Virtual Reality in Posturography - IEEE Xplore

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interaction. The system combines virtual reality visual stimula- tion with force platform posturography on a moving platform. We evaluate our contruction's utility in ...
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IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 10, NO. 2, APRIL 2006

Virtual Reality in Posturography Timo Tossavainen, Esko Toppila, Ilmari Pyykk¨o, Pia M. Forsman, Martti Juhola, and Jukka Starck

Abstract—Balance dysfunctions are common, especially among elderly people. Present methods for the diagnosis and evaluation of severity of dysfuntion have limited value. We present a system that makes it easy to implement different visual and mechanical perturbations for clinical investigations of balance and visual-vestibular interaction. The system combines virtual reality visual stimulation with force platform posturography on a moving platform. We evaluate our contruction’s utility in a classification task between 33 healthy controls and 77 patients with M´eni`ere’s disease, using a series of tests with different visual and mechanical stimuli. Responses of patients and controls differ significantly in parameters computed from stabilograms. We also show that the series of tests achieves a classification accuracy slightly over 80% between controls and patients. Index Terms—M´eni`ere’s disease, postural balance, posturography, virtual reality (VR).

I. INTRODUCTION HE ABILITY to maintain balance is fundamental for bipedal activities. The standing human body is inherently unstable and requires constant postural adjustments. Even during quiet standing, heartbeats and breathing cause postural perturbations. The organization of the postural control system is hierarchical. It is composed of afferent receptors, a central control system, and efferent motor neurons. The central system controls local subsystems that execute segmental reflexes needed in elementary motor tasks [1], [2]; it has memory and postural corrections are largely based on anticipation [3]. The complex involvement of the central nervous system (CNS) in control tasks makes it possible to stay upright in moving vehicles and to balance on slippery surfaces. Control strategy is context dependent: it is selected based on ambient conditions. Balance disorders cause problems in maintaining upright stance. For example, at the onset of M´eni`ere’s disease, difficulties occur in conjunction with dizzy spells, and later in the course of the disease even between attacks [4]. Deterioration of the vestibulo-spinal system causes instability in the elderly [5]. The disorders lead to an increased risk of slips or falls [6]. According to health statistics, accidental falls are the fifth most common cause of death in Nordic countries, surpassing even deaths caused by traffic accidents. They lead to injuries, hip fractures in about 1.5% of cases, and cause a fear of falling in

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Manuscript received December 20, 2004; revised May 10, 2005 and August 12, 2005. The work of T. Tossavainen was supported by the Tampere Graduate School of Information Science and Engineering, by the Finnish Cultural Foundation, by the Finnish Work Environment Fund, and by the Finnish Foundation for Advancement of Technology. T. Tossavainen and M. Juhola are with the Department of Computer Sciences, University of Tampere, Tampere FI-33014, Finland (e-mail: [email protected].). E. Toppila, P. M. Forsman, and J. Starck are with the Finnish Institute of Occupational Health, Helsinki FI-00250, Finland. I. Pyykk¨o is with the Department of Otolaryngology, University of Tampere, Tampere FI-33014, Finland. Digital Object Identifier 10.1109/TITB.2005.859874

about 60% of subjects. This can cause isolation, reduction of activity and agility, and degradation of quality of life. Thus, the early prevention, detection, and treatment of balance disorders is of the of utmost importance. In postural control, the CNS integrates input from sensory systems to detect body position and motion, and executes motor control of muscles to maintain balance. Visual, vestibular, and proprioceptic systems provide the most important afferent sensory influx for balance control. Semicircular canals and otolith organs in the vestibular system respond to angular and linear acceleration of the head, respectively. Pressoreceptors measure the distribution of pressure under the feet. Both systems are insensitive to very low frequency oscillations of the body [7]. Lower limb pressoreceptors and proprioceptors respond to changes in body position and movement velocity. For example, muscle spindles and golgi tendon organs (GTOs) respond synchronously to muscle and tendon distension [8], [9]. Vision provides position and movement information from retinal images and their displacements (retinal slip). It is also used to calibrate the vestibulo-spinal system. Motion on the retina can be perceived as scene-motion or as self-motion (vection). Balance disorders typically cause an increased reliance on vision, but tests of visual dependence for assessment of central and peripheral vestibular disorders have not been explored in depth. Romberg’s classical test evaluates balance in quiet standing with eyes open and closed. It is difficult, however, to produce visual perturbations that are reliable, to make effective models of the situation, and to validate their clinical relevance. The role of vision in balance is discussed in detail elsewhere [10]–[12]. Proper care of balance disorders—for example, medical or surgical treatment, reposition maneuvers, and rehabilitation— requires understanding of postural control mechanisms. Detailed analysis of a patient’s postural stability can reveal the causes underlying postural and gait disturbances. Posturography studies balance by using force platform measurements of forces applied in maintaining upright stance. Its application for rapid screening has increased. Posturography without stimulation, i.e., static posturography, measures body sway during quiet standing and with perturbing stimulation. Dynamic posturography measures the dynamic corrections of body position and velocity [13]. When Romberg’s test is carried out using posturography, measures of stability derived from measurements during quiet standing with eyes open and eyes closed are compared. Other approaches to evaluating postural stability in patients includes for example, recording electrical muscle activity (EMG) and recording deviation of the body or its segments with a camera or potentiometer [14]. The sensory systems are partially overlapping and redundant with respect to postural control. In static situations, such as quiet standing, most relevant senses are operating beneath their thresholds. Partial derangements can be virtually undetectable,

