Activity Pattern Grouping for Personalized Lifestyle Habit Guidance
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Solution Overview
Problem
Existing health support devices provide advice based solely on the average number of steps and do not account for activities other than walking or external factors, limiting their effectiveness in improving lifestyle habits.
Innovation Solution
A habit improving device that calculates and groups activity patterns over time, considering both user attributes and external factors, to provide personalized advice for achieving suitable lifestyle habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If health support devices provide advice based solely on average number of steps, then the device complexity is reduced and ease of operation is improved, but the measurement precision and reliability of lifestyle habit improvement guidance deteriorates
Solution Approach 1:
The patent segments the activity data into multiple dimensions: time-based segmentation (morning, afternoon, evening periods), activity-type segmentation (walking, exercise, daily activities), and intensity-based segmentation (low, medium, high intensity). This segmentation allows the system to provide precise guidance while maintaining ease of operation through automated categorization.
Solution Approach 2:
The patent transitions from one-dimensional step counting to multi-dimensional activity analysis by adding time dimension, activity type dimension, and intensity dimension. This dimensional expansion enables more precise measurement of lifestyle habits without increasing user burden, as the system automatically captures these dimensions through sensors and user input.
2Reliability
If health support devices consider multiple factors including activities other than walking and external factors, then the reliability of lifestyle habit improvement guidance is improved, but the device complexity increases
Solution Approach 1:
The patent implements a multi-functional system that integrates step counting, activity recognition, time tracking, and external factor monitoring into a single unified device. The control unit coordinates multiple functions including data acquisition from various sensors, pattern recognition algorithms, and personalized advice generation, achieving high reliability through comprehensive monitoring while managing complexity through integrated architecture.
Solution Approach 2:
The system employs automated pattern recognition and self-adjusting algorithms that automatically analyze collected data, identify lifestyle patterns, and generate personalized advice without requiring complex user intervention. The control unit autonomously processes multi-dimensional data and adapts recommendations based on observed patterns, reducing the operational complexity for users while maintaining high reliability.
3Measurement precision
If the system calculates and groups distribution patterns of activity amounts over time periods, then the measurement precision and personalization of advice is improved, but the loss of time for data processing increases
Solution Approach 1:
The patent implements preliminary data processing by continuously collecting and pre-organizing activity data in structured formats during daily use. The control unit pre-calculates basic statistics and maintains ready-to-analyze data structures, so that when pattern recognition is needed, the system can quickly generate insights without extensive real-time processing delays.
Solution Approach 2:
The patent replaces manual data analysis with automated computational algorithms that efficiently process multi-dimensional activity data. The control unit uses pattern recognition algorithms and machine learning techniques to rapidly analyze collected data and generate personalized recommendations, significantly reducing the time required compared to manual analysis while maintaining high measurement precision.
Data Source
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AI summary
In order to take into account both the user's activity amount and factors other than activity amount to guide the user to have suitable lifestyle habits, this habit improving device is provided with: a first calculation unit which, for every first period, acquires an activity amount of the user totaled for each first period, and which calculates a distribution pattern that indicates the change over time in the activity amount during a second period, which includes a first period; a sorting unit which classifies multiple of the distribution patterns into one or more groups; a second calculation unit which, on the basis of attribute information relating to the user's attributes, and external factor information relating to external factors, which are factors affecting the user by matters outside of the user, calculates, and associates with the groups, a target pattern as a target for the user; and a presenting unit which presents advice information to the user on the basis of the current distribution pattern and the target pattern.