacz-Kersting et al., 2007), no significant changes in the amplitude of the SOL MEP were found following any of the interventions (all p's > 0.05).

DISCUSSION
In the past we have demonstrated that suprathreshold PA mono -PAS induces long lasting increases in the excitability of the cortical projections to the target muscle (Mrachacz-Kersting et al., 2007). The present study is the first in which PAS was applied targeting the TA, using a biphasic TMS stimulation pulse. The results show that suprathreshold PA bi -PAS can significantly increase the excitability of the cortical projections to the TA similar to suprathreshold PA mono -PAS. In addition, subthreshold PA bi -PAS applied with the TA at rest is able to induce similar changes in the TA MEP amplitude. In past experiments using upper limb muscles and a monophasic TMS pulse with an AP direction, this has only been possible in pre-activated muscles (Kujirai et al., 2006). This may have important consequences for PAS as a rehabilitative tool in patients unable to fully activate their TA such as occuring during foot drop following stroke.

Experiment 1a: The Effect of Suprathreshold Monophasic Compared to Suprathreshold Biphasic TMS on PAS Induced Changes
The magnitude of change in the TA MEP amplitude following suprathreshold PA bi -PAS was on average 61% compared to pre-intervention and this effect was further enhanced 30 min after the cessation of the intervention to 123%. In contrast, the time course of the effects following PA mono -PAS were quantified as an 83% immediately following the intervention and 105% after 30 min had elapsed. In the past, changes of 96% and 88% immediately following and 30 min after the cessation of the intervention have been reported following suprathreshold PA mono -PAS (Mrachacz-Kersting et al., 2007). It thus appears that both suprathreshold PAS applied using a monophasic and a biphasic TMS pulse waveform are effective in increasing the excitability of the cortical projections to the TA.
One main difference between the changes following suprathreshold PA bi -PAS compared to suprathreshold PA mono -PAS is that in some participants (n = 3), the effect on the TA MEP was only visible 30 min following the cessation of the intervention. Similar differences in the time course for the effects of an intervention using either a biphasic or a monophasic TMS pulse form have been reported previously (Taylor and Loo, 2007). These authors used rTMS to induce a depression as quantified by a decrease in the size of the MEP in the FDI and reported that using a monophasic pulse form caused a depression immediately following the cessation of rTMS, though a depression was not seen until 20 min following the cessation of the intervention when a biphasic pulse was implemented. Results from rTMS interventions may not be directly related to those following PAS. However, as mentioned previously, a biphasic current will activate neurons within the cortex that are orientated in the AP as well as the PA direction. It may be speculated that this type of pulse form may not activate the same neural elements as a monophasic pulse form and indeed evidence on I wave recordings from the epidural space of humans confirms this (Di Lazzaro et al., 2001a). Thus, the complex waveform of the biphasic TMS may have repeatedly activated a different set or a different balance between excitatory and inhibitory interneurons compared to monophasic TMS as also suggested by Arai et al. (2007). If the excitatory neurons that are activated by a biphasic pulse (but not by a monophasic pulse) remain activated for a longer time than the corresponding inhibitory neurons, it may explain why the effect of PAS with a biphasic TMS can keep on developing, though this requires further investigation.
Experiment 2: The Effect of Subthreshold Compared to Suprathreshold Biphasic TMS on PAS Induced Changes Kujirai et al. (2006) and Roy et al. (2007) demonstrated that it was possible to facilitate the excitability of the cortical projections to FDI and TA, respectively, using PAS with subthreshold TMS. Methodological differences between the studies make direct comparisons difficult. In the present study, PAS was applied at rest with an ISI based on coincident synaptic input. Kujirai et al. (2006) applied PAS during a sustained voluntary contraction of the target muscle. It is not possible from that study to distinguish the effect of subthreshold stimulation from the effect of voluntary contraction in the MEP amplitude as voluntary contraction may enhance the effect of PAS (Mrachacz-Kersting et al., 2007). Roy et al. (2007) found that subthreshold PAS with a 20 ms 100 Hz train of CPN stimulation and arrival of the afferent inputs to the motor cortex after a TMS pulse facilitated the TA MEP amplitude. Repetitive electrical stimulation of the CPN alone has been shown in the past to facilitate the TA MEP, although with trains of pulses delivered at 0.33-1 Hz (Khaslavskaia et al., 2002;Knash et al., 2003;Khaslavskaia and Sinkjaer, 2005). In the present study, the single CPN electrical stimulus was delivered at motor threshold while Roy et al. (2007) used an intensity of 300% sensory perception threshold (which approximately equals motor threshold-own observation). The delivery of three electrical stimuli at the CPN around motor threshold may have a summation effect of the pulses such that the pulse train effectively corresponds to a suprathreshold stimulus. Nevertheless, similar to Kujirai et al. (2006), the current study provides evidence that when the TMS intensity as part of the PAS protocol is set below threshold for evoking a MEP in the target muscle, a significant increase in the excitability of the cortical projections to the TA occurs.
It was surprising that subthreshold PA bi -PAS induced similar increases in TA MEP amplitude compared to suprathreshold PA bi -PAS. It may be that the structures important in the induction of PAS have a low threshold to stimulation, though this is purely speculative and cannot be answered from data of the current study. However, Rotem and Moses (2008) investigated the effect of magnetic stimulation on one dimensional mammalian cell cultures. These authors were able to standardize the arrangements of the neurons and reported that aside from the orientation of the neurons, the neurons specific morphological and electrophysiological properties played a fundamental role in their activation through magnetic stimulation.

