Method of switching between accelerometer and gyroscope to sense pedaling frequency on bicycle

Abstract

A power meter may use acceleration data from an accelerometer to determine a treading frequency of a treading. When the power meter determines a first condition indicating that the quality of the pedaling frequency is below a desired level, the power meter activates the gyroscope and uses rotation data from the gyroscope to determine the pedaling frequency. When the power meter then determines that the first condition is no longer present, the power meter deactivates the gyroscope and uses acceleration data from the accelerometer to determine a pedaling frequency.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. patent application No. 63 / 426,640, filed on November 18, 2022, entitled “A Method for Switching Between Accelerometer and Gyroscope to Sense Cadence on a Bicycle,” the entire contents of which are incorporated herein by reference. Background Art

[0003] Processing sensor data from a gyroscope to determine the cadence of a bicycle's pedaling is more reliable than processing sensor data from an accelerometer. However, a gyroscope uses more power than an accelerometer, so using a gyroscope reduces the operating time of the battery. Summary of the Invention

[0004] One aspect of this embodiment involves recognizing that there is a trade-off between battery life and data quality when measuring cadence on a bicycle or any human-powered, pedal-driven vehicle. On the one hand, long battery life is achieved by using a low-power accelerometer to sense cadence. However, accelerometers are not robust under certain conditions, such as some degree of bumpiness from the road / path surface, other unpredictable forces applied to the bicycle, and high cadence rates, which can lead to noise in the collected accelerometer data. On the other hand, because gyroscopes directly measure rotation, they provide more reliable cadence data than accelerometers, but they require more power (e.g., twice as much) to operate. Therefore, cadence measurement devices that use accelerometers to detect cadence have longer battery life but lower data reliability, while cadence measurement devices that use gyroscopes provide more reliable data but reduced battery life. Embodiments of the present disclosure address this issue by automatically switching between using an accelerometer and a gyroscope to detect cadence when the quality of the cadence determined based on an accelerometer is poor. For example, in a cadence measurement device that includes both an accelerometer and a gyroscope, the accelerometer is used when conditions are suitable for the accelerometer, and the gyroscope is activated only when conditions are not suitable for the accelerometer. Advantageously, the gyroscope is not used continuously, so battery consumption is less than in a device that continuously uses the gyroscope to detect cadence.

[0005] In certain embodiments, the technology described herein relates to a method for switching between an accelerometer and a gyroscope in a cycling power meter to measure cadence, comprising: using acceleration data from the accelerometer to determine cadence; determining a first condition indicating that a quality of the cadence is below a desired level; activating a gyroscope based on the first condition; and using rotational data from the gyroscope to determine cadence.

[0006] In certain embodiments, the technology described herein relates to a power meter for use with a pedal-powered vehicle, comprising: an accelerometer; a gyroscope; a processor; and a memory storing machine-readable instructions that, when executed by the processor, cause the power meter to: use acceleration data from the accelerometer to determine a cadence of pedaling; determine a first condition indicating that a quality of the cadence is below a desired level; activate the gyroscope; and use rotational data from the gyroscope to determine the cadence.

[0007] In certain embodiments, the technology described herein relates to a software product for switching between an accelerometer and a gyroscope in a bicycle power meter to measure cadence, the software product comprising instructions stored on a non-transitory computer-readable medium, wherein the instructions, when executed by a controller, cause the controller to: when the gyroscope is inactive: use acceleration data from the accelerometer to determine cadence; determine a first condition indicating that the quality of the cadence is below a desired level; activate the gyroscope in response to the first condition; when the gyroscope is active: use rotation data from the gyroscope to determine cadence; determine a second condition indicating that the quality of the cadence is above a desired level; and deactivate the gyroscope. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a schematic diagram illustrating an example power meter coupled to a crank arm in an embodiment.

[0009] Figure 2 In the embodiment shown Figure 1 A block diagram of a power meter is provided for further example details.

