Abstract: A method, device and a computer program product for determining a training load distribution of a user is provided. Heart rate is continuously measured by a heart rate sensor. An aerobic training effect and load are calculated using intensity values and divided into categories of Aerobic Low and Aerobic High. An anaerobic training effect and load are calculated using determined characteristics related to high intensity periods of an exercise.

Abstract: A method and a system for determining the maximum heart rate, called HRmax of a user of in a freely performed physical exercise and using an apparatus with software and memory. An intensity model takes account of, in addition to heart rate, respiration rate and/or kinetics information.

Abstract: A method, device and a computer program product for determining a training load distribution of a user is provided. Heart rate is continuously measured by a heart rate sensor. An aerobic training effect and load are calculated using intensity values and divided into categories of Aerobic Low and Aerobic High. An anaerobic training effect and load are calculated using determined characteristics related to high intensity periods of an exercise.

Abstract: The invention relates to a method and a system for determining body's readiness to respond to physical exercise and for providing feedback to a user. In the method the user starts to perform an exercise, an earlier performance level is determined before starting a performance check having steps of: an instant performance level of the user is determined, the earlier performance level is compared to the instant performance level, a readiness index is determined according to the said comparison and optionally with background and/or training history of the user, a feedback is given according to the determined readiness index.

Abstract: A method and device for detecting a sleep state by heart-rate and movement data. By neural network software, a sleep state is detected using as input data movement data (move_count) and data derived from heart-rate data and/or data derived from inter-beat interval data such as (MAD) and respiration data (RESP). At least a portion of the variables (HRD, MHR, MAD, Resp, GRD) derived from the heart-rate data/inter-beat interval data are further modified by one, most preferably 2-4, artificial average functions and the cumulative sleep time is one input datum.

Abstract: A method and apparatus for improving recognition of anaerobic sections of exercise and to providing feedback on training load or training effect reflecting energy systems used and trained during exercise. The method determines an ‘oxygen debt’ like cumulative physiological sum (usually training effect TE as EPOC value) brought on by a change in a body homeostasis and its aerobic and anaerobic values. Particularly anaerobic value may be determined by a procedure, where a total EPOC (or TRIMP) is determined as a total sum and an aerobic part calculated in a known manner, is deducted from the total sum.

Abstract: A method and a system for determining the maximum heart rate, called HRmax of a user of in a freely performed physical exercise and using an apparatus with software and memory. An intensity model takes account of, in addition to heart rate, respiration rate and/or kinetics information.

Abstract: The invention relates to a method and system for determining the cardiorespiratory fitness level of a person, with the aid of freely performed exercise, in which training: the physiological intensity of the person is measured periodically during the exercise session, the external work output of the exercise session is measured simultaneously relative to the measured intensity, from each period's measured intensity value and external work output, the representativeness of the values measured is determined, in order to determine the fitness level using the following criteria: the physiological intensity should be stabilized relative to the external work output, the external work output should be within a selected range, the physiological intensity should be greater than a selected lower limit, in which case, a fitness level estimate is defined for each accepted period as well as a fitness from several estimates.

Abstract: A method and device for detecting a sleep state by heart-rate and movement data. By neural network software, a sleep state is detected using as input data movement data (move_count) and data derived from heart-rate data and/or data derived from inter-beat interval data such as (MAD) and respiration data (RESP). At least a portion of the variables (HRD, MHR, MAD, Resp, GRD) derived from the heart-rate data/inter-beat interval data are further modified by one, most preferably 2-4, artificial average functions and the cumulative sleep time is one input datum.

Abstract: A method and device for detecting a sleep state by heart-rate and movement data. By neural network software, a sleep state is detected using as input data movement data (move_count) and data derived from heart-rate data and/or data derived from inter-beat interval data such as (MAD) and respiration data (RESP). At least a portion of the variables (HRD, MHR, MAD, Resp, GRD) derived from the heart-rate data/inter-beat interval data are further modified by one, most preferably 2-4, artificial average functions and the cumulative sleep time is one input datum.

Abstract: A method and a system for determining the maximum heart rate of a user in a freely performed physical exercise and using an apparatus with software and memory. An intensity model takes account of, in addition to heart rate, respiration rate and/or kinetics-information. The kinetics information depicts change in excess post-exercise oxygen consumption (EPOC), more generally the direction of cumulative physiological disturbance in homeostasis, whether it is at steady state, on-response (rising) or off-response (descending) to decrease the value of the determined maximal heart rate to obtain a result.

