Bicycle Fitting System Using Body Measurements and Riding Data
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Solution Overview
Problem
Conventional bicycle fitting services often fail to provide a suitable riding posture, requiring multiple adjustments and emphasizing specialized knowledge, leading to unsatisfactory results and potential need for new bicycle models, as they treat consumers and bicycles as machines without considering individual riding styles.
Innovation Solution
A bicycle fitting method and system that receives riding information and body measurements to select a suitable bicycle model and adjust geometric parameters, including ride frequency, intensity, and surface conditions, to produce a customized bicycle that matches the cyclist's physique and riding style, using a computer program to analyze and fine-tune variables like stem length, handlebar width, and seat height.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional bicycle fitting services adjust variable modules to match consumer physique, then riding posture improves, but time and resources are wasted if the original bicycle size is incompatible
Solution Approach 1:
The system performs preliminary analysis of consumer physique and riding style before bicycle assembly, using body measurement data and riding information to pre-determine the most suitable bicycle model and frame size. This preliminary action prevents wasted fitting services by ensuring compatibility before the consumer invests time in adjustment services.
Solution Approach 2:
The system collects riding information and body measurement data as feedback to continuously optimize bicycle model recommendations. By analyzing this feedback data, the system learns from past fittings to improve future recommendations, reducing time waste through increasingly accurate pre-selection.
2Ease of operation
If variable modules are forcedly adjusted to match bicycle geometry, then riding posture may improve, but control sensitivity and aesthetics deteriorate
Solution Approach 1:
The system pre-calculates the optimal combination of bicycle model, frame size, and variable module specifications before assembly. By determining the most suitable configuration in advance based on consumer data, the system eliminates the need for forced adjustments that would compromise control sensitivity or aesthetics.
Solution Approach 2:
The system varies multiple parameters simultaneously (bicycle model type, frame size, stem length, handlebar width, seat height) to find the optimal combination that satisfies both riding posture requirements and control sensitivity requirements, rather than forcing adjustments on a fixed bicycle configuration.
3Manufacturing precision
If conventional fitting services emphasize specialized knowledge, then adjustment precision improves, but consumer understanding and satisfaction deteriorate
Solution Approach 1:
The system replaces the mechanical expert-adjustment process with an automated information processing system that analyzes consumer data and provides scientifically-based recommendations. This substitution maintains precision through algorithmic calculation while improving consumer understanding through transparent, data-driven explanations.
Solution Approach 2:
The system provides feedback to consumers in the form of understandable explanations about why specific bicycle models and configurations are recommended, based on their measured physique and riding style. This feedback loop educates consumers while maintaining scientific precision in the fitting recommendations.
4Productivity
If pre-selected bicycle models are used in conventional fitting, then initial setup speed improves, but model-consumer compatibility deteriorates
Solution Approach 1:
The system performs preliminary matching analysis between consumer characteristics and available bicycle models before assembly begins. This pre-analysis ensures high compatibility while maintaining assembly speed by pre-identifying the most suitable model from the manufacturer's lineup.
Solution Approach 2:
The system dynamically selects from multiple bicycle model categories (road bikes, mountain bikes, hybrid bikes) based on the consumer's riding style preferences and physical characteristics. This dynamic selection process adapts to individual consumers while maintaining efficient assembly through automated model recommendation.
Data Source
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AI summary
A bicycle fitting method for producing a bicycle is provided. The method includes the steps of receiving a bicycle riding information and a body measurement corresponding to a cyclist. According to the bicycle riding information, a bicycle model is provided. According to the body measurement and the selected bicycle model, a bicycle frame size and a set of bicycle geometric adjustment parameters are provided. According to the bicycle model, the bicycle frame size, and the set of bicycle geometric adjustment parameters, a bicycle which fits the cyclist is produced.