possibility of using this as a tool to improve balance in upright ... visual perception and calculated in upright stance trials. This .... and assistive technologies.
GNB2014, June 25th-27th 2014, Pavia, Italy
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A Predictive Visual Biofeedback to improve balance in upright stance trials C. D’Anna1, D. Bibbo1, T. D’Alessio1, M. Goffredo1, M. Schmid1 and S. Conforto1 1
Department of Engineering, University of Roma TRE, via Vito Volterra 62, 00146 Rome, Italy
Abstract— The aim of this study is to analyze the effect that a predictive visual biofeedback (VBF) could have on postural control. Predictive coordinates are calculated in agreement with time-to collision theory, using the centre of pressure (CoP) components, extracted directly from a force plate. CoP coordinates are used to implement two different real-time VBF, which respectively use current stability conditions (VBFCoP) and predictive stability information (VBFTtB). In both VBF, subjects know if they are or are not in the stability area by an emoticon image. Two groups of seven healthy young subjects performed the protocol in two different sequences: noVBFVBFCoP and noVBF-VBFTtB. Each condition was repeated three times, and its effect was studied by four parameters extracted directly from CoP coordinates (sway area, sway path, mean amplitude and mean frequency). Both VBFs determine a decrease of three parameters, and this decrease appeared more marked in the predictive VBF as compared to the VBFCoP for both sway path and sway area values. This opens to the possibility of using this as a tool to improve balance in upright stance. Keywords— time-to-boundary, centre of pressure, postural control, visual biofeedback
I. INTRODUCTION Postural control requires the integration of data coming from multimodal sensory channels including visual, vestibular, proprioception and tactile system. Postural stability is often evaluated by outputs from the force plate, which measures the coordinates of the centre of pressure (CoP). Parameters extracted from CoP data have been used in several studies to describe, interpret and assess the postural control [1]. Among these parameters the Time-to-Boundary function (TtB) was introduced in the framework of postural stability by moving a concept defined in the theory of the time-to-collision to visual perception and calculated in upright stance trials. This variable takes position, velocity, and acceleration of the CoP trajectory into account, to estimate the temporal margin to the stability boundaries [2]. Previous studies show that TtB may be perceived by the individual as a predictive sign of instability, as it provides information regarding the time needed to reverse a perturbation before loss of balance [3]. A number of studies have thus focused on the effect of visual biofeedback (VBF) on postural control [4], in order to determine whether it is possible to design balance rehabilitation systems that make use of this information. The use of real-time VBF from CoP during standing task is a common tool incorporated in evaluation and training of the postural control [4]. Recent study has looked the efficacy that a predictive VBF could have on postural control [6]. Therefore the goal of this preliminary study is to analyze the possibility of using this as a tool to assist the postural task.
II. MATERIALS AND METHODS A. Partecipants Experiments were conducted in 14 healthy young volunteers (age 25 ± 3 yrs, height 1.71 ± 0.07 m; weight 68.5 ± 11.1 kg). None of them reported neuropathies at the peripheral level, or vestibular pathologies, they had normal visual acuity and no colour blindness. Subjects were instructed as for the experimental procedure that will be described in the following. All of them gave written informed consent according to the declaration of Helsinki. B. Experimental set-up and procedure During the experiments the subjects stood feet together barefoot on a homemade force plate and were asked to maintain an upright posture with arms along their sides. Visual biofeedback was displayed on a computer LCD 21'' monitor placed at 1 m from the subjects (Fig.1). Three different conditions, each consisting of three 40 s repetitions, were considered: noVBF (noVBF presentation), VBFCoP (real-time processing of CoP) and VBFTtB (real-time processing of predictive CoP coordinates). For the noVBF condition participants were asked to stand upright in a natural standing posture looking in front of a fixed spot. For VBFCoP, the expression of emoticons was controlled in real time by means of CoP data. In particular, if the CoP of the subject was within stability area, an emoticon smiling was displayed on the screen, while if the CoP coordinates exceed the stability limits, an emoticon with a sad expression was displayed. In order to give an information about the direction of the stability limit exceeding, if the CoP exceeded the stability area in medio-lateral (ML) direction, a sad emoticon tilted by 30° to the left or to the right was displayed. In the same way if the CoP exceeded the stability area in antero-posterior (AP) direction a sad emotion enlarged or shrinked was displayed (Fig. 2). For VBFTtB, the CoP components were used to compute online the Time to Boundary function, according to the parabolic motion equation reported in [2]: 2 xi ( ) rx (t i ) rx (t i ) rx (t i ) / 2 yi ( ) ry (t i ) ry (t i ) ry (t i ) 2 / 2
(1)
At each time instant ti, the corresponding TtB (ti) sample is obtained as the τ value for which the parabolic motion crosses the boundary limits [2]. This is used to calculate the predictive coordinates x(i+1)(τ) and y(i+1)(τ). The limits are represented by an elliptical figure with axes size calculated by subject-specific criteria. In the same way of VBFCoP, in VBFTtB the predictive CoP coordinates are used to control
GNB2014, June 25th-27th 2014, Pavia, Italy
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the expression of emoticon. For the VBFCoP and VBFTtB participants were asked to maintain the emoticon smiling. Seven subjects executed the task in the sequence noVBFVBFCoP, while seven subjects in the sequence noVBFVBFTtB. C. Data acquisition and processing CoP data in both AP and ML directions were obtained from signals coming from a home-made force plate, sampled at 100 Samples/s (NI USB-6210, by National Instruments). Both VBFs were elaborated in real-time by a Labview application (@ National Instruments ed. 2013 64-bit). The CoP coordinates were stored for further offline processing. This included mean value removal and digital low-pass filtering (cut-off frequency of 10 Hz [1]). A subset of four measures was extracted directly from the CoP time series [1]: sway path (SP), mean amplitude (MA), sway area (SA) and mean frequency (MF). D. Statistical analysis Descriptive statistics were calculated for all the extracted parameters. All the parameters were also considered separately as dependent variables in a 2-way ANOVA test: repetition of task (RP) and type of task (TS) were the factors. The test compares the parameters of noVBF condition and VBF condition. Finally the test compares the parameters of two different biofeedbacks (VBFcop and VBFTtB). The level of significance was set at 0.05. III. RESULTS In the first group (comparison noVBF-VBFCoP), the ANOVA analysis shows no significant difference in RP and RP*TS for all parameters examined. In TS there is a significant difference in all parameters, except for SP (p