Experiment 1b: Assessment of Spinal Excitability
The results from the stretch reflex data suggest that spinal excitability (assessed by the SLR of the stretch reflex) was unchanged, indicating a supra spinal origin of the TA MEP increase following PAS. However, recent reports have argued for changes at the spinal level when the human forearm muscle flexor carpi radialis (FCR) is targeted by PAS (Meunier et al., 2007). The authors suggest an altered pre-synaptic inhibition following PAS delivered to FCR concomitant to the alterations in H-reflex. Pre-synaptic inhibition may have a cortical origin and it is well known that the H-reflex and stretch reflex have different sensitivities to pre-synaptic inhibition .
The tendency for the LLR component of the TA stretch reflex to increase in amplitude following the intervention supports a cortical origin of the changes following PAS. Past studies indicate that the LLR component is at least in part of cortical origin Christensen et al., 2001;Wallace and Miles, 2001;van Doornik et al., 2004). Further, it is the LLR which is the dominant response in the TA when a sudden externally applied ankle dorsiflexion is induced during the stance phase of human walking (Christensen et al., 2001), suggesting it has a functional role in balance regulation during walking. Facilitation in the LLR component of the TA stretch reflex through a PAS protocol may thus have functional consequences across tasks, though this is speculative at this point and requires further investigation.

Methodological Considerations
During the pre and post measures of MEP amplitude, a biphasic pulse type was used for those experiments that also used a biphasic TMS pulse shape during the intervention. Conversely, a monophasic pulse shape was used for those experiments that also used a monophasic TMS pulse shape during the intervention. The TA MEP amplitudes are thus not directly comparable. However, two previous studies investigating the effect of monophasic and biphasic low frequency rTMS on the amplitude of the FDI muscle MEP have found significant changes only when a monophasic pulse waveform was used during the intervention; one study used a monophasic pulse form for all pre and post measures (Taylor and Loo, 2007), while the other was similar to our study (Sommer et al., 2002). It is thus conceivable that if we had used the same pulse form for all of our baseline measures, similar results would have been attained.

CONCLUSION
The aims of this study were to establish whether PAS applied using a biphasic pulse shape is able to induce changes in the excitability of the cortical projections to the TA and, whether it is possible to attain significant alterations in TA MEP amplitude when a subthreshold biphasic TMS pulse is used while the target muscle was at rest. This may have important implications for the rehabilitation of patients who have suffered a lesion of the corticospinal tract as it has been shown that the connectivity of this tract is associated with functional improvements (Thomas and Gorassini, 2005). TMS using a biphasic pulse shape will activate neurons and nerve bends orientated in various directions compared to a monophasic pulse shape. This may lead to the activation of a more diverse set of neurons. It is likely that neurons situated at multiple places in the motor cortex contribute to a particular movement and TMS with a biphasic shape using a double-cone coil may activate more of these locations. This may be useful when PAS is to be used in a rehabilitation setting.

AUTHOR CONTRIBUTIONS
NM-K and AJTS conceptualized and designed the study. NM-K collected the data partly with a student group, analyzed the data and drafted the manuscript, AJTS completed the statistical analysis, commented on the manuscript and approved the final version.

FUNDING
This study was supported by the Spar Nord Foundation, and the Obelske Familiefond of Denmark.