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TOSSAVAINEN et al.: VIRTUAL REALITY IN POSTUROGRAPHY

and interactions and dependencies between different sensory systems and control mechanisms cannot be fully explored. External or voluntary perturbation is required. In balance disorders, corrective responses are usually delayed, and in many cases compensation for the deficiency is suboptimal. Dynamic posturography may reveal pathology in these conditions. Researchers have used many different external stimulation methods in dynamic posturography. Perhaps the simplest approach is to place foam rubber on top of a force platform. The foam rubber causes pressoreceptors of the sole to detect approximately constant pressure, and thus irrelevant cues of the pressure distribution under the feet [15]. Muscle proprioceptors can be misled with local vibration [2]. A translating or tilting force platform with or without irrelevant visual cues is common [16]. Only recently have video [17] and computer graphics [18] been used to visually induce sway. Mechanical constructs for visual stimulation are cumbersome and limited, as they have to obey physical laws. Virtual reality (VR) is an easy way to create relatively inexpensive visual stimuli suitable for clinical use. Computers can be programmed to create a variety of illusory visual environments. Images generated based on the position of the viewer relative to the display surface and the position of the display surface in the virtual environment make a monitor or a projection screen to appear to be a window into a virtual world. We have designed and constructed an integrated VR system for postural control research by combining VR visual stimulation with a tilting force platform. With a proper set of stimuli, we aim to obtain an extensive characterization of the human postural control system. We demonstrate our system’s utility in a discrimination task between M´eni`ere patients and healthy control subjects. The idea of using VR as visual stimulation in postural control research is not entirely new. Viirre [19] suggested using virtual reality for rehabilitation from balance disorders. Kramer et al. [20] replicated classical experiments on eye movements with virtual reality and obtained similar results. Kuno et al. [21] used a pattern moving in the depth direction to cause vection. Keshner and Kenyon [18] have studied the effects of virtual reality on postural control using the CAVE, which is a virtual reality system based on stereoscopic projections on multiple screens. The screens form a cube that surrounds the user. Jacobson et al. [22] have built a small CAVE called the Balance NAVE for balance testing, and Lee et al. [23] have applied VR to balance testing of children. In previous work, we have shown that our VR setup can affect balance [24], and that stimulation may be designed to cause different effects [25]. We have also compared different displays using the same VR stimuli [26]. II. MATERIALS AND METHODS In our integrated, transportable system the main components are a tilting force platform, a head-mounted display, a headorientation tracker, and two desktop computers (Fig. 1). VR provides programmable visual stimuli, and mechanical perturbations and measurements are carried out with the tilting force platform. Two standard desktop computers connected using

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Fig. 1. A block diagram of the measurement system. The operator’s user interface runs on a computer connected to another using LAN. The second computer generates a virtual environment based on head orientation measurements obtained from the tracker. The environment is displayed on a head-mounted display (HMD). An analog–digital converter (ADC) samples the measurements from the platform. A programmable controller controls the movements of the platform.

Fig. 2.

A test subject undergoing balance testing.

Ethernet are used to operate the system. One computer generates the virtual environment and controls the measurement, and the operator’s user interface runs on the other. This arrangement results in better synchronization between measurements and VR than distributing them to different computers. Platform measurements can also be used to control the environment, which creates a closed loop between the postural control system and the stimulus. This option is not used in the present study. Fig. 2 presents a test subject undergoing balance testing. The software is implemented in C++ using the Simple DirectMedia Layer library, OpenGL, and tools from the GNU project. The operator’s graphical user interface was implemented using the gtkmm library. Modular software design enables configuration of the system for the laboratory hardware and the desired test

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Fig. 3. The force platform. Sensors mounted at points A, B, and C measure vertical forces. The platform is mounted on a ball-joint and can be tilted using a screw-slider mechanism.

setup. At the moment, we have deployed our system in two laboratories. A. Force Platform Posturography Force platforms measure ground reaction forces caused by movements of the body. Our platform consists of a round plate mounted on top of three force transducers (Fig. 3). The transducers are arranged in an isosceles triangle. They measure vertical forces using the strain gauge principle; each has a part that reacts to pressure by changing its resistance. A motor-driven screw-slider system tilts the platform on a ball joint. The movement has two degrees of freedom: the platform can be tilted in the anteroposterior and the mediolateral direction, but not rotated around the vertical axis, or translated. A programmable controller controls the motors. Suppose that the plate is massless, rigid, and held in equilibrium by supports at noncollinear points a, b, and c on the XY-plane by forces Fa , Fb , and Fc . When a force F acts on the plate through point r, the platform is in equilibrium, if Fa + F b + F c + F = 0

(1)

a × Fa + b × Fb + c × Fc + r × F = 0.

(2)

We can solve for the vertical components Faz , Fbz , Fcz , and Fz , because the points a, b, and c are noncollinear. Writing out the system of three linear equations and solving it with Cramer’s rule gives, after some algebra, r = −Fz−1 (Faz a + Fbz b + Fcz c) and Faz + Fbz + Fcz = −Fz . The point r is called the center of pressure (COP). Horizontal forces cannot be determined without additional assumptions. An analog-digital converter (Data Translation DT-9804) samples Faz , Fbz , and Fcz from the transducers at 50 Hz with a resolution of 16 bits over −5 to 5 V; about 20 kg corresponds to 1 V. From Faz , Fbz , and Fcz , we form three independent signals: coordinates x, y of r and total vertical force Fz acting on the plate. Mediolateral direction is given by x and anteroposterior by y; The origin is at the platform center. We give a method for calibrating the platform in the Appendix. A test subject stands on the platform during measurements. An inverted pendulum model [27] is typically used (Fig. 4). The torso is modeled as a weight placed on the end of a massless rigid rod, which is connected to a triangular foot at the ankle joint. Torque applied at the ankle joint controls the pendulum. Balance can be maintained as long as the center of

