Adaptive cycling application with real-time coaching and physics-based performance optimization

Abstract

The present patent introduces an advanced cycling application that redefines training and performance coaching for cyclists. This innovative system employs real-time coaching, energy physics and aerodynamics equations, real-time data analysis and cloud-based optimization to enhance cyclists' rides. By incorporating individual real-time rider parameters and the riders' historical performance data, the application dynamically computes optimal energy expenditure, power output, velocity, and cadence for upcoming road conditions. Through interactive real-time feedback, cyclists are guided to meet uniquely and individually calculated performance, fostering improved performance and rapid skill development.

Description

ADAPTIVE CYCLING APPLICATION WITH REAL-TIME COACHING AND PHYSICS-BASED PERFORMANCE OPTIMIZATIONFIELD OF THE INVENTION

[0001] The disclosure pertains to the field of sports technology, particularly cycling applications. More particularly, the disclosure pertains to a cycling application which provides real-time coaching and performance guidance.DESCRIPTION OF THE PRIOR ART

[0002] Cyclists regularly seek ways to optimally traverse road terrains within their perceived capabilities. Ranging from the novice to the professional cyclist, obtaining instruction in understanding exactly how they can optimally perform in the succeeding road condition would assist them greatly and provide them the confidence knowing they can achieve or exceed their goal.

[0003] Some applications exist which track riding parameters or provide training programs, such as interval rides, tempo rides or high intensity interval training, as examples. However, while these applications may provide guided rides and current ride data, they do not take into consideration the current road conditions. Furthermore, they do not provide real time feedback of how a rider’s current and immediate performance compares to a target performance level.SUMMARY OF THE INVENTION

[0004] The present patent introduces an advanced cycling application that redefines training and performance coaching for cyclists. This innovative system employs real-time coaching, energy physics equations, real-time data analysis and cloud-based optimization to enhance cyclists' rides. By incorporating individual real-time rider parameters and the riders’ historical performance data, the application dynamically computes optimal energy expenditure, power output, velocity, and cadence for upcoming road conditions. Through interactive real-time feedback, cyclists are guided to meet uniquely calculated performance, fostering improved performance and skill development.

[0005] In one embodiment, the application uses route segments, power meters, rider and bike characteristics, ride goals and data analytics to provide valuable real time training feedback and post ride training analysis.

[0006] First, the application uses route segments to provide feedback specific to the rider’s current riding conditions. In the context of this disclosure, route segments are designed to break up a route in segments of the ride that provide uniform riding intensity. Such portions can include from the bottom of a hill to a portion of the hill of constant gradient, top of the hill down to a portion of the hill of constant gradient, a flat or zero gradient section of the road, a road section up to a change in direction that can include right, left or U-tum. Such road conditions are analyzed and stored as starting and ending points in the application including such parameters as their GPS coordinates, the elevation, the distance to the next point.

[0007] Second, the cyclist using commercially available power meters will then provide their riding data to the application cloud site or provide access to commonly available riding data in such applications as Strava™, Training Peaks™, Trainer Road™, Garmin™, etc. The application can then derive a cyclists’ specific profile of their performance for a multitude of recently performed rides. This profile, which includes the rider’s ability to exert a given power for a certain time duration, can be unique to each rider and provide key input to the application. The application derives an equation that is unique to the cyclist and can be used in the provision of coaching information back to the cyclist dynamically during the ride.

[0008] Third, the application can then be provided unique rider and bike parameters initially to allow the application to customize the physics / aerodynamic equations that are used by the application as the third component of the coaching. These aerodynamic equations consider such factors as type of tires, weight of the rider, weight of the bike, wind direction and intensity, approximate aerodynamic drag of the rider / bike combination, etc.

[0009] Fourth, at the commencement of every new ride, the cyclist then can provide the application with their ride specific final details. Such details include the route the cyclist looks to ride during that session, the intensity the rider plans to cycle relative to their maximal capability and the immediate climatic conditions of wind speed, wind direction and air temperature.

[0010] These elements of the cyclist’s unique conditions are used by the application once the ride commences. During the ride the cyclist is provided with instructions immediately prior to the next road segment with recommendations based on their unique set of conditions in real time. The application then records the cyclist actual performance compared to the recommended performance. At the end of the ride, the data is used to provide performanceinformation back to the rider to improve their next ride. The application also updates the rider’s capability (power versus duration) to reflect the change in their performance improvement for the next ride.

[0011] One embodiment pertains to a system for providing real time performance feedback for cyclists, the system comprising, a computing device having a processor and memory and GPS capability; a training application on said computing device, said computing device configured to be in communication with a power meter on a bicycle such that the computing device can receive data from the power meter. The training application configured to: receive rider and bike attributes; receiving a rider’s target performance goal; define or predict a rider route; divide the route into segments based on road attributes; receive GPS coordinates from said computing device and using said GPS coordinates computing the velocity of the bicycle; receiving power and cadence data from the power meter and / or cadence sensor; using the road attributes of the current route segment, rider target performance goal, calculating the rider’s target power output and target velocity; and displaying the actual power, actual velocity, target velocity, target power, actual cadence, target cadence, projected time to complete segment and length (distance) for a particular route segment on a training display of the application.

