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Classification of normal gait from pathological gait as then can be used as indicator of falling among subjects requires the correct choice of sensor location in the insole. Such a flexi force-sensor can be used underneath foot to measure vertical ground reaction force. To start with, the most relevant information (parameters) that can characterize the recorded signals are extracted from the vertical ground reaction force signals. Then Receiver Operating Characteristic curve is used to evaluate the features upon 8 sensors underneath each foot located at different locations. To confirm results obtained, features are passed upon a chosen classifier, in this paper K-nearest neighbors algorithm is chosen. Results show that the sensor located at the inner arch of the sole of the foot (i.e. at the mid foot) holds the most relevant information needed for better classification compared to other sensors.

Frequency-related parameters derived from the uterine electromyogram (EMG) signals are widely used in many pregnancy monitoring and preterm delivery prediction studies. Although they are classical parameters, they are well suited for quantifying uterine EMG signals and have many advantages over amplitude-related parameters. The present work aims to compare various frequency-related parameters according to their classification performances (pregnancy vs. labor) using the receiver operating characteristic (ROC) curve analysis. The comparison between the parameters indicates that median frequency is the best frequency-related parameter that can be used for distinguishing between pregnancy and labor contractions. We conclude that median frequency can be the representative frequency-related parameter for classification problems of uterine EMG.

In respect to the main goal of our ongoing work for predicting preterm birth, we analyze in this paper the complexity of the uterine electromyography (EMG) by using the sample entropy (SampEn) algorithm. By considering recent methodological developments, we measure the SampEn over multiple scales using the wavelet packet decomposition method. The results obtained from the analyzed data indicate that SampEn decreases along pregnancy. Furthermore, we demonstrate that the computed SampEn parameter may discriminate between the two classes (pregnancy/labor). The results are supported by statistical analysis using t-test indicating good statistical significance with a confidence level of 95%. A surrogate data test is also performed to investigate the nature of the underlying dynamics of our experimental data. The results are very promising for monitoring pregnancy and detecting labor to help identify preterm labor.

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