Activity Data Processing Apparatus for Cycling and Trekking Analysis

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Solution Overview

Problem

Existing activity recording devices lack uniformity in processing and presenting data from various types of activities, making it difficult to analyze and understand the data effectively based on the purpose and characteristics of the activity.

Innovation Solution

An activity recording data processing apparatus that includes an acquisition unit for collecting data, a memory for storing action estimation information, an extraction unit for categorizing data, a clustering unit for grouping data based on predetermined criteria, and an estimation unit for calculating power usage, allowing for accurate analysis and presentation of activity data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If data from various types of activities is processed using different methods depending on purpose or characteristics, then data analysis accuracy is improved, but processing complexity increases

Engineering Contradiction:
Improvedata analysis accuracyVSAvoidprocessing complexity
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments activity data processing by defining specific activity types (cycling, trekking, trail running) and applying dedicated processing methods for each. The clustering unit divides activity data into distinct categories, and the estimation unit applies specific algorithms tailored to each activity type, thereby improving analysis accuracy without requiring a single complex universal processing system.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The patent changes processing parameters based on activity type characteristics. Different activity types have different minimum dividing widths for data segmentation, different clustering parameters, and different power estimation formulas. This parameter adaptation allows accurate analysis for each activity while maintaining a unified processing framework.

Inventive Principle:
Principle #35Parameter changes

2Measurement precision

If activity data is divided into smaller units for detailed analysis, then analysis precision is improved, but data processing time increases

Engineering Contradiction:
Improveanalysis precisionVSAvoiddata processing time
Core Design Contradiction:
Measurement precisionVSLoss of time

Solution Approach 1:

The patent dynamically adjusts the minimum dividing width for data segmentation based on the specific activity type. For example, cycling data may use one dividing width while trail running uses another. This dynamic adjustment optimizes the balance between analysis precision and processing time for each activity type, avoiding excessive division that would increase processing time without meaningful gain.

Inventive Principle:
Principle #15Dynamics

Solution Approach 2:

Different activity types receive different levels of data division and processing detail appropriate to their characteristics. The system applies local quality principles by tailoring the granularity of data division and the complexity of analysis methods to match the specific requirements of each activity type, rather than applying uniform fine-grained division to all activities.

Inventive Principle:
Principle #3Local quality

Data Source

PatentEP3438984B1Activity recording data processing apparatus, method for processing activity recording data, and program for processing activity recording data
Publication Date: 2024.03.06 CASIO COMPUTER CO LTD
  • EP3438984B1 patent drawingFigure 1A~1B
  • EP3438984B1 patent drawingFigure 2A
  • EP3438984B1 patent drawingFigure 2B

AI summary

An activity recording data processing apparatus is provided. This apparatus includes an operation unit configured to acquire data obtained for a series of activity and action estimation information on the series of activity, the action estimation information being associated with the data; extract partial data of the data based on the action estimation information; and perform clustering of the extracted partial data.