A method that includes receiving treatment data associated with a user capable of using a treatment device to perform a treatment plan. The method also includes receiving user related data (URD) associated with the use and receiving alignment data associated with the user while the user engages in at least one activity. The method also includes identifying, based on at least the treatment data, the URD, and the alignment data, at least one alignment characteristic associated with the user and modifying at least one aspect of the treatment plan in response to receiving, from a healthcare professional, treatment plan input including at least one modification to the at least one aspect of the treatment plan.

Disclosed are example embodiments of systems and methods for exercise including an exercise machine including a multiple directional force component configured to apply a multiple directional force to an exercise machine-human body interface, the multiple directional force component further configured to provide a divergent intensity to the exercise machine-human body interface to provide the human body with a resistance during a physical exercise and an artificial intelligence (AI) component configured to control the multiple directional force component. In an example, the AI component may control the multiple directional force component based on morphological characteristics and variability of running speed parameters including at least one of stride length, stride frequency, explosive drive force, and arm drive.

An exercise machine using motion sensor to track exercise, comprising: an exercise equipment, including a mainframe, the mainframe is fixed and won't be moved by user's movement, an operating mechanism, the operating mechanism is arranged on the mainframe and it will follow the movement of the user; wherein the operating mechanism having a motion sensor, inside the motion sensor having a multiple-axis tracking sensor module and a first microprocessor for detecting the height displacement of the operating mechanism and transmitting the height displacement signal to an electronic control device, then processing by a second microprocessor arranged inside the electronic control device to know whether the motion track of the user reach the preset position during exercise to ensure the effect of exercising.

An adjustable weight exercise system is provided. A permanent weight assembly and a plurality of interlocking weights are provided. A base comprises an interlocking weight carrier, wherein the interlocking weights are supported on the interlocking weight carrier and an interlocking weight selector, wherein the interlocking weight selector is adapted to cause interlocking weights to be set to be locked to the permanent weight assembly while the permanent weight assembly is not on the base, wherein placement of the permanent weight assembly on the base causes interlocking weights that are set to be locked to the permanent weight assembly to be locked to the permanent weight assembly, and wherein placement of the permanent weight assembly on the base does not cause interlocking weights that are not set to be locked to the permanent weight assembly to be locked to the permanent weight assembly.

Embodiments provide physiological measurement systems, devices and methods for continuous health and fitness monitoring. A wearable strap may detect reflected light from a user's skin, where data corresponding to the reflected light is used to automatically and continually determine a heart rate of the user. The wearable strap may monitor heart rate data including heart rate variability, resting heart rate, and sleep quality. The systems may include a processing module that generates an indicator of physical recovery based on the heart rate data. The recovery indicator may be used to determine strain related to an exercise routine, qualitative information on the user's health, whether to alter a user's exercise plan, and so forth.

Aspects relate to a portable device that may be used to identify a critical intensity and an anaerobic work capacity of an individual. The device may utilize muscle oxygen sensor data, speed data, or power data. The device may utilize data from multiple exercise sessions, or may utilize data from a single exercise session. The device may additionally estimate a critical intensity from a previous race time input from a user.

A method of determining a CRF level for a user of a fitness tracking system includes receiving activity data from at least one activity sensor carried by the user during a number of workouts, the activity data including distance data for each of the number of workouts, and then generating workout data based on the activity data. The method further includes storing the workout data in a memory, the memory further including demographic data for the user. When the an attribute of the workout data is less than a threshold number, the method includes determining a first CRF level for the user based on a first CRF model. When the attribute of the workout data is greater than the threshold number, the method includes determining a second CRF level for the user based on a second CRF model, and displaying the first and second CRF levels.

One embodiment provides a method, including: identifying, using an information handling device, an activity engaged in by a user; determining, using a processor, whether a biometric of the user during the activity is affected by a biometric-affecting aspect; and adjusting, responsive to determining that the biometric is affected by the biometric-affecting aspect, an estimated measurement for the biometric. Other aspects are described and claimed.

Embodiments are disclosed for an electrical bicycle detector for energy expenditure estimation. In an embodiment, a method comprises: determining heart rate energy expenditure of a user wearing or holding the device; determining work rate energy expenditure of a user wearing or holding the device; determining a probability that the user is riding an electrical bike based on the heart rate energy expenditure and the work rate energy expenditure; determining whether or not the probability meets a condition corresponding to a threshold probability; and in accordance with the probability meeting the condition corresponding to the threshold probability: adjusting the work rate energy expenditure; and generating fitness data based at least on the adjusted work rate energy expenditure.