CLASS PERSONALIZATION WITHIN A CONNECTED FITNESS PLATFORM

Abstract

The systems and methods described herein receive a fitness goal or target (e.g., a user wants to be stronger, or run faster, or bike longer within a certain heart rate zone) as seed input into a guidance system, which generates a personalized plan of recommended classes/activities for the user based on their goal/target. At different points along the plan, the guidance system may modify or update its recommendations with different or enhanced classes/activities, in order to keep the user on their target or goal.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Application No. 63/516,672 filed on Jul. 31, 2023, entitled CLASS PERSONALIZATION WITHIN A CONNECTED FITNESS PLATFORM, which is hereby incorporated by reference in its entirety.

BACKGROUND

The world of connected fitness is an ever-expanding one. This world can include a user taking part in an activity (e.g., running, cycling, lifting weights, and so on), other users also performing the activity, and other users doing other activities. The users may be utilizing a fitness machine (e.g., a treadmill, a stationary bike, a strength machine, a stationary rower, and so on), or may be moving through the world on a bicycle or other equipment.

The users can also be performing other activities that do not include an associated machine, such as running, strength training, yoga, stretching, hiking, climbing, and so on. These users can have wearable devices or mobile devices (e.g., heart rate monitors) that monitor the activity or performance of the users. The users can also perform the activity in front of a user interface (e.g., a display or device) presenting content associated with the activity, or outside of any displayed content.

The user interface, whether a mobile device, a display device, or a display that is part of a machine, can provide or present interactive content to the users. For example, the user interface can present live or recorded classes (e.g., fitness or strength classes), video tutorials of activities, workout plans (e.g., a list of movements), leaderboards and other competitive or interactive features, progress indicators (e.g., via time, distance, and other metrics), and so on.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present technology will be described and explained through the use of the accompanying drawings.

FIG. 1 is a block diagram illustrating a suitable network environment for users of an exercise system.

FIG. 2 is a block diagram illustrating components of a guidance system.

FIG. 3 is a diagram illustrating a modification of a baseline sequence of exercise classes.

FIG. 4 is a diagram illustrating example groupings of similar exercise classes.

FIG. 5 is a flow diagram illustrating an example method for generating a personalized class sequence for a user of a connected fitness platform.

FIG. 6 is a flow diagram illustrating an example method for modifying an ongoing class sequence for a user of a connected fitness platform.

In the drawings, some components are not drawn to scale, and some components and/or operations can be separated into different blocks or combined into a single block for discussion of some of the implementations of the present technology. Moreover, while the technology is amenable to various modifications and alternative forms, specific implementations have been shown by way of example in the drawings and are described in detail below. The intention, however, is not to limit the technology to the particular implementations described. On the contrary, the technology is intended to cover all modifications, equivalents, and alternatives falling within the scope of the technology as defined by the appended claims.

DETAILED DESCRIPTION

Overview

Various systems and methods that enhance an exercise activity performed by a user are described. In some embodiments, the systems and methods generate class sequences and/or provide recommendations personalized to individual members or users, such as by guiding members to one or more classes or activities based on their fitness goals or targets.

For example, instead of providing content (e.g., virtual classes or fitness plans) based on certain filters or rules (e.g., presenting all new content for a certain category), a guidance system, as described herein, can generate, surface, and/or recommend content (e.g., classes, activities, workout types, interactive games, and so on) based on one or more of the following factors for an individual user or member: their content preferences, the equipment/devices they own or have access to, their determined or reported fitness levels, their wellness, workout, or fitness goals, their historical activities (in or outside of the connected fitness platform), their current or previous performances, and other personalized information.

The guidance system may determine or select classes or activities to present to the member based on the member's determined or identified goals. Thus, the guidance system may provide the member with a personalized path or journey of content as they move towards a goal within a connected fitness environment. In some cases, the guidance system may act as a personalized, virtual trainer that continuously updates its recommendations and/or instructions as the member performs recommended classes or activities, provides feedback on the classes/activities, and so on.

The systems and methods described herein, therefore, utilize a fitness goal or target (e.g., a user wants to be stronger, or run faster, or bike longer within a certain heart rate zone) as seed input into the guidance system, which generates a personalized plan of recommended classes/activities for the user based on their goal/target. At different points along the plan, the guidance system may modify or update its recommendations with different or enhanced classes/activities, in order to keep the user on their target or goal.

