Abstract: System and method for coaching based on workout history and/or readiness/recovery information. Improved solutions enable intelligent management of a user's personal fitness journey based on workout recommendations that closely align with the user's traits. In one exemplary embodiment, workout data for a population of different individuals is analyzed to identify groups of similarly performing individuals. Each group of individuals is analyzed to generate an expected profile that approximates the physiological and/or psychological traits of the group. An expected profile includes heuristics and/or performance metrics that enable dynamic coaching during workouts. Subsequently thereafter, users can be dynamically coached by their client device, based on the expected profile.

Abstract: A method for enabling dynamic coaching feedback is disclosed. The method comprises generating a plurality of expected profiles for a plurality of data records within a user history database by grouping users into the plurality of expected profiles, generating heuristics and performance metrics for each of the plurality of expected profiles, and associating a first user with a first expected profile of the plurality of expected profiles. The method further includes receiving user input relating to nutrition consumption for the first user and, in response to receiving the user input relating to nutrition consumption, recommending an activity for the first user based on the first expected profile. The first expected profile includes at least one heuristic for generating dynamic feedback with the activity, and the at least one heuristic for generating the dynamic feedback comprises a rule for modifying the activity based on the nutrition consumption for the first user.

Abstract: System and method for coaching based on workout history. Improved solutions enable intelligent management of a user's personal fitness journey based on workout recommendations that closely align with the user's traits. In one exemplary embodiment, workout data for a population of different individuals is analyzed to identify groups of similarly performing individuals. Each group of individuals is analyzed to generate an expected profile that approximates the physiological and/or psychological traits of the group. An expected profile includes heuristics and/or performance metrics that enable dynamic coaching during workouts. Subsequently thereafter, user's can be dynamically coached by their client device, based on the expected profile.

Abstract: A fitness tracking system and methods collecting fitness data for a user during a workout session are disclosed. The fitness tracking system utilizes a versatile data structure for robustly representing complex workout session templates and recording fitness data associated with individual workout sessions. The versatile data structure enables detailed fitness data to be recorded in association with complex workout session templates in a manner that enables detailed analysis and a less cumbersome user experience.

Abstract: System and method for coaching based on workout history and/or readiness/recovery information. Improved solutions enable intelligent management of a user's personal fitness journey based on workout recommendations that closely align with the user's traits. In one exemplary embodiment, workout data for a population of different individuals is analyzed to identify groups of similarly performing individuals. Each group of individuals is analyzed to generate an expected profile that approximates the physiological and/or psychological traits of the group. An expected profile includes heuristics and/or performance metrics that enable dynamic coaching during workouts. Subsequently thereafter, users can be dynamically coached by their client device, based on the expected profile.

Abstract: System and method for coaching based on workout history. Improved solutions enable intelligent management of a user's personal fitness journey based on workout recommendations that closely align with the user's traits. In one exemplary embodiment, workout data for a population of different individuals is analyzed to identify groups of similarly performing individuals. Each group of individuals is analyzed to generate an expected profile that approximates the physiological and/or psychological traits of the group. An expected profile includes heuristics and/or performance metrics that enable dynamic coaching during workouts. Subsequently thereafter, user's can be dynamically coached by their client device, based on the expected profile.

Abstract: System and method for coaching based on workout history and/or readiness/recovery information. Improved solutions enable intelligent management of a user's personal fitness journey based on workout recommendations that closely align with the user's traits. In one exemplary embodiment, workout data for a population of different individuals is analyzed to identify groups of similarly performing individuals. Each group of individuals is analyzed to generate an expected profile that approximates the physiological and/or psychological traits of the group. An expected profile includes heuristics and/or performance metrics that enable dynamic coaching during workouts. Subsequently thereafter, users can be dynamically coached by their client device, based on the expected profile.

Abstract: System and method for coaching based on workout history and/or readiness/recovery information. Improved solutions enable intelligent management of a user's personal fitness journey based on workout recommendations that closely align with the user's traits. In one exemplary embodiment, workout data for a population of different individuals is analyzed to identify groups of similarly performing individuals. Each group of individuals is analyzed to generate an expected profile that approximates the physiological and/or psychological traits of the group. An expected profile includes heuristics and/or performance metrics that enable dynamic coaching during workouts. Subsequently thereafter, users can be dynamically coached by their client device, based on the expected profile.

Abstract: A fitness tracking system and methods collecting fitness data for a user during a workout session are disclosed. The fitness tracking system utilizes a versatile data structure for robustly representing complex workout session templates and recording fitness data associated with individual workout sessions. The versatile data structure enables detailed fitness data to be recorded in association with complex workout session templates in a manner that enables detailed analysis and a less cumbersome user experience.

Abstract: Dense, massively parallel signal processing electronics are co-packaged behind associated sensor pixels. Microchips containing a linear or bilinear arrangement of photo-sensors, together with associated complex electronics, are integrated into a simple 3-D structure (a “mirror cube”). An array of photo-sensitive cells are disposed on a stacked CMOS chip's surface at a 45° angle from light reflecting mirror surfaces formed on a neighboring CMOS chip surface. Image processing electronics are held within the stacked CMOS chip layers. Electrical connections couple each of said stacked CMOS chip layers and a distribution grid, the connections for distributing power and signals to components associated with each stacked CSMO chip layer.