US20150151161A1 - Exercise training system and method - Google Patents

Exercise training system and method Download PDF

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US20150151161A1
US20150151161A1 US14/404,797 US201314404797A US2015151161A1 US 20150151161 A1 US20150151161 A1 US 20150151161A1 US 201314404797 A US201314404797 A US 201314404797A US 2015151161 A1 US2015151161 A1 US 2015151161A1
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exercise
value
activity
exercise activity
executed
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Paul Anderton
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
    • A63B24/0062—Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B19/00—Teaching not covered by other main groups of this subclass
    • G09B19/003—Repetitive work cycles; Sequence of movements
    • G09B19/0038—Sports
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
    • A63B24/0062—Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
    • A63B2024/0065—Evaluating the fitness, e.g. fitness level or fitness index

Definitions

  • the present invention relates to exercise training for skeletal muscle growth.
  • an embodiment of the present invention may find application in an exercise training programme, such as a resistance training program.
  • Resistance training is the practice of placing a subject's skeletal muscles under load, typically via eccentric and concentric contractions of a prescribed movement for a number of consecutive repetitions (“reps”) which may be repeated several times (“sets”) with a rest between each set.
  • reps consecutive repetitions
  • sets sets
  • the aim of the training is to elicit an increase in muscle size or strength or both.
  • a set/rep type exercise may be designed to create a training response which is biased towards a particular outcome, such as heavy weights with, for example, 4 reps or less for predominantly strength gains and, for example, 8 to 12 reps for size gains (hypertrophy) of a muscle group worked by the exercise.
  • a fundamental consideration when preparing a training programme incorporating exercise activities is that every person is different. Also, for each person, the various muscles will be different in their ability to handle and recover from stresses. In this respect, muscles are recognised as including various fibre “types”, broadly classified as “fast-twitch” and “slow-twitch” fibres, and also as having have several sub-categories. Each fibre “type” is recognised as having abilities biased towards particular training loads. For example, slow-twitch fibres are recognised as being better in endurance events, whereas fast-twitch fibres are recognised as being better at short term explosive or power events.
  • trainees may employ a protocol (such as a training program) that is not optimised for their physiology, or at least not tailored for, their desired objectives, or other circumstances.
  • a trainee may adopt a training program from a sports person they admire, unaware of, or ignoring the fact that, that sports person has different genetic structure, or lives a different lifestyle, amongst other things.
  • a first aspect of the present invention provides a method of analysing a resistance exercise activity executed by a subject, the method including:
  • resistance training exercise examples include strength training exercises such as isotonic and/or isometric exercises.
  • the exercise activity may involve exercise equipment such as resistance bands, free weights, or exercise machines.
  • isotonic exercises include squats, bench press, lat pull downs, dumbbell flyes, cable flyes, bar bell curls, calf raises, chin ups, sit ups, push-ups, and the like.
  • the exercise activity may involve one or more weights (wt), one or more sets (s) including one or more repetitions (R), and a total activity (elapsed) time (T e ) for the exercise.
  • the plurality of exercise parameters include:
  • R s repetition parameter value
  • the execution profile information and the load profile information include corresponding sequences of values, wherein each value represents a duration of a different exercise phase of the exercise activity.
  • the values representing the duration of different exercise phases of the exercise activity may include values representing the duration of:
  • the load profile (LP) information may be expressed as:
  • execution profile (EP) information is expressed as:
  • Processing the plurality of exercise parameters values and the activity information to determine one or more assessment parameter values for assessing the executed exercise activity may include multiplying corresponding values of the actual execution information and the load profile information to obtain a sequence of products, and summing the products to obtain a single value indicative of a working time under tension (TUT) for a muscle targeted or intended to be activated by the exercise activity.
  • TUT working time under tension
  • TUT r t 1 ⁇ d 1 +t 2 ⁇ d 2 +t 3 ⁇ d 3 +t 4 ⁇ d 4
  • each repetition in a set (s) involves the same load profile and execution profile, and each set includes a number (R s ) of repetitions.
  • the one or more assessment parameter values for assessing the executed exercise activity include:
  • the work volume parameter value (W) may be determined as:
  • the work intensity parameter value (W t ) may be determined as:
  • Te is the elapsed time of the exercise activity.
  • the stress intensity parameter value (S i ) may be determined as:
  • the hypertrophy factor parameter value (H f ) may be determined as:
  • Some embodiments of the present invention thus relate to a system and method which assesses a plurality of exercise parameters relating to resistance training, including a “hypertrophy factor” (H f ) that is proportional to stress intensity and work capacity of a muscle group(s), to guide the subject to set a tailored target for improved training results.
  • H f hypertrophy factor
  • a second aspect of the present invention provides a computer readable media including computer program instructions which are executable by a processor to implement a method according to the first aspect of the present invention.
