WO2024132164A1 - Émulation de traînée aérodynamique d'un utilisateur d'un vélo d'exercice - Google Patents

Émulation de traînée aérodynamique d'un utilisateur d'un vélo d'exercice Download PDF

Info

Publication number
WO2024132164A1
WO2024132164A1 PCT/EP2022/087565 EP2022087565W WO2024132164A1 WO 2024132164 A1 WO2024132164 A1 WO 2024132164A1 EP 2022087565 W EP2022087565 W EP 2022087565W WO 2024132164 A1 WO2024132164 A1 WO 2024132164A1
Authority
WO
WIPO (PCT)
Prior art keywords
user
drag
sensors
sensor signals
resistance
Prior art date
Application number
PCT/EP2022/087565
Other languages
English (en)
Inventor
Iordanis CHATZIPRODROMOU
Ioannis SMANIS
Georgios GIANNAKOPOULOS
Christos ALTANTZIS
Original Assignee
Airr Gmbh
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Airr Gmbh filed Critical Airr Gmbh
Priority to PCT/EP2022/087565 priority Critical patent/WO2024132164A1/fr
Publication of WO2024132164A1 publication Critical patent/WO2024132164A1/fr

Links

Classifications

    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B22/00—Exercising apparatus specially adapted for conditioning the cardio-vascular system, for training agility or co-ordination of movements
    • A63B22/06—Exercising apparatus specially adapted for conditioning the cardio-vascular system, for training agility or co-ordination of movements with support elements performing a rotating cycling movement, i.e. a closed path movement
    • A63B22/0605—Exercising apparatus specially adapted for conditioning the cardio-vascular system, for training agility or co-ordination of movements with support elements performing a rotating cycling movement, i.e. a closed path movement performing a circular movement, e.g. ergometers
    • 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
    • A63B24/0087—Electric or electronic controls for exercising apparatus of groups A63B21/00 - A63B23/00, e.g. controlling load
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B22/00—Exercising apparatus specially adapted for conditioning the cardio-vascular system, for training agility or co-ordination of movements
    • A63B22/06—Exercising apparatus specially adapted for conditioning the cardio-vascular system, for training agility or co-ordination of movements with support elements performing a rotating cycling movement, i.e. a closed path movement
    • A63B22/0605—Exercising apparatus specially adapted for conditioning the cardio-vascular system, for training agility or co-ordination of movements with support elements performing a rotating cycling movement, i.e. a closed path movement performing a circular movement, e.g. ergometers
    • A63B2022/0635—Exercising apparatus specially adapted for conditioning the cardio-vascular system, for training agility or co-ordination of movements with support elements performing a rotating cycling movement, i.e. a closed path movement performing a circular movement, e.g. ergometers specially adapted for a particular use
    • A63B2022/0658—Exercising apparatus specially adapted for conditioning the cardio-vascular system, for training agility or co-ordination of movements with support elements performing a rotating cycling movement, i.e. a closed path movement performing a circular movement, e.g. ergometers specially adapted for a particular use for cycling with a group of people, e.g. spinning classes
    • 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
    • A63B24/0087—Electric or electronic controls for exercising apparatus of groups A63B21/00 - A63B23/00, e.g. controlling load
    • A63B2024/0093—Electric or electronic controls for exercising apparatus of groups A63B21/00 - A63B23/00, e.g. controlling load the load of the exercise apparatus being controlled by performance parameters, e.g. distance or speed
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B69/00—Training appliances or apparatus for special sports
    • A63B69/16—Training appliances or apparatus for special sports for cycling, i.e. arrangements on or for real bicycles
    • A63B2069/164—Training appliances or apparatus for special sports for cycling, i.e. arrangements on or for real bicycles supports for the rear of the bicycle, e.g. for the rear forks
    • A63B2069/165—Training appliances or apparatus for special sports for cycling, i.e. arrangements on or for real bicycles supports for the rear of the bicycle, e.g. for the rear forks rear wheel hub supports
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/10—Positions
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/17—Counting, e.g. counting periodical movements, revolutions or cycles, or including further data processing to determine distances or speed
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/40—Acceleration
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/62—Time or time measurement used for time reference, time stamp, master time or clock signal
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/64—Frequency, e.g. of vibration oscillation
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/80—Special sensors, transducers or devices therefor
    • A63B2220/802—Ultra-sound sensors
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/80—Special sensors, transducers or devices therefor
    • A63B2220/83—Special sensors, transducers or devices therefor characterised by the position of the sensor
    • A63B2220/836—Sensors arranged on the body of the user
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/80—Special sensors, transducers or devices therefor
    • A63B2220/89—Field sensors, e.g. radar systems
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2225/00—Miscellaneous features of sport apparatus, devices or equipment
    • A63B2225/20—Miscellaneous features of sport apparatus, devices or equipment with means for remote communication, e.g. internet or the like
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2225/00—Miscellaneous features of sport apparatus, devices or equipment
    • A63B2225/50—Wireless data transmission, e.g. by radio transmitters or telemetry

