Disclosed herein is a dynamic load sensor system for use in microgravity environments. The dynamic load sensor system may include a first sensor pad unit, a second sensor pad unit, processing unit, and an operator interface module. Each of the first sensor pad unit and the second sensor pad unit may include a sensor base plate, a top plate, and a plurality of load cells positioned therebetween. The processing unit may include a processor operatively coupled with an internal memory device, while the operator interface module includes a display device and a plurality of operator input devices.

A sensor module includes multiple accelerometers mounted in a non-collinear arrangement onto a printed circuit board. A processor obtains linear acceleration vectors from each of the accelerometers and through the constraint imposed by the the non-collinear arrangement of the accelerometers onto the printed circuit board, the angular velocity vector of the sensor may be derived by the processor from the linear acceleration vectors.

A method of determining when a subject is experiencing anaerobic exercise during an exercise session, for determining effectiveness of a training period, and/or for determining a level of lactic acid produced by a subject during an exercise session may involve the administration of a bicarbonate comprising a carbon-13 isotope. Expired air may be monitored, and a ratio of carbon dioxide comprised of the carbon-13 isotope and carbon dioxide comprised of a carbon-12 isotope may be determined at one or more points in time. Charts of the ratios may be used to indicate onset of anaerobic exercise or effectiveness of a training period, and/or the determined ratio(s) may be used to indicate lactic acid production.

The disclosed invention relates generally to various embodiments of garments for use with electrocardiographs. In particular, the invention relates to a garment with a lead placement layer or precordial patch with incorporated ECG leads and a support layer configured to support the breasts of a user while a patient undergoes an exercise stress electrocardiograph. Various portions of the garment are easily and quickly removable.

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.

In one aspect, the present disclosure relates to a method including obtaining, by a heart rate sensor of a fitness tracking device, a heart rate measurement of a user of the fitness tracking device; obtaining, by at least one motion sensor, motion data of the user; analyzing, by the fitness tracking device, the motion data of the user to estimate a step rate of the user; estimating, by the fitness tracking device, a load associated with a physical activity of the user by comparing the heart rate measurement with the step rate of the user; and estimating, by the fitness tracking device, an energy expenditure rate of the user using the load and at least one of the heart rate measurement and the step rate.

Described herein is a method of operating an electronic device that includes collecting initial motion activity data from at least one sensor of the electronic device, and generating a initial probabilistic context of the electronic device relative to its surroundings from the initial collected motion activity data using a motion activity classifier function. The collected motion activity data is stored in a training data set, and the motion activity classifier function is updated using the training data set. The method also includes collecting subsequent motion activity data from the at least one sensor of the electronic device, and generating a subsequent probabilistic context of the electronic device relative to its surroundings from the subsequently collected motion activity data using the updated motion activity classifier function.

A method for controlling the robot of a training system according to any of the previous claims, wherein a biomechanical and/or cardiovascular stress of the user, particularly based on a measured impingement of the actuation surface, is determined and the robot is controlled using a predetermined and the measured biomechanical and/or cardiovascular stress of the user. A computer program product with a program code, which is saved on a medium readable by the computer, for implementing a method according to the previous claim.

A personal wellness system may facilitate merchandizing and/or socializing via personal wellness devices. The personal wellness devices may be portable, handheld devices configured to facilitate personal exercise as well as socializing and/or merchandizing using the devices. The personal wellness devices may be configured to facilitate electronic social networking interactions and/or gameplay with other users. This may enhance a user's personal wellness experience with a personal wellness device. The personal wellness devices may provide a portal to a virtual marketplace, where online services, virtual goods, third-party software, games, media, guru content, and/or other information may be obtained. Third-party content may be developed for the personal wellness devices, which may be obtained via the virtual marketplace. The personal wellness devices may be configured to present multimedia, present map information, and/or browse the Internet.

An apparatus for estimating an anaerobic threshold may include a heart rate detector configured to detect a heart rate from a signal sensed from a user, and an anaerobic threshold estimator configured to estimate an anaerobic threshold of the user based on a change in the detected heart rate.