Fig. 4. Inverted pendulum model. A test subject is modeled as an inverted pendulum. The center of gravity (COG) is on top of a massless rigid rod, which is attached with a joint to a triangular foot. Torque t is applied at the ankle to control the pendulum. This is reflected in the resultant ground reaction force (GRF) applied at the center of pressure (COP). In this case, the pendulum is accelerating clockwise.

gravity (COG) is above the base of support (BOS). The point where the resultant ground reaction force (GRF) is applied is called the center of pressure (COP). From the model, we can see that the COP is directly below the subject’s COG in a static situation. In dynamic situations, that is when the pendulum is moving or accelerating, it is displaced from below the COG proportionally to the height of the COG from the plate and COG’s horizontal acceleration. The difference of GRF and gravity can be used to obtain the acceleration of the COG. We also note that ground reaction forces only occur inside the base of support. According to the model, we measure the COP and the vertical component of the resultant ground reaction force. The COP as a function of time is called a stabilogram. Fig. 5 contains a typical stabilogram obtained from a test subject during 30 s of quiet standing. Analyzing the situation on the basis of a stabilogram is difficult, even if we assume that the inverted pendulum model is valid. For instance, the base of support is only approximately known, the acceleration of the COG can directly be obtained only in the vertical direction, and, consequently, the COG is difficult to track. The measured COP depends on position and acceleration of the COG; in the literature, this distinction is made by referring to the COG as the controlled variable and COP as the controlling variable. Stabilograms contain information on a complex system that maintains balance. They summarize body movements and are therefore hard to interpret. Thus, parameters computed from stabilograms are used to evaluate postural control. The range of COP in both directions, COPs standard deviations in both directions, and COP mean velocity are standard parameters. Their reliability and correlations are analyzed in [28], [29]. B. Virtual Reality In our system, virtual reality techniques are used for visual stimulation. A stereoscopic display is needed for depth

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Fig. 5. Measurement obtained from a test subject during 30 s of quiet standing. Position of COP is shown in the mediolateral (ML) and anteroposterior (AP) directions, and the vertical force signal is reported as equivalent to a mass in kilograms on the platform. Heartbeats are visible.

perception. It can be, for example, a head-mounted display (HMD), or overlaid images projected on a wall using polarized light viewed with polarizing eyeglasses. We currently use a HMD (Virtual Research V8). It weighs about 1 kg and has displays for both eyes; the diagonal field of view (FOV) is 60◦ with a resolution of 640 × 480 pixels and a 60 Hz refresh rate. Interpupillary distance is adjustable, and the optics adjust the focal plane to about 1 m from the eyes. Eyeglasses can be worn while using the HMD. A three degrees of freedom (yaw, pitch, and roll) orientation tracker (InterSense InterTrax) tracks head movements. A graphics card that supports two displays is required to display stereoscopic images. At the moment, we use nVidia GeForce series cards. VGA splitters allow the operator to see the virtual environments on monitors during operation. We have implemented virtual environments intended to disturb balance. Fig. 6 shows the cylinder and tunnel environments used in this study. In cylinder, the subject is inside a rotating cylinder colored with dots, and in tunnel, the subject is moving through a twisting tunnel. During the first 10 s, the virtual cylinder is idle. It then starts to rotate counterclockwise with constant acceleration to an angular velocity of 160◦ /s in 15 s and then decelerates in the same manner to a halt in the next 15 s. After a pause of 10 s, the rotation is repeated in the clockwise direction. In the tunnel stimulus, the movement lasts 60 s and follows a pattern composed of sinewaves that are not harmonically related. It is unpredictable from a test subject’s point of view. We used keyframe animation to implement movement control in the environments. In it, a sequence of consecutive positions, keyframes, is given, and positions between keyframes are interpolated. Movement paths were represented using Kochanek–Bartels cubic splines [30] and orientations using quaternions [31]. Speed and path control were decoupled by reparameterizing the splines by arc length. The effect of stimulation on a test subject is shown in Fig. 7. During stimulation, the subject experiences vection, and the reaction can be interpreted as resolving a sensory conflict by

Fig. 6.

Examples of virtual reality stimuli. (a) Cylinder. (b) Tunnel.

weighting the senses differently in balance control. Usually the reaction is to lean in the direction of movement; the subject tries to compensate for an illusory self-motion in the opposite direction. C. Application to Detection of Balance Disorders We carried out a comprehensive balance test using on a control group and a group of M´eni`ere patients to evaluate our VR posturography system. The measurements were

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is measured. The subject, still wearing the HMD, stands on the platform that tilts for 15 s. Finally, response to visual-vestibular stimulation is assessed using the tunnel test. The tunnel stimulus is shown, and the platform first tilts in phase with the tunnel. Halfway through the test, the phase of platform movements is inverted, which creates a sensory conflict as the tunnel and the platform move in opposite directions. D. Statistical Evaluation We filtered the measured stabilograms bidirectionally using a 2nd order Butterworth lowpass filter with a cutoff frequency of 10 Hz to remove noise and computed parameters from the measurements. We analyzed separately the two rotation directions of the cylinder test, and the sections of tunnel when the platform movement is synchronized and when it has inverted phase. We decided to use COP mean velocity, the Romberg quotient, and also introduce new parameter called vertical ground reaction force power fraction in the analysis. Mean velocity is given by n 1 d(ri , ri−1 ) MV = T i=2