Various embodiments of the system and methods will now be described. The following description provides specific details for a thorough understanding and an enabling description of these embodiments. One skilled in the art will understand, however, that these embodiments may be practiced without many of these details. Additionally, some well-known structures or functions may not be shown or described in detail, so as to avoid unnecessarily obscuring the relevant description of the various embodiments. The terminology used in the description presented below is intended to be interpreted in its broadest reasonable manner, even though it is being used in conjunction with a detailed description of certain specific embodiments.

Examples of a Suitable Exercise Platform

The technology described herein is directed, in some embodiments, to providing a user with an enhanced user experience (e.g., a personalized or guided experience) when performing an exercise activity or a sequence of exercise activities, such as a group or block of activities as part of a connected fitness system or other exercise system. FIG. 1 is a block diagram illustrating a suitable network environment 100 for users of an exercise system.

The network environment 100 includes an activity environment 102, where a user 105 is performing an exercise activity, such as a cycling activity. In some cases, the user 105 can perform the activity with an exercise machine 110, such as an exercise bicycle, a treadmill, a rowing machine, a stair climber, and so on.

In some cases, the exercise activity performed by the user 105 can include a variety of different workouts, activities, actions, and/or movements not associated with a machine, such as movements associated with lifting weights 112 (as shown), stretching, meditation, yoga, pilates, rowing, running, cycling, jumping, sports movements (e.g., throwing a ball, pitching a ball, hitting, swinging a racket, swinging a golf club, kicking a ball, hitting a puck), and so on.

The activity environment 102 may include a home or home gym of the user, a commercial fitness center or gym, a hotel, a specific sports facility (e.g., yoga studio, batting cage, indoor track, and so on), an outdoor venue (e.g., a track, running path, street, park, and so on) or other workout facility or location that includes exercise machines, workout devices or accessories (e.g., weights, heavy bags, and so on). The platform 100 may access, obtain, and/or store information about the facility/location, such as the types of exercise machines or activities available to users, the hours available to utilize the facility/location, and so on.

The exercise machine 110 can assist or facilitate the user 105 to perform the movements and/or can present interactive content to the user 105 when the user 105 performs the activity. For example, the exercise machine 110 can be a stationary bicycle, a stationary rower, a treadmill, a weight machine, or other machines. As another example, the exercise machine 110 can be a display device that presents content (e.g., streamed classes, dynamically changing video, audio, video games, instructional content, workout instructions, and so on) to the user 105 during an activity or workout.

The exercise machine 110, in some embodiments, can include a media hub 120 and a user interface 125. The media hub 120, in some cases, captures images and/or video of the user 105, such as images of the user 105 performing different movements, or poses, during an activity. The media hub 120 can include a camera or cameras, a camera sensor or sensors, or other optical sensors configured to capture the images or video of the user 105.

In some cases, the media hub 120 includes components configured to present or display information to the user 105. For example, the media hub 120 can be part of a set-top box or other similar device that outputs signals to a display, such as the user interface 125. Thus, the media hub 120 can operate to both capture images of the user 105 during an activity, while also presenting content (e.g., time-based or distance-based experiences, streamed classes, workout statistics, and so on) to the user 105 during the activity.

The user interface 125 provides the user 105 with an interactive experience during the activity. For example, the user interface 125 can present user-selectable options that identify live classes available to the user 105, pre-recorded classes available to the user 105, historical activity information for the user 105, progress information for the user 105, instructional or tutorial information for the user 105, and other content (e.g., video, audio, images, text, and so on), that is associated with the user 105 and/or activities performed (or to be performed) by the user 105.

Further, in various embodiments, the media hub 120 can facilitate the tracking of movements or actions performed by the user 105, such as when the user is performing different movements as part of a strength or lifting class. The media hub 120, as described herein, can track and identify movements performed by the user 120. Various associated systems can perform form tracking, repetition counting, or other actions that assist the user 105 during the class or activity. Further details regarding the actions performed for the user 105 can be found in PCT Application No. PCT/US22/26032, filed on Apr. 22, 2022, entitled USER EXPERIENCE PLATFORM FOR CONNECTED FITNESS SYSTEMS, which is hereby incorporated by reference in its entirety.

In some cases, a heart rate monitor (HRM) 127 or other wearable device (e.g., smart watch, headphones, fitness trackers, AR/VR/XR displays or devices, and so on) can capture biometric information about the user 105, such as heart rate, movement information, sleep information, and so on. The HRM 127 can capture the user's heart rate and other information during machine-based activities and/or other activities, such as offline or class-based activities that do not utilize the exercise machine 110. In some cases, the exercise machine can include components configured to capture biometric information for the user 105, such as heart rate information. Further, the HRM 127 or smart watch may also provide a user interface (e.g., the user interface 125) to the user.