[0012] In an aspect, there is provided a system for providing real time performance feedback for a rider. The system includes a computing device comprising a processor; a memory; a receiver for obtaining ride data from at least one sensor; and a location module for determining a rider location. The memory includes instructions stored thereon that, when executed, causes the processor to: receive rider attributes and bike attributes; receive a target performance goal; define or predict a rider route; divide the rider route into route segments based on road attributes; receive the rider location from the location module; using the rider location and the rider target performance goal, calculate a target performance metric based on the road attributes of one of the route segments; receive the ride data from the at least one sensor; calculate the actual performance metric based on the ride data; and display the target performance metric, and actual performance metric for the one of the route segments.

[0013] In an embodiment, the rider route is predetermined. In another embodiment, the rider route is predicted based on the rider location, direction of travel, or both. In another embodiment, the rider route is predicted based on the rider location and direction of travel.

[0014] In an embodiment, the at least one sensor includes a power meter, heart rate monitor, speed sensor, cadence sensor, environmental sensor, gyroscope, accelerometer, or a combination thereof.

[0015] In an embodiment, the at least one sensor comprises a power meter.

[0016] In an embodiment, the target performance metric comprises a target power output, a target speed, a target cadence, a target time in segment, or a combination thereof.

[0017] In an embodiment, the target power output is determined by:where,Watt2 is the target power output of the rider for the one of the ride segmentsVrider is a target speed,Watt2A is a product of a rolling resistance, a combined mass of the rider and the bike, and gravity,Watt2B is a product of 0.5 times density of air, and the Coefficient of Aerodynamic drag of the rider and the bike,Watt2c is a product of the combined mass of the rider and the bike, and gravity, Vwind is the speed of the wind,WindDirection, in radians, is the perceived wind direction encountered by the rider, andSlope is the slope of the route segment.

[0018] In the above equation, speeds are provided in Kilometers per hour.

[0019] In an embodiment, WindDirection is calculate from a CyclistBearing, which is the clockwise angle measurement relative to true north the rider is heading in, subtracting the actual wind direction, which is the clockwise angle measurement the wind is blowing from relative to true north.

[0020] In an embodiment, Slope is calculated by taking the difference between the segment end altitude (meters) and the segment start altitude (meters) divided by the segment length in meters.

[0021] In an embodiment, the Coefficient of Aerodynamic drag is estimated based on resistance of the combined bike and rider’s frontal area as they move through the air. In some embodiments, this estimate is adjusted for atmospheric conditions.

[0022] In an embodiment, the atmospheric conditions are received from the at least one sensor, determined based on the rider’s location and received from Cloud -based application, or a combination thereof.

[0023] In an embodiment, the actual performance metric comprises an actual power, an actual speed, an actual cadence, an actual heart rate, or a combination thereof.

[0024] In an embodiment, the speed of the rider is computed using rider location, for example, by using the change in location per unit time.

[0025] In an embodiment, the one of the route segments is the route segment in which the rider location is located.

[0026] In an embodiment, the one of the route segments is determined based on the rider location within to the end of the route segment in which the rider location is located.

[0027] In an embodiment, the instructions further cause the processor to display an actual time spent in the one of the route segments.

[0028] In an embodiment, the route segments provide uniform riding intensity within a segment.

[0029] In an embodiment, the rider attributes are updated based on the actual performance metric. In a preferred embodiment, the rider attributes include a rider power capability calculated according towherein,Watti is the power capability for a set time (seconds) duration,Riderintensity is the rider’s chosen intensity for the ride,WattiA is a steady state power output the rider can deliver over at least 30 minutes, Watt IB is a maximum power output that the rider is capable of delivering in 1 second, Distance is the length of the current segment in meters,Wattic is the time in seconds at a midpoint between the maximum power and the steady state power, where time in seconds is calculated by taking the current segment length in meters and dividing by the estimated Speed (Vrider) in Kilometers per hour / 3.6,Watt ID is the Hill’s coefficient representing the slope of the curve at the inflection point between the maximum and steady state power values, and Vrider is the speed of the rider.

[0030] In the above equation, speeds are in Kilometers per hour.

[0031] In an embodiment, the system further comprises a display for displaying the target performance metric, and actual performance metric for the one of the route segments.