Fig. 7. Stabilograms obtained from a subject during (a) quiet standing, and (b) cylinder stimulus administered using HMD. The plot presents the path traced by the COP during the measurement in centimeters centered to COP mean position during the first 5 s. Increased mediolateral swaying is apparent, and the typical reaction to lean forward slightly for increased stability during stimulation is also visible.

performed at the Hearing Center of the Tampere University Central Hospital. The control group consists of 33 male subjects (age 24–46, mean 33, std 5) and the M´eni`ere group of 77 patients, 23 male and 54 female (age 38–82, mean 60, std 10). The test consists of five parts: quiet standing eyes open and closed, cylinder, tilting platform, and tunnel. The aim of the test setup is to gain understanding on the functioning of the subject’s postural control system under different conditions. During testing, the test subject stands on a force platform in a standardized measurement position. The test sequence starts with the Romberg test: measurements are taken during 15 s of quiet standing on the platform with eyes open and eyes closed. A fixation point is provided on a monitor located 1.4 m in front of the patient. Quiet stance with eyes open was also used as a baseline when assessing the results from other tests. Next, the subject is attached to the ceiling with a safety harness to prevent falls during the tests with external stimulation. There is enough slack so that the harness will not support the subject during normal swaying. The subject is given the headmounted display and is asked to adjust it with the help of an adjustment screen. External stimulus commences with the cylinder test, where the subject is shown the cylinder stimulus. Next, response to mechanical perturbation without visual stimulation

(3)

where T is duration of the measurement, d is the Euclidean distance, and ri = (xi , yi ), i = 1, 2, . . . , n, are the sampled COP positions. The Romberg quotient RQ, is given by RQ = MVec /MVeo , where MVec and MVeo are COP mean velocity eyes closed and eyes open, respectively. This parameter is frequently used in the force platform version of Romberg’s test. The vertical ground reaction force (VGRF) is rarely analyzed ¨ in the literature. Onell [32] suggested that it contains information on how well the subject damps perturbations from breathing and heartbeats. A subject’s weight can be approximated from the mean VGRF acting on the plate over a period of time; otherwise, the subject would be moving vertically. Intuitively, VGRF is instantaneous weight on the force platform. The design of the safety harness in our tests, unfortunately, made it possible for subjects to get some support outside of the platform. We noticed this in our preliminary tests as a sudden decrease of weight or its slow drifting in the measurements. This can occur, for example, when a test subject bends his knees, or during large swaying movements. The vertical ground reaction force power fraction (VFPF) parameter quantifies this phenomenon. Suppose that c0 , c1 , . . . , cN −1 are the discrete Fourier transform (DFT) coefficients of a (real-valued) signal x0 , x1 , . . . , xN −1 . The coefficient ck corresponds to frequency f = fs k/N , where fs is the sampling frequency; thus the frequency interval (0, 1] Hz roughly corresponds to k = 1, . . . , N/fs . The VFPF parameter is given by N /f s |ck |2 =1 VFPF = 2 k N × 100% −1 2 k =1 |ck | where multiplication by two comes from symmetry (cN −k = c∗k and corresponds to the same frequency). In practice, VFPF is computed by zero padding the signal to a length of a power of two, and then using FFT to obtain the ck . The resulting parameter is a rough estimate of the fraction of signal power in (0, 1] Hz. It detects slow changes and drifting, such as hanging

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on the safety harness, without regard to scale of the original signal. The choice of 1 Hz upper limit was somewhat arbitrary; it could, in principle, be optimized for classification. We removed outliers from each group separately in each test in the statistical analysis. A value was omitted if it was more than 1.5 times the interquartile range (IQR) of group above the group’s upper quartile, or similarly below the lower quartile. This prunes measurement errors or single incidents; for example, a slight staggering in quiet standing may result in abnormally large mean velocities in the relatively short measurement. The other option is to remove all measurements from a subject differing from the group. This could be used to remove misclassified cases or exceptionally unstable patients from the analysis. Our goal is to discriminate between M´eni`ere patients and controls. We approach this problem by building classifiers from parameters computed from measurements. Each test subject has corresponding vector of observed parameters. Classifiers take a set of labeled examples, the training set, and try to construct a mapping from the space of observations to class labels that minimizes the classification error rate. We do not know the actual distributions, so accuracy has to be estimated from the observations. Using the classifier error on the training set gives optimistic results. The error rate should be estimated on an independent test set. Due to scarcity of data, we use the leaveone-out cross-validation (LOOCV) procedure. We remove each single case in turn from the observations, build a classifier using the remaining cases as the training set, classify the removed case using the classifier, and finally estimate accuracy from the results of individual cases. The classifier has no information on the case it is classifying, but uses all other cases to classify it. LOOCV yields nearly unbiased estimates of the classification performance; the drawback is large variance. We use simple univariate classifiers to compare the different parameters and multivariate classifiers to evaluate the classification performance of the whole series of tests and parameters. The majority classifier is an intuitive baseline. It classifies all cases to the largest group, and minimizes the classification error in the absence of other information. The univariate classifiers select a cutoff value and classify smaller values to one class, and greater values to the other class. We choose the cutoff value at the center of the region that minimizes the descriptive error (the error on the training set). This is done by sorting the observations and performing an exhaustive search of rules that place the cutoff between two consecutive observations with different classes. We used Fisher’s linear discriminant analysis (LD) and k nearest neighbor (kNN) classifiers [33], and also performed small experiments with AdaBoost [34]. Fisher’s linear discriminant analysis assumes that the class-conditional distributions are multivariate normal with equal covariances. The kNN classifier classifies a new case by finding k most similar cases; i.e., neighbors, in the training set, and assigning it into the most frequent class in the k neighbors. The notion of similarity is defined using a metric. Formally, suppose the training set X = {x1 , x2 . . . , xn } and that the classes C1 , C2 , . . . , Cm ⊂ X partition X. To classify an observation x, we sort X into increas-

