Sensor-Based Motion Analysis Using Shock-Triggered Segmentation
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Solution Overview
Problem
Existing technologies for determining motion patterns in sports lack accuracy and efficiency, as they rely on manual observation and require significant effort, and previous automated systems increase processing load and stability issues with segment setting.
Innovation Solution
An information processing system that uses a shock sensor and a motion sensor to identify target segments in time-series data, including pre-shock and post-shock portions, to improve the accuracy of motion pattern determination by setting suitable analysis segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated motion analysis is performed by repeatedly searching for segments in sensor data, then motion patterns can be extracted automatically, but processing load increases and determination accuracy decreases
Solution Approach 1:
The system performs preliminary segmentation of sensor data into meaningful motion phases (e.g., swing phases in golf) before pattern recognition. By pre-identifying and isolating relevant time segments corresponding to specific motion phases, the system prepares data in advance for more accurate and efficient pattern determination, avoiding the need for repeated full-data searches.
Solution Approach 2:
The patent divides continuous sensor data into distinct segments based on motion phase identification. Each segment corresponds to a specific phase of motion (e.g., backswing, downswing, follow-through in golf). This segmentation allows the system to analyze each phase separately with appropriate parameters, improving both processing efficiency and determination accuracy by focusing computational resources on relevant portions of the data.
2Measurement precision
If manual observation is used to determine motion patterns, then determination accuracy can be maintained through expert judgment, but significant effort and time are required
Solution Approach 1:
The system replaces manual mechanical observation with automated sensor-based detection and computational analysis. Motion patterns are determined through algorithmic processing of sensor data rather than human visual observation, maintaining accuracy through precise sensor measurements while dramatically improving productivity by eliminating the time-consuming nature of manual analysis.
Solution Approach 2:
The system enables users to perform their own motion analysis automatically without requiring external experts. By providing automated pattern recognition capabilities through the sensor device and processing system, users can independently analyze their own motion patterns, eliminating the need for coaches or scorers while maintaining determination accuracy through objective sensor data.
3Device complexity
If segment setting is performed without stable criteria, then processing can be simplified, but accuracy of motion pattern determination is not high
Solution Approach 1:
The system uses specific parameter thresholds and criteria to define motion segments automatically. By establishing quantitative parameters for identifying motion phase transitions (e.g., acceleration thresholds, velocity change rates), the system creates stable and objective segment boundaries without requiring complex manual configuration, thereby maintaining both simplicity and accuracy.
Data Source
AI summary
An information processing system includes processing circuitry that is configured to receive input data from a shock sensor which outputs data based on a shock on the shock sensor, and identify a target segment of time-series data that is output from a motion sensor that senses a motion of an object. The target segment includes a pre-shock portion that occurs before the shock event and a post-shock portion that occurs after the shock event, the shock event is recognized based on the data from the shock sensor.