  • a system for analysing a resistance exercise activity executed by a subject including:
  • a method of determining plural sets of instructions including one or more exercise parameters for execution of a resistance exercise activity by a subject including:
  • a system for determining one or more exercise parameters for execution of a resistance exercise activity by a subject including:
  • Yet another aspect of the present invention provides a method of analysing a resistance exercise activity executed by a subject, the method including:
  • Embodiments of the present invention may provide a method of analysis and/or predictive modeling of resistance training parameters which provides a tool which may be used to customise exercise activities for an individual based on their individual physiological characteristics and training response as determined from past performance.
  • embodiments of the present invention may provide analysis of, and predictive modelling for, a subject (such as an individual trainee) to firstly determine training parameters for achieving their training goals via iterative analysis and guidance. It is further possible that embodiments may provide feedback to adjust a program of exercise activity to maintain progress towards the subject's training objectives irrespective of potential changes in the trainee's physiology or other variables that can affect the training response.
  • Embodiments of the present invention may thus assist in overcoming obstacles with regard to ‘knowing’ a plurality of factors that contribute to athletic performance for example, a trainee's muscle make-up (via biopsies) and recuperative abilities, energy systems, oxygen delivery, hormonal responses, (i.e. ‘the system’ characteristics) and the subsequent assumptions of the ‘optimal’ training that may therefore result, by assessing the actual performance of the total ‘system’ and providing feedback and guidance to ‘peak’ training parameters via predictive modelling.
  • Embodiments of the present invention may provide a means of analysing performance of an exercise activity via exercise parameters which may be recorded during execution of an exercise activity without requiring complex technical equipment or measurement instruments. Embodiments of the present invention may thus be simple to implement in a training environment without requiring any modifications to the existing training equipment, such as gym equipment.
  • training outputs performance statistics outlined earlier
  • training input for example, weight, repetitions, number of sets, load profile, time parameters outlined earlier
  • embodiments of the present invention may tailor or at least customise to some extent the training stimulus for a trainee.
  • This approach adopted by the present invention is expected to provide improved accuracy compared to technical equipment or measurement instruments which focus on one or more factors of performance (for example, muscle fibre type) and which do not take into account other factors of performance (for example, energy systems or oxygen delivery) that may ultimately affect the actual stress that can be imposed on the muscle and thus may result on a lower stress to the muscle than determined by the assumptions.
  • factors of performance for example, muscle fibre type
  • other factors of performance for example, energy systems or oxygen delivery
  • the present invention may involve determining the optimal peak stress and workload and “hypertrophy factor” by monitoring and analysing exercise parameters and analysing parameters to provide performance statistics as well as predictive training parameters and targets to accelerate progress towards the subject's training goals.
  • Embodiments of the present invention may relate to an automated system for resistance training which thus provides tailored training protocols for each individual trainee to guide them to their optimal training parameters for each muscle and exercise. It is preferred that the system uses analysis of training parameters that may be measured without specialised equipment and uses analysis and predictive modeling to iteratively determine the optimal training mode for each exercise and/or muscle for each trainee.
  • Some embodiments of the present invention may address shortcomings with training protocols which rely on prescribed set, repetition, and recovery schemes and ignore the physiological differences between people.
  • Such training protocols may only be suitable for a small percentage of people whose physiology suits those protocols and, even then, for a period of time until a trainee adapts to that protocol or perhaps reaches a different “stage” in their life either due to their age, health, living conditions, or other circumstances.
  • An advantage of the present invention is that it may provide an assessment and iterative process which is capable of tailoring exercise activities for the subject.
  • FIG. 1 is a block diagram of a system according to an embodiment of the present invention
  • FIG. 2 is a flow diagram for a method according to an embodiment of the present invention.
  • FIG. 3 is a table including load profile and execution profile information for an exercise activity
  • FIG. 4 is a table including load profile and execution profile information for a plurality of exercise activities
  • FIG. 5 is a diagram illustrating example bio-mechanical processes for executing a bicep curl exercise activity to illustrate the effects on exercise performance on the load profile;
  • FIG. 6 is a table including load profile and weight information for a plurality of exercise activities
  • FIG. 7 is a table listing weight and repetition information with reference to a one-repetition maximum value for an exercise activity.
  • FIG. 8 is an example output report including plural instructions for executing an exercise activity.
  • processing may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGAs field programmable gate arrays
  • processors controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof.
  • Software modules also known as computer programs, computer codes, or instructions, may contain a number of source code or object code segments or instructions, and may reside in any computer readable medium such as a RAM memory, flash memory, ROM memory, EPROM memory, registers, hard disk, a removable disk, a CD-ROM, a DVD-ROM or any other form of computer readable medium.
  • the computer readable medium may be integral to the processor.
  • the processor and the computer readable medium may reside in an ASIC or related device.
  • the software codes may be stored in a memory unit and executed by a processor.
  • the memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.