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ing order x(1) , x(2) , . . . , x(n ) with respect to d(x, xi ), where d is a metric giving the distance between x and xi . The kNN rule assigns x to class arg max |{x(1) , x(2) , . . . , x(k ) } ∩ Ci | i=1,2,...,m

where | · | is the number of elements in the set. The main parameters are the choice of metric d and size of the neighborhood k. As usual, we choose an odd k so that the rule is unambiguous when there are no ties in the distances. Ties are broken arbitrarily. We used the Euclidean metric on standardized variables. Note that the M´eni`ere group is somewhat larger than the normal group, which means that the kNN classifiers will favor it. The parameter k was chosen automatically to maximize LOOCV accuracy. This can be done using the distance matrix between points of the training set. When computing LOOCV accuracy, the kNN classifier will classify each case using the k nearest other cases, so we can simply count how many cases in the training set are classified correctly for each choice of k. The multivariate classifiers can be built including all the variables, but selection of a suitable variable subset may improve the performance of the classifier. We use the wrapper approach [35] that does this while directly optimizing the classifier performance. Each subset of variables gives a different classifier. We can try to optimize the subset for LOOCV accuracy by performing a search on the set of all subsets of variables. An exhaustive search of all variable subsets is not feasible when there are several variables, so a heuristic search has to be used. We used sequential forward selection (SFS), sequential backward selection (SBS), and sequential forward floating selection (SFFS) [36] for variable selection. The SFS algorithm starts from an empty set of variables and adds the single variable whose addition results in the best performance, and continues until all variables are included. SBS does the same in reverse; it starts from all variables and removes one variable at a time. These algorithms only consider nested subsets of variables, which has been seen as a weakness. The SFFS algorithm does more extensive search, and does not suffer from the nesting problem. It adds a single variable as SFS, and then does SBS steps as long as removal improves on the best previously found subset of the same size. SFFS also starts from the empty subset. Note that in SFFS variable addition may worsen performance, but not removal. Among others, Kudo and Sklansky [37] have recommended floating search for small and medium size variable subset selection problems. We ran the algorithm until it had included all the variables. We always chose the best variable subset considered by the search algorithm, and in case of ties, the smallest one. Variable selection optimizes the classifier for LOOCV accuracy. Thus, a classifier that uses the best variable subset found will have an optimistic LOOCV accuracy estimate if tested with the observation set from which the choice of variables was made. The choice of best subset may also be uncertain. To get a less biased evaluation, we consider the variable selection process as a part of the classifier and add another level of LOOCV to evaluate the final performance of this variable subset selecting classifier. AdaBoost uses another algorithm, a base learner, that is guaranteed to do slightly better than guessing on every distribution.

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TABLE I ROMBERG QUOTIENT (EYES CLOSED/EYES OPEN) AND MEAN VELOCITIES (CM/S)

It constructs multiple weak classifiers on different weightings (distributions) of the training data and combines the results of the individual weak classifiers into one strong classifier. We will not go into details, as the development is quite long. The main parameters of AdaBoost are the number of weak classifiers to use and the base learner to contruct the weak classifiers. We used decision stumps that choose the optimal cutoff value along a coordinate axis (i.e., a single variable) as the base learner. The cutoff value was determined otherwise as in the univariate classifiers previously described, but taking the weighting into account. This method also implicitly includes feature selection. III. RESULTS All control subjects were able to complete the tests. A total of 67 patients completed the cylinder test, 69 patients completed the moving platform test, and 56 patients completed the tunnel test. In the tables, the separately analyzed section are cylinder counter clockwise (CCW) and clockwise (CW) rotation, and tunnel with synchronized platform and inverted phase platform movements. Results for mean velocities and Romberg quotients are shown in Table I. The number of outliers removed from the statistical analysis is indicated in the tables. We used the student’s t-test with unequal variances to test for difference of means between control and M´eni`ere groups. The p-value given is the approximate probability of erroneously claiming a difference in means. Significant differences (p < 0.05) in mean velocity between controls and patients were found in quiet stance with eyes closed, in the moving platform test, and in the tunnel test. Romberg quotients also differed significantly. The number of outliers in the cylinder measurement suggest that it detects subjects with an increased susceptibility to visual stimulation.

TABLE II VERTICAL GROUND REACTION FORCE POWER FRACTION BELOW 1 Hz (VFPF)

Patients have a smaller mean velocity in the moving platform and the tunnel tests, which is surprising, but this can be explained by the use of the safety harness for extra support. The VFPF results are shown in Table II. Differences of means between controls and patients are significant in all cases. The parameter can also differentiate between controls and patients in the eyes open and eyes closed tests conducted without the safety harness. The phase inversion in the tunnel caused no statistically significant differences. We included all cases with complete data, 33 normals and 55 M´eni`ere, in the classifier training set. The results of classification are shown in Table III. Note that a single case accounts for 1.14%, 1.8%, and 3.0% in overall, M´eni`ere, and control group classification accuracy, respectively, so there is no information to be gained from reporting the results in higher precision. The rule for single variable classifiers indicates the decision region for the M´eni`ere group. When the classifier is inaccurate the rule may not be sensible, as the classifier may be close to a majority classifier. This is reflected in the accuracy of the smaller control group. Overall, the results show that classification is quite difficult with individual parameters. The Romberg’s test is poor at discrimination. Although the means of controls and patients differ significantly, Romberg’s quotient fails to discriminate on the individual level. With the exception of platform test and tunnel with inverted phase with 68 and 71% accuracy, mean velocity discriminates poorly, close to majority classification. VFPF parameters have a fairly good classification accuracy of 68–81%. Here, VFPF consistently outperforms MV. In the multivariate case, LD has an accuracy of 76%–83%, the kNN rule 82%–85%, and AdaBoost 81%. Variable subset selection algorithms chose generally four to seven variables from the 15 available; the performance was similar to classifiers that included all variables. As expected, variable selection resulted in overly optimistic LOOCV estimates for the selected subsets. The selected subsets had LOOCV accuracies of 86%–89% for LD, and, 92%–93% for the kNN rule. The best subset found was