The exercise machine 110, the media hub 120, and/or the user interface 125 can send or receive information over a network 130, such as a wireless network. Thus, in some cases, the user interface 125 is a display device (e.g., attached to the exercise machine 110), that receives content from (and sends information, such as user selections) an exercise content system 140 over the network 130. In other cases, the media hub 120 controls the communication of content to/from the exercise content system 140 over the network 130 and presents the content to the user via the user interface 125.

The exercise content system 140, located at one or more servers remote from the user 105, can include various content libraries (e.g., classes, movements, tutorials, and so on) and perform functions to stream or otherwise send content to the machine 110, the media hub 120, and/or the user interface 125 over the network 130.

A content database 150 stores content 155 (e.g., video files) that presents a pre-recorded class to a user. The content can include images, video, and other visual information that present the class, music and other audio information to be played during the activity, and various overlay or augmentation information that is presented along with the audio/video content. Further, the database 150 can include various content libraries (e.g., classes, movements, tutorials, and so on) associated with the content presented to the user during a selected experience.

In some cases, the content may include simple or targeted workout plans or activities that includes lists of movements or activities (e.g., perform 3 sets of a certain movement or run for 10 minutes at medium pace), and is not associated with video or audio classes or content.

Further, in some cases, aspects of the content may be dynamically generated during an activity, such as during a running class or activity. The content may be generated in response to the user performing the activity, in response to the user moving through an environment (e.g., real or virtual), and so on.

As described herein, a guidance system 145 can include various components configured to provide recommendations or personalized guidance to users of a connected fitness platform. For example, the guidance system 145 can determine a personalized set of recommendations (e.g., a plan or journey) based on a known fitness goal/target for the user 105 and/or various factors determined for the user 105, and output or generate the plan or journey (e.g., a class sequence or activity sequence) for the user 105. Further details regarding the guidance system 145 are described herein.

FIG. 1 and the components, systems, servers, and devices depicted herein provide a general computing environment and network within which the technology described herein can be implemented. Further, the systems, methods, and techniques introduced here can be implemented as special-purpose hardware (for example, circuitry), as programmable circuitry appropriately programmed with software and/or firmware, or as a combination of special-purpose and programmable circuitry. Hence, implementations can include a machine-readable medium having stored thereon instructions which can be used to program a computer (or other electronic devices) to perform a process. The machine-readable medium can include, but is not limited to, floppy diskettes, optical discs, compact disc read-only memories (CD-ROMs), magneto-optical disks, ROMs, random access memories (RAMs), erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), magnetic or optical cards, flash memory, or other types of media/machine-readable medium suitable for storing electronic instructions.

The network or cloud 130 can be any network, ranging from a wired or wireless local area network (LAN), to a wired or wireless wide area network (WAN), to the Internet or some other public or private network, to a cellular (e.g., 4G, LTE, or 5G network), and so on. While the connections between the various devices and the network 130 and are shown as separate connections, these connections can be any kind of local, wide area, wired, or wireless network, public or private.

Further, any or all components depicted in the Figures described herein can be supported and/or implemented via one or more computing systems, services (e.g., cloud instances), or servers. Although not required, aspects of the various components or systems are described in the general context of computer-executable instructions, such as routines executed by a general-purpose computer, e.g., mobile device, a server computer, or personal computer. The system can be practiced with other communications, data processing, or computer system configurations, including: Internet appliances, hand-held devices, wearable devices, or mobile devices (e.g., smart phones, tablets, laptops, smart watches), all manner of cellular or mobile phones, multi-processor systems, microprocessor-based or programmable consumer electronics, set-top boxes, network PCs, mini-computers, mainframe computers, AR/VR devices, gaming devices, and the like. Indeed, the terms “computer,” “host,” and “host computer,” and “mobile device” and “handset” are generally used interchangeably herein and refer to any of the above devices and systems, as well as any data processor.