[0010] Figure 3A The following is an example of a mode switching algorithm used during a ninety second period of riding on a bicycle. Figure 2 Figure 2 shows an example of switching between an accelerometer and a gyroscope.

[0011] Figure 3B is a graph illustrating the different qualities of the signals from the accelerometer and the gyroscope in an embodiment.

[0012] Figure 4 In the embodiment shown Figure 2A data flow diagram of example operations of a mode switching algorithm for automatically switching between using an accelerometer and using a gyroscope to determine cadence.

[0013] Figure 5 The embodiment shown is Figure 2 The mode switching algorithm and cadence algorithm generate Figure 1 FIG. 4 is a graph showing an example comparison between a switching cadence signal of a power meter and a cadence signal obtained from sensor data based only on an accelerometer in the prior art. DETAILED DESCRIPTION

[0014] U.S. Patent No. 10,060,738 and U.S. Patent No. 11,033,217 (each of which is incorporated herein by reference in its entirety) disclose a power meter and a cadence meter for a bicycle. Cadence, also known as pedaling rate, is a measure of the number of revolutions per minute of the crank. It is a measure of angular velocity that is proportional to, but not identical to, wheel speed.

[0015] Although prior art bicycle meters may include both an accelerometer and a gyroscope, these prior art bicycle meters are unable to automatically switch between using the accelerometer and the gyroscope in order to conserve battery power while maintaining the quality of the detected cadence.

[0016] Figure 1 1 is a schematic diagram illustrating an example power meter 101 coupled to a crank arm 100. The crank arm 100 drives a circular chainring 106 around a crank bearing 110 based on a pedal (not shown) attached to a hole 108 at the distal end of the crank arm 100, thereby driving at least one wheel of a pedal-driven vehicle (e.g., a bicycle). In some embodiments, a housing 102 of the power meter 101 is adhered to the crank arm 100. In other embodiments, the power meter 101 is built into the crank arm 100. The housing 102 may include a battery door 104 to allow replacement of the battery of the power meter 101. At least one function of the power meter 101 is to measure the cadence applied to the crank arm 100 by a cyclist using the bicycle. Although not shown, a second power meter may be attached to a second crank arm that is coupled to the crank bearing 110 and positioned at the opposite side of the pedal-driven vehicle.

[0017] Figure 2 It shows Figure 11 is a block diagram of further example details of the power meter 101. The power meter 101 includes a battery 202 (optionally rechargeable), a controller 204 (e.g., a microprocessor or microcontroller having at least one processor 250 and memory 252 storing machine-readable instructions - software, firmware, etc. - that implement the functionality of the power meter 101 as described herein), at least one accelerometer 206 (using the X-axis and Y-axis), a gyroscope 208, and a wireless interface 210. In some embodiments, the accelerometer 206 and the gyroscope 208 are implemented in a single package such as the LSM6DS0 inertial measurement unit (IMU) manufactured by STMicroelectronics. The power meter 101 may include other sensors and components without departing from the scope of the present invention. For example, the power meter 101 may include one or more strain gauges for sensing the force applied to the crank arm 100. The controller 204 may include at least one analog-to-digital converter for digitizing an analog signal that can be stored and / or processed using a cadence algorithm 207 that determines cadence 209 based on input from an accelerometer 206 or a gyroscope 208. The controller 204 may control a wireless interface 210 to communicate with one or more smartphones 220, a bicycle computer 230, and another computer 240. Cadence is an important metric for a cyclist and can be used in other metrics, such as determining the work performed by the cyclist or power. Therefore, accurate determination of cadence should be ensured.