[0032] In an aspect, there is provided a method for providing real-time training feedback. The method includes receiving rider attributes and bike attributes; receiving a target performance goal; defining or predicting a rider route; dividing the rider route into route segments based on road attributes; receiving a rider location; calculating a target performance metric based on the rider location, the target performance goal, and the road attributes of one of the route segments; receiving ride data from at least one sensor; calculating an actual performance metric based on the ride data; and displaying the target performance metric, and actual performance metric for the one of the route segments.BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The features of the invention will become more apparent in the following detailed description in which reference is made to the appended drawings wherein:

[0034] Figure 1 shows an overview of the cycling training system;

[0035] Figure 2 is a flow chart of logic used by the cycling training application;

[0036] Figure 3 is a screenshot of the main menu of the application;

[0037] Figure 4a is a screenshot of the rider details section of the application;

[0038] Figure 4b is a screenshot of the bike gear parameters section of the application;

[0039] Figure 4c is a screenshot of the sensors screen of the application;

[0040] Figure 4d is a screenshot of the rider parameters screen of the application;

[0041] Figure 5a shows a screenshot of the application in training mode;

[0042] Figure 5b shows a screenshot of the application in training mode according to an alternate embodiment;

[0043] Figure 6a shows a screenshot of the application when a ride is paused or stopped;

[0044] Figure 6b shows a screenshot of the application confirming exiting of the application;

[0045] Figure 7 shows and example power (wattage) output vs. time duration curve; and

[0046] Figure 8 is a magnified view of the map of the training screen showing route segments.DETAILED DESCRIPTION OF THE INVENTION

[0047] The present disclosure relates to an application to be used when cycling to maximize performance and / or provide real-time coaching feedback. The application uses different inputs and analysis techniques to provide real time coaching instruction to the cyclist. These elements include but are not limited to, road segments based on road attributes, power meters and / or cadence sensors, rider and bike parameters, and ride details.

[0048] Figure 1 shows a high-level overview of the overall system 2. The system comprises a bike 4, a power meter 6 and a computing device 8. The computing device 8 can be any suitable device but in the preferred embodiment is a smart phone with GPS capability and data capabilities. In the preferred embodiment, the computing device 8 is mounted to the handlebars 3 of the bike 4. Installed on the computing device 8 is and application 10. The application 10 is capable of communicating with the power meter 6 to receive information therefrom. The application 10 is preferably configured to be available and compatible with a variety of commercially available operating systems of mobile devices and / or can be integrated into bike computers such as Garmin™, Karoo™ and others.

[0049] The power meter 6 is used to determine the power generated at the pedals by the rider and could be any commercially available power meters. In one embodiment, the power meter 6 can be a combination of a power meter and a cadence sensor. In the preferred embodiment, the power meter 6 has Bluetooth™ capabilities to facilitate connection to the computing device 8. The power meter 6 provides the power and cadence data of the rider to the application 10. In an alternative embodiment, the power meter communicates with a cloud-based application accessible by the mobile device 8. In yet another embodiment, the application 10 is permitted to access to commonly available applications which collect and transmit power readings and / or riding data such as Strava™, Training Peaks™, Trainer Road™, Garmin™ etc.

[0050] The computing device 8 is optionally in communication (preferably via the internet) with a cloud-based data storage and processing system 11.Initial Application Set Up

[0051] The Application 10 is customizable to the individual rider. It can be appreciated that the application 10 is configured to accommodate several different rider profiles, each with their own settings and preferences. Thus, as part of the initial set up, the rider creates a profile in which their personal information and riding history can be saved.

[0052] As part of the initial set up, the rider inputs information about themselves and their equipment. For example, with reference to Figure 3, a main menu 100 of the application 10, a user can access a rider information screen 102a (as shown in Figure 4a) and gear parameters screen 104a (as shown in Figure 4B) by selecting features 102 and 104, respectively. Although figures 3 to 4d illustrate examples of how the menu and preset information can be organized in application 10, alternative organization categories and layouts would be known to a person skilled in the art.

[0053] The user can further set the gear parameters associated with their bike by selecting the rider information feature 102 from the menu shown in figure 3. Within the rider information screen 102a, shown in figure 4a, the rider can set a bike type, 108, a bike weight 110 and a tire type 112.

[0054] The user can further set the gear parameters associated with their bike by selecting the gear parameters feature 104 from the menu shown in figure 3. An example of gear parameter input screen 104a is shown in figure 4b.

[0055] In a preferred embodiment, a user can save different bike profiles. For example, a rider may have several different bikes, including but not limited to, road, mountain, gravel or commuter bikes. The type of bike field 108 can be linked to the bike weight, tire type and bike gear parameters. This allows for a user to select the bike they are using and the weight, tire type and bike gear parameter is automatically correlated with the bike selection. In some embodiments, this information is saved locally to the device 10. In other embodiments, this information is retrieved from a database. Such parameters are used by the application to provide specific performance recommendations to the cyclist that are customized to that person on that specific bike. For example, the tire type is used by the application to determine a known or estimated coefficient of rolling resistance for that tire, which can be used to determine required power to complete a portion of the route at a predetermined intensity.

[0056] Furthermore, the type of bike can also be linked to specific sensors, such as power meters. In this way, if a particular bike is selected for a ride, the application would automatically look to connect to the power meter associated with the selected bike. Additionally, various sensors can be set up in the “Connect to Sensors” screen 106a accessible from feature 106 on menu 100 (see 106 of figure 3 and figure 4c). In addition to various power meters, other sensor types include, but are not limited to, heart rate monitors, cadence sensors, speed sensors, combined speed and cadence sensors, smart lights, and external GPS devices.