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TABLE III LOOCV ACCURACY ESTIMATES OF CLASSIFIERS (%)

often estimated to be over 10% more accurate than the whole selection process when tested on independent data. The parameters MV eyes closed, VFPF eyes open, VFPF cylinder CW, and VFPF tunnel inverted were most often chosen during the search. AdaBoost training error converged to zero with about 30 base classifiers. The LOOCV accuracy varied from 78% to 84% for 1–50 base classifiers, but this seemed to be due to random variation; no trends were visible in the LOOCV error. The reported accuracy is for 50 base classifiers, which is representative. Using more base classifiers does not change the situation (we tested up to 4000). IV. DISCUSSION The combination of VR visual stimulus and posturography on a moving platform is flexible, and can be programmed to stimulate in an unpredictable way the different sensory systems used in postural control. It can be used to evaluate both the hierarchy of the postural system and its ability to adapt to changes in the environment. In man, motility and agility determine sensory preferences. The internal preferences between different receptive systems may be altered in vestibular patients. Our prior assumption in the design of the test setup was that postural control gives more weight to visual input and also, to a lesser degree, to proprioception after injury of the vestibular system [38]. Patients with vestibular dysfunction can stand and walk with their eyes closed, which shows that they use exteroceptive and prorioceptive inputs in the control of upright equilibrium during both free stance and locomotion. However, the patients require a rigid

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and flat support surface to keep the upright position intact [7]. A foam rubber surface makes the position unstable and the subjects tend to fall, unless supported with visual feedback [15], [7]. The sensory preferences tend to change in favor of visual input among people older than 50 years, even without vestibular dysfunction. This trend is apparent among elderly people over 75 years of age [39]. Virtual reality posturography measures the performance of the subject in conditions where visual input is inadequate or misleading. It reveals excessive use of visual input, but not its cause. Visual dependence leads to poor postural control when quick reactions are needed. The patient group consisted of M´eni`ere-patients; during attacks, M´eni`ere’s causes an acute vestibular deficiency, which is compensated using vision. This may lead to dominance of vision over vestibular information, also between attacks, and increase sensitivity to visual stimulation, as observed in the present study. In clinical evaluation of vestibular deficiency, Black et al. [40] showed that in visual control conditions the patients with compensated and uncompensated loss of vestibular function showed normal or nearly normal responses to postural perturbation. The results were somewhat variable in nonvisual control conditions, but when the platform and visual surrounding were perturbed simultaneously, all patients displayed abnormal body posture control. According to Black et al. [40], the patients were vestibular dependent, and in normal conditions compensated to environmental demands, but became unstable when forced to rely on vestibular information, or when forced to select between conflicting visual and vestibular cues. In patients with distorted vestibular disorder, as is proposed to be in benign paroxysmal positional vertigo, a conflict situation between visual and vestibular influx was always resolved in favor of vision. A significant number of subjects in this group also performed abnormally when exposed to erroneous visual feedback only. Thus, in these subjects, a reorganization had taken place in favor for vision. Pyykk¨o et al. [41] used a static platform technique in patients with M´eni`ere’s disease. They observed with visual feedback the sway amplitude was abnormal in about 1/3 of the subjects, and without visual feedback, the sway amplitude was abnormal in about half of the subjects. Similar results were also observed in this study. Compared to the control group, about 1/3 of the M´eni`ere group had abnormally high Romberg quotient (abnormal is defined as differing from the mean of controls by more than two standard deviations). However, a significant difference in means does not necessarily lead to good discrimination. We evaluated the discriminative power of parameters and tests using classifiers. Romberg’s test offers no improvement over majority classification on subjects able to complete all the tests. Removal of patients unable to complete all the tests from the classifier training set is in part responsible for this result. Romberg’s quotient for the patients had means 1.89 and 2.35 for the classifier training set and the removed cases respectively. The difference was statistically significant (p = 0.012). Thus Romberg’s quotient seems to be less sensitive than VFPF or the tests with external perturbations. The VFPF parameters had better discrimination between controls and patients than MV and RQ.

