US-12623117-B2 - Cold start calibration

US12623117B2US 12623117 B2US12623117 B2US 12623117B2US-12623117-B2

Abstract

A first set of performance information pertaining to a previous performance of a first exercise movement is received, the first set of performance information comprising a first weight, a first velocity, and a first range of motion. Target parameters for a target exercise movement based at least in part on the first set of performance information is predicted, wherein the target exercise movement is different from the first exercise movement. An exercise machine is configured to facilitate performing of the target exercise movement based at least in part on the predicted target parameters.

Inventors

Joshua Ben Shapiro
Giuseppe Barbalinardo

Assignees

TONAL SYSTEMS, INC.

Dates

Publication Date: 20260512
Application Date: 20230627

Claims (20)

1 . A system, comprising: a processor configured to: receive a first set of performance information pertaining to a previous performance of a first exercise movement, the first set of performance information comprising a first weight, a first velocity, and a first range of motion; predict target parameters for a target exercise movement based at least in part on the first set of performance information, wherein the target exercise movement is different from the first exercise movement; and wherein an exercise machine motor torque is adjusted to facilitate performing of the target exercise movement based at least in part on the predicted target parameters and wherein the exercise machine motor torque is associated with user resistance for an exercise machine; and a memory coupled to the processor and configured to provide the processor with instructions.
2 . The system recited in claim 1 , wherein the processor is further configured to: receive a second set of performance information pertaining to a previous performance of a second exercise movement, the second set of performance information comprising a second weight, a second velocity, and a second range of motion; and predict the target parameters for the target exercise movement based at least in part on both the first set of performance information and the second set of performance information.
3 . The system recited in claim 1 , wherein the target parameters comprise at least one of weight, velocity, and range of motion.
4 . The system of claim 1 , wherein the first range of motion comprises an aggregate range of motion value across a set comprising performance of a plurality of repetitions of the first exercise movement.
5 . The system of claim 1 , wherein the first set of performance information comprises an indication of a first movement family that the first exercise movement is included in.
6 . The system of claim 1 , wherein the first set of performance information comprises an indication of a first movement family that the first exercise movement is included in and wherein the target exercise movement is not included in the first movement family.
7 . The system of claim 1 , wherein the first set of performance information comprises a first muscle utilization.
8 . The system of claim 1 , wherein the predicting is triggered prior to a workout comprising one or more exercise movements to be performed, the workout comprising the target exercise movement.
9 . The system of claim 1 , wherein the first set of performance information is associated with a calibration set.
10 . The system of claim 1 , wherein the first set of performance information is received based at least in part on a determination that the previous performance of the first exercise movement is within a threshold period of time.
11 . The system of claim 1 , wherein the predicting is performed using a machine learning model, and wherein the first set of performance information is included in an input feature vector to the machine learning model.
12 . The system of claim 1 , wherein the predicting is performed using a machine learning model, and wherein a first set of population performance information associated with the first exercise movement is included in an input feature vector to the machine learning model.
13 . The system of claim 1 , wherein the predicting is performed using a machine learning model, and wherein an output label of the machine learning model comprises a suggested weight for the target exercise movement.
14 . The system of claim 1 , wherein the first weight is associated with a one rep maximum determined for the first exercise movement.
15 . The system of claim 1 , wherein the first weight and the first velocity are associated with a determination from a progressive weight mode for the first exercise movement, wherein the progressive weight mode progressively increases weight for a user at least in part to determine a user force-velocity profile.
16 . The system of claim 1 , wherein the first weight is associated with a determination from an isokinetic weight mode for the first exercise movement, wherein the isokinetic weight mode matches a user applied force at a constant velocity at least in part to determine a user force-velocity profile.
17 . The system of claim 1 , wherein the system comprises a backend server, and wherein the processor is further configured to transmit, over a network, the predicted target parameters to the exercise machine.
18 . The system of claim 1 , wherein the system comprises the exercise machine.
19 . A method, comprising: receiving a first set of performance information pertaining to a previous performance of a first exercise movement, the first set of performance information comprising a first weight, a first velocity, and a first range of motion; predicting target parameters for a target exercise movement based at least in part on the first set of performance information, wherein the target exercise movement is different from the first exercise movement; and wherein an exercise machine motor torque is adjusted to facilitate performing of the target exercise movement based at least in part on the predicted target parameters and wherein the exercise machine motor torque is associated with user resistance for an exercise machine.
20 . A computer program product embodied in a non-transitory computer readable medium and comprising computer instructions for: receiving a first set of performance information pertaining to a previous performance of a first exercise movement, the first set of performance information comprising a first weight, a first velocity, and a first range of motion; predicting target parameters for a target exercise movement based at least in part on the first set of performance information, wherein the target exercise movement is different from the first exercise movement; and wherein an exercise machine motor torque is adjusted to facilitate performing of the target exercise movement based at least in part on the predicted target parameters and wherein the exercise machine motor torque is associated with user resistance for an exercise machine.

