Personalized Motion Assistance via User-Specific Data Conversion
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Solution Overview
Problem
Current wearable robots lack the ability to provide personalized motion assistance tailored to individual users' physical characteristics, such as leg length and age, which can affect the effectiveness of motion training and rehabilitation.
Innovation Solution
An electronic device system that includes a communication module, processor, and sensors to generate and share motion data sets, allowing users to receive personalized motion assistance by adjusting motion trajectories and speeds based on their physical characteristics, and providing real-time feedback for accurate posture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard motion data is used for all users, then device complexity is reduced, but adaptability to individual physical characteristics deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and storing motion data from multiple users with different physical characteristics before actual use. Motion data sets are pre-generated for various body types, ages, and physical conditions, allowing the device to quickly adapt to individual users without complex real-time calculations during operation.
Solution Approach 2:
The system creates copies of motion data from one user and adapts them for another user based on their physical characteristics. Motion trajectories, speeds, and ranges are copied and modified according to the target user's body measurements, age, and health condition, enabling personalized rehabilitation without requiring completely new motion programs for each individual.
2Adaptability or versatility
If motion data is customized for each user, then adaptability improves, but loss of time for data processing increases
Solution Approach 1:
Motion data sets are prepared in advance for various user profiles and physical characteristics. The system pre-processes and stores multiple versions of motion data that can be quickly selected and applied based on the user's profile, eliminating the need for time-consuming real-time customization during rehabilitation sessions.
Solution Approach 2:
The system automatically matches users with appropriate motion data sets based on their physical characteristics without requiring manual customization. The processor automatically selects and adjusts the most suitable pre-prepared motion data based on user input data, reducing both processing time and manual intervention.
3Reliability
If motion trajectories are adjusted for individual users, then exercise effectiveness improves, but device complexity increases
Solution Approach 1:
The system adjusts motion parameters such as trajectory range, speed, and amplitude based on user-specific parameters like leg length, age, and physical condition. By modifying these parameters within pre-defined motion patterns rather than creating entirely custom trajectories, the system achieves personalized effective exercise while maintaining relatively simple device architecture.
Data Source
AI summary
An electronic device may include a communication module and a processor operatively connected to the communication module, wherein the communication module may communicate with a server and an external electronic device including a sensor for measuring user motion, and the processor may control that sensing data obtained by measuring the user motion is acquired from the external electronic device, through the communication module, a motion data set including standard/reference motion data related to another user having a standard/reference body is generated by converting, on the basis of information about the user, motion data including a time-dependent body motion trajectory based on the sensing data, and the motion data set is transmitted to the server through the communication module.