  • the term “software,” as used here in, includes but is not limited to one or more computer readable and/or executable instructions that cause a computer or other electronic device to perform functions, actions, and/or behave in a desired manner.
  • the instructions may be embodied in various forms such as routines, algorithms, modules or programs including separate applications or code from dynamically linked libraries.
  • Software may also be implemented in various forms such as a stand-alone program, a function call, a servlet, an applet, instructions stored in a memory, part of an operating system or other type of executable instructions. It will be appreciated by one of ordinary skilled in the art that the form of software is dependent on, for example, requirements of a desired application, the environment it runs on, and/or the desires of a designer/programmer or the like.
  • Embodiments of the present invention apply analysis to exercise parameters recorded during or after execution of an exercise activity to determine assessment parameters for providing feedback to the trainee (hereinafter, the “subject”) of the actual training statistics as well as a predictive target of exercise parameters that will modify the parameters according to the subject's abilities.
  • the trainee hereinafter, the “subject”
  • the present invention could be implemented for use by a trainer, coach, or the like. Indeed, it is possible that embodiments of the present invention could be implemented on-line (suitable, for example, for Internet based remote coaching) or as a mobile application.
  • FIG. 1 there is shown a block diagram for a system 100 according to an embodiment of the present invention.
  • the system 100 includes a processing device 102 such as a desktop computer, a lap top computer, a note book computer, a hand held computer, a programmed electronic device equipped with a programmed or programmable controller (such as a microcontroller), a smart phone, a personal digital assistant or the like.
  • a processing device 102 such as a desktop computer, a lap top computer, a note book computer, a hand held computer, a programmed electronic device equipped with a programmed or programmable controller (such as a microcontroller), a smart phone, a personal digital assistant or the like.
  • processing device is intended to denote any type of device including a processor capable of executing a set of software instructions to perform a function.
  • the processing device 102 includes a processing unit 103 , a memory 104 , a computer software program 106 resident in a first store of information in the form of a memory 104 , communications interface 108 , input interface 110 , display interface 112 , mass storage device 114 , and power supply 116 , however, it will be appreciated that any configuration is acceptable such that the functionality of accepting data inputs, processing data and providing outputs in any format can be accommodated, either within a hardware device or any “virtual” or “cloud” device or process-capable medium, hereinafter encapsulated by the term “processing device” or “device 102 ”.
  • processing device or “device 102 ”.
  • all references to specific elements of the device are taken to also address any other configurations of the processing system.
  • the memory 104 may be installed on-board the processing device 102 or may be connectable or accessible to the processing device 102 via a suitable communications interface, such as communications interface 108 .
  • a suitable communications interface includes a universal serial bus (USB) interface.
  • the memory 104 may include volatile (for example, RAM, DRAM, SRAM) or non-volatile memory, such as a ROM memory (for example, PROM, EPROM, EEPROM), or NVRAM (such as FLASH) or the like.
  • a suitable memory is a USB FLASH memory adapted to communicate with the processing unit 103 via a USB interface.
  • the memory 104 is programmed with a set of executable instructions in the form of the computer software program 106 .
  • references to the term “memory” are to be understood to denote the total memory available to the processing unit.
  • the software 106 will also include an operating system for controlling system functions. Suitable operating systems will depend on the processing unit 103 and would be well known to a skilled addressee.
  • the memory 104 may also store a database 107 .
  • the database 107 may be stored on a remote device, such as a server, or a device which is accessible to the processing device 102 via the or another communications interface 108 which may support wired or wireless communications with a network 118 , such as the Internet.
  • the communications interface 108 may thus include a wired interface such as a USB, Ethernet or the like, or alternatively may include a wireless interface such as a Wi-Fi interface, a Bluetooth interface, or the like. Other suitable communications interfaces would be known to a skilled addressee.
  • the communications interface 108 may support data communication which allows the database 107 to be accessible to the processing device 102 via a cloud.
  • the database 107 may include multiple databases distributed across a plurality of network accessible devices.
  • the database 107 stores information for one or more exercise activities, such as weight training type exercises.
  • the stored information includes load profile information (LP) for one or more exercise activities as the approximate “load” of the weight on the working muscle(s) (that is, the muscle intended to be exercised by the exercise activity) at four distinct phases of the exercise, expressed as components
  • LP load profile information
  • the database 107 also stores additional information for the one or more exercise activities such as the weight component lifted as a ratio of the weight loaded on the machine or bar (for example, 10%, 50%, 100%, 150%) as a result of leverage, cams, or angle of lift versus angle of incidence with gravity, the inherent and “minimum” weight as a result of the weight of the machine (for example, the foot plate of a leg press machine), and the component of bodyweight lifted in the exercise activity (for example, 10%, 50%, 100%, 150%).
