Cyclist fatigue detection

Abstract

The present invention relates to a method for operating a bicycle. The method comprises detecting (12) a pedal crank actuation and determining (14) the fatigue of a bicycle rider based on the detected pedal crank actuation. Furthermore, the invention relates to an operating device for a bicycle.

Description

Technical field

[0001] The present invention relates to a method for operating a bicycle. The invention also relates to an operating device for a bicycle. State of the art

[0002] Bicycles, such as city bikes, mountain bikes or cargo bikes, are powered by operating their pedal crankshaft. Riding a bike can be particularly efficient and gentle on the rider if the rider pedals evenly and selects an appropriate gear. The rider can become tired while riding, making it more difficult for them to propel the bike. The rider can also be tired at the start of the ride. A tired rider can operate the bike in an unsuitable manner. For example, a tired rider may select an unsuitable gear, making it more difficult to propel the bike. Attention in traffic can also be impaired due to fatigue. However, people often only realize their fatigue when they are very exhausted and are unaware of previous limitations caused by fatigue when riding a bike.

[0003] Driver fatigue detection is already common practice in motor vehicles. For example, DE 10 2010 013 356 A1 describes a driver fatigue alarm on a windshield head-up display. However, the fatigue detection systems and methods used in motor vehicles are not applicable to bicycles, or only with considerable effort. Description of the invention

[0004] The object of the present invention is to detect cyclist fatigue and thus enable better use of a bicycle. This object is achieved by the subject matter of the independent patent claims.

[0005] A first aspect relates to a method for operating a bicycle. The bicycle can be designed, for example, as a touring bike, racing bike, or mountain bike. The bicycle can also be designed as a pedelec. A pedelec has a drive motor designed to assist a cyclist in propelling the bicycle. The drive motor can be designed, for example, as an electric machine. The bicycle can have a pedal crankshaft at which a drive force can be introduced into a drive train of the bicycle by the cyclist. The cyclist can propel the bicycle, for example, by pedaling.

[0006] The method comprises a step of detecting a pedal crank actuation. In this case, a rotational speed and, alternatively or additionally, a torque can be detected. The pedal crank actuation can, for example, be detected as a curve. The detection can take place continuously, with a sensor frequency, at specific times and, alternatively or additionally, at specific angular positions of the pedal crankshaft. The angular position of the pedal crankshaft can, for example, correspond to pedal positions, which can influence the actuation. This influence can then be largely filtered out by the detection. For example, the detection can take place after each revolution or a multiple of a revolution, whereby, for example, pedal position influences can be filtered out.

[0007] The method comprises a step of determining the fatigue of a bicycle rider based on the detected pedal crank actuation. The fatigue can be determined, for example, as one or more characteristic values. For example, a value between 0 and 100 can be determined for fatigue. The fatigue can be a measure of the rider's exhaustion. The pedal crank actuation allows a conclusion to be drawn about the rider's fatigue, which is not possible with other vehicles, such as motor vehicles and aircraft. Fatigue is detected, for example, based on a change in pedal crank actuation over time. For example, a tired rider may pedal less smoothly, have a less consistent cadence, or take short breaks while pedaling more frequently. Likewise, vibrations may become greater when the pedals are loaded.This allows fatigue determination to be achieved using simple means, such as a single speed sensor. The method can be used solely to determine rider fatigue or can also further intervene in the operation of the bicycle, for example, by outputting the determined fatigue or by controlling bicycle components.

[0008] A gradient of the pedal crank operation can also be determined depending on the detected pedal crank operation. The determination of a bicycle rider's fatigue can be carried out depending on the determined gradient of the pedal crank operation. The gradient of the detected pedal crank operation can form the basis for determining the rider's fatigue, either alone or together with the detected pedal crank operation. If the pedal crank operation has several parameters, the gradient can be determined for one, several, or all of these parameters. The respective detected and determined values can be stored on a data storage device, which can, for example, be part of a central server, the bicycle, or a portable device such as a smartphone.This data can then be used for subsequent evaluation of driver fatigue, user profiling and, alternatively or additionally, for improving fatigue determination.

[0009] In a further embodiment of the method, it can be provided that the detection of the pedal crank actuation comprises the detection of a rider torque. For this purpose, the bicycle can have a torque sensor by means of which a torque introduced by the rider at the pedal crankshaft can be detected. The torque can be detected as described above for the detection of pedal crank actuation, for example, as a curve in certain angular positions of the pedal crankshaft. By detecting the rider torque, very precise conclusions can be drawn about severe rider fatigue, since the rider can then usually no longer drive the pedal crankshaft as hard as usual.