Description

BACKGROUND OF THE INVENTION Strength training may be a poorly understood activity for a strength training user. One aspect of this is lack of knowledge about assessing one's own strength. When starting a strength training regimen, this lack of knowledge may have the strength training user choosing an inappropriate weight level for a given movement. This may cause a dangerous injury for a user and/or discourage a user because of a lack of progress. BRIEF DESCRIPTION OF THE DRAWINGS Various embodiments of the invention are disclosed in the following detailed description and the accompanying drawings. FIG. 1 is a block diagram illustrating an embodiment of an exercise machine capable of digital strength training. FIG. 2 illustrates an example of strength determination based on isokinetic seed movements. FIG. 3A illustrates an example of rep equivalent determination based on an 1RM fraction curve. FIG. 3B illustrates one embodiment of linear weight percentage reduction for a particular muscle in a workout. FIG. 4 illustrates an embodiment of a system for progressive strength calibration. FIG. 5 is an illustration of progressive weight mode. FIG. 6 is an illustration of a velocity and weight recommendation model. FIG. 7 is a flow diagram illustrating an embodiment of a process for a velocity and weight recommendation. DETAILED DESCRIPTION The invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor. In this specification, these implementations, or any other form that the invention may take, may be referred to as techniques. In general, the order of the steps of disclosed processes may be altered within the scope of the invention. Unless stated otherwise, a component such as a processor or a memory described as being configured to perform a task may be implemented as a general component that is temporarily configured to perform the task at a given time or a specific component that is manufactured to perform the task. As used herein, the term ‘processor’ refers to one or more devices, circuits, and/or processing cores configured to process data, such as computer program instructions. A detailed description of one or more embodiments of the invention is provided below along with accompanying figures that illustrate the principles of the invention. The invention is described in connection with such embodiments, but the invention is not limited to any embodiment. The scope of the invention is limited only by the claims and the invention encompasses numerous alternatives, modifications and equivalents. Numerous specific details are set forth in the following description in order to provide a thorough understanding of the invention. These details are provided for the purpose of example and the invention may be practiced according to the claims without some or all of these specific details. For the purpose of clarity, technical material that is known in the technical fields related to the invention has not been described in detail so that the invention is not unnecessarily obscured. Efficient strength determination and/or strength calibration of a strength training user is disclosed. The user's weight capability on a target exercise movement they have not performed is estimated based on analysis of a multi-dimensional performance capture on a few historical exercise movements for the user as well as a population-level performance capture on the few historical exercise movements and the target exercise movement. For example, the user may have a first captured performance of weight, velocity, and range of motion for a bench press movement, and a second captured performance of weight, velocity, and range of motion for a gobble squat movement, and may wish to target a bicep curl movement next without any previous performance history. Based on analysis of the user's first and second captured performance and population-level performance across the bench press movement, gobble squat movement, and bicep curl movement, a predicted weight capability for the user for the bicep curl movement is determined. Progressive weight mode is disclosed. A strength training user may traditionally use an isokinetic weight mode to directly model the user's weight capacity for a given movement velocity as a force velocity curve, in part to determine a one rep maximum for the user. Progressive weight mode is an improvement in user comfort over isokinetic weight mode that uses a higher velocity calibration with lower weight to model the force velocity curve. Another improvement with progressive weight mode is that a “target speed” referred to herein as a threshold percentage of the user maximum concentric velocity for a given movement may be determined. Suggested Weights. Sugg