Definitions

  • the invention relates in general to the field of methods, systems, and computer program products, for emulating aerodynamic drag of a user of an exercise bike having a pedalling system subjected to a variable resistance.
  • it is directed to a method that processes sensor signals received from a set of sensors worn by the user, who is training on the exercise bike, to track the posture of the user, accordingly compute a drag force, and transmits a control signal encoding this quantity to a control unit for it to accordingly modify the resistance applied to the pedalling system.
  • exercise bikes also known as stationary bikes, exercise bicycles, spinning bikes, or exercycles
  • indoor exercises allow users to continue training, take advantage of structured workouts, and avoid bad weather.
  • the popularity of indoor cycling has even led to indoor cycling competitions.
  • racing cyclists or triathletes often train on stationary bicycles, whether designed as wheelless exercise machines or as systems consisting of regular bicycles mounted on rollers or trainers (also known as turbo trainers or smart trainers). Such systems allow them to warm up before racing or to train indoors.
  • the present invention is embodied as a method of emulating aerodynamic drag of a user of an exercise bike.
  • the exercise bike has a pedalling system, which is subjected to a variable resistance.
  • the method comprises repeatedly performing a series of steps at a processing device. This series of steps comprises processing sensor signals, computing a drag-related quantity, generating a control signal encoding the computed quantity, and transmitting the generated control signal to a control unit for it to modify the variable resistance in accordance with the transmitted control signal.
  • the processed sensor signals are signals that are initially received from a set of sensors worn by the user as the latter trains on the exercise bike by actuating the pedalling system. Thus, the sensor signals depend on a body posture of the user.
  • the proposed solution revolves around tracking the user posture thanks to sensors worn by the user to emulate the aerodynamic drag by modifying the pedalling resistance in accordance with signals received from the sensors.
  • This solution precisely tracks the posture to compute a drag- related quantity and accordingly emulate the drag. It does not require additional equipment, subject to the processing device, although the latter may form part of the exercise bike.
  • the present solution may notably be used to emulate aerodynamic drag effects for more realistic indoor biking training or indoor biking contests.
  • this quantity is computed by updating a 3D model of the user and then determining one or more drag equation parameters from the updated 3D model.
  • This allows realistic drag equation parameters to be obtained at a very affordable computational cost.
  • the drag-related quantity is eventually obtained based on the drag equation parameters.
  • the 3D model involves a skeletal structure of joints linked by segments.
  • the 3D model is updated by updating coordinates of the joints based on the processed sensor signals.
  • the determined drag equation parameters preferably include two sets of parameters values, which are values of the drag coefficient and the frontal area.
  • the drag equation parameters include a single set of values, e.g., products of values of the drag coefficient and the frontal area. Each of the drag coefficient and the frontal area is preferably considered to depend on a geometry of the user and the body posture of the user.
  • the drag equation parameters are determined based on a lookup table (LUT) stored in a memory of the processing device, for better accuracy and computational efficiency.
  • the LUT maps index values onto output values of the one or more parameters.
  • the index values correspond to predetermined categories of body postures and user geometry. So, the one or more drag equation parameters are determined by identifying one or more given values of the index values in accordance with the updated 3D model (the identified one or more given values respectively correspond to one or more of said categories), and looking up corresponding output values in the LUT.
  • the corresponding output values consist of one or more output values that correspond to the identified one or more given index values.
  • the LUT may map a set of index values onto a single set of output values, corresponding to products of values of the frontal area and the drag coefficient.
  • the LUT maps the set of index values onto two distinct sets of output values (i.e., values of the frontal area and the drag coefficient).
  • the output values are values that are precomputed for respective ones of the predetermined categories. So, the drag equation parameters are determined by identifying index values and looking up corresponding output values in the LUT.
  • determining the drag equation parameters further comprises correcting said output values to take account of one or each of a bike type and one or more aerodynamic auxiliaries.
  • the leg rotation motion can be taken into account too. Applying such corrections a posteriori does not impact the efficiency of the computations of the drag equation parameters.
  • the computed quantity is preferably a drag force; it is computed in accordance with the drag equation, based on the drag coefficient, the frontal area, a mass density of air, and an air flow velocity relative to the user.
  • the method may further comprise receiving, from the control unit, a feedback signal reflecting a virtual speed of the user.
  • the processing unit further receives data related to an altitude or the mass density of air, and accordingly updates a value of the air flow velocity relative to the user to accordingly compute the drag force in accordance with the drag equation.
  • processing the sensor signals comprises sampling sensor measurement values (as encoded in the received sensor signals) at a first frequency that is in a range extending from 2 to 100 Hz, preferably from 10 to 60 Hz. Said quantity is repeatedly computed at a second frequency, whereas the control signal is repeatedly generated and transmitted at a third frequency.
  • Each of the second frequency and the third frequency is less than or equal to 10 Hz, the third frequency being preferably less than or equal to 5 Hz.
  • Such frequencies are chosen to achieve a desired response time of the system, while also accommodating characteristics of the sensors.
  • the method further comprises, prior to transmitting the generated control signal, connecting to a processing unit of the exercise bike or a resistance-control system that includes said control unit, via an application programming interface (API) to be able to transmit the generated control signal via said API.
  • API application programming interface
  • the method further comprises receiving, at the control unit of the pedalling system, an internal signal based on the transmitted control signal, and modifying said resistance in accordance with the received internal signal.
  • the invention is embodied as an aerodynamic drag emulation system for emulating aerodynamic drag of a user of an exercise bike.
  • the exercise bike has a pedalling system subjected to a variable resistance.
  • the system comprises a processing device, which is configured to interface with each of a set of sensors (e.g., wirelessly) and a control unit adapted to modify said variable resistance.
  • the processing device is further configured to repeatedly perform a series of steps.
  • this series of steps includes processing sensor signals received from the set of sensors, computing a drag-related quantity based on the processed sensor signals, whereby this quantity depends on the body posture, generating a control signal encoding the computed quantity, and transmitting the generated control signal to a control unit for it to modify said resistance in accordance with the transmitted control signal.
  • the system further comprises the set of sensors.
  • the latter include inertial sensors (preferably inertial measurement units), microradar sensors, and/or strain sensors.
  • the sensors consist of inertial sensors only.
  • the sensors include both inertial sensors and microradar sensors.
  • the system may further include a garment, which is designed to integrate at least part of the sensors.
  • the processing device is further configured to sample measurement values encoded by the received sensor signals at a first frequency that is in a range between 2 and 100 Hz, preferably between 10 and 60 Hz, so as to repeatedly compute the quantity at a second frequency and repeatedly generate and transmit control signals at a third frequency;
  • a first frequency that is in a range between 2 and 100 Hz, preferably between 10 and 60 Hz, so as to repeatedly compute the quantity at a second frequency and repeatedly generate and transmit control signals at a third frequency;
  • Each of the second frequency and the third frequency is less than or equal to 10 Hz, the third frequency being preferably less than or equal to 5 Hz.
  • the system further includes the exercise bike.
  • the processing device is a remote processing device, distinct from the exercise bike.
  • the exercise bike is otherwise configured to run an API, while the remote processing device is configured to connect to a processing unit of the exercise bike via said API to repeatedly transmit the generated control signals to said control unit via said API.
  • the processing device connects, via an API, to a resistance-control system that includes said control unit and is accordingly designed to control the resistance of the pedalling system.
  • the resistance-control system may for instance form part of system comprising rollers or a turbo trainer.
  • the invention is embodied as a computer program product for emulating aerodynamic drag.
