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.

The invention is directed to systems and methods that can determine a user's fitness based on a single factor such as oxygen lung volume (pmVO2). A heart rate measurement is conducted on the user after the user performs a physical test specified by a fitness application of the system. The heart rate measurement is converted into a pmVO2 value and compared to pmVO2 values of other individuals. The system assigns the user a fitness grade based on the comparison result. The system further includes an artificial intelligent system that helps the user to move from a grade to a higher grade.

Methods, systems and devices are provided for motion-activated display of messages on an activity monitoring device. In one embodiment, method for presenting a message on an activity monitoring device is provided, including the following method operations: downloading a plurality of messages to the device; detecting a stationary state of the device; detecting a movement of the device from the stationary state; in response to detecting the movement from the stationary state, selecting one of a plurality of messages, and displaying the selected message on the device.

In one embodiment, a method for generating a message to a friend of a user is provided, comprising: processing activity data of a first user measured by an activity monitoring device to update a value of an activity metric for the first user; identifying a change in an inequality relationship between the value of the activity metric for the first user and a value of the activity metric for a second user; in response to identifying the change in the inequality relationship, prompting the first user to generate a message to the second user.

Physical fitness assessment systems and methods and wellness assessment systems and methods are disclosed. One physical fitness assessment system includes an exercise device and a host computer. The exercise device is configured to track movement of the exercise device by a user, determine a current physical activity data of the user based on, at least, the tracked movement, and transmit the current physical activity data of the user. The host computer is configured to receive from the exercise device the current physical activity data of the user, receive a physical fitness assessment selection to apply to the current physical activity data, compare the current physical activity data against benchmark physical activity data correlated with the exercise device, determine a physical fitness assessment of the user, generate the physical fitness assessment image based on the physical fitness assessment of the user, and present the physical fitness assessment image.

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.

A method includes receiving data pertaining to a user that uses a treatment apparatus to perform a treatment plan. The data includes characteristics of the user, the treatment plan, and a result of the treatment plan. The method includes assigning the user to a cohort representing people having similarities to the characteristics of the user. The method includes receiving second data pertaining to a second user, the second data comprises characteristics of the second user. The method includes determining whether at least some of the characteristics of the second user match with at least some of the characteristics of the user, assigning the second user to the first cohort, and selecting, via a trained machine learning model, the treatment plan for the second user, and controlling, based on the treatment plan, the treatment apparatus while the second user uses the treatment apparatus.

Disclosed is an integrated measurement system for measuring health-related fitness factor and, more particularly, to an integrated measurement system for measuring health-related fitness factor, the integrated system being achieved by integrating separate health-related fitness measurement devices to be able to measure health-related fitness factors through one system, and raising convenience in management and guide by a manager and measurement by a measurer, and objectivity in a result by outputting measurement process and result in real time through a display.

It relates to the field of displaying the heart-rate data collected in an exercise test, in particular to a method for achieving a panoramic display during Electrocardiogram Exercise Test by using a time axis, comprising following steps: (a) building a horizontal axis (X axis) in units of time as a time axis, and building a vertical axis (Y axis) in units of heart rate, therefore forming a two-dimensional tendency chart; (b) collecting and storing real-time 12-lead ECG raw data, acquiring and storing real-time heart rate data, (c) setting a time vernier, wherein in a default status, the time vernier is located at the current moment, and it is connected with 12-lead ECG raw data.

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.