[0010] In a further embodiment of the method, it can be provided that the detection of the pedal crank actuation comprises the detection of a cadence. For this purpose, the bicycle can have a speed sensor by means of which a rotational speed of the pedal crankshaft can be detected. The cadence can be detected as described above for the detection of the pedal crank actuation, for example, as a curve in certain angular positions of the pedal crankshaft. A cadence can be a pedaling frequency. By detecting the cadence, even slight fatigue in the rider can be detected, since even with lower levels of fatigue, many riders pedal less evenly.

[0011] In a further embodiment of the method, it can be provided that the determined fatigue is output to the rider. The output can be acoustic, for example, and alternatively or additionally visual. The output can be provided, for example, by an output device on the bicycle and alternatively or additionally via a portable device such as a smartphone or wireless headphones. The output can, for example, be continuous, at specific time intervals or over specific travel distances, only upon request, or when the determined fatigue of the rider exceeds a threshold. Through the output, the rider can, for example, better distribute his energy, plan rest breaks carefully, and alternatively or additionally optimize his workload and training. The rider can also select gears adapted to his fatigue.The output may, for example, include fatigue indicators, a recommended action for the driver, such as changing gear or pedaling more slowly and evenly, and alternatively or additionally, a fatigue warning.

[0012] In a further embodiment of the method, it can be provided that the bicycle is controlled depending on the determined fatigue. This allows for control adapted to the rider's exhaustion. For example, the control of a gearshift, a braking system, an assist motor, and alternatively or additionally a chassis can be modified, or control can generally be carried out depending on the determined fatigue. For example, lower gears can be automatically engaged for a very tired rider than for a fresh rider. This can make it easier to propel the bicycle when exhausted. In addition, the rider can be encouraged to ride more slowly, which can be safer when attention is reduced due to fatigue.Alternatively or additionally, the assistance power provided by the assistance motor can be adjusted to a higher level, the greater the rider's fatigue is determined. For example, different control characteristics can be selected depending on the determined fatigue.

[0013] In a further embodiment of the method, it can be provided that the method further comprises a step of detecting a bicycle state. For example, the bicycle state can be detected by a sensor on the bicycle or by a device carried by the rider, such as a smartphone. For example, the bicycle state can include a riding speed, a bicycle inclination, bicycle vibration, a steering angle, an angular velocity, an acceleration, a selected gear, for example in comparison to a current rider torque, and alternatively or additionally a riding speed. The bicycle state can be detected as described above for pedal crank actuation detection, for example as a curve in certain angular positions of the pedal crank shaft.

[0014] The determination of rider fatigue can also be made depending on the recorded bicycle condition. This allows specific bicycle conditions that may influence pedal crank operation to be taken into account when determining fatigue. This makes the determination of rider fatigue more accurate. For example, the cyclist typically pedals differently when cornering tightly, when going uphill, and when going downhill. A riding situation can be determined based on the recorded bicycle condition, for example, on the basis of characteristic variables. The rider fatigue can be determined depending on the specific riding situation. The riding situation can be used as an additional input variable when determining fatigue.Alternatively or additionally, the detected pedal crank operation can be modified depending on the specific riding situation and the determination of the rider's fatigue can be determined depending on the modified detected pedal crank operation.

[0015] In a further embodiment of the method, it can be provided that the method further comprises a step of detecting an environmental state.

[0016] For example, the environmental condition can be recorded by a sensor on the bicycle or by a device carried by the rider, such as a smartphone. The environmental condition can be received alternatively or additionally, for example as weather data or map data from the internet. The environmental condition can, for example, include traffic data, weather data, a current position of the bicycle, air temperature, wind, air pressure, traffic, slope gradient and, alternatively or additionally, map data. For example, a headwind, terrain topography, altitude above sea level and, alternatively or additionally, an obstruction while riding due to slow-moving traffic can be taken into account when determining fatigue. The environmental condition can be recorded as described above for pedal crank actuation recording, for example as a curve in certain angular positions of the pedal crank shaft.

[0017] The determination of the rider's fatigue can also be made depending on the detected environmental conditions. This allows respective environmental conditions, which can also influence the pedal crank operation, to be taken into account when determining the fatigue. This makes the determination of the rider's fatigue more accurate. For example, the rider tires more quickly in thin mountain air or a hot ambient temperature. In strong crosswinds or headwinds, the rider may pedal differently. The detected environmental conditions can be used as an additional input variable when determining the fatigue. Alternatively or additionally, the detected pedal crank operation can be modified depending on the detected environmental conditions and the determination of the rider's fatigue can be determined depending on the modified detected pedal crank operation.