  • the computer program product comprises a computer readable storage medium having program instructions embodied therewith.
  • the program instructions are executable by processing means of a processing device to cause the latter to repeatedly perform a series of steps according to the present methods.
  • FIG. 1 A is a side view of a user exercising on an exercise bike coupled to a turbo trainer capable of varying the pedalling resistance, whereby the user experiences a pedalling resistance feedback controlled by an external processing device, via a resistance-control system, in accordance with measurement values obtained from sensors worn by the user, where this resistance feedback emulates aerodynamic drag, according to embodiments.
  • a portable device laptop, tablet, smartphone
  • FIG. IB is a variant of FIG. 1A, where the user exercises on a stationary bike, as in embodiments. Again, the user experiences a pedalling resistance feedback, here controlled by a processing device mounted in the bike, according to embodiments;
  • FIG. 2 is a block diagram schematically illustrating selected components of a system for emulating aerodynamic drag, according to embodiments
  • FIG. 3 is a flowchart illustrating high-level steps of a method of emulating aerodynamic drag, according as in embodiments;
  • FIG. 4 illustrates a 3D model of a cyclist, where the 3D model is based on a skeletal structure of joints linked by segments, which is used to track a body posture of the cyclist to determine a quantity depending on a drag coefficient and a frontal area of the cyclist, according to embodiments; and
  • FIG. 5 schematically represents a general-purpose computer architecture, suited for implementing one or more method steps as involved in embodiments of the invention.
  • the present invention aims at providing solutions to realistically emulate the aerodynamic drag for users of exercise bikes.
  • the mechanical resistance translates in energy losses due to friction as bike parts rub together while moving.
  • the rolling resistance relates to the energy loss due to the friction experienced by the rolling tires on the road.
  • the mechanical and rolling resistances are essentially constant and can easily be emulated as constant losses in the trainer.
  • Gravitational resistance, or inertial resistance represents the kinetic energy losses and its transformation to potential energy as the biker rides uphill. The corresponding losses can be captured by an analytical function of the terrain slope; they can easily be incorporated in a variable resistance trainer.
  • the aerodynamic resistance relates to the force exerted by the air on the biker and the bike, parallel and opposite to the direction of flow relative to the biker.
  • This type of resistance depends on the velocity of the rider, the air density and the instantaneous body shape and posture. This resistance is time dependent and highly nonlinear. Realistic emulation of aerodynamic resistance is such a complex issue that it has not been implemented in commercial exercise bikes yet. Therefore, users of exercise bikes cannot experience aerodynamic drag as they would in real conditions. With this in mind, the present inventors came to develop solutions to realistically emulate aerodynamic drag.
  • FIGS. 1A - 3 A first aspect of the invention is now described in detail, in reference to FIGS. 1A - 3.
  • This aspect concerns a method of emulating aerodynamic drag, allowing a user 2 of an exercise bike 20, 20a to experience the drag effect, notwithstanding the stationarity of the exercise bike. This is achieved by modifying the resistance to which the pedalling system 22, 22a is subjected, in accordance with the posture of the user, where the posture is tracked thanks to sensors.
  • the present method and its variants are collectively referred to as the “present methods”. All references Sn refer to methods steps of the flowcharts of FIG. 3, while numeral references pertain to devices, components, and concepts, involved in embodiments of the present invention.
  • the method is implemented at a processing device 10, which repeatedly performs a series of steps, which essentially include the steps denoted by references S30, S40, S50, and S60, in the flow of FIG. 3.
  • steps revolve around processing S30 sensor signals, accordingly computing S40 a drag-related quantity, generating S50 a control signal encoding the computed quantity, and finally transmitting S60 the generated control signal to a control unit 25 for it to modify S70 the resistance of the pedalling system 22, 22a in accordance with the transmitted control signal.
  • a control unit 25 is used to modify the resistance to which the pedalling system is subjected.
  • the control unit 25 may for instance form part of a resistance control system 21 of a turbo trainer 23 (as in FIG. 1A), of rollers, or of a stationary bike (as in FIG. IB).
  • Such steps are repeatedly performed, as necessary to continually emulate the aerodynamic drag in accordance with the tracked posture of the user 2.
  • the sensor signals processed at step S30 are signals that are initially received S20 from a set of sensors 50, e.g., a network of wearable sensors 50, such as inertial sensors. Such sensors are worn by the user 2 as the latter trains S10 on the exercise bike 20, 20a (i.e., the user actuates S 10 the pedalling system 22, 22a). As a result, such sensor signals depend on, and thus capture, the posture of the user 2 as this posture changes over time.
  • the quantity computed at step S40 is a key quantity used to emulate the aerodynamic drag. In other words, the aerodynamic drag to be emulated is determined by this quantity, which is computed based on the processed sensor signals. As a result, this quantity depends on, and reflects, the posture of the user 2. In the following, this quantity is referred to as a drag-related quantity.
  • the exercise bike 20, 20a is here defined as a device used as exercise equipment for cycling (typically indoors). It may be a mere stationary bicycle 20a (also known as exercise bicycle, spinning bike, exercycle, etc.), typically wheelless, as assumed in FIG. IB, where the front wheel is a weighted flywheel.
  • the control unit 25 is a control unit of the pedalling system of the bike, i.e., a unit capable of modifying a resistance setting of the flywheel.
  • the exercise bike is a bicycle 20, which is mechanically coupled to rollers or a trainer 23, such as a so-called turbo trainer or a smart trainer (as assumed in FIG. 1 A).
  • rollers When using rollers (not shown), one or each wheel of the bike 20 rolls on one or more rollers.
  • the latter form part of a resistance-control system, which includes a control unit to vary the resistance applied to the pedalling system 22 through friction with the rollers.
  • a turbo trainer 23 When using a turbo trainer 23, the back wheel of the bicycle 20 is removed but the bike chain is coupled to a gear system having a variable resistance.
  • the gear system forms part of a resistance-control system 21, which includes a control unit to vary the resistance applied to the pedalling system 22 through the gear system.
  • the exercise bike is a fan bike (also called air bike), where the resistance is primarily caused by blades moving through air.
  • the pedalling system may again be coupled to a resistance-control system configured to modify the effective resistance, thanks to a control unit of the resistance-control system.
  • the exercise equipment 20, 20a on which the user sits or stands includes, or is coupled to, a variable resistance-control system, itself including or controlled by a control unit that can modify the resistance setting of the resistance-control system, e.g., whether mechanically or magnetically.
  • the resistance setting indicates how hard the user has to pedal (i.e., actuate the pedalling system). This resistance is used to mimic exertion as when riding a bicycle outdoors.
  • the pedalling system 22, 22a is typically designed as a chainset (also called crankset), including crankarms, to which the pedals 24, 24a attach. However, the pedalling system does not necessarily include a chain or a belt in a stationary bike.
  • the processing device 10 can be a standalone device (i.e., distinct from the exercise bike or the resistance-control device), placed at any suitable location, e.g., in front of the user (as in FIGS. 1 A, IB) or in the vicinity of the bike. It is typically embodied as a small portable device.
  • the processing device 10 may be a smartphone or a tablet.
  • it is preferably designed as a dedicated device, which notably includes a processing unit, memory, buffers, and interfaces, as latter described in reference to FIGS. 2 and 5.
  • This dedicated device may also possibly be integrated in a garment 40 worn by the user 2.
  • This garment 40 may otherwise include the sensors 50, or some of the sensors, as assumed in FIGS. 1A, IB.
  • the dedicated device 10 may further include one or more of the sensors 50.
  • the processing device 10 forms part of the bike 20a, as in FIG. IB. E.g., it may be (or form part of) the processing unit 26 of the bike 20b itself. In all cases, the processing device 10 must be interfaced with a control unit 25 that is able to modify the resistance applied to the pedalling system, e.g., by way of magnets and a conducting flywheel operating as an eddy current brake.
  • This control unit 25 is (or includes) an input/output VO unit, which defines (or contributes to define) changes to the resistance applied to the pedalling system.
  • the essential function of the processing device 10 is to collect and process sensor signals, so as to compute the drag-related quantity, and accordingly provide feedback to the bike, typically via an application programming interface (API), with a view to modifying the pedalling resistance and thereby emulating the drag effect.
  • API application programming interface
