Gait-Based Foot Accessory Determination Using Sensor Classification
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Solution Overview
Problem
Conventional methods for determining foot accessories are unreliable, leading to discomfort, poor posture, additional burden, and increased injury risk due to improper fit, as they do not account for individual foot shapes and gaits.
Innovation Solution
A determination model using machine learning algorithms and sensor data to classify specific types of movement, enabling the selection of appropriate foot accessories based on gait analysis and physiological parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional try-on method is used to determine foot accessory, then the process is simple and quick, but the reliability and accuracy of fit determination is poor
Solution Approach 1:
The patent replaces the mechanical try-on method with a data-driven determination system using machine learning algorithms. The system collects sensor data (acceleration, angular velocity, magnetic field) and physiological parameters, then uses trained models to automatically determine suitable foot accessories, eliminating the need for physical trial and error.
Solution Approach 2:
The patent introduces sensor devices and processing systems as intermediaries between the user's foot characteristics and the foot accessory selection. These intermediaries collect and analyze movement data, serving as a bridge that translates physical gait characteristics into actionable recommendations for foot accessory selection.
2Object-affected harmful factors
If individualized foot accessory selection based on gait analysis is implemented, then comfort and injury prevention are improved, but the time and resources required for determination increase
Solution Approach 1:
The patent performs preliminary actions by pre-collecting and storing sensor data and physiological parameters during gait analysis. The machine learning models are trained in advance on comprehensive datasets, enabling rapid determination of suitable foot accessories without requiring extensive real-time analysis or multiple trial sessions.
Solution Approach 2:
The system enables self-service determination where users can independently complete gait analysis and receive personalized foot accessory recommendations without requiring professional fitting services. The automated machine learning models provide instant determination results, eliminating the need for time-consuming expert assessment.
Data Source
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.