Aspects of the system can be embodied in a special purpose computing device or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions explained in detail herein. Aspects of the system may also be practiced in distributed computing environments where tasks or modules are performed by remote processing devices, which are linked through a communications network, such as a Local Area Network (LAN), Wide Area Network (WAN), or the Internet. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Aspects of the system may be stored or distributed on computer-readable media (e.g., physical and/or tangible non-transitory computer-readable storage media), including magnetically or optically readable computer discs, hard-wired or preprogrammed chips (e.g., EEPROM semiconductor chips), nanotechnology memory, or other data storage media. Indeed, computer implemented instructions, data structures, screen displays, and other data under aspects of the system may be distributed over the Internet or over other networks (including wireless networks), or they may be provided on any analog or digital network (packet switched, circuit switched, or other scheme). Portions of the system may reside on a server computer, while corresponding portions may reside on a client computer such as an exercise machine, display device, or mobile or portable device, and thus, while certain hardware platforms are described herein, aspects of the system are equally applicable to nodes on a network. In some cases, the mobile device or portable device may represent the server portion, while the server may represent the client portion.

Examples of Providing Guidance to Users of a Connected Fitness Platform

The connected fitness platform, as described herein, can utilize information associated with a member or user performing various exercise activities, such as strength or cardio activities, within the platform. For example, the guidance system 145 can generate a plan or journey (e.g., a class sequence or workout sequence) that is personalized for the user 105 and based on a current fitness goal or target for the user.

FIG. 2 is a block diagram illustrating example modules of the guidance system 145. The guidance system 145, which can be a system of components, modules, or devices, functions to generate content for users, such as members of a connected fitness platform or service. The components and/or modules of the guidance system 145 can be implemented with a combination of software (e.g., executable instructions, or computer code) and hardware (e.g., at least a memory and processor). Accordingly, as used herein, in some embodiments, a component/module is a processor-implemented component/module and represents a computing device having a processor that is at least temporarily configured and/or programmed by executable instructions stored in memory to perform one or more of the functions that are described herein. The guidance system 145 includes a sequence module 210, a modification module 220, and an output module 230.

In some embodiments, the sequence module 210 is configured and/or programmed to generate a baseline sequence of exercise classes associated with a fitness goal or target for a user of a connected fitness platform. For example, the sequence module 210 may employ and/or utilize a machine learning (ML)/artificial intelligence (AI) model 215 or framework when generating the baseline sequence of exercise classes, such as a workout plan of classes and/or activities. As described herein, a workout sequence may include classes (e.g., streamed instructor-led classes) or activities (e.g., a game, a set of workout instructions, a set of guided movements, and so on).

In some cases, the ML model 215, which may be part of multiple different ML models or frameworks, may receive, as input, a goal/target for a user, as well as some preference information, and generate candidate classes and rank the candidate classes. For example, the ML model 215 may perform the following task—“for a user u, at time t, given a class type (e.g. grouping) of activities having an intended fitness purpose x_u,t, a duration d_u,t and a music super-genre g_u,t, generate a single class c_u,t recommendation that maximizes a conversion to a workout within a generated sequence. Of course, the task may include other features or factors (e.g., instructors, user preferences, and so on).

The ML model 215 may query a class library (e.g., content database 150) for all candidates created and/or available within the platform for a certain time period (e.g., the last 365 days), and/or dynamically generate exercise content (based on the library or other sources of information). Using the available candidates, the ML model 215 ranks, using user preferences and other filters (e.g., recency, difficulty, and so on) the candidates to generate seed classes for the baseline sequence.

Further, in some cases, the ML model 215 may utilize one or more specific preferences to boost certain classes within the rankings. For example, the users of the platform often select classes based on music played during the classes, and the ML model 215 may boost or apply a higher weight to music (or artist) preferences when ranking candidate classes.

Thus, the sequence module 210 may perform some or all of the following when generating a baseline sequence (or one or more seed classes) for a user:

• • access a library of exercise classes available to the user of the connected fitness platform and generate the baseline sequence of exercise classes by applying an ML model to the accessed library of exercise classes to select distinguished exercise classes to include in the baseline sequence of exercise classes;
  • access a library of exercise classes available to the user of the connected fitness platform and generates the baseline sequence of exercise classes by applying an ML model to the accessed library of exercise classes to select one or more class types of exercise classes to include in the baseline sequence of exercise classes;
  • access a selection of exercise classes available to the user of the connected fitness platform that was performed by fitness experts associated with the connected fitness platform and generates the baseline sequence of exercise classes by applying an ML model to the accessed selection of exercise classes to select one or more class types of exercise classes to include in the baseline sequence of exercise classes;
  • generate a baseline sequence of exercise classes by applying an ML model to identify multiple types of exercise activities to include in the baseline sequence of exercise classes and selects, from a library of exercise classes available to the user of the connected fitness platform, multiple exercise classes that include the identified multiple types of exercise activities; and so on.