[0018] As described above, gyroscope 208 can more accurately detect cadence than accelerometer 206, particularly in certain situations. However, gyroscope 208 uses more power than accelerometer 206. Controller 204 includes a mode switching algorithm 205 that characterizes data from accelerometer 206 and detects situations where accelerometer 206 is underperforming (e.g., bumpy roads, high cadence) and automatically switches between using data from accelerometer 206 and using data from gyroscope 208. In certain embodiments, controller 204 processes sensor data (e.g., acceleration data) from accelerometer 206 to determine a vibration metric that indicates the amount of vibration sensed by accelerometer 206. When the vibration metric is above a high vibration threshold, the vibration metric indicates that the quality of cadence 209 calculated using the sensor data from accelerometer 206 is poor. Consequently, controller 204 activates gyroscope 208 (e.g., applies power to gyroscope 208) only when accelerometer 206 is underperforming. In these situations, the controller 204 can also modify the sampling rate of the accelerometer 206. Because these situations (when the accelerometer 206 performs poorly) only occur during a certain percentage of the operating time of the power meter 101 during typical bicycle riding, battery life goals can still be met while reducing the chance of the user encountering low-quality cadence and power data and without requiring the rider to adjust the settings of the power meter 101 while riding. In particular, the rider is unaware of the switch between using the accelerometer 206 and the gyroscope 208. Advantageously, the controller 204 uses the mode switching algorithm 205 to activate the gyroscope 208 only when necessary to maintain the quality of the cadence 209, thereby avoiding excessive battery power consumption by using the gyroscope 208 when it is not needed.

[0019] The mode switching algorithm 205 uses the vibration metric to determine when to switch between using the accelerometer 206 and the gyroscope 208. The following pseudo code provides an example algorithm for calculating the vibration metric:

[0020]

[0021] The following pseudo code shows an example of the mode switching algorithm 205:

[0022]

[0023] As shown in the pseudo code above, a vibration metric is calculated and compared to a threshold (e.g., a high vibration threshold above which it is known that the quality of cadence calculations based on accelerometer data is poor) to determine when to activate the gyroscope 208 to improve the quality of the cadence 209 and to deactivate the gyroscope 208 when it is no longer needed. The vibration metric is determined by processing sensor data from the accelerometer 206 and determining the amount of vibration (e.g., road noise) sensed by the accelerometer 206. For example, when the bicycle is traveling on a smooth surface, the vibration metric will be low, while when the bicycle is traveling on a rough surface, the vibration metric will be high.

[0024] The pseudocode for mode switching algorithm 205 further illustrates other conditions that may cause mode switching algorithm 205 to switch between using accelerometer 206 and gyroscope 208 to determine cadence 209. For example, mode switching algorithm 205 deactivates gyroscope 208 when any of the following conditions occur: (a) the rider has stopped pedaling; (b) the calculated vibration metric is below a high vibration threshold; (c) a panic flag is cleared; and (d) a timer associated with the panic flag has expired. In another example, mode switching algorithm 205 activates gyroscope 208 when any (or more) of the following conditions occur: (a) the panic flag is set; (b) the calculated vibration metric is above a high vibration threshold; and (c) the cadence is above a high cadence threshold. Other software within controller 204 may set the panic flag when an anomaly is detected. For example, cadence algorithm 207 may set the panic flag when an error is detected during the calculation of cadence 209. In situations where a cyclist pedals at a cadence above a high cadence threshold (e.g., 110 revolutions per minute), it is recognized that sensor data from accelerometer 206 results in a poor calculation of cadence. In summary, mode switching algorithm 205 activates gyroscope 208 when any of the aforementioned conditions occur, and deactivates gyroscope 208 when any of the aforementioned conditions no longer exist or persist. That is, mode switching algorithm 205 deactivates gyroscope 208 only when the conditions that caused gyroscope 208 activation no longer exist. For example, if cadence increases above the high cadence threshold and gyroscope 208 is activated, then when cadence drops below the high cadence threshold, gyroscope 208 is deactivated only if no other conditions occur (e.g., a vibration metric is above the high vibration threshold or an emergency handling flag is set). However, when cadence is zero (e.g., the cyclist has stopped pedaling), mode switching algorithm 205 deactivates gyroscope 208 even if other conditions still exist.

