VGRF database is obtained from PhysioNet [4]. A collection of signal measures VGRF (in Newton) as a function of time extracted from 8 sensors (Ultraflex Computer DuynoGraphy, Infotronic Inc.) underneath each of the right and left foot. In addition, two signals representing the summation of each of the 8 signals recorded from each foot are taken as a reference.

Sensor locations are shown in Figure 1 inside the insole as lying approximately at the following (X, Y) coordinates measured as a person is comfortably standing with both legs parallel to each other. The origin (0, 0) is just between the legs and the person is facing towards the positive side of the Y axis.

Two groups of persons were recorded: the normal case, also named control and they count for 18 persons, and 29 patients with Parkinson disease. Each participant walked for two minutes at their own natural pace and with acquisition sampling rate of 100 Hz. Figure 2 shows the force generated by eight sensors underneath right and left foot in addition to their total for both normal and Parkinson diseased person of age 72 years old.

Before utilizing data for any purpose, preprocessing must be performed on the data to remove any undesirable characteristics that produced during acquisition. For instance, Filtering is used in cleaning and removing any unwanted disturbance in GRF data. That’s because the presence of noise can totally mask the true information in data. In addition, it’s significant to eliminate sources of variation on the measured VGRF like the influence of mediolateral and anterior-posterior variations. For illustration, Butterworth low pass filter of second order can be used for that purpose [5] [6]. Such a filter is used to filter the data with cut-off frequency of 30 Hz. This choose is due to studying the mean frequency of high oscillations denoted as Intrinsic mode function given by empirical mode decomposition of each signal.

In the other side, if one variable is to be normalized to another variable, it is important to understand the relation between them [3]. That’s why, data normalized bynorm-2 as to make the data comparable to each other.

In this part, simple features were extracted after the data being pre-processed. Then their performance was tested. The features used here are used from literature, we found 23 features. In this paper the nine most relevant features were retained. A list of the commonly wide used features are shown below:

・ Mean: Signal averaging

・ Median: numerical value separating the higher half of a data from the lower half.

・ standard deviation: measures the amount of variation or dispersion from the average

・ range: the difference between the maximum and minimum values

・ Interquartile range: robust estimate for the spread of the data being equal to the difference between the upper and lower quartiles IQR = Q₃ − Q₁.

・ 95% percentiles of the distribution of the signal. A percentile (or a centile) is a measure used in statistics indicating the value below which a given percentage of observations in a group of observations fall.

・ Skewness: measure of lack of symmetry

・ Kurtosis: measure of whether the data are peaked or flat relative to a normal distribution.

・ Power of the signal

・ Mean power frequency

・ Magnitude of peak frequency

As too many statistical features could be extracted and evaluated in time domain analysis and frequency domain, in this section one feature is used to demonstrate the evaluation. However this is done for all features among all sensors for the 47 subjects. Let’s take for instance the skewness as in many studies related to vertical ground reaction force [2] shows its capability in distinguishing between normal and Parkinson’s diseased person.

The results of ROC evaluation among all the 47 subjects are shown in Table 1 for each sensor.

Analyzing Table 1 indicates thatunlike other studies similarto [2] that consider total summation of force signals from all sensors as the most important, however, its clearly shown that sensor 5 is the most important sensor (AUC = 0.9) to consider in building acquisition system to acquire data for analyses. Figure 4 shows the ROC curve of the total ground reaction force from sensors of the right foot. The sensitivity is recorded to be 0.6552 while the specificity is 0.5556. This yield AUC = 0.5460 which refers a fail level of accuracy in classification.

This conclusion is generalized as the same procedure is applied over the rest of the features chosen in this study. If expanding data is needed, then adding sensor 7 and 6 corresponds as main sensors also to be considered for classification as shown in Figure 5.

Not to add, the average of the strides of the 2 classes corresponding to the 120 seconds of walking is also considered. As a result, each series of strides is represented by its average, that is, one stride. Next, 3 features were extracted: the amplitude of the first peak, that is, the peak that corresponds to the heel contact (in case of total force), time to the first peak, stride time. The ROC evaluation also infers a better accuracy for sensor 5 compared to other sensors.

In order to verify results, the features are passed through a chosen classifier, k-nearest neighbors (KNN) in this case. As mentioned, KNN is not used to test its power in classification, but to test the power of sensor 5 in a given classifier. Ten subjects from each class are chosen as training and the rest are tested by the classifier.

In this study, this is done in two ways. First, select one sensor among all subjects and then choose two features randomly and iterate between them. The feature chosen will have a high score from ROC evaluation and then feed them to KNN-classifier one example is shown in Figure 6.

In a second case, fix the feature and iterate a number of sensors among the KNN classifier. The results of KNN classifier indicate an accuracy of around 83% on average in most cases where sensor five exist. Other sensor shows a relatively smaller value. While the total force when used shows an accuracy of around 15% smaller than sensor five.