An exercise recommendation system determines recommended weights for users to perform exercises with. The exercise recommendation system accesses a plurality of exercise pairs, each labeled with performance statistics of users who performed the exercises. Each exercise in an exercise pair is associated with a weight. The exercise recommendation system trains a machine learning model on the plurality of exercise pairs to determine a weight to recommend to a user for a first exercise based on performance statistics of the user associated with one or more second exercises, which are each in an exercise pair with the first exercise. The exercise recommendation system retrieves performance statistics of a target user including weights for exercises previously performed by the target user. The exercise recommendation system applies the machine learning model to the performance statistics to determine a target weight to recommend and modifies a user interface to include the target weight.

An exercise recommendation system determines a current capability of a user. The exercise recommendation system accesses an exercise history for a user. The exercise history comprises an exercise performed by the user and a capability of the user each time the user performed the exercise. The exercise recommendation system partitions the exercise history into a plurality of time periods, and, for each time period, computes an aggregate capability of the user for the exercise during the time period. The exercise recommendation system calculates a moving average capability of the user for the exercise based on the aggregate capabilities and determines a current capability of the user for the exercise based on the moving average capability. The current capability of the user may be discounted at least in part based on how recently the user performed the exercise.

The present disclosure relates to system and method for coordinating and providing overall feedback for one or more users performing one or more physical activities at one or more locations. The feedback may be generated as AI feedback or human feedback. The method involves data capturing and coordinating the physical activities of the multiple users. The information to be captured is regarding performance activity of the multiple users and processing the same information in real time using AI assisted model. The method includes comparing each user's activity performance data including various performance parameters having a set of target activity performance parameters. The method includes generating feedbacks based on the comparison of the performance parameters. The feedbacks generated are shared with the users and rendered on the multimedia output device available to the users. The method includes sending the feedback to external portals via corresponding Application Programming Interfaces (APIs).

Aspects of the subject disclosure may include, for example, a method operating at a processing system including a processor, including detecting a difference between current biometric information of a user and target biometric information of the user exceeding a threshold at a performance event of the user to identify a biometric deficiency, detecting an absence of a desired environmental component for the user at the performance event, selecting a remediation experience responsive to the detecting the biometric deficiency and the detecting the absence of the desired environmental component, facilitating presentation of the remediation experience to the user at the performance event to generate a modified performance by the user, and adjusting the presentation of the remediation experience to the user at a termination of the performance event. Other embodiments are disclosed.

An apparatus having processing circuitry configured to receive a selection of a predetermined activity, receive baseline data, and continuously perform tracking of user progress with respect to the predetermined activity as a function of the baseline data. The processing circuitry generates tracking data based on the tracking, continuously generates progress change data as a function of the tracking data, and outputs the progress change data to an external device. The progress change data is processed by the external device to output the progress change data to a display screen as a Graphical User Interface (GUI), a color scheme of the GUI being continuously updated as a function of being redisplayed based on the continuously generated progress change data.

A system for computer-implemented golf shot analysis includes a tracking device and a computing device. The computing device accesses ball flight data generated by the tracking device in view of a predetermined gradient map and associated functionality to derive a performance metric that penalizes shots deemed to be offline.

Electromechanical rehabilitation of a user can include receiving a pedal force value from a pedal sensor of a pedal; receiving a pedal rotational position; based on the pedal rotational position over a period of time, calculating a pedal velocity; and based at least upon the pedal force value, a set pedal resistance value, and the pedal velocity, outputting one or more control signals causing an electric motor to provide a driving force to control simulated rotational inertia applied to the pedal.

An electromechanical device for rehabilitation includes pedals coupled to radially-adjustable couplings, an electric motor coupled to the pedals via the radially-adjustable couplings, and a control system including a processing device operatively coupled to the electric motor. The processing device configured to, responsive to a first trigger condition occurring, control the electric motor to operate in a passive mode by independently driving the radially-adjustable couplings rotationally coupled to the pedals. The processing device also configured to, responsive to a second trigger condition occurring, control the electric motor to operate in an active-assisted mode by measuring revolutions per minute of the radially-adjustable couplings, and cause the electric motor to drive the radially-adjustable couplings when the measured revolutions per minute satisfy a threshold condition, and responsive to a third trigger condition occurring, control the electric motor to operate in a resistive mode by providing resistance to rotation of the radially-adjustable couplings.

An apparatus, method, and a non-transitory programmed medium provide a structure to move a heavy bag to simulate sparring along with a method and programmed media to provide selective training experiences. Drive motors in a Cartesian gantry provide multi-axis lateral motion in a horizontal plane. The heavy bag is instrumented to generate information indicative of performance of the user. Also, the heavy bag receives information to prompt a user with indicators such as LEDs. A moving target simulates a boxing match. A user responds to motions of a heavy bag in an X-Y plane whether toward or away from the user. It may be used as a sparring partner. Electronic controls provide a pre-selected pattern of movement. Training programs include sequences designed by professional boxers and trainers. Controls may be adaptive to vary responses of the apparatus in response to user performance.

In some embodiments, apparatuses and methods are provided herein useful to monitor athletic performance. In some embodiments, one or more control circuits and sensors are used to analyze performance as it compares to music tempo that is played during an exercise session or class, which may be done both directly and/or indirectly. In one embodiment, athletic performance during an exercise period is monitored and compared to the tempos of music played, where the music tempo is identified by one of measuring the actual tempo of the music played and/or obtaining the tempo from a database or otherwise associating the selection(s) played with an identified tempo of the music itself. In another embodiment, the music tempo is indirectly identified or analyzed, such as by analyzing the performance or cadence of a group of exercisers and comparing the performance parameters sensed to obtain a benchmark tempo from which to compare individual users.