  • the above series of steps must be repeatedly performed. However, some of these steps may be performed at different frequencies, as necessary to achieve a desired response time of the system, while accommodating characteristics of the sensors.
  • the drag-related quantity is a quantity that relates to aerodynamic drag, i.e., the friction caused by fluids like air and water.
  • the aerodynamic drag is the force that an object needs to overcome as it moves through this fluid at a certain velocity.
  • the drag-related quantity may for instance be the drag force, which depends on the frontal area (itself depending on the body posture and the body size), the drag coefficient, and the fluid velocity relative to the object.
  • the quantity computed at step S40 is a quantity (e.g., the drag coefficient), or a combination of quantities (e.g., the frontal area and the drag coefficient), based on which the drag force can be estimated.
  • this quantity can be any number or an array of numbers. It may for instance be an array of values including the frontal area and the drag coefficient, or a quantity proportional to a product of the frontal area and the drag coefficient, for example.
  • the proposed solution relies on tracking the user posture thanks to sensors 50 worn by the user 2 to emulate the aerodynamic drag by modifying the pedalling resistance in accordance with signals received from the sensors.
  • This solution precisely tracks the user posture to accordingly compute a drag-related quantity and accordingly emulate the drag.
  • No additional equipment is required, subject to the processing device 10, although the latter may form part of the bike’s electronics.
  • the present solution may notably be used to realistically emulate aerodynamic drag effects for realistic indoor bike training or indoor biking contests.
  • GUI graphical user interface
  • This GUI may for instance be run at the bike 20a, should it integrate a suitable display 28, as assumed in FIG. IB, and/or at a nearby computer 30, as in FIG. 1 A.
  • the processing device 10 is used to estimate the aerodynamic drag on the fly, based on the signals received from the sensors 50.
  • the drag force can be estimated by first determining drag parameters including the frontal area A and the drag coefficient Cd. This is preferably achieved by looking up such values in a lookup table (LUT) 18, the index values of which correspond to predetermined body postures and body sizes. A response signal is then sent to the resistance-control system’s control unit 25 defining the change of the resistance in accordance with the emulated aerodynamic drag.
  • LUT lookup table
  • the drag-related quantity is preferably computed S40 by updating S42 a 3D model 15 of the user 2 and then determining S44 parameter values of the drag equation, based on the updated 3D model 15.
  • the 3D model can be devised as a skeletal structure of joints 17 linked by segments, as assumed in FIG. 4.
  • the 3D model is updated S42 by updating coordinates 19 of the joints based on the previously processed sensor signals.
  • the determined parameters preferably include both the drag coefficient and the frontal area. In variants, the determined parameters consist of the product of the drag coefficient and the frontal area or, even, the drag coefficient alone, because the frontal area can be subjected to approximations.
  • each of the drag coefficient and the frontal area depends on the geometry and the current posture of the user 2.
  • the geometry of the user corresponds to the size and shape of the user. This geometry is assumed to be constant; it does not change over time.
  • the user’s posture changes over time because the user happens to change positions as s/he trains on the exercise bike. I.e., together, the geometry and the posture capture a time-dependent, effective geometry of the user as s/he trains on the bike.
  • the frontal area A corresponds to a frontal, cross-sectional area of the system considered, i.e., the user 2, the user 2 + a given type of bike 29, or the user 2, the type of bike 29, and aerodynamic auxiliaries (such as aerodynamic wheels, helmets, shoes, clothes, glasses, etc.), if any.
  • the frontal area A is considered to be constant over time; it can thus simply be determined in accordance with characteristics of the user 2 (height, weight, shoulder distance, torso size, etc.), a type of bike 29, and aerodynamic auxiliaries, if any.
  • the frontal area A is considered to be a time-varying quantity, as mostly assumed in the following. This quantity primarily depends on the posture of the user 2.
  • the area A will at least capture the frontal area of the sole user 2, though it may additionally incorporate a frontal area of a given bike 29 too, as well as aerodynamic auxiliaries’, as noted above.
  • the area A is the frontal area of the system composed of both the user and the bike 29.
  • the latter 29 does not necessarily correspond to the exercise equipment 20, 20a that is actually used by the user 2.
  • the simulated bike 29 is a bike as intended for emulation purposes.
  • Several types of bikes can possibly be considered in the initial calibration.
  • the frontal area A corresponds to an orthographic projection of a certain system (whether consisting of the sole user or both the simulated bike and the user) in a plane perpendicular to the theoretical direction of motion.
  • the area A reflects the user only, meaning that the contribution of the bike is neglected.
  • the user legs have a fairly negligible impact on the change of the frontal area. So, the impact of the legs may possibly be neglected too.
  • the frontal area A can be computed as a function of the time-dependent frontal cross-section of the sole upper body of the user.
  • the impact of the legs is considered, e.g., as an additional constant, a multiplicative factor, or a simple analytical function of the frontal area of the sole upper body.
  • Another possibility is to take into account the impact of the rotation of the legs, using a function of the rotation speed of the legs (effectively equal to the cadence [rpm]) and the virtual bike velocity (or, more precisely, the virtual air flow velocity relative to the user).
  • the contribution of the bike 29 can be taken into account as well, as noted above.
  • a temporary change in the simulated environment (as when turning and thus cornering the bike) can be taken into account too, by way of simple corrections.
  • the frontal area A is a dynamic quantity that essentially depends on the posture and geometry of the upper body.
  • a change in the frontal area A can safely be approximated as being solely due to how the frontal cross section of the upper body changes over time.
  • the frontal area A may be computed as a function of the time-dependent frontal cross-section of the sole upper body of the user, whereas the contributions from the user legs and/or the bike 29 can be taken into account as additional constants, factors, etc.
  • additional corrections can similarly be made, e.g., to take account of a particular equipment (e.g., aerodynamic auxiliaries), the leg rotation speed, and/or a temporary change in the simulated environment, as noted above.
  • the drag coefficient Cd is a dimensionless coefficient related to the effective geometry of the target system as it changes over time; this coefficient is used to quantify the drag of the target system in a fluid environment (here the air).
  • the drag coefficient of any object results from the effects of the two basic contributions to fluid dynamic drag, namely the skin friction (depending on the surface roughness) and the form drag, which depends on the shape and size of the object.
  • the 3D model 15 of the user involves a pseudo-skeletal structure, which can be captured as a simple graph structure (typically a tree). It is a reference backbone of joints 17 linked by segments (shown as dashed lines in FIG. 4). This skeletal structure does not necessarily correspond to body joints and limbs of the use, as it does not need to. Nevertheless, this 3D model 15 adequately reflects the current posture of the user.
  • the 3D model preferably incorporates the user geometry (size and shape). Contrary to the depiction in FIG. 4, the 3D model may further encompass the bike 29, thus providing a dynamic model of a system comprising both the user 2 and the bike 29. The bike can be considered as mostly having a constant frontal area.
  • the 3D model can further be calibrated based on different types of bikes 29 (e.g., time trial, endurance, road bike, etc.) and, if necessary, different types of aerodynamic auxiliaries.
  • the coordinates of the joints 17 may be computed using any suitable coordinate system.
  • the coordinate representation may for instance use an internal coordinate representation (e.g., similar to the z-matrix of an atomic system in quantum chemistry), involving true independent coordinates of the joints only.
  • the coordinate system may include segment lengths, segment angles, and dihedral angles. That said, the segments of the 3D model 15 may have constant lengths, such that only the relative angles may have to be updated. Such angles can be derived from the sensor signals.
  • Euclidean coordinates 19 ⁇ x ; , z, ⁇ of the joints 17 are relied on, as assumed in FIG. 4.
  • the drag equation parameters are preferably determined S44 based on a LUT 18, which basically maps index values onto output values.
  • the index values correspond to predetermined categories of postures and user geometry. I.e., N predetermined postures and AT predetermined user geometry may be turned into a 2D input array of N x AT index values.
  • the input array is a multidimensional array (e.g., a 3D or 4D array), where the various dimensions may notably pertain to user geometry, postures, aerodynamic auxiliaries, and hand positions.
  • the dimensionality of the LUT depends on the optimisation of the lookup algorithm employed.
  • the LUT such index values to a single set of output values, e.g., the drag coefficient or the product of the frontal area and the drag coefficient.