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Combining the various parameters for classification using feature selection methods and multivariate classifiers yielded accuracies of 76%–85%. Failing to complete the tests is a strong indication of M´eni`ere’s. If we consider those cases correctly classified, then the overall accuracy improves about 4% to 80– 88%. This is probably close to the theoretical maximum and a significant improvement over Romberg’s test. It must be noted that M´eni`ere’s disease is fluctuant and its severity varies among the patients. A further study should be carried out to find how the degree of vestibular derangement affects the test results. The relative performance of the classifiers can not be determined with certainty, as the best results of the multivariate classifiers are within four cases of each other. AdaBoost is a hard-margin classifier that eventually achieves zero error on the training set. This leads to overfitting, as the data set is noisy and the classes overlap. Its performance could probably be improved by changing the base learner. The kNN rule, while simple and also prone to overfitting, can handle noise by increasing k (done here in a data-dependent manner). LD is rigid and limited to linear decision boundaries. A. Remarks on Test Setup Use of the safety harness was necessary. The risk of falling over is high, especially when the platform is moving. Falling over during a test might also result in severe injuries and equipment breakage. As a safety precaution, the harness has been successful; no falls have occurred during the tests. In case of loss of balance, the subjects have used the harness as additional support. This is reflected in the instantaneous weight measurement as a significant decrease of weight. We used the effect as performance measure and it provided good discrimination between healthy subjects and patients. The need to use ground reaction forces to maintain balance is reduced when relying on the safety harness for support. This results in smaller MV values compared to controls in the M´eni`ere group, but the use of external support is detected by the VFPF parameter. It is likely that MV would have detected more of the patients without the safety harness. The VFPF parameter was originally designed to catch leaning and gains power from the safety harness. It may be better at discrimination than MV even without one. This certainly seems to be the case on the basis of the quiet standing tests, but this issue requires further study. VGRF is a direct measure of COG vertical acceleration, and VFPF can be seen as a rough measure of vertical COG movements. The MV parameter on the other hand is an indirect measure of COG horizontal accelerations. As mentioned, the COP signal is a mixture of COG position and acceleration with acceleration dominating at higher frequencies. The differencing in the parameter computation acts as a highpass filter. Minimizing the support from the harness is difficult, as too much slack may be hazardous for the test subject. We must also take into account the vertical movement that occurs during leaning especially in moving platform tests. The moving platform tests may have been slightly too difficult saturating the reactions and thus obscuring the differences between controls

and patients. The heavy rope may also provide an extra vertical reference. The HMD has some drawbacks. It is cumbersome, and subjects occasionally have difficulties with adjustments. It was also difficult to use especially with the elderly. Correct use of HMDs requires care. For instance, display adjustments are user-dependent, and incorrectly implemented adjustment patterns may cause convergence errors [42]. B. Remarks on Virtual Reality Virtual reality cannot achieve the same level of realism as mechanical constructions. Naturally, display resolution and FOV are important. Refresh rate—the rate at which new images are generated and displayed—is critical to motion perception. Delays in data acquisition from position sensors—sensor lag—may cause the environment to react slowly to head-movements and cause simulator sickness. Image realism is affected by factors such as lighting, shadows, and surface textures. Since the resolution of our HMD is quite low, one may expect objectionable rendering artifacts, or “jaggies,” in the images. Modern graphics accelerators use antialiasing techniques to produce reasonably good results even with low resolutions. The design of a visual stimulus depends on the desired effect. In general, good environments are ones that can cause a convincing illusion of self-motion in subjects. Their design involves considering perceptual issues as well as technological limitations. Our stimuli were intentionally kept simple, as complex environments make the results difficult to interpret. Improving realism would provide better immersion and probably make the environments better at disturbing balance. On the other hand, more realistic images are slower to generate. The frame rate should be high enough for smooth motion and quick response to head movements; low frame rates may lessen immersion and, consequently, the effect on balance. Our stimuli mostly run at 60 frames per second—the hardware’s maximum. Graphics generation can be sped up, for example, by culling objects visible to the user using spatial data structures and bounding volumes, and optimizing hardware access to geometry data [43], [44]. A VR stimulus should be predictable and precisely timed for repeatability, but on desktop computers without realtime operating systems, the execution may be interrupted at inconvenient times and the program may miss a deadline for image generation. This causes the animation to skip frames, which has to be observed particularly when analysis of the response depends on accurate timing. One solution is to implement the stimuli so that they conceptually sample a continuously running stimulus based on absolute time. V. CONCLUSION We presented a system for clinical evaluation of balance. The preliminary tests show that we attained significant improvements over the standard Romberg test in discrimination between healthy controls and patients with M´eni`ere’s disease using balance tests with VR visual stimulation and a moving force platform.