[0018] In a further embodiment of the method, it can be provided that determining the rider's fatigue comprises a comparison of the detected pedal crank actuation with previously stored pedal crank actuations. The previously stored pedal crank actuations can, for example, be historical data of the method. For example, a change compared to the start of the journey can be used to determine fatigue. For example, the stored pedal crank actuations can allow the creation of a user profile and, alternatively or additionally, can be assigned to a specific fatigue of the rider. For example, the previously stored pedal crank actuations can be data determined experimentally or generated using a simulation. For example, fatigue can have been manually assigned to the previously stored pedal crank actuations.This allows the rider to determine fatigue levels either by themselves or through experiments or simulations. For example, the rider can specify how tired they were based on previously saved pedal strokes. Alternatively, the rider can confirm whether the fatigue displayed is correct or not based on the previously saved pedal strokes. To do this, the rider can, for example, swipe left or right on the fatigue displayed on a touchscreen, similar to what is often done on dating apps.

[0019] In a further embodiment of the method, it can be provided that the determination of the rider's fatigue comprises a key figure analysis of the detected pedal crank actuation. The key figure analysis can be, for example, a spectral analysis or an order analysis. For example, a proportion of the pedal crank actuation at certain frequencies or harmonics can be determined. This proportion can shift if the rider becomes fatigued, whereby fatigue can be easily determined. For example, the key figure analysis can be used to determine Fourier coefficients, amplitudes, phase positions and, alternatively or additionally, spectral power densities at one or more frequencies, for example at integer multiples of the average cadence in the detection period. The determination of the rider's fatigue can be carried out depending on the result of the key figure analysis, for example based on a threshold comparison or by an algorithm.The thresholds can, for example, have been previously determined experimentally and can be set and modified alternatively or additionally by the driver. For example, the driver can confirm whether the indicated fatigue is correct or not. To do so, the driver can, for example, swipe the fatigue displayed on a touchscreen display to the left or right, similar to what is often done with dating apps.

[0020] In a further embodiment of the method, it can be provided that the determination of the rider's fatigue takes place using a neural network. The neural network can use the determined pedal crank actuation and, alternatively or additionally, other of the previously described variables as input variables. As an output variable, the neural network can, for example, output a modified pedal crank actuation or directly output the determined fatigue. The neural network can, for example, only perform the key figure analysis. The actual determination of the rider's fatigue can then again be carried out analytically based on the output variables of the neural network. The neural network can, for example, be trained using historical data and, alternatively or additionally, simulated data. The neural network can at least partially eliminate the need for a complex analytical determination of fatigue.For example, the neural network can easily take environmental conditions, such as wind, into account when determining fatigue. The neural network can, for example, be driver-specific or generally applicable. The neural network can form an AI. The neural network can, for example, have a topology and weight and link respective input variables. The neural network can, for example, be further trained by the driver by providing feedback on the determined fatigue. This feedback can be used to determine a reward for the neural network, for example to change the weightings in connections of the neural network. For example, the driver can confirm whether the displayed fatigue is correct or not. To do this, the driver can, for example, swipe the fatigue displayed on a touch display to the left or right, similar to what is often done on dating apps.

[0021] In a further embodiment of the method, it can be provided that the method comprises an assessment of a specific level of fatigue of the rider by the rider. For example, the rider can confirm whether the displayed fatigue is correct or not in their opinion. To do so, the rider can, for example, swipe the fatigue displayed on a touch display to the left or right, similar to what is often done with dating apps. For example, the rider can also correct a measure of the specific fatigue and thus assess the specific fatigue. A future determination of the rider's fatigue can be modified depending on the rider's assessment. For example, the neural network can be trained based on the assessment and, alternatively or additionally, an analytical evaluation of the recorded pedal crank actuation can be modified. For example, an algorithm can be adapted depending on the assessment.

[0022] In a further embodiment of the method, it can be provided that the method comprises selecting a user profile. The determination of the rider's fatigue can be carried out depending on the selected user profile. For example, the user profile can modify the evaluation of the detected pedal crank actuation. Respective evaluations can also be stored in a user-specific manner. The user profile allows a fatigue determination to be adapted to the rider. However, the user profile can also be adapted to different usage situations, for example, for a bicycle race or a city ride.

[0023] A second aspect relates to an operating device for a bicycle. The operating device can be configured to carry out the method according to the first aspect. Further features, embodiments, and advantages can be found in the descriptions of the first aspect. Conversely, features, embodiments, and advantages of the second aspect also represent features, embodiments, and advantages of the first aspect.