In some embodiments, the modification module 220 is configured and/or programmed to modify the baseline sequence based on class preferences associated with the user of the connected fitness platform. For example, for each seed class of a baseline sequence, the module 220 identifies other classes that have a similarity score or metric that is above a threshold value based on a variety of factors, such as factors associated with the class (e.g., movements or segments of the class), factors associated with an intensity or difficulty of the class, and so on.

Each class may include a number or sequence of movements, where a movement has one or more features, including a movement name, a movement type, equipment used, muscle group targeted, embedded or learned similarities to other movements, and so on. FIG. 3 is a diagram 300 illustrating a modification of a baseline sequence 310 of exercise classes.

The baseline sequence 310 may include various seed classes 312-318, such as classes selected by the sequence module 210. The modification module 220 identifies other classes and replaces the seed classes 312-318 with new classes 322-328, such as classes that better match certain user class preferences (e.g., favorite instructors or music genres) and/or user characteristics (e.g., contextual features, such as time of day or weather, a readiness of a user, physiological data, and so on) before commencement of a workout plan and/or during performance of a workout plan.

For example, each class can have the following features: whether the class is a movement tracking class (e.g., a class compatible with a device or experience that can track the user's movements), whether the class can perform repetition counting or other similar operations, equipment (e.g., machines, weights, accessories, gyms, tracks, and so on) accessible by the user (e.g., at a workout location), user levels or fitness metrics, impact to the user's fitness, user class preferences (e.g., music, instructor, class type, and so on), and so on. The modification module 220 may determine similarity metrics for each of the classes (e.g., for some or all movements within the classes) to identify and/or select classes for the modified (and personalized) class sequence.

For example, the modification module 220 may utilize a feature vector map, learned representations (e.g., movement2vec) based on sequences of movements in known or existing classes, learned representations based on two-tower structure/triplet loss, SME labels of movement similarity, and so on. FIG. 4 depicts an example mapping 400 of similarity scores for classes, where classes may be part of similarity clusters 405 and replace any other classes with the cluster (or within certain regions of the mapping 500).

Thus, the modification module 220 may replace seed classes with similar classes, such as classes that are new to the platform after the baseline sequence is generated and/or in response to certain triggers during performance of a personalized sequence by the user (e.g., upon a determination that a user is falling behind their goal or target). The modification module 220 may perform some or all of the following when modifying, adjusting, or enhancing the baseline sequence:

• • determine music preferences, instructor preferences, movement preferences, or class length preferences for the user and modify the baseline sequence to include exercise classes associated with the determined music preferences, instructor preferences, or class length preferences for the user;
  • determine an exercise level, fitness level, experience level, or current effort level for the user and modify the baseline sequence to include exercise classes associated with the determined level for the user;
  • determine an exercise location, exercise machine, or exercise facility available to the user and modify the baseline sequence to include exercise classes supported by the exercise location, machine, or facility associated with the user; and so on.

In some embodiments, the output module 230 is configured and/or programmed to present a class sequence based on the modified baseline sequence to the user of the connected fitness platform. The output module 230 may perform various actions to present a sequence of classes (e.g., a personalized plan) to the user.

For example, the output module 230 may present the class sequence to the user via a display of an exercise machine (e.g., exercise machine 110) associated with the user and/or via a mobile device associated with the user. The output module 230 may cause the exercise machine 110 and/or mobile device (e.g., smart watch, AR/VR/XR display or device, and so on) to present one or more streaming exercise classes, one or more class recommendations, one or more exercise activities or content, and so on.

Thus, in various embodiments, the guidance system 145 receives input from a user (e.g., an identified goal and associated information) and generates a personalized plan (e.g., a sequence of classes) directed to the user achieving the goal. The following is an example implementation:

The guidance system 145, accessed by a mobile application or web portal, receives a message from a member of the connected fitness platform 100. The message indicates a workout plan duration and timing (e.g., number of weeks and days/times when the members can workout), their level of experience, and their goal (e.g., get stronger, lose weight, add size to certain muscle group, build endurance, run faster, and so on). In some cases, the message can include other details provided by a user (or determined for a user), such as their activity preferences, the type of equipment available to the user, movements they want to avoid, movements they love, and so on.

The guidance system 145 queries the ML model 215 (e.g., a large language model (LLM)) of the platform for a certain number of baseline plans or sequences. The LLM returns one or more class sequences, and the guidance system 145 modifies the sequences before or during performance of the plan by the user, as described herein.

