Abstract: A procedure for the detection of stress state is disclosed, wherein ambulatory heart beat signal is measured. In the first phase segments are defined from heart beat signal with a chosen rule for segmentation. Then at least one segment describing a physiological state with elevated cardiac activity due to physical workload and/or increased metabolic rate is identified and excluded, if exists, and segments other than the excluded segments are detected for a potential stress state, which is identified using a predetermined rule for the heart beat signal.

Abstract: The invention relates to a method for determining accumulated body fatigue index (BFI), wherein one or more parameters from the measurement of one or more signals are obtained sequentially as input and these parameters are information on the intensity of physical activity. BFI has a predetermined initial value, and next BFI value is always a sum of BFI-value and a difference. The difference is combination of upslope and optional downslope components of BFI determined with the said parameters and the upslope component and the optional downslope component are each determined with a function, which is scaled by a preset physiological character.

Abstract: The invention relates a procedure for the detection of stress state, wherein ambulatory heart beat signal is measured. In the first phase segments are defined from heart beat signal with a chosen rule for segmentation. Then at least one segment describing a physiological state with elevated cardiac activity due to physical workload and/or increased metabolic rate is identified and excluded, if exists, and segments other than the excluded segments are detected for a potential stress state, which is identified using a predetermined rule for the heart beat signal.

Abstract: The present invention describes a method of tracking minute ventilation from R-R intervals that are based on the acquisition of ECG signal. Sub-methods (RDF1), which are mainly based on heart rate variability, are used, wherein the respiratory frequency is determined from a pattern of rhythmic changes in heart beat data. The sub-methods (RDF1-n) are combined using an expert function, wherein each sub-method (RDF1-n) determines an estimate of the respiratory frequency.