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: An apparatus and method for determining injury risk from plurality of exercises using a device with a heart rate sensor, a processor, memory including a resident memory, an output device and software. For example the method may: determine and store values of Training load of each exercise in the resident memory obtaining a register, perform a HRV-based Recovery Test and storing Recovery values in the register, calculate indices depicting Short Term Training Load, ratio of Short Term Training Load to Long Term Training Load, and Recovery, calculate weighting factors for each said index, correct each index with corresponding weighting factor to obtain weighted indices, calculate a value of the Injury Risk using the weighted indices, and display the value of Injury Risk.

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: 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, 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: An apparatus and method for determining injury risk from plurality of exercises using a device with a heart rate sensor, a processor, memory including a resident memory, an output device and software. For example the method may: determine and store values of Training load of each exercise in the resident memory obtaining a register, perform a HRV-based Recovery Test and storing Recovery values in the register, calculate indices depicting Short Term Training Load, ratio of Short Term Training Load to Long Term Training Load, and Recovery, calculate weighting factors for each said index, correct each index with corresponding weighting factor to obtain weighted indices, calculate a value of the Injury Risk using the weighted indices, and display the value of Injury Risk.

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.