[0025] Figure 3A is a graph 300 illustrating the operation of the bicycle by the mode switching algorithm 205 during a period of approximately one minute of riding on the bicycle. Figure 2 Graph 300 illustrates an example switching between the accelerometer 206 and the gyroscope 208. Graph 300 shows a true cadence signal 302 (e.g., a reference signal derived by post-processing gyroscope data to determine the true cadence as accurately as possible), an example cadence signal 304 determined by the cadence algorithm 207 (e.g., cadence 209), and a switching line 306 output from the mode switching algorithm 205 indicating when the cadence algorithm 207 switches between using data sensed by the accelerometer 206 and data sensed by the gyroscope 208.

[0026] During a first period 310, the switch line 306 is low, and the cadence algorithm 207 determines the cadence signal 304 based on the data sensed by the accelerometer 206. Although the cadence signal 304 is not perfect during the first period 310 (e.g., it fluctuates around the true cadence signal 302), the cadence signal 304 remains within an acceptable tolerance threshold (e.g., one revolution per minute) of the true cadence signal 302. At time 312, the mode switch algorithm 205 transitions the switch line 306 to non-zero, indicating that the cadence algorithm 207 should determine the cadence signal 304 based on the data sensed by the gyroscope 208 because the rider has increased the cadence above the accelerometer cadence threshold (e.g., 110 revolutions per minute, above which the sensor data from the accelerometer 206 is less reliable), and the gyroscope 208 is activated. After a short period (e.g., a quarter of a second) after cadence algorithm 207 begins using the data sensed by gyroscope 208, cadence signal 304 improves to accurately follow true cadence signal 302. Thus, during period 314, cadence 209 is derived based on gyroscope 208. At time 316, mode switching algorithm 205 transitions switching line 306 to zero, indicating that cadence algorithm 207 should determine cadence signal 304 based on the data sensed by accelerometer 206, and gyroscope 208 is deactivated. During period 318 after time 316, cadence signal 304 deviates from true cadence signal 302.

[0027] Figure 3B is a graph 350 showing the different qualities of the signals from the accelerometer 206 and the gyroscope 208. The graph 350 presents Figure 3A300, and shows the actual cadence signal 302 and the switching line 306 for reference. Line 352 presents an accelerometer-based estimate of cadence 209 (e.g., a frame-by-frame estimate), which is based on calculating the proportion of a circle completed by accelerometer 206 during the last update period (e.g., the interval between consecutive sensor data readings from the accelerometer, such as 1 / 26 second). Line 354 presents the instantaneous reading of sensor data from gyroscope 208. As can be seen, line 352 has a dramatic, periodic high-low cycle. While the main signal maintains a 1g force of gravity in circular motion, the oscillations are believed to be caused by actual acceleration / deceleration of the crank / bicycle / cyclist. Throughout the sampling period, line 354 remains reliably close to the actual cadence signal 302, demonstrating the superior quality of gyroscope 208 over accelerometer 206.

[0028] The sensor data from the accelerometer 206 can be processed in other ways to determine the cadence 209. In one embodiment, the cadence algorithm 207 can measure the time span between peaks, zeros, and troughs in the sensor data from the accelerometer 206 to determine the cadence 209. In another embodiment, the cadence algorithm 207 measures the slope of atan2 (e.g., a 2-argument arctangent function) of the x-axis sensor data and the y-axis sensor data from the accelerometer 206 over time, where the slope is defined in radians per second and can be multiplied to give the cadence 209 (e.g., revolutions per minute). In another embodiment, the cadence algorithm 207 uses a frequency domain transform of the sensor data from the accelerometer 206. In another embodiment, the cadence algorithm 207 implements a trained neural network that uses the sensor data from the accelerometer 206 to determine the cadence 209. In another embodiment, the cadence algorithm 207 determines the cadence 209 by curve fitting a sine wave or a cosine wave to the sensor data from the accelerometer 206 .

