The objective of the test procedure is to obtain bio signals from Photoplethysmograph and Electrocardiograph sensors on selected consumer devices and to statistically validate the data for use with a drowsiness estimation method.The method selected for validation uses LF/HF ratio calculated by a set of R-R interval data to estimate drowsiness state of a human. The value LF to HF ratio calculates balance between sympathetic and parasympathetic activity that can be measured from HRV (Heart rate variability) signals. The statistical data collected are processed by using Fast Fourier Transform and HRV frequency domain analysis on a set of test participants.There is a correlation between medical ECG equipment control output and Matlab tool’s HRVAS (Burg) output of data processed from ECG based wearable smart sensor when the LF/HF ratio is calculated in all observed volunteer data. The results for Photoplethysmograph sensors of this test correlate with other tested tools but level of the values is lower, and data from optical biosensor devices which are designed to measure HRV time-domain properties as pulse did not confirm with ECG equipment results for frequency-domain analysis required for use with selected drowsiness estimation method. The result affecting factors are sensor placement, motion artefacts and discrete vendor-specific signal pre-processing of wearable device output data.The following results confirm the use of consumer grade biosensor that produces discretely pre-processed R-R interval data for the frequency based HRV method and application validation against directly processed ECG data from certified medical equipment.

Human fatigue manifests in slower reactions, reduced ability to process information, memory lapses, absent-mindedness, decreased awareness, lack of attention, underestimation of risk, reduced coordination etc. Chronic, decompensated and acute fatigue in form of drowsiness and falling asleep can lead to errors and accidents, ill-health and injury, and reduced productivity of sectors as in equipment operations, transportation. Their detection is necessary where they provide an option for the quantification and objective evaluation of subjective fatigue levels. Many studies are dealing with this topic for automotive and workability usage to design a fatigue detection and countermeasure device. The paper describes research of recent attitude to the development of the fatigue condition detection methods with the usage of human biological signal combinations, like electroencephalography (EEG), photoplethysmography (PPG), electromyography (EMG), galvanic skin response (GSR), temperature, position, respiration and percentage of eye closure (PERCLOS) to obtain diagnostic parameters reflecting the state of central nervous, cardiovascular, respiratory and muscular system and for monitoring of physiological vital changes. The current research focuses on aspects of non-obtrusive sensor signal quality and placement, and selection factors and evaluation of usability and potential integration into a wearable platform with the use of current sensor technologies that extend the application of sensors from laboratory to everyday environment. The sensor review aims to support development of a platform with multilevel fatigue monitoring and workability evaluation system designed in order to provide an integrated service in the area of operational safety.

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Abstract-This article covers the process of software testing. Test management and creation methods are described within the scope of the research. The process of test selection through several stages of project development is discussed and practical examples of appliance are given for the test organization and decision making with the help of topological models of software. The criteria of test ranging are described within scope of each of the testing levels. The paper indicates the use of topological structural models in software test creation, and planning.

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