[0057] As shown in figure 3 and figure 4a, rider parameters associated with a particular rider profile can also be set. Attributes such as rider weight 114 can be stored in the rider profile, while other attributes, such as rider max power, rider recover power can be dynamically determined by the application based on rider performance. In one embodiment, the rider completes a “calibration ride” to determine rider max power 116 and rider recovery power 118. In another embodiment, the application uses information from a plurality of rides to estimate rider max power 116 and rider recovery power 118. In yet another embodiment, these fields can be either dynamically determined or are simply provided as an input by the rider. In one embodiment, two equations govern the specific rider / bike combination, and their capabilities can be adjusted by adjusting the seven unique coefficients. These can be entered, viewed, stored or edited in a Rider App Parameter screen 121 a as shown in figure 4d, and accessed from the menu 100 through the Rider App Parameter feature 121 as shown in figure 3.

[0058] Any Parameters that are not known or not entered, can be estimated by the application and, over time, derived specifically using application algorithms for the cyclist and bike combination. As the Application 10 learns the rider’s unique performance characteristics in different road conditions, such parameters that were initially unknown are over time inferred and applied back to the rider’s unique coaching.Set Up Before a Ride

[0059] Once a rider has logged into their profile and selected the bike that they are planning on using, they can set their ride parameters. This is shown as step 14 in figure 2. In the embodiment shown in the figures, the ride parameters are included in the rider information screen 102 (figure 4a), however it can be appreciated that the ride parameters could be included in a separated screen. Ride parameters can include, but are not limited to,environmental conditions, rider type, route, ride intensity, and functional threshold power (FTP).

[0060] Environmental conditions, including but not limited to Wind Velocity 120, Wind Direction 122, Air Temperature 124, and Humidity, can be optional inputs and can either be entered manually or automatically gathered from connected sensors or an external service providing local weather (such as a cloud-based application that uses the rider’s location). The rider type 126 can include a variety of preset rides (such as interval rides, endurance rides, tempo rides) or can be a custom ride, the parameters of which are set by the user. Furthermore, preset rider types could be downloaded or accessed via the internet in database or rider types set by other users or by the administrator.

[0061] The rider can also choose a preset Route in the route field 128. The options for this field can in one embodiment, be preset using a map function. In this embodiment, the rider would map their route on a map and save the same to their profile. In the preferred embodiment, the rider can complete rides and have the option of saving their ride post session to make the ride route available in the future. Furthermore, in one embodiment, the application can download suggested routes via the internet from an administration database of rides. In some embodiments, the application can use the mobile device’s GPS coordinates to suggest routes for the user. These, and additional route selection techniques will be discussed in further detail below.

[0062] For each ride, the user can pick a Rider Intensity 130 (Figure 4a) depending on their goal for the upcoming ride. While there are multiple ways to estimate intensity of a ride known to a person skilled in the art, in the preferred embodiment, rider intensity is estimated as a percentage of the rider’s maximum effort. Maximum effort can be measured or estimated in many ways, including but not limited to measuring the rider’s V02max, rider’s max power, and by collecting a rider’s perceived exertion during or after a ride. The rider intensity can be changed based on the purpose of the ride or the preference of the rider. For example, if the rider is going out for a recovery ride, they may set their rider intensity at 50%, whereas if they are going out for a tempo ride, they may set their rider intensity at 60 to 80 percent. While the example depicted in the figures uses rider intensity to set an effort level for the rider, it can be appreciated that other methods would be known to a person skilled in the art. For example, a rider may set an overall time, or distance goal. Furthermore, a plurality of goals could be set for various segments of the ride as will be described below.

[0063] In a preferred embodiment, the Application 10 collects a comprehensive set of cyclist parameters prior, during and following each ride. Parameters specific to the cyclist such as weight, rider classification, FTP, maximum cadence rate, maximum power output, recovery power post maximum power output and the desired Rider Intensity are captured by the application and / or entered by the cyclist. In this embodiment, rider intensity is a qualitative number with 100% representing the historical maximum power output of the rider in all time duration ranges that they have performed. In this way, the number representing 100% automatically and dynamically changes as the rider improves throughout their training program. In this embodiment, the application uses a 4 Parameter Logistic Regression to determine the relationship between Time (seconds) and Watage, for example, Equation 1, for each cyclist. A sample graph showing such a relationship for a cyclist’s maximal power (watage) capability for a time duration is shown in Figure 7. The coefficients of that nonlinear equation (WATTIA, WATTIB, WATTIC, and WATT1D in Figure 4d) are unique to each rider, calculated by the cloud application and then applied back into the application for that rider.Equation 1:WhereWati is the power capability of the rider for a set time (seconds) duration,Riderintensity is the rider’s chosen intensity for the ride,WatiA is the steady state power that the rider can deliver at 30 minutes and later,WatiB is the maximum power that the rider can deliver in 1 second,Watic is the time in seconds at a midpoint between the maximum power and the steady state power, where time in seconds is calculated by taking the current segment length in meters and dividing by the estimated Speed in Kilometers per hour / 3.6, Distance is the length of the current segment in meters,Wat ID is the Hill’s coefficient representing the slope of the curve at the inflection point between the maximum and steady state power values, andV rider is the speed of the rider.

