Recognition of people based on their way of movement is one of the most interesting issues of behavioural biometrics. Among the basic characteristics of each biometric system is accuracy. It is currently considered that greater accuracy of biometric can be achieved by ensemble of two and more classifiers. The aim of this study is to present our own method for the usage of ensemble classifiers in the biometrics of the human gait based on ground reaction forces. In the presented ensemble of k-nearest neighbor classifiers the input signals were formed by dividing the ground reaction forces into sub phases characteristic of the support phase of the gait cycle. The research was carried out based on measurements from 200 people (more than 3500 gait cycles). The correct classification rate for proposed here method is more than 97.37%.

For years, researchers have been recognizing patterns in gait for purposes of medical diagnostics, rehabilitation, and biometrics. A method for observing gait is to measure ground reaction forces (GRFs) between the foot and solid plate with tension sensors. The presented dataset consists of 13,702 measurements of bipedal GRFs of one step of normal gait of 324 students wearing shoes of various types. Each measurement includes raw digital signals of two force plates. A signal comprises stance-related samples but also preceding and following ones, in which one can observe noise, interferences, and artifacts caused by imperfections of devices and walkway. Such real-world time series can be used to study methods for detecting foot-strike and foot-off events, and for coping with artifacts. For user convenience, processed data are also available, which describe only the stance phase of gait and form ready-to-use patterns suitable for experiments in GRF-based recognition of persons and footwear, and for generating synthetic GRF waveforms. The dataset is accompanied by Matlab and Python programs for organizing and validating data.

Biometrics is currently an area that is both very interesting as well as rapidly growing. Among various types of biometrics the human gait recognition seems to be one of the most intriguing. However, one of the greatest problems within this field of biometrics is the change in gait caused by footwear. A change of shoes results in a significant lowering of accuracy in recognition of people. The following work presents a method which uses data gathered by two sensors: force plates and Microsoft Kinect v2 to reduce this problem. Microsoft Kinect is utilized to measure the body height of a person which allows the reduction of the set of recognized people only to those whose height is similar to that which has been measured. The entire process is preceded by identifying the type of footwear which the person is wearing. The research was conducted on data obtained from 99 people (more than 3400 strides) and the proposed method allowed us to reach a Correct Classification Rate (CCR) greater than 88% which, in comparison to earlier methods reaching CCR’s of <80%, is a significant improvement. The work presents advantages as well as limitations of the proposed method.

Gas turbines and their blades in particular might be damaged in the course of the aviation turbojet engine operation process. The degradation process of the blade microstructure is most evident from the change in the colour of its surface. This is assessed using the optoelectronic method. The article presents the concept of non-destructive methods, which are used to assess the degree of degradation of the alloy of a gas turbine blade. The proposed optoelectronic method is the basic method for the preliminary determination of color changes in the surface. Appropriate videoscopes or video-analyzers, capable of recording a test object in various electromagnetic wave ranges (infrared radiation, visible range), are used for this purpose. These preliminary results of the diagnosis make it possible to infer further studies of the blade. For accurate investigation of the state of the alloy microstructure, a non-destructive thermographic method (xenon flash pulsed thermography) was used. The results of studies of the degradation of the microstructure with the use of non-destructive methods were verified through the use of metallographic investigations. Appropriate metallographic specimens were created in order to recognize phase γ′ degradation (i.e., the phase strengthening the blade alloy). The results of the presented research allowed for an in-depth assessment of the degree of microstructure degradation of operated blades.

Human gait recognition is one of the most interesting issues within the subject of behavioral biometrics. The most significant problems connected with the practical application of biometric systems include their accuracy as well as the speed at which they operate, understood both as the time needed to recognize a particular person as well as the time necessary to create and train a biometric system. The present study made use of an ensemble of heterogeneous base classifiers to address these issues. A Heterogeneous ensemble is a group of classification models trained using various algorithms and combined to output an effective recognition A group of parameters identified on the basis of ground reaction forces was accepted as input signals. The proposed solution was tested on a sample of 322 people (5980 gait cycles). Results concerning the accuracy of recognition (meaning the Correct Classification Rate quality at 99.65%), as well as operation time (meaning the time of model construction at <12.5 min and the time needed to recognize a person at <0.1 s), should be considered as very good and exceed in quality other methods so far described in the literature.