  • the LUT maps index values to two distinct sets of output values, respectively corresponding to values of the frontal area and the drag coefficient, as precomputed for respective ones of the categories. So, suitable values of the frontal area and the drag coefficient can be obtained by merely identifying an index value corresponding to the current sensor measurements. More precisely, the drag equation parameters are determined S44 by identifying S442 one or more given index values and then looking up S444 the corresponding output values in the LUT 18, see FIG. 4. The index values are identified in accordance with the updated 3D model 15. The identified index values correspond to respective categories that potentially match the sensor measurements.
  • a preferred scenario is one where the LUT maps index values onto two distinct sets of output values, corresponding to values of frontal area A and drag coefficient Cd. That is, such output values are precomputed according to different body types and postures.
  • each of the values of A and Cd are preferably tabulated (for efficiency)
  • the drag coefficient is a highly nonlinear quantity, which can normally not be formulated as an analytical function (i.e., a closed- form solution).
  • the LUT 18 can be stored on a memory 16 of the processing device 10 (see FIG. 2), which can for instance be equipped with an NVMe Flash memory.
  • the LUT may possibly be implemented in hardware, next to the memory 16, by way of one or more dedicated circuits, which can be regarded as memory circuits.
  • the LUT can be implemented as a hardcoded circuit, designed to provide the necessary parameter values, similar to a read-only memory (ROM) circuit, or as a rewritable memory circuit, such as a random-access memory (RAM), to achieve a reprogrammable LUT.
  • ROM read-only memory
  • RAM random-access memory
  • the upper-body posture is preferably identified based on angles between the joints 17, i.e., virtual reference points of a virtual backbone mapped onto the user 2 thanks to the sensor measurements, as assumed in FIG. 4.
  • the lengths of the segments between the joints are normally needed too by the underlying algorithm. Still, the segment lengths can normally be considered constant, notably if the sensors 50 are included in or on a garment worn by the user. In this case, the segment lengths can be computed from known, predetermined positions of the sensors in the garment. The positions of the sensors may also be inferred in accordance with the size of the garment incorporating the sensors, if necessary.
  • the segment lengths may further be derived from detailed inputs from the user 2 (e.g., inputs concerning height, weight, shoulder distance, torso size, etc.), as part of an initial calibration procedure. Such inputs may also be obtained using other computer vision techniques, e.g., implemented at a nearby computer 30 exploiting images obtained from an integrated webcam. Once the segment lengths have been registered, these can be used to compute the backbone configuration 15 (by suitably updating angles between such segments), from which the coefficients A and Cd can be determined, preferably thanks to the LUT 18.
  • the identification of relevant index values may for instance be performed thanks to a suitably trained classifier (i.e., a machine learning model), using sensor measurements as input.
  • a suitably trained classifier i.e., a machine learning model
  • mere heuristics e.g., based on decision rules and arithmetic operations
  • interpolation may be used. That is, one may interpolate output values in accordance with two or more index values identified from the sensor measurements.
  • a machine learning model is used to directly perform inferences (as to the drag equation parameters) from the sensor signals received (i.e., without requiring any LUT at all).
  • an elaborate cognitive model may directly infer s and Cd values from arrays of values produced from the sensor signals.
  • a cognitive model should be distinguished from a classifier used to identify index values, as the latter scenario requires a LUT, contrary to the former.
  • the present inventors concluded that the best approach is to rely on a heuristic to extract the current body posture (from a suitably updated 3D model) and finally determine the frontal area and drag coefficient values via a LUT 18. That is, using the 3D model 15, a joint and spine motion analysis is performed to identify the new posture, based on the updated angles and segment lengths, and finally determine the values of A and Cd corresponding to the updated posture, using the LUT. Tests performed by the inventors show that using a LUT leads to more accurate results and is more efficient, computationally speaking, such that it also requires less power in practice.
  • the processing device is a standalone (e.g., a mobile, battery-powered) device 10, distinct from the exercise equipment 20, 20a.
  • the LUT 18 may additionally consider information about the bike type and aerodynamic auxiliaries.
  • this requires adding corresponding categories in the LUT and increases the size of the input array, which impairs the computational efficiency. So, it is more efficient to separately compute the impact of the bike type and aerodynamic auxiliaries by correcting the instantaneous drag coefficient and/or frontal areas, as necessary, using ad hoc correction methods.
  • the determination of the drag equation parameters may further involve a correction S446 of the looked-up values to take account of a bike type and/or one or more aerodynamic auxiliaries such as exemplified above.
  • the drag equation parameters can be simply corrected by way of corrective aerodynamic gain percentages.
  • the corresponding information can be initially provided by the users 2 themselves, e.g., via GUI run on the exercise bike 20a or a nearby computer 30. This information is then transmitted to the processing device 10, should the latter be a standalone external processing device 10, as assumed in FIGS. 1A and IB. This information may also be directly transmitted to and processed by the processing unit 26 of the exercise bike 20, 20a itself, should the processing device form part of the bike 20, 20a.
  • such information may be translated as corrective gain percentages with respect to standard reference values as stored in the LUT.
  • This makes it possible to easily tune the drag-related quantity, e.g., the drag force Fd.
  • corrections may more accurately be applied separately to the values of A and Cd, hence the advantage of mapping two distinct sets of output values for A and Cd.
  • Such operations can be easily and efficiently implemented by an arithmetic (ARI) unit, whether in the device 10 or in the processing unit 26 of the exercise bike 20a.
  • the parameters or quantities appearing in the above equation correspond to the mass density p of air, the frontal area 4, the drag coefficient Cd, and the air flow velocity u relative to the user.
  • the air flow velocity u corresponds to an air flow velocity that the user would experience in real conditions, because of his/her speed.
  • the processing device 10 collects different types of signals, beyond the sensor signals, in order to accurately compute the drag force Fd.
  • the processing device 10 may receive feedback signals reflecting the current virtual speed of the user 2.
  • the virtual speed signal may be any signal indicative of the current virtual speed, e.g., including the current power output from the resistance-control system or the pedalling system (should the resistance-control system be distinct from the pedalling system).
  • Such signals are received from any unit (such as the control unit 25 of the pedalling system) or any device capable of measuring an out quantity indicating the virtual speed. This allows the processing device 10 to derive the air flow velocity u relative to the user 2.
  • the virtual wind velocity may be assumed to be zero, as in quiescent conditions, where the relative air flow velocity u is solely determined by the speed of the user. It is nevertheless possible to add a virtual wind profile on top of the virtual air flow velocity resulting from the virtual speed, i.e., as part of the relative air flow velocity u.
  • the wind velocity parameter can be a predetermined input parameter, possibly set by the user or competition organizers.
  • the virtual wind velocity may also vary according to a predetermined wind profile, which may be a function of time and/or the virtual distance travelled. Note, the air flow velocity can typically be updated at a lower frequency than other quantities that depend on the upperbody posture.
  • the air density p Another parameter needed to compute the drag is the air density p.
  • the latter may be assumed to be a fixed parameter, e.g., a constant stored in the processing device 10.
  • the device 10 further receives data related to the air mass density, if necessary.
  • the air density may also be calculated as a simple function of altitude, such that the device 10 may rely on data related to a virtual altitude too, instead of the air mass density.
  • the device 10 will accordingly update a value of the air flow velocity relative to the user 2 and compute the drag force in accordance with the drag equation.
  • the signals transmitted S60 by the processing device 10 to modify the pedalling resistance capture the current drag force Fd.
  • the transmitted signals may be any relevant drag quantity, e.g., the drag coefficient alone (assuming the frontal area is constant), the frontal area and the drag coefficient, or a product of the frontal area and the drag coefficient, whereby the bike’s internal processing unit may still adequately compute the drag force to accordingly modify the pedalling resistance.
  • the sensor signals are preferably processed S30 by sampling the sensor measurement values (as encoded in the received sensor signals) at a first frequency /i, which typically is between 2 to 100 Hz (preferably between 10 to 60 Hz).