[0064] In the above equation, speeds are provided in Kilometers per hour.

[0065] Using the selected ride intensity, the application uses various physics equations, including but not limited to aerodynamics and fluid dynamics equations, to dynamically compute the energy required for an upcoming segment of the route. One example of such an equation is shown below as Equation 2. The coefficients of Equation 2 (WATT2A, WATT2B, and WATT2C of Figure 4d) are unique to each rider. By using the cyclist Equation 1 (Time versus Wattage) created by the application, the application then does a two-way match for a given road segment condition and velocity. Using the rider’s chosen intensity, the application can then determine the energy expenditure for a segment of the route and a theoretical velocity is determined using the known segment distance. The application can then calculate a theoretical time to traverse the road segment. Using that time, the application can look at the rider’s Wattage vs. Time Duration curve (200 as shown in figure 7) to determine a target power for a particular road segment. Once there is a match between these conditions, that becomes the coaching recommended power, velocity, and time for the cyclist. Knowing the gearing ratio combinations, the application can then compute the optimal cadence within the capable range of the cyclist.Equation 2: x 3Vrider fVrider + Vwind ' COS( WlndDlreCtion) \Watt2= Watt2A■ + Watt2B■ - — — - - - + Slope ■ Watt3.6 y 3.6 J2C_ Kider3.6WhereWatt2 is the target wattage of the rider for the current ride segment and a projected Speed (in Kph) (“Vrider”),Watt2A is the product of the Rolling Resistance times the combined mass of the rider and bike times gravity,Watt2B is the product of 0.5 times density of air multiplied by the Coefficient of Aerodynamic drag of the rider and bike,Watt2c is the product of the combined mass of rider and bike times gravity, Vwind is the speed of the wind,WindDirection, in radians, is the perceived wind direction encountered by the cyclist; andSlope is the slope of the route segment.

[0066] In the above equation, speeds are provided in Kilometers per hour.

[0067] In an embodiment, WindDirection is calculated from a CyclistBearing, which is the clockwise angle measurement relative to true North the rider is heading in, by subtracting the actual wind direction, which is the clockwise angle measurement the wind is blowing from relative to true North. In an embodiment, the WindDirection can be calculated based on sensors, for example, using an anemometer.

[0068] In an embodiment, Slope is calculated by taking the difference between the segment end altitude (meters) and the segment start altitude (meters) divided by the segment distance in meters. In an embodiment, Slope is determined using an inclinometer or a gyroscope and accelerometer.

[0069] In an embodiment, the Coefficient of Aerodynamic drag is estimated based on resistance of the combined bike and rider’s frontal area as they move through the air. In some embodiments, this estimate is adjusted for atmospheric conditions.Route Building

[0070] The route or dynamic route is specified in step 16 of figure 2. As referenced above, there are many ways in which routes can be built and saved as would be known to a person skilled in the art. However, in the preferred embodiment, five different ways that the application can build a route are outlined below.

[0071] Auto Learn: In an embodiment, the rider can select this mode in the dashboard and then press the START button. The App would record the route taken by the rider from start to finish. The App records GPS data including Latitude, Longitude and Altitude; and rider’s power output, cadence, heart rate and velocity concurrently. At the end of the ride, the route with this data is uploaded to the cloud. A program then processes the geographic data to create start / end points that are unique to that route. Those points are then added to a library of points in that geographic area for the rider and other riders to subsequently use.

[0072] Pre-Plan: The rider goes to the Map and indicates start / end points in the major changes in direction (left, right or u turn), traffic lights, stop signs, changes in elevation. The rider then saves that with a unique route name. In some embodiments, changes in elevation are denoted by the segments of constant slope between a start position and an end position. For example, a hill may be denoted by a base, a summit, and the slope between the base and the summit. Alternatively, where the hill has multiple sections of different slopes, the hillmay be entered as segments, each having a starting point (e.g. base), end point (e.g. summits), and slope.

[0073] Route Selection: Previously recorded and named routes are provided as options to the rider at the start of the ride. Rider picks one from the list.

[0074] Processing of other bike computer rides: By providing the cloud with routes previously recorded in any of the commercially available formats (e.g. *.fit, *.GPX, ...) the program processes the ride and creates a list of start / end geographic points (latitude, longitude and altitude). These are added to the library of previous points in that geographic area for the rider and other riders to use. The route is then available for riders to use on their next ride.