As described herein, the guidance system 145 may perform various processes or methods when personalizing plans for users. FIG. 5 is a flow diagram illustrating an example method 500 for generating a personalized class sequence for a user of a connected fitness platform. The method 500 may be performed by the guidance system 145 and accordingly, is described herein merely by way of reference thereto. It will be appreciated that the method 500 may be performed on any suitable hardware.

In operation 510, the guidance system 145 generates or selects a baseline sequence based on a goal of a user. For example, the system 145 can select or generate a number of seed or baseline classes or other fitness/workout activities that are mapped to a goal of the user 105 or a fitness path of the user 105 (e.g., where the fitness path is determined or defined as a delta between a current fitness state of the user 105 and a goal fitness state of the user 105).

In some cases, the system 145 can build a library of seed classes based on information across some or all members of the platform (e.g., most popular classes, most liked classes, and so on), based on input or selections from experts (e.g., subject matter experts, or SMEs, for the various fitness disciplines), based on output of the ML model 215, and so on.

In operation 520, the guidance system 145 captures information associated with fitness activities of the user. For example, the system 145 can determine, select, access, or measure a current fitness level of the user 105, historical performance information for the user 105 (e.g., from classes/activities within or outside of the platform, such as cadence/speed metrics, resistance/incline metrics, output or work metrics, and so on), biological information for the user (e.g., heart rate, weight, lean body mass, and so on), polled or surveyed information, fitness test information, and so on.

The system 145, therefore, may assess or quantify the user 105 as having a certain fitness level (for one, some, or all types of fitness activities), based on various types of accessed or captured information. For example, the system 145 may quantify the user 105 based on her performance in a few most recent strength classes, her reported weight, and information retrieved from a 3^rd party application that tracked her most recent 5K race.

In operation 530, the guidance system 145 identifies class preferences for the user within the connected fitness platform. For example, the system 145 can access information (e.g., metadata or tagged information) associated with classes taken by the user 105, 3^rd party information (e.g., music playlists or social network information) to identify class preferences for the user 105, such as preferences for music or music genres, instructors, types or lengths of classes, exercise machines and/or accessories available to the user, and so on.

In operation 540, the guidance system 145 modifies the baseline sequence based on the user fitness information and the class preferences. For example, the system 145 can identify classes that are similar to classes within the baseline classes (e.g., one or more of the seed classes) and generate a personalized sequence of classes using the identified classes. In some cases, the guidance system 145 performs an action to present the updated baseline sequence of virtual classes to the user of the connected fitness platform, as described herein.

As described herein, the guidance system 145 may modify, in real-time or during performance of a plan of classes (e.g., between one of multiple classes), one or more classes of a remaining sequence of classes. FIG. 6 is a flow diagram illustrating an example method 600 for modifying an ongoing class sequence for a user of a connected fitness platform. The method 600 may be performed by the guidance system 145 and accordingly, is described herein merely by way of reference thereto. It will be appreciated that the method 600 may be performed on any suitable hardware.

In operation 610, the guidance system 145 accesses a fitness goal associated with a user of the connected fitness platform and a sequence of multiple fitness activities generated for the user and based on the fitness goal associated with the user. For example, the system 145 access and/or tracks a user performing a plan generated for the user upon a request to reach a certain fitness goal.

In operation 620, the guidance system 145 predicts the user is not on pace to achieve the fitness goal. For example, the guidance system 145 may be triggered by an action, or inaction of the user, such as the user missing a class, the user exceeding or not meeting certain class metrics or thresholds, and so on.

As another example, the guidance system 145 may quantify a fitness state of the user during performance of the sequence of multiple fitness activities and determine the quantified fitness state is outside of a threshold fitness state. The system 145 may generate a score or metric for different aspects of a user's fitness, health, effort, or activity level, such as for a user's strength, cardio, mobility, power, flexibility, and so on. The system 145 may utilize performance information (e.g., output and/or speed/cadence levels from bicycling or running classes and/or weight information from strength classes), heart zone or effort information, self-reported information, and so on, when generating or quantizing a fitness state.

The system 145, in some cases, may determine a fitness state based on a collection of workout metrics (e.g., output, calories burned, and so on) and associated context information (e.g., workout modalities, equipment metrics, class types or sequences, actual or perceived difficulty level, and so on). The system 145 may utilize the ML model 215 to determine the fitness state of the user at any given point of a sequence or plan, such as before commencement of the plan, after each class, and so on.

