The present disclosure relates to systems and methods for elastic delivery, processing, and storage for wearable devices based on system resources. For example, a wearable apparatus may have at least one battery; at least one sensor configured to measure at least one property associated with a user of the wearable apparatus; at least one memory storing measurements from the at least one sensor and instructions; at least one transmitter configured to send data to a device remote from the wearable apparatus; and at least one processor configured to execute the instructions to: receive, from the at least one battery, an indicator of a charge; and based on the received indicator, sending a command to the at least one sensor to operate in a low-power mode or a high-power mode, the low-power mode having. a lower sampling rate than the high-power mode.

A method, an electronic apparatus and a recording medium for automatically configuring a plurality of sensing devices, applicable to an electronic apparatus having at least one sensor and a communication device, is provided. In the method, a first sensing data is detected by using the at least one sensor. A plurality of second sensing data is respectively received from the plurality of sensing devices by using the communication device. The first sensing data and each of the second sensing data are analyzed to obtain a moving pattern of the electronic apparatus and each of the sensing devices. A position on a user's body of each of the sensing devices is configured by comparing the moving patterns with at least one movement model.

An apparatus having a gesture sensor is provided. The gesture sensor includes an image sensing unit and a processing unit. The image sensing unit can capture at least a gesture image of user. The processing unit is electrically connected to the image sensing unit. The processing unit can send at least a control command according to the gesture image to operate the apparatus.

A method for planning parameters of a fitness course is disclosed. The method includes the following steps: generating a plurality of limb motion signals by sensing a plurality of limb motions of a body builder through a sensing module, and sensing a physiological state of the body builder to generate a physiological state signal via the sensing module; obtaining a workout characteristic index (WCI) by performing a first calculation related to the plurality of limb motion signals, and obtaining a physiological effect index (PEI) by performing a second calculation associated with the physiological state signal; obtaining a workout effect index (WEI) by performing a third calculation associated with the WCI and the PEI; and evaluating a plurality of categorical factors associated with the WEI to plan the parameters of the fitness course.

The appropriateness of a motion of a motion actor who makes a motion in response to a motion of at least one moving body other than him/herself is visualized. To display motion information of a motion actor who makes a motion in response to a motion of at least one moving body other than him/herself, an image showing one sequence which is visible or a plurality of sequences which are visible is output. In this case, a relative time which is a position on a relative time axis corresponds to a position on a specific axis of the image. The one sequence or each of the plurality of sequences graphically shows at least a position on the axis which corresponds to a moving body time that is a relative time at which at least one predetermined motion of the moving body was made, and a position on the axis which corresponds to a motion actor time that is a relative time at which at least one predetermined motion of the motion actor was made.

Systems and methods are disclosed for generating a 3D avatar using a biomechanical analysis of observed actions with a focus on representing actions through computer-generated 3D avatars. Physical quantities of biomechanical actions can be measured from the observations, and the system can analyze these values, compare them to target or optimal values, and use the observations and known biomechanical capabilities to generate 3D avatars.

Among other things, a processor executes instructions to: update, in real time during a current instance of an exercise activity by a subject competitor, a graphical user interface displayed on a display device, the display device being included in a first exercise machine operated by the subject competitor or included in a mobile electronic device. The graphical user interface includes: a ranking of the subject competitor and a second competitor based on (i) a projected performance metric of the subject competitor over a predefined scope of the exercise activity compared to (ii) a historical performance metric of the second competitor over the predefined scope of the exercise activity in a previous instance of the exercise activity, an illustration of a margin between the historical performance metric of the second competitor and the projected performance metric for the subject competitor, and an illustration of the projected performance metric.

A system includes wearable devices positioned on a subject in different locations. Each wearable device includes motion sensors that measure the subject's movement in three dimensions. The motion sensors generate raw sensory data as the subject performs a physical movement. A data filter is selected based on a condition of the subject and a designated movement corresponding to the physical movement, and used to convert the raw sensory data into formatted data. A level of compliance of the physical movement with a movement model for the designated movement is determined by applying comparative modeling techniques to the formatted data and the movement model. Real-time feedback is delivered dynamically to the subject by the wearable devices during the performance of the physical movement based on the level of compliance. The movement model can be generated, and the comparative modeling techniques can be selected, based on the condition of the subject.

This disclosure relates to systems, media, and methods for quantifying and monitoring exercise parameters and/or motion parameters, including performing data acquisition, analysis, and providing scientifically valid, clinically relevant, and/or actionable diagnostic feedback. Embodiments may be related to systems, devices, methods, and computer-readable media for providing baseline-adjusted real-time feedback to a user. Embodiments may include determining a type of activity for the user. Embodiment may include receiving data from the one or more motion sensors indicating a time-dependent series of three axis acceleration data and three-axis orientation data. Embodiments additionally may include providing a graphical user interface with a real-time representation of the received data. The real-time representation may include a scaled representation of at least one dimension of the time-dependent series of three axis acceleration data and three-axis orientation data for at least one of the one or more motion sensors based on the updated baseline adjustment.

An interactive exercise system includes a mechanical support system and a display module held by the mechanical support system. At least one force-controlled component is engageable by a user and connected to the mechanical support system, with the force-controlled component configure to reduce force when applied user force is great enough to cause tip-over or unwanted movement of the mechanical support system. The force-controlled component can be graspable by a user and allows for a range of exercise types and programs.