A device includes a first arm, a second arm, and a lateral arm. The first arm may include a first length, a first socket disposed at a distal end, and a length fit indicator disposed at a proximal end. The second arm may include a second length, a first indication ball disposed at a distal end, and a first insertion point able to couple with the first socket. In addition, the second arm may include a second socket. The lateral arm may comprise a third length, a second indication ball, and a second insertion point to couple with the second socket.

A method and system for activity classification. A pressure sensor receives input data resulting from physical activity of a subject performing an activity. The input data includes pressure data from at least one pressure sensor, and may include other data acquired through other types of sensors. A deep learning neural network is applied to the input data for identifying the activity. The neural network is trained with reference to training data from a training database. The training data may include empirical data from a database of previous data of corresponding activities, synthesized data prepared from the empirical data or simulated data. The training data may include data from physical activity of the subject being monitored by the system. Different aspects of the neural network may be trained with reference to the training data, and some aspects may be locked or opened depending on the application and the circumstances.

A child's shoe sizing kit, system, and method are disclosed. The kit includes a variety of shoe sizers, or cut out templates of a foot sized in a variety of sizes. The kit includes whole and half sizes shoe sizers for: Infant, Baby Walker, Toddler, Little Kid, and Big Kid. The kit would have the size on each sizer also marked with Left and Right. Each kit of sizers would come in a small clear carrying bag with handle for easy transport to stores. The child steps down on the sizers to determine which size the child's foot is. Select the sizer which fits the size and shape of the child's foot. Take the correct sizer that matches the child's foot and place it inside of a shoe. Place your hand in the shoe to feel where the sizer ends compared to where the shoe ends.

A bicycle cleat-fitting tool system may include a body with a superior face and an inferior face. The body defines a cleat receptacle for receiving a cleat to be fitted, with the cleat receptacle perpendicular to a medial line of the body. The superior face of the body includes a cleat retaining mechanism to selectively hold a cleat to be fitted in place within the cleat receptacle. The body also includes angle measurement indicia to indicate a degree of rotation of the body and the cleat receptacle with respect to a bicycle shoe to be fitted with the cleat. Methods for determining an accurate toe-out angle and angle of adjustment for the cleat are also described.

A footbed for use in footwear is provided. The footbed assembly includes a resilient, flexible material (e.g. EVA or PU foam) that extends the entire length of the footbed. a heel plate made from a material that is more rigid (plastic, carbon fiber) than the resilient foam attaches to the flexible material. The shape of the resilient footbed defines a geometry that provides support and comfort to the user by reducing peak pressures, improving cushioning, and enhancing foot support. The resilient foam and heel plate defines the shape of the midfoot R2 and rearfoot R1 of the footbed while the resilient flexible foam continues forward to define the shape of the forefoot R3 region of the footbed.

A method and system for activity classification. A pressure sensor receives input data resulting from physical activity of a subject performing an activity. The input data includes pressure data from at least one pressure sensor, and may include other data acquired through other types of sensors. A deep learning neural network is applied to the input data for identifying the activity. The neural network is trained with reference to training data from a training database. The training data may include empirical data from a database of previous data of corresponding activities, synthesized data prepared from the empirical data or simulated data. The training data may include data from physical activity of the subject being monitored by the system. Different aspects of the neural network may be trained with reference to the training data, and some aspects may be locked or opened depending on the application and the circumstances.

A measurement device that allows all users to conveniently and discreetly determine appropriate general footwear measurements at any age is disclosed. The device may include a housing having a contact edge configured to receive the heel or a first side edge of the user's foot, a retractable tape configured to extend outwardly from the housing, and a toe flange proximate the distal end of the retractable tape that is configured to receive the toe or a second side edge of the user's foot. The retractable tape may have indicia of barleycorn units of measurement that can easily be used to determine footwear sizes (length and width) in English speaking countries.

In a markerless foot size estimation device 100, a shape data input unit 10 acquires three-dimensional shape data of a foot of a subject and stores the three-dimensional shape data in a shape data storage unit 20. A characteristic extraction unit 30 extracts foot shape characteristics from the three-dimensional shape data of a subject's foot stored in the shape data storage unit 20, and stores the foot shape characteristics in a shape characteristic storage unit 40. A machine learning unit 50 uses, as teacher data, foot shape characteristics and arch height stored in the shape characteristic storage unit 40 to create, through machine learning, an estimation model used to estimate arch height based on foot shape characteristics, and stores the estimation model in an estimation model storage unit 60. An estimation output unit 70 uses the estimation model stored in the estimation model storage unit 60 to estimate arch height based on extracted shape characteristics and outputs the arch height.

A measurement device that allows all users to conveniently and discreetly determine appropriate general footwear measurements at any age is disclosed. The device may include a housing having a contact edge configured to receive the heel or a first side edge of the user's foot, a retractable tape configured to extend outwardly from the housing, and a toe flange proximate the distal end of the retractable tape that is configured to receive the toe or a second side edge of the user's foot. The retractable tape may have indicia of barleycorn units of measurement that can be easily be used to determine footwear sizes (length and width) in English speaking countries.

A rendering constraint is detected for a maximum time between capture of each a plurality of frames of a captured video stream of a consumer and a rendering of each corresponding frame of a consumer representation comprising a rendered three dimensional model rendered to depict a consumer appearance and a movement wearing a particular merchandise item. Processor elements accessible to the at least one computing system are selected that are able to generate the rendered three dimensional model within the time period specified in the rendering constraint. At least one processing task is directed to the selected processor elements to build the consumer representation comprising the three dimensional model and generate a rendering of the three dimensional model wearing the particular merchandise item as the captured video stream is received in real time at the at least one computer system.