In operation 630, the guidance system 145 modifies the sequence of multiple fitness activities based on the prediction. For example, the system 145 adds classes to the sequence and/or updates classes of the sequence. The system 145 may determine the fitness state of the user has moved below a threshold state to reach the goal and adjust subsequence classes and/or add classes that are predicted to cause the fitness state to move back above the threshold state to reach the goal.

In some cases, the guidance system 145 may modify a sequence based on various triggers or events, in order to flexibly provide a personalized sequence for the user that changes along with the user's exercise journey. For example, the system 145 may determine the user is performing at least one fitness activity of the multiple fitness activities at a certain location (e.g., a new gym or workout facility), identify exercise machines available to the user at the certain location (e.g., exercise machines not previously available to the user), and modify the sequence of multiple fitness activities to include one or more fitness activities that incorporate the exercise machines available to the user at the certain location.

The guidance system 145, therefore, may modify a plan for a user in a dynamic or real-time manner and based on various trigger events or other determined changes, including:

The user has not followed the plan (or certain classes of the plan) and/or has exceed the plan;

• • The user fitness state or other tracked metrics have changed or moved outside of a threshold or target state;
  • The user has access to additional exercise machines or workout facilities;
  • The user has access to fewer exercise machines or workout facilities; and so on.

Thus, in various embodiments, the systems and methods can provide guided personal training and/or recommendations for users of a connected fitness platform, among other benefits. The systems and methods can utilize data specific to a user to modify or enhance class recommendations and/or build sequences of classes or activities for the user, either before or during the user performing an activity or class.

The user can input a goal (e.g., build strength, lose weight, get faster), and the systems and methods can generate classes (of different movements), class sequences, or other activity recommendations that are specific to the user's goal and their fitness and class preferences, among other benefits. Further, the systems and methods may adapt the plan to certain changes associated with the user, enabling the user to keep working towards a goal or target, among other benefits.

CONCLUSION

Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense, as opposed to an exclusive or exhaustive sense; that is to say, in the sense of “including, but not limited to.” As used herein, the terms “connected,” “coupled,” or any variant thereof, means any connection or coupling, either direct or indirect, between two or more elements; the coupling of connection between the elements can be physical, logical, or a combination thereof. Additionally, the words “herein,” “above,” “below,” and words of similar import, when used in this application, shall refer to this application as a whole and not to any particular portions of this application. Where the context permits, words in the above Detailed Description using the singular or plural number may also include the plural or singular number respectively. The word “or”, in reference to a list of two or more items, covers all of the following interpretations of the word: any of the items in the list, all of the items in the list, and any combination of the items in the list.

The above detailed description of embodiments of the disclosure is not intended to be exhaustive or to limit the teachings to the precise form disclosed above. While specific embodiments of, and examples for, the disclosure are described above for illustrative purposes, various equivalent modifications are possible within the scope of the disclosure, as those skilled in the relevant art will recognize.

The teachings of the disclosure provided herein can be applied to other systems, not necessarily the system described above. The elements and acts of the various embodiments described above can be combined to provide further embodiments.

Any patents and applications and other references noted above, including any that may be listed in accompanying filing papers, are incorporated herein by reference. Aspects of the disclosure can be modified, if necessary, to employ the systems, functions, and concepts of the various references described above to provide yet further embodiments of the disclosure.

These and other changes can be made to the disclosure in light of the above Detailed Description. While the above description describes certain embodiments of the disclosure, and describes the best mode contemplated, no matter how detailed the above appears in text, the teachings can be practiced in many ways. Details of the technology may vary considerably in its implementation details, while still being encompassed by the subject matter disclosed herein. As noted above, particular terminology used when describing certain features or aspects of the disclosure should not be taken to imply that the terminology is being redefined herein to be restricted to any specific characteristics, features, or aspects of the disclosure with which that terminology is associated. In general, the terms used in the following claims should not be construed to limit the disclosure to the specific embodiments disclosed in the specification, unless the above Detailed Description section explicitly defines such terms. Accordingly, the actual scope of the disclosure encompasses not only the disclosed embodiments, but also all equivalent ways of practicing or implementing the disclosure under the claims.

From the foregoing, it will be appreciated that specific embodiments have been described herein for purposes of illustration, but that various modifications may be made without deviating from the spirit and scope of the embodiments. Accordingly, the embodiments are not limited except as by the appended claims.