  • the weight component lifted as a ratio of the weight loaded on the machine or bar for example, 10%, 50%, 100%, 150%) as a result of leverage, cams, or angle of lift versus angle of incidence with gravity
  • the inherent and “minimum” weight as a result of the weight of the machine (for example, the foot plate of a leg press machine)
  • the component of bodyweight lifted in the exercise activity for example, 10%, 50%, 100%, 150%).
  • the total weight (wt) may be expressed as:
  • the database 107 also stores additional information in the form of a repository of historical training (exercise activities) records for exercise activities which have been executed by a subject to enable recall and analysis either for comparison purposes or for processing by the processing device 102 to generate a set of training instructions for executing the exercise activity based on prior execution information.
  • additional information in the form of a repository of historical training (exercise activities) records for exercise activities which have been executed by a subject to enable recall and analysis either for comparison purposes or for processing by the processing device 102 to generate a set of training instructions for executing the exercise activity based on prior execution information.
  • the input interface 110 provides a receiving means for receiving a plurality of exercise parameter values for an executed exercise activity into the processing device 102 , and/or a means for providing output information.
  • the user interface 110 may provide:
  • the input interface 110 may include an interactive graphical user interface (GUI) which permits a subject to, for example, enter and select parameter values for an exercise activity.
  • GUI graphical user interface
  • Other suitable input options may include sensor devices (for example, accelerometer(s), or strain gauges) adapted with communication means, or specifically built training apparatus (with in-built detectors and data transmission systems) which are adapted to communicate data to an input device such as a smart phone, tablet, laptop computer, desktop computer, wrist watch based device, or other intelligent device, via a suitable wired or wireless interface.
  • sensor devices may include devices which are worn by the subject during the exercise activity.
  • the sensor devices may communicate with an input device via a suitable wired or wireless interface, or may feed data directly to a communications network or to a processing device locally or via the cloud.
  • the plurality of exercise parameter values will include information representing an execution profile (EP) for an executed exercise activity.
  • EP execution profile
  • the processing unit 103 processes a plurality of received exercise parameters values for an executed exercise activity, including information representing the Load profile (LP), and possibly other information, to determine one or more assessment parameter values for assessing the executed exercise activity.
  • the processing unit 103 retrieves from the database 107 , information representing the exercise parameters, which may include the ratio of weight lifted, % bodyweight lifted, inherent machine weight, load profile (LP) for the executed exercise activity, and processes the plurality of exercise parameters values including the execution profile (EP) and the information representing the load profile (LP) to determine one or more assessment parameter values for assessing the executed exercise activity.
  • FIG. 2 shows a flow diagram for a method according to an embodiment of the present invention. As shown, the illustrated method involves receiving, at step 202 , a plurality of received exercise parameter values for the executed exercise activity, wherein the information for the executed exercise activity includes information representing the execution profile (EP).
  • the illustrated method involves receiving, at step 202 , a plurality of received exercise parameter values for the executed exercise activity, wherein the information for the executed exercise activity includes information representing the execution profile (EP).
  • EP execution profile
  • the exercise activity executed by a subject includes a resistance training exercise activity involving one or more weights (wt), one or more of sets (s) including one or more repetitions (R), and a total activity time (T e )
  • the plurality of exercise parameter values will also include:
  • information representing the load profile for the executed activity is then retrieved, at step 204 , from the database 107 .
  • the plurality of exercise parameters values and the information representing the load profile is then processed, at step 206 , to determine one or more assessment parameter values for assessing the executed exercise activity.
  • the load profile information includes information which identifies the phases of the exercise activity during which a muscle targeted by the exercise activity is intended to perform work, and/or contribute to stress on the muscle.
  • the execution profile information includes information which relates to the duration of each phases of the exercise activity, as measured during execution of the exercise activity, irrespective of which phase was intended to contribute to stress for that exercise activity.
  • a resistance training type exercise activity involving manipulating (such as lifting and lowering) a weight (wt) for a set including a number of repetitions includes four phases of a repetition, namely an eccentric phase, an eccentric-pause phase, a concentric phase, and a concentric-pause phase, with each phase having an associated duration.
  • the load profile may be represented as a binary digit code or “mask”, including values which identify the phases of an exercise activity that are intended to contribute to stress for that exercise activity.
  • the load profile (LP) may be expressed in a general form as:
  • load profile may be expressed in a different form.
  • the load profile for a “squat” requiring an eccentric phase duration, an eccentric-pause duration, a concentric phase duration, and no concentric-pause phase duration the LP may be expressed, using the above-described general form, as:
  • the subject undertaking the exercise activity may be able to select the appropriate LP expression for the exercise by selection of the respective exercise activity from the database.
  • the execution profile information may represent a sequence of values, wherein each value represents a duration of one of the exercise phases of the exercise activity.