[0029] Figure 4 It shows Figure 2 A data flow diagram 400 of an example operation of the mode switching algorithm 205 for Figure 2 The accelerometer 206 is automatically switched to the gyroscope 208 for determining the cadence 209. During operation of the power meter 101, the accelerometer 206 is active and Figure 1The cadence algorithm 207 continuously sends acceleration data 402 during operation of the power meter 101. The gyroscope 208 is activated only when needed and, when activated, sends rotation data 404 to the cadence algorithm 207. The cadence algorithm 207 includes software that sends the acceleration data 402 and rotation data 404 (when available) to the mode switching algorithm 205 and applies the acceleration data 402 to the acceleration algorithm 410 and the rotation data 404 to the gyroscope algorithm 412, which runs only when the gyroscope 208 is activated. The following pseudo code shows an example of the acceleration algorithm 410:

[0030]

[0031] The pseudo code above detects zero crossings in the sensor data from the accelerometer 206 to determine the period of the previous cycle in the sensor data, and then calculates the cadence 209 based on the previous period. In the event that the acceleration calculation method 410 detects an anomaly, for example, the time of the last zero crossing occurs in the future, emergency processing is initiated to cause the mode switching algorithm 205 to activate the gyroscope 208 to ensure the quality of the cadence 209.

[0032] The following pseudo code shows an example of the gyroscope algorithm 412:

[0033] function calculateGyroCadence(sensorReadingRadiansPerSecond)

[0034] #convert raw sensor reading to RPM

[0035] return60*sensorReadingRadiansPerSecond / (2*PI).

[0036] As shown in the pseudo code above, the sensor data (eg, rotation data) from the gyroscope 208 is already in radians per second and can therefore be used directly to calculate the cadence 209 .

[0037] Acceleration algorithm 410 generates at least one filtered estimate of the cyclist's current cadence based on acceleration data 402. However, when gyroscope 208 is activated, gyroscope algorithm 412 determines an accurate cadence value based on rotation data 404 and can provide an accurate cadence value 414 to update or replace the cadence value determined by acceleration algorithm 410. Acceleration algorithm 410 also includes software that detects when acceleration data 402 generates a poor quality cadence 209. For example, acceleration algorithm 410 can determine when the generated cadence 209 requires unrealistic acceleration, includes severe oscillations, and / or when acceleration data 402 contains excessive noise. When acceleration algorithm 410 determines that cadence 209 is of poor quality, acceleration algorithm 410 sends an "urgent action" notification 416 to mode switch algorithm 205, which generates a gyroscope activation signal 420 to activate gyroscope 208.

[0038] The mode switching algorithm 205 may also determine when the quality of the acceleration data 402 is poor to generate a gyroscope activation signal 420. Additionally, the mode switching algorithm 205 may determine when the quality of the acceleration data 402 is improved and deactivate the gyroscope activation signal 420 to power off the gyroscope 208 to conserve battery power.

[0039] The cadence 209 and / or the precise cadence value 414 are input to a power algorithm 430, which also receives torque sensor data 432 from a power sensor 434. In some embodiments, the power sensor 434 includes at least one strain gauge applied to the crank arm 100 to measure the torque applied by the cyclist to the crank arm 100. The power algorithm 430 calculates the cyclist's power (in watts) based on the torque sensor data 432 and the measured torque indicated by the cadence 209 or the precise cadence value 414 (when available).

[0040] The following pseudo code shows an example of the power algorithm 430:

[0041]

[0042] As shown in the pseudo code above, the torque applied by the rider to the cranks is measured (e.g., using strain gauges applied to the cranks), and a power value is calculated using this torque and cadence 209. Other methods of calculating power may be used without departing from the scope of this disclosure.

[0043] Real-world comparison

[0044] Figure 5 The graph 500 shows the Figure 2 The mode switching algorithm 205 and the cadence algorithm 207 generate Figure 1FIG2 is an example comparison between the switching cadence signal 502 of the power meter 101 and the prior art cadence signal 504 derived from sensor data based solely on an accelerometer. The power meter 101 and the prior art cadence meter were simultaneously mounted on a mountain bike, and the cadence 209 of the power meter 101 (shown as the switching cadence signal 502) and the prior art cadence from the prior art cadence meter (shown as the prior art cadence signal 504) were captured over a period of approximately one minute in duration.