Claims

1. A system, comprising:

a sequence module that generates a baseline sequence of exercise classes associated with a fitness goal or target for a user of a connected fitness platform;

a modification module that modifies the baseline sequence based on class preferences associated with the user of the connected fitness platform; and

an output module that presents a class sequence based on the modified baseline sequence to the user of the connected fitness platform.

2. The system of claim 1, wherein the sequence module:

accesses a library of exercise classes available to the user of the connected fitness platform; and

generates the baseline sequence of exercise classes by applying a machine learning (ML) model to the accessed library of exercise classes to select distinguished exercise classes to include in the baseline sequence of exercise classes.

3. The system of claim 1, wherein the sequence module:

accesses a library of exercise classes available to the user of the connected fitness platform; and

generates the baseline sequence of exercise classes by applying a machine learning (ML) model to the accessed library of exercise classes to select one or more class types of exercise classes to include in the baseline sequence of exercise classes.

4. The system of claim 1, wherein the sequence module:

accesses a selection of exercise classes available to the user of the connected fitness platform that was performed by fitness experts associated with the connected fitness platform; and

generates the baseline sequence of exercise classes by applying a machine learning (ML) model to the accessed selection of exercise classes to select one or more class types of exercise classes to include in the baseline sequence of exercise classes.

5. The system of claim 1, wherein the sequence module:

generates the baseline sequence of exercise classes by applying a machine learning (ML) model to identify multiple types of exercise activities to include in the baseline sequence of exercise classes; and

selects, from a library of exercise classes available to the user of the connected fitness platform, multiple exercise classes that include the identified multiple types of exercise activities.

6. The system of claim 1, wherein the modification module:

determines music preferences, instructor preferences, or class length preferences for the user; and

modifies the baseline sequence to include exercise classes associated with the determined music preferences, instructor preferences, or class length preferences for the user.

7. The system of claim 1, wherein the modification module:

determines an exercise level for the user; and

modifies the baseline sequence to include exercise classes associated with the determined exercise level for the user.

8. The system of claim 1, wherein the modification module:

determines an exercise location associated with the user; and

modifies the baseline sequence to include exercise classes supported by the exercise location associated with the user.

9. The system of claim 1, wherein the output module presents the class sequence to the user via a display of an exercise machine associated with the user.

10. The system of claim 1, wherein the output module presents the class sequence to the user via a mobile device associated with the user.

11. A non-transitory, computer-readable medium whose contents, when executed by a computing system, causes the computing system to perform a method, the method comprising:

receiving, at a machine learning (ML) model, an input that identifies a fitness goal for a user of a connected fitness platform;

determining, via the ML model, a baseline sequence of virtual classes based on the fitness goal for the user;

obtaining preference information associated with the user; and

updating the baseline sequence of virtual classes based on the obtained preference information associated with the user.

12. The non-transitory, computer-readable medium of claim 11, further comprising:

performing an action to present the updated baseline sequence of virtual classes to the user of the connected fitness platform.

13. The non-transitory, computer-readable medium of claim 12, wherein performing the action includes displaying a list of recommended virtual classes to the user via a display of an exercise machine associated with the user.

14. The non-transitory, computer-readable medium of claim 12, wherein performing the action includes presenting a virtual class to the user via a mobile device associated with the user.

15. The non-transitory, computer-readable medium of claim 11, wherein the preference information associated with the user includes class preference information that identifies characteristics of virtual classes previously taken by the user via the connected fitness platform.

16. The non-transitory, computer-readable medium of claim 11, wherein the preference information associated with the user includes difficulty information for virtual classes previously taken by the user via the connected fitness platform.

17. The non-transitory, computer-readable medium of claim 11, wherein the preference information associated with the user includes exercise machines available to the user.

18. A method performed by a guidance system of a connected fitness platform, the method comprising:

accessing a fitness goal associated with a user of the connected fitness platform and a sequence of multiple fitness activities generated for the user and based on the fitness goal associated with the user;

predicting the user is not on pace to achieve the fitness goal; and

modifying the sequence of multiple fitness activities based on the prediction.

19. The method of claim 18, wherein predicting the user is not on pace to achieve the fitness goal includes:

quantifying a fitness state of the user during performance of the sequence of multiple fitness activities;

determining the quantified fitness state is outside of a threshold fitness state.

20. The method of claim 18, wherein modifying the sequence of multiple fitness activities based on the prediction includes:

determining the user is performing at least one fitness activity of the multiple fitness activities at a certain location;

identifying exercise machines available to the user at the certain location; and

modifying the sequence of multiple fitness activities to include one or more fitness activities that incorporate the exercise machines available to the user at the certain location.