  • the execution profile (EP) may be expressed in a general form as:
  • the duration values t 1 , t 2 , t 3 , and t 4 may be recorded during execution of an exercise activity using an suitable means, such as a stop watch. Verification to check approximate accuracy of the estimate may be achieved by the easier method of timing a set (for example, if the estimated/targeted profile is 1110 and each repetition is 10 seconds in duration, the total duration of the set is 30 seconds). The duration values may then be input into the system at the completion of the exercise activity for analysis by the system.
  • the duration values t 1 , t 2 , t 3 , and t 4 may be automatically recorded by a sensing device worn by the subject or attached to a machine, or incorporated in an exercise apparatus being operated by the subject.
  • a sensing device may include, for example, a wireless sensor.
  • a wireless sensor include a band or strap incorporating a sensor which is worn on, for example, the wrist of the subject to detect movements during exercise activities such as a bench press, pull-downs, bicep curls, tricep extensions.
  • the sensing device may be worn as a waist band, or as a necklet during chin-ups, or relocated to the ankle during leg curls or leg extensions.
  • the strap would be relocated to the ankle or the weight plate, or in the case of a cable-weight machine, the device could be attached to the weight stack. Indeed, in most instances where a cable-weight stack is used, the weight stack may provide a preferable location for the sending device regardless of the body movements, as the weight stack is subject to the true range of motion.
  • the activity database for the system may include reference data for interpreting sensor data obtained from a sensing device attached to the weight stack, including:
  • Embodiments of the present invention preferably access an activity database which is indexed to identify the above criteria, and for the subject and/or coach to make adjustments in the interpretation of sensor data if necessary.
  • the execution profile for a “squat” including an eccentric phase duration of 2 seconds, an eccentric-pause phase duration of 0 seconds, a concentric phase duration of 1 second, and a concentric-pause phase duration of 2 seconds may be expressed as:
  • processing the load profile and execution profile information involves determining a single value indicating the total duration of the phases intended to contribute to stress during the execution of the exercise activity.
  • the single value may be considered as the actual “total time under tension” (TUT) resulting from the identified phases.
  • processing the load profile (LP) information and execution profile (EP) information provides a single value indicating the accumulated time under stress contributed by phases identified by the load profile information during execution of the exercise activity.
  • the processing determines a single value as the total time under tension caused by the identified phases as the sum of the products of the values of the load profile and the values of the execution profile. Accordingly, the TUT for each rep in a set may be expressed as:
  • TUT r t 1 ⁇ d 1 +t 2 ⁇ d 2 +t 3 ⁇ d 3 +t 4 ⁇ d 4
  • a TUT may be determined for each set as the average TUT per repetition.
  • FIG. 4 includes further examples of TUT derivations for different exercise activities.
  • exercise parameters may be determined for each individual repetition rather than using an average of the TUT per repetition. Such an approach may provide additional data in embodiments of the system where performance over time of each set is of interest.
  • exercise activities may be performed in ways to modify the load profile.
  • a “barbell row” type exercise activity involving manipulation of a weight (wt) to exercise the latissimus dorsi muscle
  • TUT has an impact, in terms of the contribution to stress, during the “up” (that is, the concentric phase) and “down” (that is, the eccentric phase) phase.
  • the weight is held at the contracted-pause phase, and also at the bottom since the subject is still supporting the weight under stretch in the eccentric-pause phase.
  • the weight is “cheated up”
  • the TUT will be reduced since the momentum carries the weight up. In this example, this effect occurs because most of the weight is borne by the lower back in “throwing the weight”.
  • the activity database may include “Barbell row 1011”, “Barbell row 1010”, “Barbell row—cheat 0100” or any other means of conveying the exercise performance style and hence the actual load profile.
  • embodiments of the system may provide an option for exercise activities to be performed with a stipulation of continuous tension/load.
  • Embodiments of the present invention may cater for these variations, and permit the system to determine which variation was used for each execution of an exercise activity.
  • the variation may be stipulated by the subject or his/her coach (i.e. by specifying in a recording device which option of performance was used).
  • the system may detect the option by incorporating, for example, a sensor(s) which conduct a “full range of movement calibration” such that a full range of movement is performed initially with either no weight or a light weight prior to the execution of the actual exercise activity so the system can detect the full eccentric and concentric positions and possibly automatically assign, for each repetition, the appropriate load profile.
  • the subject may execute a full range repetition.
  • the system may detect the 0 and 180 degree positions, then, as the subject executes the barbell curls and the first repetition (“Rep 1”) stops at 75 degrees, the concentric pause of the LP is assigned as “1”, but for the second repetition (“Rep 2”), the arm is extended to a vertical position, in which case the concentric pause of LP is assigned as “0”.
  • the product of the execution profile and the load profile for the executed exercise activity may be determined as:
  • the system may estimate the load profile information throughout the range of movement using, for example, a scalar or percentage as opposed to simply assigning a “1” (on load) or “0” (no load). For example, with bicep curls, obtaining the angle of the forearm at the top of the range of movement may allow for the load profile to be estimated as having a value less than 1 (for example, 0.5) to indicate that the bicep is being stressed at 50% during that phase. Furthermore, it is possible that the load profile and duration could be sampled at various points over the range of movement to further improve accuracy.