[0045] Notably, prior art cadence signal 504 exhibits aggressive cadence oscillations in region 506 (eg, near the middle of the displayed time period) and also consistently exhibits more noise than switched cadence signal 502 .

[0046] Gyroscope working time data

[0047] Depend on Figure 1 The Power Meter 101's internal and external test instruments capture detailed logs representing real-world riding conditions and are used to determine Figure 2 The test data is used to adjust the parameters of the mode switching algorithm 205 to maximize cadence accuracy while keeping power consumption below the budgeted battery life target.

[0048] For example, assuming a 200mAh CR2032 battery and a desired battery life of 800 hours, the average current draw should not exceed 250uA. The following formula determines the average power consumption:

[0049] (GyroOnTimeFraction)*(GyroOnCurrent)+(GyroOffTimeFraction)*(GyroOffCurrent).

[0050] Therefore, by measuring the GyroOnCurrent and GyroOffCurrent of the power meter 101, the maximum GyroOnTimeFraction can be determined. For example, a measured gyroOffCurrent of 180uA and a measured GyroOnCurrent of 540uA result in a maximum gyroscope on-time of 20%. The collected logs show that, as expected, the gyroscope on-time increases with increasing ride roughness, as shown in the gyroscope on-time in Table 1. In this dataset, 32.6% of the time corresponds to smooth riding conditions, such as riding on a treadmill or stationary bike, 45.8% of the riding time corresponds to riding on road-type surfaces, and 21.5% of the riding time corresponds to riding on gravel or mountain trail-type surfaces. When riding on a flat road, the activation ratio of the gyroscope 208 is minimized (0.02%). When riding on a road-type surface, the gyroscope is on for 32.5% of the time. When riding on a gravel surface or mountain trail, the gyroscope is on for 48.3% of the time.

[0051] Table 1 Gyroscope working time

[0052] Riding Type Percentage of test set Gyroscope working time Trainer / Fixture 32.6％ 0.02％ highway 45.8％ 32.5％ Gravel / MTB 21.5％ 48.3％

[0053] Modifications may be made to the above-described methods and systems without departing from the scope of the present disclosure. It should be noted, therefore, that the matter contained in the above description or shown in the accompanying drawings is to be interpreted as illustrative and not restrictive. The appended claims are intended to cover all general and specific features described herein, and all statements of the scope of the methods and systems of the present disclosure, as far as language is concerned, that could be said to fall therein.

[0054] Combination of features

[0055] The features described above and in the appended claims may be combined in various ways without departing from the scope of the present disclosure. The following examples illustrate some possible non-limiting combinations:

[0056] (A1) A method for switching between an accelerometer and a gyroscope in a bicycle power meter to measure cadence, comprising: using acceleration data from the accelerometer to determine cadence; determining a first condition indicating that the quality of the cadence is below a desired level; activating the gyroscope based on the first condition; and using rotation data from the gyroscope to determine the cadence.

[0057] (A2) In the embodiment of (A1), determining the first condition includes processing the acceleration data to determine a vibration metric indicative of an amount of vibration sensed by the accelerometer, wherein the first condition occurs when the vibration metric is above a vibration threshold.

[0058] (A3) In any of embodiments (A1) and (A2), determining the first condition further includes determining that an emergency processing flag is set, wherein the emergency processing flag is set by software when an abnormality is detected.

[0059] (A4) In any of embodiments (A1)-(A3), determining the first condition further comprises determining when the cadence is above a high cadence threshold.

[0060] (A5) In any of embodiments (A1)-(A4), the method further includes: determining that the first condition no longer exists; deactivating a gyroscope based on the first condition; and determining a cadence using acceleration data from an accelerometer.