  • the frequency f may typically differ from the frequency fo at which the sensors send data.
  • the drag-related quantity can be repeatedly computed S40 at a second frequency /2, whereas the control signal is repeatedly generated S50 at a third frequency fa.
  • the frequency at which the generated signal is transmitted is normally equal to the third frequency fa.
  • the second and third frequencies fa, fa may differ from the first frequency fa.
  • each of the second frequency and the third frequency may be less than or equal to 10 Hz.
  • the communication protocol may possibly be imposed by the type of sensors 50.
  • Data acquisition capabilities of the sensors as considered herein may typically be on the order of kHz. In the present context, however, there is typically no need for the processing device 10 to exploit frequencies higher than 10 Hz. Even, the useful transmission frequency/ can be limited to 5 Hz.
  • the sensor signals can be throttled by sampling values produced by the sensors and storing the sampled values in a buffer.
  • the data acquisition frequency may possibly be controlled directly from the sensors 50, the sensor devices permitting. In that case, the sensing frequency is preferably set directly on the sensors 50, which will result in lower data transfers. Depending on the sensor type, this may also result in increasing the precision of the sensors.
  • the system captures and analyses the user body at an average frequency fa of approximately 5 Hz (every 0.2 seconds). Still, in order to capture the body motion and posture at this frequency, the processing device will typically need a higher sampling frequency /i, which will roughly be an order of magnitude higher, e.g., around 50 Hz. A higher sampling frequency is advantageous for smoothing and filtering input signals from the sensors. This may notably be used to detect body posture changes that are substantial and long-term enough, as part of pre-processing steps. Eventually the drag quantity computed may possibly be averaged over time (e.g., based on two or three successive time points), whereby the signals transmitted to the resistance-control system may have a lower frequency /?.
  • the third frequency may be less than or equal to 5 Hz in practice. This value depends on the rate at which the system can still respond to the input signal in a smooth manner.
  • the frequency fa at which the control unit 25 updates the resistance setting may differ from the frequency fa. In practice, such frequency values may have to be adapted, depending on actual characteristics of the system components (like sensors, pedalling system, etc.).
  • the processing device 10 is a standalone device (as in FIG. 1A or IB), use is typically made of an API to connect and communicate with the resistance-control system 21 of the bike 20 (or the bike 20a itself). That is, the present methods may further comprise connecting to a processing unit 26 of the resistance-control system 23 or the bike 20a (via an API running on this processing unit 26), so as for the device 10 to be able to transmit S60 the generated control signals via the API. In turn, the control unit 25 of the pedalling system 22, 22a may receive an internal signal from said API and accordingly modifying S70 the pedalling resistance. Note, the processing device 10 may connect wirelessly to the resistance-control system 21 (as in FIG. 1A) or the bike 20a (as in FIG.
  • the device 10 connects via a USB or Thunderbolt connection, for example.
  • the choice of the connection protocol depends on the capabilities of the API as well as the amount of data to be transferred to the resistance control system via the API. In general, the amount of data and the frequency of transfers are compatible with typical wireless protocols.
  • the processing device forms part of the stationary bike 20a or the resistance-control system 21. E.g., the processing device may be the processing unit 26 of the bike 20a or the resistance-control system 21, as noted earlier.
  • the processing device 10 may connect to a remote computer 30 enabling a GUI that allows information to be displayed to the user 2, as assumed in FIG. 1.
  • this GUI may provide indications for the user to improve her/his posture and reduce the drag.
  • This GUI may further display information as to the current drag force, or the parameters A and/or Cd. It may also display a dynamic drag score derived from such parameters.
  • the system 1, la includes a processing device 10, which may possibly form part of the bike 20a or a separate resistance-control system 21, as discussed earlier.
  • the control unit 25 may possibly form part of the bike 20a or a separate resistance-control system 21. In all cases, however, this control unit 25 is adapted to modify the pedalling resistance.
  • the device 10 may have any suitable computer architecture, such as exemplified in FIG. 5 and described in detail in section 2.2.
  • the device 10 is designed to interface with the set of sensors 50 and the control unit 25. Note, the device 10 may only be indirectly interfaced with the control unit 25, e.g., via an API running on a processing unit 26 of the bicycle 20a or the resistance-control system 21, as discussed above.
  • the device 10 is further configured to repeatedly perform a series of steps S30 - S60, at suitable frequencies, for the control unit 25 to modify S70 the pedalling resistance, as described earlier in reference to the present methods.
  • the system 1, 1a may possibly be provided with the set of sensors 50.
  • the system may include a garment 40, which is designed to integrate such sensors 50.
  • the integrated sensors 50 merely provide information as to the upper-body posture.
  • they can be complemented by one or more additional sensors, e.g., integrated in or fixed to a helmet or headband, and/or wristbands, which may form part of the system 1, 1a too.
  • the sensors 50 can be passive or active sensors.
  • the sensors 50 may include inertial sensors, preferably inertial measurement units (IMUs), microradar sensors, and/or strain sensors. Preferred is to rely on inertial sensors 50 such as IMUs.
  • IMU sensors can be placed at specific locations on the user body to provide high accuracy measurements, e.g., through a garment 40.
  • the garment 40 can be designed similar to a motion-capture suit. It may for example be a jersey with pockets defining optimal sensor locations.
  • the number of sensors can be reduced to, e.g., less than 19 sensors, preferably less than 13 sensors.
  • a 3D model 15 as discussed earlier does not require a high-definition, which eases real-time computations. Rather, this model requires accurate measurements is respect of specific locations.
  • the optimal sensor locations can be obtained thanks to experimental measurements in wind tunnels.
  • the number of sensors can vary depending on the desired accuracy and power consumption.
  • passive sensors such as Lidar sensor, ultrasound sensors, and/or microradar sensors (also called Microwave Doppler Radar Motion Sensors or microwave sensors) can be used too, which require a light source or a sonic source (not shown). This source may form part of the system 1, la too.
  • elastic strain sensors Another possibility is to use elastic strain sensors.
  • passive sensors and elastic strain sensors typically provide less accurate information than IMUs. That said, different types of sensors may be combined, in principle. For example, use can be made of IMUs and Lidar or microradar sensors. Another possibility is to combine microradar sensors and strain sensors. Plus, the sensors may be complemented by a camera to leverage computer vision, if necessary.
  • the garment 40 may be designed with imprinted circuitry including electromyography sensors, to extract tension of certain muscles of the torso and shoulders. Such signals provide useful information for flexibility training, helping the user 2 to improve her/his posture and comfort on the bike.
  • Some sensors may further be integrated in one or two wristbands and a headband.
  • the sensors may be completed by feedback devices, such as vibrating systems, e.g., involving small permanent magnet DC motors. This way, the feedback devices may provide in-situ feedback for the biker to improve her/his posture and decrease the drag.
  • Communication between the processing device 10 and the sensors 50 preferably rely on wireless protocols such as Bluetooth, ANT+, Zigbee, Thread, UWB, and Wi-Fi, at rates that typically are between 1 and 65 MBps.
  • wireless protocols such as Bluetooth, ANT+, Zigbee, Thread, UWB, and Wi-Fi
  • a point cloud extracted from lidar sensors has a considerably larger size compared to IMUs.
  • a point cloud extracted using lidar sensors typically has a memory footprint between 1 and 10 MB for each time point (time instance).
  • time instance At an acquisition rate of 30 Hz, this means that the data transfer needs are of approximately 30 to 300 MBps.
  • Bluetooth-related technologies cannot accommodate such data-transfer rates.
  • using lidar or microradar sensors advocates the use of Wi-Fi.
  • Bluetooth Other sensors require transferring less data, whereby protocols like Bluetooth or ANT+ protocols can be used, which operate at 1 - 2 MBps.
  • IMU data-transfer rates are on the order of KBs per second, and can thus easily be handled by Bluetooth-related protocols.
  • the system 1, 1a may further comprise the resistance-control system 21, if not the bike 20, 20a itself.
  • a stationary bike 20a can be specifically designed in accordance with the present purpose.
  • the bike may include an additional holder configured to receive the processing device 10, e.g., on top of a display 28.
  • a detailed description of preferred systems 1, la is provided in section 2.1.
  • a final aspect of the invention concerns a computer program product, which comprises a computer readable storage medium having program instructions embodied therewith.
