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 system for determining training status of a user from exercises using a device with a heart rate sensor, a processor, memory, an output device and software. The training status is selected from a fixed group of alternatives. Each exercise is monitored using the heart rate sensor. Chosen exercise characteristics of each executed exercise are determined using obtained heart rate data and the determined characteristics of each executed exercise are stored in a memory. The chosen exercise characteristics include values of at least following variables: a date of the exercise, a value depicting physical readiness level for exercise during the exercise, a value depicting a training load of the exercise. When the exercises have been executed, values of selection variables are calculated using the stored exercise characteristics in the memory.

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 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 system for determining training status of a user from exercises using a device with a heart rate sensor, a processor, memory, an output device and software. The training status is selected from a fixed group of alternatives. Each exercise is monitored using the heart rate sensor. Chosen exercise characteristics of each executed exercise are determined using obtained heart rate data and the determined characteristics of each executed exercise are stored in a memory. The chosen exercise characteristics include values of at least following variables: a date of the exercise, a value depicting physical readiness level for exercise during the exercise, a value depicting a training load of the exercise. When the exercises have been executed, values of selection variables are calculated using the stored exercise characteristics in the memory.

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 system for determining training status of a user from exercises using a device with a heart rate sensor, a processor, memory, an output device and software. The training status is selected from a fixed group of alternatives. Each exercise is monitored using the heart rate sensor. Chosen exercise characteristics of each executed exercise are determined using obtained heart rate data and the determined characteristics of each executed exercise are stored in a memory. The chosen exercise characteristics include values of at least following variables: a date of the exercise, a value depicting physical readiness level for exercise during the exercise, a value depicting a training load of the exercise. When the exercises have been executed, values of selection variables are calculated using the stored exercise characteristics in the memory.

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: A method and apparatus for determining training status from a group of alternatives from a plurality of exercises, where a user has frequently monitored exercises with at least heart rate being measured by a host process, which outputs selected variables for calculating the training status by a child process.

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 system for determining training status of a user from exercises using a device with a heart rate sensor, a processor, memory, an output device and software. The training status is selected from a fixed group of alternatives. Each exercise is monitored using the heart rate sensor. Chosen exercise characteristics of each executed exercise are determined using obtained heart rate data and the determined characteristics of each executed exercise are stored in a memory. The chosen exercise characteristics include values of at least following variables: a date of the exercise, a value depicting physical readiness level for exercise during the exercise, a value depicting a training load of the exercise. When the exercises have been executed, values of selection variables are calculated using the stored exercise characteristics in the memory.

Abstract: A method and apparatus for determining training status from a group of alternatives from a plurality of exercises, where a user has frequently monitored exercises with at least heart rate being measured by a host process, which outputs selected variables for calculating the training status by a child process.

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.