[0024] The operating device can have a detection device designed to detect pedal crank actuation. The detection device can have sensors that are arranged, for example, on the bicycle. The sensor signals can be evaluated, for example, by an evaluation unit of the detection device. The evaluation unit can be a microprocessor arranged on the bicycle. The evaluation unit can also be formed by a portable device, such as a smartphone, or a central server to which the sensor signals are transmitted. The operating device can have a determination device designed to determine the fatigue of a bicycle rider depending on the detected pedal crank actuation. The determination device can be a microprocessor arranged on the bicycle.The determination device can also be formed by a portable device, such as a smartphone, or a central server, to which the sensor signals and, alternatively or additionally, the detected pedal crank actuation are transmitted. The determination device and the evaluation unit of the detection device can be formed jointly by the same device. The operating device can have an output device, for example with a touchscreen or a loudspeaker. The determined fatigue of the rider can be output there. The operating device can have a control device. The control device can be designed to control the bicycle depending on the determined fatigue. For this purpose, respective control signals can be output to the bicycle. Short description of the characters Fig. 1 schematically shows a method for operating a bicycle. Detailed description of embodiments

[0025] Fig. 1 illustrates a method for operating a bicycle. In an optional step 10, a user profile is selected. In a step 12, a pedal crank operation of the bicycle by the rider is detected, wherein the pedal crank operation comprises a rider torque and a cadence. In step 14, a determination of the fatigue of the bicycle rider takes place depending on the detected pedal crank operation. In one embodiment, the determination 14 of the fatigue of the rider takes place by means of a key figure analysis using a spectral analysis. The determination 14 of the fatigue of the rider then takes place depending on the result of the key figure analysis. In another embodiment, the determination 14 of the fatigue of the rider takes place using a previously trained neural network.

[0026] In an optional step 16, the determined fatigue level is output to the rider. The rider can then evaluate the determined fatigue level and thus modify the future determination 16 of fatigue in their user profile. In a likewise optional step 18, the bicycle is controlled depending on the determined fatigue level. For example, the control 18 modifies a characteristic curve of an assist motor and, alternatively or additionally, of an automatically shifting derailleur. Reference symbol Step 10: Select a user profile 12 Step: Capture pedal crank operation 14 Step: Determining fatigue 16 Step: Output of the specific fatigue 18 Step: Control the bike based on the determined fatigue QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] DE 10 2010 013 356 A1

[0003]

Claims

[1] A method for operating a bicycle, the method comprising at least the following steps: - detecting (12) a pedal crank actuation; and - Determining (14) a fatigue of a rider of the bicycle as a function of the detected pedal crank actuation. [2] Method according to claim 1, characterized by that the detection (12) of the pedal crank actuation comprises a detection of a driver torque. [3] Method according to claim 1 or 2, characterized by that the detection (12) of the pedal crank actuation comprises a detection of a cadence. [4] Method according to one of the preceding claims, characterized by that the determined fatigue is output to the driver (16). [5] Method according to one of the preceding claims, characterized by that the bicycle is controlled depending on the specific fatigue (18). [6] Method according to one of the preceding claims, characterized bythat the method further comprises a step of detecting a bicycle condition and the determination (14) of the rider's fatigue is additionally carried out as a function of the detected bicycle condition. [7] Method according to one of the preceding claims, characterized by that the method further comprises a step of detecting an environmental condition and the determination (14) of the driver's fatigue is additionally carried out as a function of the detected environmental condition. [8] Method according to one of the preceding claims, characterized by that determining (14) the rider's fatigue comprises a comparison of the detected pedal crank actuation with previously stored pedal crank actuations. [9] Method according to one of the preceding claims, characterized bythat the determination (14) of the rider's fatigue comprises a key figure analysis of the detected pedal crank actuation and the determination (14) of the rider's fatigue is carried out depending on the result of the key figure analysis. [10] Method according to one of the preceding claims, characterized by that the determination (14) of the driver's fatigue is carried out by means of a neural network. [11] Method according to one of the preceding claims, characterized by that the method comprises an assessment of a certain fatigue of the driver by the driver and a future determination (14) of the fatigue of the driver is modified depending on the assessment by the driver. [12] Method according to one of the preceding claims, characterized by that the method comprises selecting (10) a user profile and determining (14) the driver's fatigue as a function of the selected user profile. [13] Operating device for a bicycle, wherein the operating device has a detection device which is designed to detect (12) a pedal crank operation, and a determination device which is designed to determine (14) a fatigue of a rider of the bicycle as a function of the detected pedal crank operation.

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