Motion Time Calculation for Wearable Riding Detection
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Solution Overview
Problem
Existing intelligent wearable devices inaccurately determine the riding state and calculate riding time due to interference, leading to incorrect motion mode determination and time calculation.
Innovation Solution
A method involving a three-axis accelerometer to acquire motion flags by calculating resultant acceleration energy and peak numbers, distinguishing between riding and non-riding modes through specific thresholds, and counting time based on consecutive flag determinations to accurately determine motion mode entry and exit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the existing riding algorithm uses three-axis accelerometer to calculate angle and amplitude with simple threshold conditions, then the device complexity is low and ease of operation is high, but the measurement precision deteriorates leading to incorrect motion mode determination
Solution Approach 1:
The patent segments the motion detection process into multiple independent judgment dimensions: angle threshold judgment, amplitude threshold judgment, and time threshold judgment. Each dimension independently evaluates a specific aspect of the motion, and all must be satisfied simultaneously for riding mode determination. This segmentation allows complex multi-criteria validation without requiring a single complex algorithm, maintaining computational simplicity while improving accuracy.
Solution Approach 2:
The patent adds a time dimension to the traditional angle-amplitude detection by introducing continuous time-point validation. Instead of relying solely on spatial parameters (angle and amplitude), the system now requires that these parameters satisfy threshold conditions across multiple time points within a specified time window. This temporal dimension filters out transient interference signals that may momentarily satisfy spatial thresholds, thereby improving measurement precision without significantly increasing algorithmic complexity.
2Reliability
If simple threshold conditions are used for determining riding state, then the calculation speed is fast and productivity is high, but the reliability deteriorates due to interference actions
Solution Approach 1:
The patent performs preliminary validation by checking whether the angle and amplitude parameters satisfy threshold conditions before proceeding to time-based validation. This preliminary action filters out obviously non-riding motions early in the detection process, avoiding unnecessary time consumption for invalid cases. Only when preliminary spatial thresholds are satisfied does the system proceed to the more time-consuming temporal validation, thus balancing reliability improvement with time efficiency.
Solution Approach 2:
The patent applies partial validation by requiring threshold satisfaction at multiple discrete time points within a time window rather than continuous validation. This partial sampling approach provides sufficient reliability improvement over single-point detection while avoiding the excessive time cost of truly continuous monitoring. The system checks at key moments within the riding pattern cycle, achieving reliable detection without proportional time penalty.
Data Source
AI summary
A motion time calculation method, device and apparatus and a computer-readable storage medium are disclosed, the motion time calculation method comprises: acquiring a motion flag corresponding to a first preset time period, wherein the motion flag comprises a first motion flag and a second motion flag; if motion flags corresponding to two consecutive first preset time periods are first motion flags, entering a motion mode; after entering the motion mode, if motion flags corresponding to three consecutive first preset time periods are second motion flags, exiting the motion mode; and counting a time from entering the motion mode to exiting the motion mode into a motion time. Thus, more accurate calculation of the motion time is achieved.