  • the program instructions are executable by processing means 11 of a processing device 10 to cause the latter to repeatedly perform steps S30 - S60 as described above.
  • some of the program instructions may be executed on several devices 10, 20a, 30, e.g., in a distributed manner, if necessary.
  • This aspect is discussed in detail in section 2.3.
  • the above embodiments have been succinctly described in reference to the accompanying drawings and may accommodate a number of variants. Several combinations of the above features may be contemplated. Examples are given in the next section.
  • a preferred implementation of the system 1, la is one where the system comprises a processing device 10, an exercise bike 20, 20a, and an external computer 30 running a GUI, as illustrated in FIGS. 1 A, IB, and 2.
  • the turbo trainer 23 includes a resistance-control system 21 having a processing unit, which runs an API for communicating with the device 10.
  • the bike 20 includes a pedalling system 22, coupled to the trainer 23.
  • the resistance-control system 21 applies a variable resistance when the pedals 24 are turned.
  • the system 21 includes a control unit 25, which is adapted to receive signals from the processing unit (via the API) and apply signals to modify the pedalling resistance as the user 2 trains on the bike 20.
  • the bike 20a includes a processing unit 26, which runs an API 27 for communicating with the device 10.
  • the bike 20a includes a pedalling system 22a, which is coupled to a mechanism that provides resistance when the pedals 24a are turned.
  • the bike 20 further includes a control unit 25, which is adapted to receive signals from the processing unit 26 (via the API 27) and apply signals to modify the pedalling resistance as the user 2 trains on the bike 20a.
  • the bike 20a may further include a display 28, used to display any useful information as to the performance of the user 2.
  • the user 2 wears a garment 40, which integrates sensors 50 (e.g., IMUs).
  • sensors 50 e.g., IMUs
  • the sensors 50 emit signals, which are picked up by the processing device 10, via a receiver 12 of the device 10.
  • the receiver typically contains analogue-to-digital converters (ADCs), converting signals received from the sensors into digital signals.
  • ADCs analogue-to-digital converters
  • the processing device 10 further includes general-purpose processing means 11 (these including at least one processor, with an arithmetic unit) and a memory 16 (e.g., a Flash memory) storing the LUT 18.
  • the processing means are notably used to write tabulation data in the LUT at build time.
  • the processing means 11 are used to compute the body posture (i.e., by updating a 3D model 15) to identify index values corresponding to a current category matching the body posture (and the user body geometry), and accordingly look up drag parameters in the LUT 18.
  • a suitable drag quantity such as the drag force Fa
  • the processing means essentially thanks to arithmetic operations performed by the arithmetic unit
  • a corresponding signal is sent to an I/O unit 14, which relays such signals to the resistance control system (whether forming part of the turbo trainer 23 or the bike 20a), via the API 27 run at the processing unit 26.
  • the API 27 forwards additional data (e.g., a power output of the pedalling system 22, 22a or other data related to the virtual speed of the user 2) to the processing device 10 for the processing means 11 to compute the drag force Fa.
  • the I/O unit 14 is further connected to the receiver 12 to relay digital signals from the receiver 12.
  • the I/O unit 14 may further communicate with the external computer 30, e.g., to forward drag-related data, which are exploited by the GUI run at the computer 30 to display useful information to the user 2.
  • the computer 30 may further send calibration data to the exercise bike 20, which may possibly be entered by the user 2, initially, or by competition organizers.
  • the system 1, 1a may further be operated to work in ergometer mode (ERG mode), i.e., a setting in which the training platform fixes the power output by automatically adjusting the pedalling resistance to match the biker’s cadence.
  • EMG mode ergometer mode
  • the resistance can be set to a specific value, such that the virtual speed may be computed in accordance with both the biker’s posture and cadence (rpm). That is, the pedalling resistance changes in accordance with the user’s cadence but the power output is converted to a speed that further depends on the body posture, based on concepts as disclosed herein.
  • the method steps S30 - S70 described herein are implemented in software, e.g., as one or more executable programs, executed by the processing device 10, which may form part of the exercise bike 20a or an external system 23. Additional steps may further be performed at the bike 20a, the system 23, and the external computer 30. All such devices 10, 20a, 23, 30 may generally have an architecture as shown in FIG. 5. However, the architectures of the processing device 10 and the processing means 26 of the bike 20a or the system 23 may possibly be simplified. E.g., such devices do not necessarily include a display 130 and a display controller 125.
  • An example of suitable architecture for a computerized system 100 involves one or more processing elements, such as one or more processors 105 and a memory 110 (meant to serve as a main memory), coupled to a memory controller 115.
  • the processors 105 are hardware devices for executing software, as loaded in the main memory of the system 100.
  • the processors 105 can be any custom made or commercially available processors; they may possibly include graphics processing units (GPUs), which can be leveraged to perform machine learning inferences, if necessary.
  • GPUs graphics processing units
  • the memory 110 may include a combination of volatile memory elements (e.g., random access memory) and nonvolatile memory elements, e.g., solid-state devices.
  • the software in memory may include one or more separate programs, each of which may for instance comprise an ordered listing of executable instructions for implementing logical functions.
  • the software in the memory 110 includes methods described herein in accordance with exemplary embodiments and a suitable operating system (OS).
  • OS essentially controls the execution of other computer (application) programs and provides scheduling, I/O control, file, data and memory management, and communication control as well as related services.
  • the system 100 further include one or more input and/or output (I/O) devices 145, 150, 155 (or peripherals) communicatively coupled via a local input/output controller 135.
  • the input/output controller 135 can comprise or connect to one or more buses 140 or other wired or wireless connections.
  • the I/O controller 135 may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, and receivers, etc., to enable communications.
  • a local interface may include address, control, and/or data connections to enable appropriate communications among the aforementioned components.
  • I/O devices 145 - 155 may include other hardware devices, which communicate both inputs and outputs.
  • the system 100 may further include a display controller 125 coupled to a display 130.
  • the system 100 may further include a network interface 160 or transceiver for coupling to a network (not shown).
  • the methods described herein shall typically be, at least partly, in the form of executable program, script, or, more generally, executable instructions.
  • one or more of the processing elements 105 execute software stored within the memory 110 (separate memory elements may possibly be dedicated to each processing element), to communicate data to and from the memory 110, and to generally control operations pursuant to software instructions.
  • the methods described herein, in whole or in part are read by one or more of the processing elements 105, typically buffered therein, and then executed.
  • the methods described herein are implemented in software, the methods can be stored on any computer readable medium for use by or in connection with any computer related system or method.
  • Computer readable program instructions described herein can be downloaded to processing elements 105 from a computer readable storage medium, via a network, for example, the Internet and/or a wireless network.
  • a network adapter card or network interface 160 in the device may receive the computer readable program instructions from the network and forwards the program instructions for storage in a computer readable storage medium 120 interfaced with the processing elements.
  • a computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.
  • the computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device.
  • the computer readable storage medium may for example be an electronic storage device, a magnetic storage device, an optical or electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. Examples of such storage media include: a hard disk, a random-access memory (RAM), a static random-access memory (SRAM), an erasable programmable read-only memory (EPROM or Flash memory), a memory stick, and any suitable combination of the foregoing.
  • a computer readable storage medium is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fibre-optic cable), or electrical signals transmitted through a wire.
  • Each computer-implemented block in the flowchart or the block diagram may represent a module, or a portion of instructions, which comprises executable instructions for implementing the functions or acts specified therein.
  • the functions or acts mentioned in the blocks may occur out of the order specified in the figures.
  • two blocks shown in succession may actually be executed in parallel, concurrently, or still in a reverse order, depending on the functions involved and the algorithm optimization used.
  • each block and combinations thereof can also be adequately distributed through special-purpose hardware components.