[0061] (A6) In any of embodiments (A1)-(A5), determining that the first condition no longer exists includes processing the acceleration data to determine a vibration metric indicative of an amount of vibration sensed by the accelerometer, wherein the first condition no longer exists when the vibration metric is below a vibration threshold.

[0062] (A7) In any of embodiments (A1)-(A6), determining that the first condition no longer exists includes determining that an emergency processing flag is not set, wherein the emergency processing flag is set by software when the abnormality is detected and has ended.

[0063] (A8) In any of embodiments (A1)-(A7), determining that the first condition no longer exists includes determining when the rider stops pedaling.

[0064] (A9) In any of embodiments (A1)-(A8), determining that the first condition no longer exists includes determining when the cadence is below a high cadence threshold.

[0065] (A10) In any of embodiments (A1)-(A9), using the acceleration data to determine the cadence further comprises using the acceleration data to calculate a proportion of a circle completed by the accelerometer during an update period between consecutive readings of the acceleration data.

[0066] (A11) In any of embodiments (A1)-(A10), using the acceleration data to determine the cadence further comprises calculating time spans between peaks, zeros, and troughs in the acceleration data.

[0067] (A12) In any of embodiments (A1)-(A11), determining the cadence using the acceleration data further comprises calculating a slope of an output of the atan2 function of the x-axis component and the y-axis component of the acceleration data over time.

[0068] (A13) In any of embodiments (A1)-(A12), using the acceleration data to determine the cadence further comprises using a frequency domain transform of the acceleration data.

[0069] (A14) In any of embodiments (A1)-(A13), using the acceleration data to determine the cadence further comprises processing the acceleration data using a trained neural network.

[0070] (A15) In any of embodiments (A1)-(A14), using the acceleration data to determine the cadence further comprises performing a sine wave or cosine wave curve fitting on the acceleration data.

[0071] (B1) A power meter for use with a pedal-powered vehicle, comprising: an accelerometer; a gyroscope; a processor; and a memory storing machine-readable instructions that, when executed by the processor, cause the power meter to: use acceleration data from the accelerometer to determine a cadence of pedaling; determine a first condition indicating that a quality of the cadence is below a desired level; activate the gyroscope; and use rotational data from the gyroscope to determine the cadence.

[0072] (B2) In an embodiment of (B1), the memory further includes machine-readable instructions that, when executed by the processor, cause the power meter to: determine that the first condition no longer exists; deactivate the gyroscope; and use acceleration data from the accelerometer to determine cadence.

[0073] (B3) In any of the embodiments of (B1) and (B2), the memory further includes machine-readable instructions that, when executed by the processor, cause the power meter to: process the acceleration data to determine a vibration metric indicating an amount of vibration sensed by the accelerometer; and compare the vibration metric to a high vibration threshold to determine the first condition.

[0074] (B4) In any of the embodiments of (B1)-(B3), the memory also includes machine-readable instructions that, when executed by the processor, cause the power meter to: determine that the first condition exists when an emergency processing flag is set, wherein the emergency processing flag is set by software when an abnormality is detected; and determine that the first condition exists when the cadence is higher than a high cadence threshold.

[0075] (C1) A software product for switching between an accelerometer and a gyroscope in a bicycle power meter to measure cadence, the software product comprising instructions stored on a non-transitory computer-readable medium, wherein the instructions, when executed by a controller, cause the controller to: when the gyroscope is inactive: use acceleration data from the accelerometer to determine cadence; determine a first condition indicating that the quality of cadence is below a desired level; activate the gyroscope in response to the first condition; when the gyroscope is active: use rotation data from the gyroscope to determine cadence; determine that the first condition no longer exists; and deactivate the gyroscope.

Claims

1. A method for switching between an accelerometer and a gyroscope in a bicycle power meter to measure cadence, comprising: determining the cadence using acceleration data from the accelerometer; determining a first condition indicating that a quality of the cadence is below a desired level; activating the gyroscope based on the first condition; as well as The cadence is determined using rotational data from the gyroscope.