  • the “standard” load profile may be “1110” since at the top (that is the concentric-pause phase) of the movement, the load disappears from the quadriceps if the knees are locked.
  • the actual TUT is not 8 seconds, but is instead 2 seconds.
  • the set will have a total duration of 58 seconds (noting that the duration is not 64 since on the last rep the subject will effectively ‘rack’ the weight for 6 seconds).
  • the effective TUT that is, the actual working TUT as determined by the method according to the present invention is 16 seconds.
  • Example A the subject's arm 500 is moved to ensure constant load on the biceps 502 , at the start the elbow 504 is slightly forward so the biceps 502 work from the start, and at the top of the movement, the forearm 506 is only just above parallel so the biceps 502 are working hard to contract.
  • Example B the elbow 504 is retracted at the start in cheat style meaning that half of the work is already done without actually loading the biceps 502 simply by bringing the elbow forward. (ref.
  • Example B is a less effective way to perform a curl but is unfortunately used by many subjects to use very heavy weights in the belief that they are making progress, even though this approach is less effective.
  • the execution profile information may be represented as:
  • the execution profile information may be represented as:
  • EP 0.5,0,0.5,0
  • embodiments of the present invention then process the TUT with the plurality of exercise parameter values for the executed exercise activity to determine one or more assessment parameter values for assessing the executed exercise activity.
  • processing the plurality of exercise parameter values to determine one or more assessment parameter values for assessing the executed exercise activity includes determining:
  • the weight (wt) may include part of the subject's bodyweight. For instance, with squats, about 80% of the body is also lifted (as the lower leg region of each leg is ‘supported’ on the floor and is not lifted, and the upper leg region of each leg is partially supported at the knee joint). With chin-ups, about 90% of the body is lifted (as the hands and forearms are “locked” to the bar and are not lifted). Hence, it is possible that for some exercise activities the weight (wt) may be a proportion of the subject's body weight.
  • the weight lifted (wt) is not the “actual” or apparent weight on the bar.
  • the effective weight is a proportion of the total weight manipulated by the subject.
  • There may also be part of the weight of the apparatus to take into account i.e. a minimum weight such as the weight of the slide or base plate etc.) before the % weight calculation is applied.
  • the assessment parameter values may be determined as follows.
  • a work volume (W) parameter value may be determined as:
  • a stress (S) value may be determined as:
  • the stress intensity assessment parameter value may then be determined as:
  • the hypertrophy factor assessment parameter may be determined as:
  • a subject completed a bench press exercise activity with exercise parameters as listed in table 1 over a total exercise activity duration of 510 seconds.
  • the exercise activity was performed as a bench press in which no component of subject's bodyweight was lifted.
  • the actual weight loaded was the weight lifted (including the bar), and the total work volume was determined as:
  • the TUT for sets 1 to 4 was determined as:
  • TUT t 1 ⁇ d i +t 2 ⁇ d 2 +t 3 ⁇ d 3 +t 4 ⁇ d 4
  • the stress (S) was determined as:
  • the work intensity (W i ) was determined as:
  • the above example provides assessment values which may permit a subject to readily compare the effectiveness of different exercise activities in terms of their effectiveness on stressing a targeted muscle, as is explained with reference to the below example.
  • a subject completed a bench press exercise activity with exercise parameters as listed in table 2 over an activity duration of 600 seconds.
  • Example 2 The following assessment parameters were then determined using the process described with reference to Example 1, which for clarity omits other assessment parameters such as the no. of sets, drop sets, max weight, avg weight per rep, total reps, and avg reps per set.
  • Example 2 The assessment parameter values determined in Example 2 were then compared with the corresponding values determined for Example 1 as follows:
  • Example 1 The comparison indicated that the exercise activity described in Example 1 was more effective than the exercise activity described in Example 2 in terms of stressing the muscle intended to be exercised by the exercise activity for the goal of hypertrophy or conditioning.
  • the present invention may assist a subject to identify permutations of each exercise activity to achieve maximum effective stress to elicit hypertrophy (or performance response) in each of their given muscles.
  • the output instructions may provide a more ideal target for subjects seeking growth (i.e. H f , S i , W i ) than the traditional methods which are typically pre-set or “prescribed” performance parameters that do not take into account a subject's unique physiology.
  • targets may also be “set” such as maximum work performed or work intensity for the purpose of improving performance in a sport or for increasing calorie expenditure.
  • Embodiments of the present invention may store assessment parameter values for subsequent analysis to generate instructions for one or more exercise parameters for the future execution of the exercise activity by the subject, such as in a periodised training program.
  • the instructions may provide a subject with plural program selections or options based around their identified ideal performance parameters for each muscle and thus assist the subject in aiming for the new ideal targets via periodised training, to avoid over-training, as explained in further detail as follows.
