Sensor-Based Foot Accessory Selection From Gait Phase Analysis
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Solution Overview
Problem
Conventional methods for determining foot accessories based on fit are unreliable, leading to discomfort, poor posture, additional burden, and increased injury risk due to improper fit.
Innovation Solution
A determination model using machine learning and sensor data analysis to identify specific types of movement, such as over-pronation, pronation, and supination, to recommend appropriate foot accessories like insoles, shoes, or socks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a person tries on foot accessories to determine fit, then the person can physically test the accessory, but the determination is unreliable and may lead to improper fit
Solution Approach 1:
The patent replaces the mechanical trial-and-fit system with an automated sensor-based detection system. Sensors mounted on footwear collect gait data during walking, and a processing system analyzes this data to automatically determine foot accessory requirements, eliminating the need for manual trying-on while significantly improving determination reliability
Solution Approach 2:
The system enables self-service by allowing the footwear to automatically detect and analyze the wearer's gait characteristics without requiring external intervention. The sensors and processing unit work autonomously to provide personalized foot accessory recommendations based on real-time gait analysis
2Object-affected harmful factors
If conventional trial methods are used, then the process is simple, but it causes discomfort, poor posture, and increased injury risk due to improper fit
Solution Approach 1:
The patent replaces subjective manual fitting with objective automated gait analysis using sensors and machine learning algorithms. This automation accurately identifies individual gait characteristics and recommends appropriate foot accessories, eliminating the harmful effects of improper fit such as discomfort, poor posture, and injury risk
Solution Approach 2:
The system implements feedback by continuously monitoring gait parameters through sensors and using this information to determine optimal foot accessory recommendations. The processing unit analyzes sensor data in real-time and provides personalized recommendations based on the detected gait patterns, ensuring proper fit and preventing injury
Data Source
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AI summary
A method to establish a determination model for determining a foot accessory is to be implemented by an electronic device. The electronic device stores training data sets that correspond to sampled objects. The method includes: for each of the training data sets, grouping entries of sensor data of the training data set into sensor-data groups that correspond to phases of a specific activity; establishing a classification model based on the entries of sensor data that belong to a target one of the sensor-data groups of each of the training data sets; and combining the classification model and a lookup table by using an output of the classification model as an input of the lookup table so as to obtain the determination model.