Wearable Device Tread Frequency Calculation via Acceleration Signal Analysis
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Solution Overview
Problem
Wearable devices cannot accurately calculate tread frequency or identify a riding state without pre-obtaining parameters of the rotation carrier or mounting on the carrier.
Innovation Solution
A method that determines a feature point set from an acceleration signal with periodic distribution and reasonable amplitude, then calculates the quantity of circles and tread frequency based on these features, allowing accurate calculation without pre-obtaining carrier parameters or mounting the device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an accelerometer is mounted on a bicycle wheel to obtain rotational speed, then the rotational speed can be accurately measured, but the device is inconvenient to use and requires independent mounting
Solution Approach 1:
The wearable device on the rider's body serves itself by using its own acceleration sensor to detect riding state and calculate tread frequency, eliminating the need for separate mounting on the bicycle wheel while maintaining measurement capability
Solution Approach 2:
The wearable device performs multiple functions: it monitors the rider's body motion, detects riding state, calculates tread frequency, and estimates rotational speed without requiring separate dedicated sensors on the bicycle
2Ease of operation
If a wearable device is worn on the foot to calculate step quantity, then the device is easy to use, but it cannot calculate tread frequency or identify riding state
Solution Approach 1:
The device changes the parameters used for analysis from simple step-counting parameters to multi-dimensional acceleration signal parameters including periodic distribution characteristics and amplitude features, enabling accurate tread frequency calculation from body-worn sensor data
Solution Approach 2:
The device transitions from one-dimensional step counting to analyzing multi-dimensional acceleration signals with temporal and amplitude characteristics, adding dimensionality to the data processing to extract riding state and tread frequency information
3Adaptability or versatility
If the wearable device calculates tread frequency without pre-obtaining carrier parameters, then the device is more versatile and easy to use, but the calculation complexity increases
Solution Approach 1:
The device extracts only the essential features from acceleration signals (periodic distribution and amplitude characteristics) needed for tread frequency calculation, removing the need for complex carrier parameter inputs while maintaining calculation accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate calculation of tread frequency and identification of the riding state on a wearable device without the need for pre-obtaining rotation carrier parameters or mounting, improving data accuracy and usability.
Implementation Method 1
an acceleration sensor is mounted on a rotation carrier, for example, a bicycle wheel to obtain a rotational speed or a speed of the rotation carrier
Implementation Method 2
A centrifugal force acts on the acceleration sensor, and the acceleration sensor outputs a periodic signal in a time domain or a frequency domain
Data Source
AI summary
A method for calculating a tread frequency of a bicycle includes obtaining a first state of an acceleration signal in a first time window, determining a feature point set from the acceleration signal when the first state includes a riding state, where time distribution of a feature point in the feature point set meets a preset periodic distribution condition, and amplitude meets a physical rule, determining that the first state is the riding state when a feature point subset in the feature point set meets a preset reasonableness condition, and calculating, based on the feature point subset, a quantity of circles and a tread frequency that are in the first time window.