[0075] Dynamic prediction: The rider would activate this mode, and the App then looks at the current GPS location at finite time intervals. By looking at several successive intervals, the APP then determines the direction the rider is travelling in along the road on mapping software, such as Google Map. The APP would know the starting Latitude, Longitude and Altitude. Based on that information the APP then determines the next logical end / start point along the road by looking ahead through the map and the road. Once the end / start point was determined, the APP would then compute the coaching instruction to the rider. Once the rider was within a predetermined distance to the end / start point, the APP would then start looking at the GPS real time data and once beyond the end / start point would determine the direction they were travelling in. With this new direction the APP would then look along the road and determine then next end / start point repeating the process for each such point. At the end of the ride, the rider is provided the option to save and name the route just completed. The route is preferably uploaded to the cloud and those start / end points added to the library of points including the route name for other riders or the same rider to use in subsequent rides.Route Segments

[0076] In a preferred embodiment, each route is broken down into a series of segments. The way in which the present application divides routes differently from those of other programs. The present application can use a plurality of landmarks or natural features of the land to break a route into meaningful route segments which can be utilized to provide meaningful real-time feedback.

[0077] In one embodiment, the route is broken up into segments based on uniform road conditions. In the context of this disclosure, road conditions are defined as portions of a roadthat provide uniform riding intensity. Such portions can include from the bottom of a hill to a portion of the hill of generally constant gradient, top of the hill down to a portion of the hill of generally constant gradient, a flat or zero gradient section of the road. Alternatively, or additionally, sections of a route can be determined at natural direction changes, such turns (preferably over 45 degrees), or a U-turn in the route. Furthermore, alternatively or additionally, route sections can be determined using traffic lights, stop signs, or yields. Additionally, or alternatively, the application 10 can use existing maps, such as Google Maps™ or other map software, to determine road changes or features which would lead to route segments. It can be appreciated that when a route is originally built, the route sections may be estimated using available resources such as traffic signs, elevation and natural direction changes. However, these sections can change based on actual rider data. For example, if the application breaks a portion of the route into two sections, but the actual rider data shows that the rider intensity remains consistent between those two sections, the application can adapt on a future ride represent the two sections as a single section. Each route section has a start point and an end point which, among other properties, are used to provide feedback to the rider. During each ride, route properties such as road conditions, elevation and direction changes are updated using at least rider feedback, elevation and / or GPS coordinates.

[0078] In the preferred embodiment, the route segments are depicted in a map view 169 (figure 8) of the training screen 140 (Figure 5 a). As shown in the magnified view of the route map 169, the route segments can be displayed in different colours or dashes / dots to help a rider visually understand when the next segment is expected. In one embodiment, the rider’s location is tracked on the map and is typically in the center of the route map.Application in Use on a Ride

[0079] When a rider initiates a training ride, they open the application (step 12 of figure 2), set their input parameters and goals as described above (step 14), and specify their route or dynamic route (step 16). At step 18, the application waits for an indication that the rider has started their intended route. For example, the GPS location may be changing along the specified route, and the application could start automatically. Alternatively, the application could wait for the rider to activate the “Start” button 142 as shown in training screen 140 of figure 5a. The activation of the ride triggers the application to commence to display therider’s performance data and initiates the coaching instructions. In a preferred embodiment, the ride information is recorded and stored for retrieval at a later time.

[0080] The application can then use the GPS coordinates of the rider to determine their starting position (step 20). In the preferred embodiment, this includes the starting GPS coordinates and altitude. Based on the selected route or dynamic prediction, the application and rider intensity or other rider goals, the application computes initial riding instructions, including targets for the current road segment, or other coaching cues (step 26).

[0081] As can be seen in figure 5a, the application can provide real time feedback for the rider. In the preferred embodiment, the application shows the recommended target that the rider is theoretically capable of achieving for the selected rider intensity and road condition. In the preferred embodiment, the training screen 140 displays the rider’s actual power 144 as well as the target power 146 for the current segment. Similarly, the rider’s actual speed 148, target speed 150, actual pedal cadence 152 (preferably in revolutions per minute), target pedal cadence 154, actual time since start of segment 156, target time since start of segment 158, actual distance 160 and target distance 162 are displayed. Furthermore, information about the current or upcoming segment, such as slope 166 and current intensity for the current or upcoming segment 168 can be displayed. The display enables cyclists to gauge their performance against the calculated targets, allowing adjustments and continuous improvement. In an alternate embodiment, shown in figure 5b, the application provides a third column denoting estimates pre-generated at a given intensity. These can be used to fine tune the coefficients of the calculations.

[0082] During the ride, the application is evaluating the distance from the rider’s current position to the start of the next segment (step 24 of figure 2). This can be done either continually, or at predetermined time intervals. At step 30, the application evaluates if the rider is within a predetermined distance of the next segment. If they are not, the application continues to calculate, track and / or display the rider data on the training screen 140 (step 32). Then using the preset rider intensity or other rider goals, the application can display coaching cues (such as speed up, slow down, notes about performing over or under targets, power out of expected range, etc.) and / or target outputs as described above (step 34). This is continued until the rider is within the predetermined distance to the next segment. At this point, the application can provide targets for the upcoming segment, such as by refreshing the Target column of Figure 5a as shown in step 40 of figure 2. Coaching cues or notes about theupcoming segment can also be displayed to the rider. For example, if the rider is approaching a hill, the application may display a note regarding the upcoming change in slope to the rider.