  • Embodiments of the present invention may analyse, for example, stored assessment parameter values to identify “peak” exercise activities having, for example, the highest combination of stress intensity and work and set a new target or objective based on these identified “peak” exercise activities.
  • the subject may then work towards a new target by developing strength and work capacity “either side” of the target whilst also allowing recovery between exercise activities.
  • the exercise activities may, for example, “cycle” a load between higher weights and lower volume (and stress) and lighter weight and higher volume, and also cycle periods of higher stress intensity but lower volume (hence lower H f ) to stimulate the muscles to develop, whilst reducing the likelihood of physically or psychologically “burn-out”.
  • the meso-cycles may be determined by calculating variations about the “target” H f exercise activities and varying the loads to avoid over-training as well as provide the necessary stimulus for the subject's muscles to achieve the target.
  • Embodiments of the present invention may thus provide a guide, in the form of a set of instructions, to cycle loads and periodise exercise activities, and may also provide guidelines on how to adjust the exercise activities instinctively.
  • the periodisation may be determined via suitable algorithms.
  • An embodiment of the present invention may also thereby provide statistics and guidelines for the subject in relation to the exercise parameters over longer periods of time such as micro-cycles, meso-cycles, and macro-cycles. In this manner, the subject will also gather performance and response data for the parameters for any given muscle that have meaning over the longer term, such as total work and analysis of the periodic stress and stress intensity over the longer term.
  • both of the above exercise activities equate to the same hypertrophy factor H f (that is, Stress intensity ⁇ Work volume).
  • H f that is, Stress intensity ⁇ Work volume
  • the system has identified an alternative target for the subject than the traditional “maximum weight” or “max weight for 8 to 12 reps”.
  • the subject may identify exercise activity combinations which improve the likelihood of the subject achieving the target work capacity, stress intensity and hypertrophy factor that the system determines for him/her.
  • the subject may train with varying parameters determined and/or predicted by the system and designed to ultimately develop the aspects of the muscle to achieve that performance, that is, a combination of strength stamina, and work capacity.
  • specifying “equivalent” targets effectively identifies the preferred parameters for peak performance in an iterative guidance manner.
  • the varying parameters mentioned above may be determined by the system in a ‘meso-cycle’ program which will stress the muscles around an optimum operating mode as well as providing ‘detraining’ sessions to prevent burn-out.
  • the system may also aim to have the subject achieve peak stimulation not just per workout but also per week, month, and meso-cycle in order to achieve and advance the peak capacity as quickly as possible.
  • Embodiments of the present invention may analyse recorded exercise activity information to determine one or more exercise activities which involve a slightly increased H f , that also match the subject's capabilities. For example, knowing that the subject can manage an average of 11 reps with 58 kg, provides a guide to the weights possible with other anaerobic repetition ranges.
  • the historical database of executed exercise activities may categorise exercise activities that have similar effects on the musculature so that exercise activities can be compared as opposed to simply comparing only sessions of the exact same exercise activity.
  • the database may have similar ID coding for flat barbell bench press and flat dumbbell bench press. This approach may enable comparisons of all workout histories of these exercise activities.
  • the average weight for an exercise activity was determined as 58 kg for 12 reps.
  • the system may determine a value expressing the average weight in terms of a proportion, or percentage of 1 RM (that is, the subject's one repetition maximum weight for the exercise activity) by indexing, for example, a table in the system database “%1 RM vs Reps”. Additional repetition ranges, for different weights, may then be estimated from their typical %1 RM values as shown in FIG. 7 . This type of table may be calculated for each exercise activity when predicting new target workouts.
  • Embodiments of the present invention may employ, for example, an existing or standard % 1 RM vs Reps reference table, and then further refine it over time for each subject in response to the subject's execution of an exercise activity.
  • a standard table might suggest that 8 reps is typically possible with 60%1 RM, and by extrapolation that the 70%1 RM weight may be determined and, by reverse look-up of the table, 6 reps should be possible.
  • the subject may demonstrate, on execution of the exercise activity that only 5 reps are possible.
  • the table may be modified for subject and exercise (over time with the gathering of data), and hence for future predictions when determining new targets.
  • weight values can be determined for each Rep value by retrieving the %1 RM for the actual reps and pro-rating the values for all other rep values.
  • the above tables can be referred to since the predicted reps have been determined based on the actual reps and weight lifted and by relating them to the R v %1 RM table.
  • the rep options may be as follows:
  • an embodiment of the present invention may determine and/or predict the likely weights and repetition ranges.
  • the subject, or the system may then program an increase of H f or other key parameters, such as:
  • the targeted reps/set may not necessarily be limited to the repetitions used in the example.
  • targets may be determined from the predicted 1 RM and working on %1 RM as follows:
  • the system may determine a plurality of instructions for executing an exercise activity which meet the training criteria, as follows:
  • embodiments of the present invention then output the results in the form of a set of instructions for executing the exercise.