Landscapes

  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Physical Education & Sports Medicine (AREA)
  • Cardiology (AREA)
  • Vascular Medicine (AREA)
  • Professional, Industrial, Or Sporting Protective Garments (AREA)

Abstract

L'invention concerne notamment un procédé d'émulation de la traînée aérodynamique d'un utilisateur (2) d'un vélo d'exercice (20, 20a). Le vélo d'exercice comprend un système de pédalage (22, 22a) qui est soumis à une résistance variable. Le procédé consiste à mettre en œuvre, de manière répétée, une série d'étapes au niveau d'un dispositif de traitement (10). Cette série d'étapes comprend le traitement de signaux de capteur, le calcul d'une quantité liée à la traînée, la génération d'un signal de commande codant la quantité calculée, et la transmission du signal de commande généré à une unité de commande (25) pour qu'elle modifie ladite résistance en fonction du signal de commande transmis. Les signaux de capteur traités sont des signaux qui proviennent initialement d'un ensemble de capteurs (50) portés par l'utilisateur lorsque ce dernier s'entraîne sur le vélo d'exercice par actionnement du système de pédalage. Ainsi, les signaux de capteur dépendent d'une posture corporelle de l'utilisateur (2). La quantité liée à la traînée est une quantité qui détermine la traînée aérodynamique. Il peut notamment s'agir d'une force de traînée, ou d'une combinaison d'une zone frontale et d'un coefficient de traînée, par exemple. Cette quantité est calculée sur la base des signaux de capteur traités. Elle dépend donc également de la posture corporelle de l'utilisateur. La présente invention concerne en outre des systèmes associés et des produits-programmes d'ordinateur associés.
PCT/EP2022/087565 2022-12-22 2022-12-22 Émulation de traînée aérodynamique d'un utilisateur d'un vélo d'exercice WO2024132164A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/EP2022/087565 WO2024132164A1 (fr) 2022-12-22 2022-12-22 Émulation de traînée aérodynamique d'un utilisateur d'un vélo d'exercice

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/EP2022/087565 WO2024132164A1 (fr) 2022-12-22 2022-12-22 Émulation de traînée aérodynamique d'un utilisateur d'un vélo d'exercice

Publications (1)

Publication Number Publication Date
WO2024132164A1 true WO2024132164A1 (fr) 2024-06-27

Family

ID=84981569

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2022/087565 WO2024132164A1 (fr) 2022-12-22 2022-12-22 Émulation de traînée aérodynamique d'un utilisateur d'un vélo d'exercice

Country Status (1)

Country Link
WO (1) WO2024132164A1 (fr)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130059698A1 (en) * 2011-09-01 2013-03-07 Icon Health & Fitness, Inc. System and Method for Simulating Environmental Conditions on an Exercise Bicycle
EP2583887A1 (fr) * 2010-06-17 2013-04-24 Pioneer Corporation Dispositif de mesure du taux de travail
KR101759096B1 (ko) * 2016-02-05 2017-07-19 채수길 마그네틱브레이크를 이용한 실내 자전거 및 그 시스템
US20220395723A1 (en) * 2021-06-11 2022-12-15 Vision Quest Virtual, LLC System and method for using drag force data to optimize athletic performance

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2583887A1 (fr) * 2010-06-17 2013-04-24 Pioneer Corporation Dispositif de mesure du taux de travail
US20130059698A1 (en) * 2011-09-01 2013-03-07 Icon Health & Fitness, Inc. System and Method for Simulating Environmental Conditions on an Exercise Bicycle
KR101759096B1 (ko) * 2016-02-05 2017-07-19 채수길 마그네틱브레이크를 이용한 실내 자전거 및 그 시스템
US20220395723A1 (en) * 2021-06-11 2022-12-15 Vision Quest Virtual, LLC System and method for using drag force data to optimize athletic performance

Similar Documents

Publication Publication Date Title
US20220203196A1 (en) System and method for controlling a bicycle trainer
Baca et al. Ubiquitous computing in sports: A review and analysis
EP3448533B1 (fr) Équipement d'exercice et ensemble équipement d'exercice, et appareil et procédé permettant de simuler un environnement d'exercice dans un équipement d'exercice
US10576348B1 (en) System and method for controlling a bicycle trainer
US9216320B2 (en) Method and apparatus for measuring power output of exercise
CN105664469B (zh) 具有模拟场景的运动系统及其实现方法
KR102461484B1 (ko) 메타버스 트레이닝 서비스 제공 방법, 서버 및 컴퓨터 프로그램
CN104181875A (zh) 基于物联模式的运动健身系统
US10617913B2 (en) Training system for an e-bike
KR102241414B1 (ko) 머신 러닝 모델을 이용한 특정 움직임에 대한 피드백을 제공하는 전자 장치 및 그 동작 방법
US20160058373A1 (en) Running Energy Efficiency
CN105561535A (zh) 一种基于物联网技术的体育健身管理系统
KR102445543B1 (ko) 메타버스 트레이닝 서비스를 제공하기 위한 시스템
JP2016063871A (ja) 運動解析装置、方法、およびプログラム
CN106880945A (zh) 基于虚拟现实眼镜的室内骑行健身系统
CN205909833U (zh) 一种计步鞋垫装置
CN107376305A (zh) 一种基于智能手环的拳击击打能量计算方法
CN109758154A (zh) 一种运动状态确定方法、装置、设备及存储介质
WO2024132164A1 (fr) Émulation de traînée aérodynamique d'un utilisateur d'un vélo d'exercice
CN203017719U (zh) 一种基于动作识别技术的足球头球攻门训练辅助系统
WO2018098655A1 (fr) Système de détection d'alimentation pour bicyclette
US20230381586A1 (en) System and method for improving an athletic body position
JP6798124B2 (ja) ゴルフクラブのフィッティング装置、方法及びプログラム
CN109011516A (zh) 交互式虚拟漫游健身车
WO2022085069A1 (fr) Dispositif d'instruction pour l'optimisation d'exercice, procédé d'instruction pour l'optimisation d'exercice et programme d'instruction pour l'optimisation d'exercice

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22844088

Country of ref document: EP

Kind code of ref document: A1