[0083] As the rider continues along their route, the application records all the relevant inputs to advise on coaching, such as, but not limited to, the data from the power meter, data from an optional heart rate monitor, actual GPS and time and date data. The application computes the riders’ immediate velocity. Alternatively, if available on the mobile device, this could be sensed at the mobile device level and used as input into the application.

[0084] The application provides real-time feedback on how they are performing on the present segment of the route. For example, the application may update them on their time since the start of the segment of the route to allow the cyclist to update their performance. The application can further provide the cyclist with updated values of their immediate performance and allow them to compare that to the coaching instructions provided by the application specifically for their unique conditions. The application can also allow them to dynamically alter the Rider Intensity should the cyclist’s unique conditions change during the ride allowing them to further adjust the coaching to their immediate physical condition. It is noted that the application stores the current cyclist performance parameters, the recommended cyclist performance targets and other data for subsequent analysis and rider review.

[0085] The application continues to coach and / or collected data until the ride is stopped. This can be accomplished in several ways. For example, as shown in figure 6a, the rider can either pause the ride using “Pause” button 170 or end the ride using the “End Ride” button 172. As shown in the screenshot of Figure 6b, the application provides cyclist a final prompt to exit the full application prior to shutting down and disconnecting from all connected devices.Analytics

[0086] As described above, during each ride, the Application collects a myriad of data generated by the cyclist or provided by devices with the cyclist. Data is acquired and then stored for every unit time of the ride in a file that can be accessed by the cyclist and optionally uploaded to a cloud-based storage system. Data elements within the file can include, but is not limited to, seconds from the start of the ride, pedal cadence generated by the person, GPS Latitude, Longitude, elevation and heading, velocity of the cyclist / bike, power generated by the person, heart rate, road segment number, distance from the start ofthe ride, slope, wind direction, wind speed, rider intensity, temperature and other key cycling parameters.

[0087] In one embodiment, the data in the rider’s data file can be uploaded and / or imported into commercially available applications for further analysis. In the preferred embodiment, post ride analytics are available to the rider. While the analytics can be performed at any level of the system, in the preferred embodiment, the cloud platform performs data analysis and comparisons to determine the rider's performance against chosen intensity levels. Data analysis includes the updating of the Cyclist performance characteristics including the power versus time duration relationship to feed that back to the application for the next ride. By continuing to update the performance characteristics of the cyclist, any improvements in ride performance are quickly provided back to the cyclist to continue to optimize and fine tune their performance.

[0088] Using the atmospheric conditions entered by the cyclist or determined automatically from sensors or an external (e.g. cloud-based) application service providing local weather and user a rider’s location, and the actual cyclist performance under different road conditions, the application can calculate some of the cycling aerodynamic characteristics that may not have been entered by the cyclist at any time or further refine based on real road conditions. The application can utilize portions of the route the cyclist completed with their actual performance and infer the aerodynamic parameters for the cyclists.Benefits

[0089] The cycling application presented offers numerous unique benefits to cyclists. By using the rider parameters and historic performance data, the application can provide realtime coaching and energy optimization that are specific to the rider’s capability. By providing real-time and optionally continuous coaching recommendations to the cyclist specific to each road segment, the rider can evaluate their performance during the ride. That provides them with the positive feedback to know they can achieve that set of performance and thus drive them to build experience during the ride and during the specific road segment. The Real-time feedback ensures cyclists can aim to meet targeted values, promoting better performance. Furthermore, the interactive feedback encourages skill refinement during rides. By providing the rider with real time coaching guidance, the cyclist’s skills can grow as they recognize they can achieve certain goals that are within their capabilities.

[0090] The data analytics facilitated by the application enhances performance evaluation and goal setting. They also provide a closed system feedback allowing for continual performance improvement as the application learns the Rider’s capabilities over every recorded ride.

[0091] The applications use of physics to provide evidence-based strategies for energy management allows for personalized target ranges and energy expenditure estimates. The energy equations are also continually optimized as the application leams from variety of riders, road conditions and environmental conditions.

[0092] The adaptive cycling Application described in this patent merges cloud-based data analysis, physics-based modeling, and real-time coaching to revolutionize cyclists' training and performance optimization. This system is poised to elevate cyclists' experiences and contribute to enhanced proficiency across varying rider capabilities, road conditions and intensity levels.

[0093] Although the invention has been described with reference to certain specific embodiments, various modifications thereof will be apparent to those skilled in the art without departing from the spirit and scope of the invention as outlined in the claims appended hereto. The entire disclosures of all references recited above are incorporated herein by reference.

Claims

What is claimed is:

1. A system for providing real time performance feedback for a rider, the system comprising: a computing device comprising: a processor; a memory; a receiver for obtaining ride data from at least one sensor; and a location module for determining a rider location; wherein the memory includes instructions stored thereon that, when executed, causes the processor to: receive rider attributes and bike attributes; receive a target performance goal; define or predict a rider route; divide the rider route into route segments based on road attributes; receive the rider location from the location module; using the rider location and the rider target performance goal, calculate a target performance metric based on the road attributes of one of the route segments; receive the ride data from the at least one sensor; calculate the actual performance metric based on the ride data; and display the target performance metric, and actual performance metric for the one of the route segments.