2. The method of claim 1 , determining the first condition comprising processing the acceleration data to determine a vibration metric indicative of an amount of vibration sensed by an accelerometer, wherein The first condition occurs when the vibration metric is above a vibration threshold.

3. The method of claim 1, wherein determining the first condition further comprises determining that an emergency processing flag is set, wherein: When an abnormality is detected, the emergency processing flag is set by software.

4. The method of claim 1 , determining the first condition further comprising determining when the cadence is above a high cadence threshold.

5. The method of claim 1 , further comprising: determining that the first condition no longer exists; deactivating the gyroscope based on the first condition; as well as The cadence is determined using acceleration data from the accelerometer.

6. The method of claim 5, determining that the first condition no longer exists comprises: The acceleration data is processed to determine a vibration metric indicative of an amount of vibration sensed by the accelerometer, wherein the first condition no longer exists when the vibration metric is below a vibration threshold.

7. The method of claim 5, determining that the first condition no longer exists comprises: It is determined that an emergency processing flag is not set, wherein the emergency processing flag is set by software when an abnormality is detected and has ended.

8. The method of claim 5, determining that the first condition no longer exists comprises: Determine when the cyclist stops pedaling.

9. The method of claim 5, determining that the first condition no longer exists comprises: It is determined when the cadence is below a high cadence threshold.

10. The method of claim 1 , wherein using acceleration data to determine the cadence further comprises: The acceleration data is used to calculate a proportion of a circle completed by the accelerometer during an update period between successive readings of the acceleration data.

11. The method of claim 1 , wherein using acceleration data to determine the cadence further comprises: The time spans between peaks, zeros, and troughs in the acceleration data are calculated.

12. The method of claim 1 , wherein using acceleration data to determine the cadence further comprises: Calculate the slope of the output of the atan2 function of the x-axis component and the y-axis component of the acceleration data as a function of time.

13. The method of claim 1 , wherein using acceleration data to determine the cadence further comprises: A frequency domain transform of the acceleration data is used.

14. The method of claim 1 , wherein using acceleration data to determine the cadence further comprises: The acceleration data is processed using a trained neural network.

15. The method of claim 1 , wherein using acceleration data to determine the cadence further comprises: Perform sine wave or cosine wave curve fitting on the acceleration data.

16. A power meter for use with a pedal-powered vehicle, comprising: accelerometer; gyroscope; processor; as well as a memory storing machine-readable instructions that, when executed by the processor, cause the power meter to: using acceleration data from the accelerometer to determine a pedaling cadence; determining a first condition indicating that a quality of the cadence is below a desired level; activating the gyroscope; as well as The cadence is determined using rotational data from the gyroscope.

17. The power meter of claim 16, the memory further comprising machine-readable instructions that, when executed by the processor, cause the power meter to: determining that the first condition no longer exists; deactivating the gyroscope; and The cadence is determined using acceleration data from the accelerometer.

18. The power meter of claim 16, the memory further comprising machine-readable instructions that, when executed by the processor, cause the power meter to: processing the acceleration data to determine a vibration metric indicative of an amount of vibration sensed by the accelerometer; and The vibration metric is compared to a high vibration threshold to determine the first condition.

19. The power meter of claim 16, the memory further comprising machine-readable instructions that, when executed by the processor, cause the power meter to: When the emergency processing flag is set, it is determined that the first condition exists, wherein, The emergency processing flag is set by software when an abnormality is detected; and When the cadence is higher than a high cadence threshold, it is determined that the first condition exists.

20. A software product for switching between an accelerometer and a gyroscope in a bicycle power meter to measure cadence, the software product comprising instructions stored on a non-transitory computer-readable medium, wherein: When the instructions are executed by the controller, the controller: When the gyroscope is inactive: determining the cadence using acceleration data from the accelerometer; determining a first condition indicating that a quality of the cadence is below a desired level; activating the gyroscope in response to the first condition; When the gyroscope is active: using rotational data from the gyroscope to determine the cadence; determining that the first condition no longer exists; and Deactivate the gyroscope.

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