Abstract: A method and system for determining anaerobic threshold intensity (AnT) of a user in a freely performed physical exercise. A physiological response of a user is measured by heart rate and measured heart rate values are recorded as heart rate data. An external workload values are recorded and are each associated with one measured heart rate values to form a plurality of data points. The data points are filtered to form accepted data points, which are classified within a plurality of heart rate segments representing a heart rate within an anaerobic threshold (AnT) of the user. A data point with highest probability is stored for each segment. A first probability factor for each accepted data point is calculated. The calculated first probability factor is compared to a stored probability factor in each segment, and the higher probability factor is retained. AnT is calculated using the stored probabilities in each segment.

Abstract: A system of estimating acclimatization to environmental conditions, and providing a corrected fitness level estimates based on said acclimatization level. The environmental conditions are particularly related to heat and altitude. At first previous training history is provided including a plurality of day records, where each day record includes a date stamp and information regarding altitude and optionally temperature and humidity and training history including timestamp and training load data from executed exercises in stored records.

Abstract: A method, device and a computer program product for determining a training load distribution of a user is provided. Heart rate is continuously measured by a heart rate sensor. An aerobic training effect and load are calculated using intensity values and divided into categories of Aerobic Low and Aerobic High. An anaerobic training effect and load are calculated using determined characteristics related to high intensity periods of an exercise.

Abstract: A method and apparatus for determining an individualized need for sleep and real-time indication of sleep pressure from physiological data, including at least heart rate data. The method may first involve collecting certain demographic information about the user, such as age data. A sleep need value may then be calculated for the user, based on their expected need for sleep, based on factors such as high chronic stress, acute and chronic training load (for the individual and as an absolute matter), and any other factors such as altitude or alcohol consumption. A sleep pressure value may then be calculated throughout the day, based on the user's activity level and their sleep need (including, for example, sleep debt information), which may then be used to provide feedback to the user as to when they should go to bed.

Abstract: A method and a system for determining the maximum heart rate, called HRmax of a user of in a freely performed physical exercise and using an apparatus with software and memory means.

Abstract: The invention relates to a method and a system for determining body's readiness to respond to physical exercise and for providing feedback to a user. In the method the user starts to perform an exercise, an earlier performance level is determined before starting a performance check having steps of: an instant performance level of the user is determined, the earlier performance level is compared to the instant performance level, a readiness index is determined according to the said comparison and optionally with background and/or training history of the user, a feedback is given according to the determined readiness index.

Abstract: The invention relates to a method and a system for determining body's readiness to respond to physical exercise and for providing feedback to a user. In the method the user starts to perform an exercise, an earlier performance level is determined before starting a performance check having steps of: an instant performance level of the user is determined, the earlier performance level is compared to the instant performance level, a readiness index is determined according to the said comparison and optionally with background and/or training history of the user, a feedback is given according to the determined readiness index.

Abstract: The invention relates to a method and system for determining the cardiorespiratory fitness level of a person, with the aid of freely performed exercise, in which training: the physiological intensity of the person is measured periodically during the exercise session, the external work output of the exercise session is measured simultaneously relative to the measured intensity, from each period's measured intensity value and external work output, the representativeness of the values measured is determined, in order to determine the fitness level using the following criteria: the physiological intensity should be stabilized relative to the external work output, the external work output should be within a selected range, the physiological intensity should be greater than a selected lower limit, in which case, a fitness level estimate is defined for each accepted period as well as a fitness from several estimates.

Abstract: The invention relates to a method and system for determining the cardiorespiratory fitness level of a person, with the aid of freely performed exercise in which training: •the physiological intensity of the person is measured periodically during the exercise session (5) •the external work output of the exercise session is measured simultaneously relative to the measured intensity (6), •from each period's measured intensity value and external work output, the representativeness of the values measured is determined (8.1, 8.2), in order to determine the fitness level using the following criteria: ?the physiological intensity should be stabilized relative to the external work output, ?the external work output should be within a selected range, ?the physiological intensity should be greater than a selected lower limit, in which case, a fitness level estimate is defined (8.3) for each accepted period as well as a fitness from several estimates.