Algorithmic L/R Balance Estimation for Single-Sided Power Meters via High-Frequency Crank Telemetry

Hi everyone,

I wanted to pitch a software concept for a Karoo Companion App / Extension that bridges a frustrating hardware limitation for budget-conscious riders: calculating an estimated Left/Right pedalling balance for users with single-sided power meters.

Since the Karoo SDK gives us so much flexibility to build custom data fields and post-ride analytics, I think this could be a highly popular utility for the ecosystem.

Here is a breakdown of how the logic would work, the technical hurdles, and how it could look on the device.

🛠️ The Core Mechanics (The Physics)

Even when the Karoo displays a steady cadence (e.g., 90 RPM), the crank arm does not move at a constant speed. It continuously micro-accelerates during a leg's downstroke (roughly 2 o’clock to 5 o’clock) and micro-decelerates through the dead spots (12 o’clock and 6 o’clock).

Spider-based power meters already use these pulses to estimate balance.

For a single-sided meter, the extension's algorithm would approach it like this:

1. Phase Segmentation: Segment a single 360° revolution into two distinct 180° phases based on the rotational time domain.

2. Pulse Analysis: Measure the amplitude and duration of the power pulse during Phase A (the measured leg's downstroke) and compare it to the momentum and micro-acceleration generated during Phase B (the unmeasured leg's downstroke).

3. The Split: By calculating the variance between these two phases, the software derives a real-time L/R percentage split.

💻 Proposed Architecture 1.

The Data Processing Layer:

• High-Frequency Sampling: Poll incoming ANT+/BLE power and cadence profiles at their maximum refresh rates, tapping into raw torque or rotation payloads if exposed by the hardware.

• Adaptive Baseline Smoothing: At high cadences (110+ RPM) or on rough terrain (gravel/cobbles), velocity fluctuations naturally blur. The algorithm would need a dynamic rolling filter to damp out environmental noise.

• Rider Position Classifier: Standing vs. seated riding completely alters the shape of the power pulse. The software could utilize a threshold classifier (detecting sharp torque spikes combined with minor cadence drops) to shift calculation profiles on the fly.

2. The Karoo OS Layer

• Custom SDK Data Field: Expose the metric as a clean, native-looking data field ("Est. L/R Balance") for real-time biofeedback.

• FIT File Integration: Inject the calculated balance data into the developer fields of the .FIT file, ensuring seamless post-ride analysis on platforms like TrainingPeaks or Intervals.icu.

⚠️ Technical Trade-Offs & Challenges to Solve

• The Upstroke Blindspot: This calculation cannot detect "negative resistance." If a rider has a lazy leg that rests heavily on the pedal during its upstroke, the algorithm will likely misinterpret the resulting deceleration as a weaker push from the opposite leg.

• Hardware Broadcast Limitations: Standard Bluetooth/ANT+ channels typically broadcast at 1Hz to save battery. The accuracy of this extension relies heavily on whether the power meter transmits high-rate rotational data packets (like Crank Torque Frequency).

I’d love to get the community's thoughts on this.

1. Has anyone looked into the raw data packet structures of popular single-sided meters (like 4iiii or Stages) to see if they expose the required high-frequency cadence telemetries over ANT+?

2. Would the current Karoo SDK allow for the low-latency mathematics required to process this in real time without causing battery drain?

Thanks for considering.