Measuring muscle load in athletic activities, and associated systems and methods are described herein. In an embodiment, a method for monitoring muscle load of an athlete includes: determining a muscle effort (ME) of the athlete by a wearable electromyography (EMG) sensor, and determining at least one inertial measurement unit (IMU) output of the athlete. The method further includes comparing the ME and the IMU output of the athlete, and, based on comparing, determining a performance of the athlete.

The present disclosure relates to an information processing apparatus, an information processing method, and a program capable of providing more effective learning content in learning motion of a body.

An adjustment unit generates an adjusted second virtual object by adjusting, on the basis of feature point information of a first person included in a first virtual object reflecting a body motion of the first person, a second virtual object reflecting a body motion of a second person to be superimposed on the first virtual object. The technology according to the present disclosure can be applied to, for example, an information processing apparatus such as a smartphone.

A multi-mode athleticism movement measurement system includes an athlete-borne acceleration sensor and an athleticism processing device to determine athleticism information based upon one or more timing measurements from the athlete-borne acceleration sensor, the athleticism information corresponding to any of multiple athleticism measurement modes available on athleticism processing device and selectable by a user. A data link between the athlete-borne acceleration sensor and the athleticism rating processing device transmits the one or more timing measurements from the athlete-borne acceleration sensor to the athleticism rating processing device.

A sports injury sensing system and method are provided. The sports injury sensing system includes a sports injury sensing apparatus. The apparatus receives a plurality of inertial sensing data of a user, and each of the inertial sensing data corresponds to a body part of the user. The apparatus determines an exercising body part of the user based on the inertial sensing data. The apparatus receives a thermal image sensing datum corresponding to the exercising body part, wherein the thermal image sensing datum indicates a body temperature state of the user. The apparatus analyzes a temperature change of surrounding muscles of the exercising body part based on the thermal image sensing datum. The apparatus calculates a sports injury assessment based on the inertial sensing data and the temperature change of surrounding muscles.

Provided herein in some embodiments is a resistance-training system including an upper-body garment having a first section of a single-layered construction and a second section of a multi-layered construction. The single-layered construction can include a first fabric layer. The multi-layered construction can include the first fabric layer, a second fabric layer, and a resistance-providing layer in-between the first fabric layer and the second fabric layer. The resistance-training system also includes a lower-body garment having the multi-layered construction, optionally with a different resistance-providing layer than the upper-body garment. The resistance-providing layer of the upper-body garment and the lower-body garment can be configured to provide resistance to one or more user movements for a user donning the resistance-training system.

Techniques for music selection based on exercise detection are disclosed. In one aspect, a method of operating a wearable device may involve determining, based on output of one or more biometric sensors, that a user of the wearable device has started an exercise and playing music for the user of the wearable device in response to determining the start of the exercise. For example, playing the music may involve turning on a music player based on the start of the exercise. In another example, the wearable device includes the music player.

A method and system of predicting a muscle characteristic using ultrasound. The characteristic may include a tenderness characteristic and/or a strength characteristic. An analysis of muscle structure is performed for a sample using ultrasound data of the sample. The analysis may include determining a relative number of bundles, fascicles, sarcomeres, fibers, and/or sheath thickness from the ultrasound data. Thereafter, the muscle characteristic is predicted for the sample based on the analysis.

Assessment of cognitive function affected by brain injuries is achieved. This included, but is not limited to, assessment of cognitive function affected by brain injuries, for example injuries sustained during sporting activities. A virtual reality system is used to apply controlled cognitive loading to the subject, via a series of distinct test types which in combination apply an increasing cognitive load over time. Results are optionally assessed in conjunction with data from an instrumented mouthguard and/or Finite Element Analysis (FEA) model.

An apparatus and method to evaluate the endurance of a muscle group of a user by measuring velocities of an engagement assembly coupled to a resistance element and moved by the user through a plurality of exercise strokes. A muscle group is assessed by determining a rate of fatigue for the muscle group. The method can include adjusting the controllable resistance to a resistance level, monitoring the movement of the engagement assembly against the resistance level over a plurality of repetitions, determining a relationship between the movement of the engagement assembly by the user over the plurality of repetitions, and using the relationship to determine the rate of fatigue.

Methods, systems, and devices for heart rate detection are described. A method for measuring heart rate for a user may include receiving physiological data associated with the user, wherein the physiological data may include photoplethysmogram (PPG) data and motion data collected throughout a first time interval via a wearable device associated with the user. The method may include determining a set of candidate heart rate measurements within the first time interval based at least in part on the PPG data, selecting a first heart rate measurement from the set of candidate heart rate measurements based on the received motion data, and determining a first heart rate for the user within the first time interval based on the selected first heart rate measurement.