Frequency-Domain Motion Analysis for Exercise Effectiveness
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Solution Overview
Problem
Existing methods for measuring the effectiveness of periodic motion during physical exercises, such as running or swimming, lack precision in distinguishing efficient from inefficient motion, particularly due to terrain and non-ideal conditions, leading to inaccurate assessments of energy usage and technique.
Innovation Solution
A system utilizing a combination of motion sensors, including accelerometers, gyroscopes, and magnetometers, processes motion measurement data through frequency-domain analysis to calculate an effectiveness parameter by determining the ratio of energy in periodic signal components to total energy, incorporating peak detection and harmonic distortion analysis, and providing real-time feedback and correction factors for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion sensors are used to measure periodic motion effectiveness, then measurement capability is provided, but measurement precision is insufficient to distinguish efficient from inefficient motion under terrain and non-ideal conditions
Solution Approach 1:
The motion measurement data is segmented into frequency-domain samples through spectral analysis. The frequency spectrum is divided into distinct components: periodic signal components (fundamental frequency and harmonics) and aperiodic signal components. This segmentation allows separate evaluation of different motion characteristics, enabling precise identification of efficient periodic motion versus inefficient aperiodic motion even under varying terrain conditions.
Solution Approach 2:
The patent transforms time-domain motion data into frequency-domain representation using Fast Fourier Transform (FFT). This dimensional transformation from time to frequency domain enables the system to distinguish between periodic and aperiodic components based on their spectral characteristics. The frequency-domain analysis reveals hidden patterns in motion that are not visible in raw time-domain data, significantly improving measurement precision and reliability.
2Measurement precision
If simple motion sensing is used, then device complexity is low, but energy usage assessment accuracy is insufficient
Solution Approach 1:
The patent replaces complex mechanical measurement systems with electronic signal processing. Instead of using multiple sophisticated sensors and mechanical measurement apparatuses, the system uses standard motion sensors combined with computational algorithms (FFT, peak detection, energy ratio calculation) to achieve accurate energy usage assessment. This substitution maintains low hardware complexity while significantly improving measurement precision through software-based analysis.
Solution Approach 2:
The system changes the parameters used for analysis from raw time-domain motion data to frequency-domain parameters. By transforming the data representation and analyzing parameters such as fundamental frequency, harmonic content, and spectral energy distribution, the system achieves accurate energy usage assessment. This parameter transformation allows simple sensor data to reveal complex motion characteristics without requiring complex hardware.
3Measurement precision
If frequency-domain analysis is applied to motion data, then effectiveness differentiation is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by focusing frequency-domain analysis only on specific frequency ranges and components relevant to motion effectiveness. Instead of analyzing the entire frequency spectrum in detail, the system identifies and processes only the fundamental frequency and significant harmonics. This selective approach reduces processing time while maintaining sufficient precision for effectiveness differentiation. The system also uses threshold-based filtering to ignore negligible frequency components.
Data Source
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AI summary
The present document discloses a solution for estimating effectiveness of periodic motion during a physical exercise such as a running exercise. According to an aspect, a computer-implemented method for estimating the effectiveness of the periodic motion comprises: measuring, by using at least one motion sensor, periodic motion of a user and thus acquiring motion measurement data during a time interval of a physical exercise; transforming the motion measurement data into frequency-domain samples; extracting, amongst the frequency-domain samples by using peak detection, a first subset of frequency-domain samples representing periodic motion; computing a metric indicating a ratio between energy on the first subset of frequency domain samples and energy on other frequency domain samples; and mapping the computed ratio to an effectiveness parameter by using a determined mapping rule and outputting the effectiveness parameter via an interface.