  • FIG. 8 shows one example of an output 800 for display.
  • the illustrated output 800 provides a user-friendly presentation of the weight/rep/TUT and rest combinations to achieve a target H f , or W and S i .
  • Another element shown in the output shown in FIG. 8 is a preference tool of rest and TUT options to help the subject select an exercise parameter combination as may be done for example via intuitive periodisation or for gaining more data about a muscles working capacity in order to better identify the optimum training parameters.
  • the subject may have trained heavier last workout (with less reps and therefore lower TUT per set) and therefore may wish to choose a workout with higher reps and longer TUT per set (as part of the meso-cycle and adaptation process). They may also have trained with longer rests and want to shorten the rests to increase the intensity.
  • the subject may then “tick”, in this example, the rest options “31-51” and “51-61” to identify rest periods 31 to 61 seconds, and highlight those workouts (as identified in the TUT column with “ ⁇ ” or by other highlighting means).
  • various options for the next workout targets may be filtered for display to assist with the selection process. For example:
  • An embodiment of the present invention may also include guidance for using drop-sets in the target workouts.
  • a system in accordance with the present invention may provide an option as above known as a “standard” drop set, or allow selection of a “customised” drop set criteria.
  • a customised drop set criteria may enable the subject or coach to pre-set the system with choices of various combinations such as:
  • the system may perform the calculations for the current exercise activity in real-time and provide feedback to the subject in real-time, or near real-time, on their progress to meeting their target.
  • the system might indicate that based on the Work and times thus far in the exercise activity and that only one set remains, that if the subject starts the set in 20 seconds and performs the desired reps in the specified form, the subject will achieve their target.
  • embodiment of the present invention display a total H f score that will be achieved if the subject starts the last set in the within a particular duration (for example, 20 seconds), and then updates this score periodically (for example, every 5 seconds) and displays the updated score against the previous best score.
  • a particular duration for example, 20 seconds
  • the system may provide an alert for each phase of the exercise (for example, for each phase of the targeted execution profile for each rep) to assist the subject with the timing of the phases during the exercise activity.
  • the system may provide an audible tome or “beep” at the start of each repetition phase, in other words, the eccentric phase, the eccentric pause phase, the concentric-phase, or the concentric-pause phase to indicate the tempo of an exercise activity.
  • the subject for example, is in the eccentric phase and commences the concentric pause phase before the next “beep”, they know they are going too fast, etc.
  • the present invention provides a plurality of exercise activity instructions of varying weight, reps, TUT, rest that all match a targeted Hf or Si and W. Furthermore, the present invention is expected to assist a subject identify exercise activities which match their preference when planning their next workout. Preferably, this process is accomplished in one operation for all exercises activities that the subject plans for the next workout. The trainee can then select the preferred parameters for each exercise by “ticking” the workout that matches their preferred characteristics.
  • the system then downloads this workout data to a printable area as well as a field that can be exported to, for example, SMS or other e-media output or displayed.
  • the present invention can be implemented in numerous ways, including as a process, an apparatus, a system, or a computer readable medium such as a computer readable storage medium or a computer network wherein program instructions are sent over wireless, optical, or electronic communication links. It should be noted that the order of the steps of disclosed processes may be altered within the scope of the invention.

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US11351420B2 (en) 2015-02-23 2022-06-07 Smartweights, Inc. Method and system for virtual fitness training and tracking devices
US10881907B2 (en) * 2015-02-23 2021-01-05 Smartweights, Inc. Method and system for virtual fitness training and tracking service
US20210264811A1 (en) * 2015-06-08 2021-08-26 Pilates Metrics, Inc. Monitoring and assessing subject response to programmed physical training
US10290227B2 (en) 2015-06-08 2019-05-14 Pilates Metrics, Inc. System for monitoring and assessing subject response to programmed physical training, a method for encoding parameterized exercise descriptions
US20170282014A1 (en) * 2015-06-08 2017-10-05 Pilates Metrics, Inc. Monitoring and assessing subject response to programmed physical training
US10997870B2 (en) * 2015-06-08 2021-05-04 Pilates Metrics, Inc. Monitoring and assessing subject response to programmed physical training
US20170340920A1 (en) * 2016-05-31 2017-11-30 Polar Electro Oy System for monitoring physiological activity
US10967221B2 (en) * 2016-11-29 2021-04-06 James L. O'Sullivan Device and method for monitoring exercise performance
US20190160333A1 (en) * 2017-11-28 2019-05-30 International Business Machines Corporation Adaptive fitness training
US20210295240A1 (en) * 2020-03-23 2021-09-23 Fujitsu Limited Information processing apparatus and evaluation support method
US11701546B1 (en) * 2022-01-17 2023-07-18 Tonal Systems, Inc. Exercise machine struggle detection
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