2. The system of claim 1, wherein the rider route is predetermined.

3. The system of claim 1, wherein the rider route is predicted based on the rider location.

4. The system of any one of claims 1 to 3, wherein the at least one sensor comprises a power meter, heart rate monitor, speed sensor, cadence sensor, environmental sensor, gyroscope, accelerometer, or a combination thereof.

5. The system of claim 4, wherein the at least one sensor comprises a power meter.

6. The system of any one of claims 1 to 5, wherein the target performance metric comprises a target power output, a target speed, a target cadence, a target time in segment, or a combination thereof.

7. The system of claim 6, wherein the target power output is determined by:wherein,Watt2 is the target power output of the rider for the one of the ride segmentsV rider is a target speed,Watt2A is a product of a rolling resistance, a combined mass of the rider and the bike, and gravity,Watt2B is a product of 0.5 times density of air, and the Coefficient of Aerodynamic drag of the rider and the bike,Watt2c is a product of the combined mass of the rider and the bike, and gravity,Vwind is the speed of the wind,WindDirection, in radians, is the perceived wind direction encountered by the rider, and Slope is the slope of the route segment.

8. The system of claim 7, wherein the Coefficient of Aerodynamic drag is estimated based on resistance of the combined bike and rider’s frontal area as they move through the air.

9. The system of claim 8, wherein the atmospheric conditions are received from the at least one sensor, determined based on the rider’s location and received from atmospheric database, or a combination thereof.

10. The system of any one of claims 1 to 9, wherein the actual performance metric comprises an actual power, an actual speed, an actual cadence, an actual heart rate, or a combination thereof.

11. The system of any one of claims 1 to 10, wherein the speed of the rider is computed using rider’s change in location per unit time.

12. The system of any one of claims 1 to 11, wherein the one of the route segments is the route segment in which the rider location is located.

13. The system of any one of claims 1 to 11, wherein the one of the route segments is determined based on the rider location being within a predetermined distance to the end of the route segment in which the rider location is located.

14. The system of any one of claims 1 to 13, wherein the instructions further cause the processor to display an actual time spent in the one of the route segments.

15. The system of any one of claims 1 to 14, wherein the route segments provide uniform riding intensity within a segment.

16. The system of any one of claims 1 to 15, wherein the rider attributes are updated based on the actual performance metric.

17. The system of claim 16, wherein the rider attributes include a rider power capability calculated according to\Riderintensity . (Watt1B- Watt1A)Watt]100 + - Distance^L) .WMtJ }Wherein,Watti is the power capability for a set time (seconds) duration, Riderintensity is the rider’s chosen intensity for the ride,WattiA is a steady state power output the rider can deliver over at least 30 minutes, Watt IB is a maximum power output that the rider is capable of delivering in 1 second, Wattic is the time in seconds at a midpoint in time between the maximum power and the steady state power, where time in seconds is calculated by taking the current segment length in meters and dividing by the estimated Speed in Kilometers per hour / 3.6,Distance is the length of the current segment in meters,Watt ID is the Hill’s coefficient of the slope of the curve at an inflection point between maximum and steady state power values, andV rider is the speed of the rider.

18. The system of any one of claims 1 to 16, further comprising a display for displaying the target performance metric, and actual performance metric for the one of the route segments.

19. A method for providing real-time training feedback comprising: receiving rider attributes and bike attributes; receiving a target performance goal; defining or predicting a rider route; dividing the rider route into route segments based on road attributes; receiving a rider location; calculating a target performance metric based on the rider location, the target performance goal, and the road attributes of one of the route segments; receiving ride data from at least one sensor; calculating an actual performance metric based on the ride data; and displaying the target performance metric, and actual performance metric for the one of the route segments.

20. A device for providing real time performance feedback for a cyclist comprising: a processor; a receiver for obtaining ride data from at least one sensor; a locater for determining a rider location; and a memory having instructions stored thereon that, when executed, causes the processor to: receive the rider location from the locater; determine road attributes of a route segment based on the rider location; calculate a target performance metric using rider attributes, bike attributes, a rider target performance goal and the road attributes; receive the ride data from the at least one sensor; calculate an actual performance metric based on the ride data; anddisplay the target performance metric, and actual performance metric for the route segment.

21. A system for providing real time performance feedback for cyclists, the system comprising: a computing device having a processor and memory and GPS capability; a training application on said computing device; said computing device configured to be in communication with a power meter on a bicycle such that the computing device can receive data from the power meter; said training application configured to: receive rider and bike attributes; receiving a rider’s target performance goal; define or predict a rider route; divide the route into segments based on road attributes; receive GPS coordinates from said computing device and using said GPS coordinates computing the velocity of the bicycle; receiving power data from the power meter; using the GPS calculating velocity, using the road attributes of the current route segment, rider target performance goal, calculating the rider’s target power output, target velocity and target cadence; and displaying the actual power, actual velocity, target velocity, target power and target cadence for a particular route segment on a training display of the application.

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