TOSSAVAINEN et al.: VIRTUAL REALITY IN POSTUROGRAPHY

APPENDIX CALIBRATION OF FORCE PLATFORM Calibration of the platform is easy once it is noted that measurements of COP position are not needed. Here we assume that the mass of the platform is such that force transducers can be considered linear. With this assumption, the platform can be calibrated using linear model fitting. Suppose that the voltages Va , Vb , Vc , measured from each transducer are linearly related to forces Fa , Fb , Fc acting on them by Fi = Gi (Vi − Di ), i = a, b, c, where Gi is an unknown gain and Di is an unknown dc offset. If a calibrated weight of mass m is placed on the platform then we should have Fa + Fb + Fc = mg; the forces caused by gravity acting on the platform are calibrated to be 0. We first measure the offsets Da , Db , Dc with an empty platform. Then we take measurements with the weight placed uniformly at several positions subtracting the offsets Da , Db , Dc . Using these measurements and setting Fa + Fb + Fc = mg, we get an overdetermined system of linear equations for Ga , Gb , Gc , that can be solved; for example, using the method of least squares. In an ideal situation, three measurements from three noncollinear points would do, but our method averages the error over the platform. Naturally, all measurements of Va , Vb , Vc should be averaged over a period of time for better accuracy. REFERENCES [1] L. M. Nashner, “Strategies for Organization of Human Posture,” in Visual and Vestibular Control on Posture and Locomotor Equilibrium, M. Igarashi and F. O. Black, Eds. Basel, Switzerland: Karger, 1985, pp. 1– 8. [2] I. Pyykk¨o, H. Aalto, H. Seidel, and J. Starck, “Hierarchy of different muscles in postural control,” Acta Otolaryngol. Suppl. (Stockh.), vol. 468, pp. 175–180, 1989. [3] J. Droulez and A. Berhoz, “Servo controlled (conservative) versus topological (projective) mode of sensory motor control,” in Disorders of Posture and Gait, W. Bles and T. Brandt, Eds. Amsterdam, The Netherlands: Elsevier, 1996, pp. 83–98. [4] I. Pyykk¨o, S. Eklund, H. Ishizaki, and H. Aalto, “Postural compensation after gentamicin treatment of m´enier`e’s disease,” J. Vestibular Res., vol. 9, pp. 19–26, 1999. [5] I. Pyykk¨o, P. J¨antti, and H. Aalto, “Postural control in the elderly subjects,” Age and Ageing, vol. 19, pp. 215–221, 1990. [6] P. J¨antti, I. Pyykk¨o, and A. Hervonen, “Falls in the elderly nursing home residents,” Public Health, vol. 107, pp. 89–96, 1993. [7] M. Magnusson, H. Enbom, R. Johansson, and I. Pyykk¨o, “Significance of pressor information from the human feet in anterior-posterior postural control—the effect of hypothermia on vibration-induced body sway,” Acta Otolaryngol. (Stockh.), vol. 110, no. 182–188, 1990. [8] A. Prochazka and P. Wand, “Tendon organ discharge during voluntary movements in cats,” J. Physiol. (London), vol. 303, pp. 385–390, 1980. [9] J. Desmedt, “Size principle of motoneuron recruitment and the calibration of muscle force and speed in man,” in Advances in Neurology, New York: Raven Press, 1983, vol. 39, pp. 227–251. [10] W. M. Paulus, A. Straube, and T. Brandt, “Visual stabilization of posture,” Brain, vol. 107, pp. 1143–1163, 1984. [11] T. Brandt, W. Paulus, and A. Straube, “Vision and posture,” in Disorders of Posture and Gait, W. Bles and T. Brandt, Eds. Amsterdam, The Netherlands: Elsevier, 1986, pp. 157–175. [12] M. S. Redfern, L. Yardley, and A. M. Bronstein, “Visual influences on balance,” J. Anxiety Disord., vol. 15, pp. 81–94, 2001. [13] I. Pyykk¨o, E. Toppila, H. Aalto, H. Ishizaki, E. Kentala, T. Hirvonen, and P. Honkavaara, “Determination of parameters for computing postural stability,” Automedica, vol. 19, pp. 39–62, 2000. [14] R. O. Andres and D. J. Andersson, “Designing a better postural measurement system,” Am. J. Otolaryngol., vol. 1, pp. 197–206, 1980.

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disease and in vestibular schwannoma when studied on linearly moving platform,” in Control of Posture and Gait, J. Duysens, B. C. M. SmitsEngelsman, and H. Kingma, Eds. Maastricht, The Netherlands: Int. Soc. Postural and Gait Res., 2001, pp. 230–233. [42] R. S. Kalawsky, The Science of Virtual Reality and Virtual Environments. Reading, MA: Addison-Wesley, 1993, pp. 253–277. [43] T. M¨oller and E. Haines, Real-Time Rendering. Natick, MA: A. K. Peters, 1999. [44] D. H. Eberly, 3D Game Engine Designs. San Mateo, CA: Morgan Kaufmann, 2001.

Timo Tossavainen received the M.Sc. degree in computer science in 1999 from the University of Tampere, Tampere, Finland, where he is currently working toward the Ph.D. degree. He is currently a Researcher in the Department of Computer Sciences, University of Tampere. His research interests include biomedical signal analysis and compression, pattern recognition, and virtual reality.

Esko Toppila received the M.Sc. and Ph.D. degrees in physics from the University of Helsinki, Helsinki, Finland, in 1978 and 2000, respectively. He is an Associate Professor in the Department of Physics, Finnish Institute of Occupational Health, Helsinki, Finland. His research interests include modeling of the inner ear function, modeling the development of noise induced hearing loss and balance control, and modeling the biological causes of cochlear damage.

Ilmari Pyykk¨o received the M.D. and Ph.D. degrees from the University of Helsinki, Helsinki, Finland, in 1971 and 1974, respectively. He is a Professor and Head of the Department of Otolaryngology, University of Tampere, Tampere, Finland. He works on clinical otoneurology and dizzy patients. His main research interests are vestibular testing, inner ear disorders, and falls in the elderly, about which he has published about 500 papers. Previous positions include Professor, University of Helsinki, 1990–1995, Professor and Head of the Department of Otolaryngology, Karolinska Institutet, Stockholm, 1995–2002.

Pia M. Forsman received the Ph.Lic. degree in physics from the University of Helsinki, Helsinki, Finland, in 2005. She is currently working toward the Ph.D. degree at the Finnish Institute of Occupational Health, Helsinki, Finland. She is currently a Researcher in the Department of Physics at the Finnish Institute of Occupational Health. Her research is focused on modeling postural stability.

Martti Juhola received the M.Sc., Ph.Lic., and Ph.D. degrees in computer science from the University of Turku, Turku, Finland, in 1982, 1985, and 1987 respectively. Previously, he was an Academic Assistant, Lecturer, and Researcher at the University of Turku, and later a Professor at the University of Kuopio, Finland. Since 1997, he has been a Professor of Computer Science at the University of Tampere, Tampere, Finland. His research interests include bioinformatics, medical informatics, medical signal analysis, artificial intelligence, neural networks, pattern recognition, population studies, and information retrieval.

Jukka Starck received the M.Sc. degree from the University of Helsinki, Helsinki, Finland, in 1969, and the Ph.D. degree from the University of Kuopio, Kuopio, Finland, in 1984, both in physics. He is a Professor and Head of the Department of Physics at Finnish Institute of Occupational Health, Helsinki, Finland. His research is focused on physical factors at the work place and their health effects, alone and in combination with other environmental and individual factors.