Wearable Gait Biofeedback for Real-Time Motion Correction
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Solution Overview
Problem
Conventional methods for improving running or walking form are limited by the availability of real-time, personalized feedback, often requiring a coach's presence and being time-intensive, and lack accuracy due to inadequate sensor capabilities.
Innovation Solution
A system comprising wearable sensors and processing elements that detect motion characteristics during activities like running or walking, providing real-time biofeedback through audible, visual, and tactile sensations to adjust user motion towards ideal ranges or thresholds, tailored to individual skill levels and goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional coaching methods are used to provide gait instruction, then personalized feedback can be obtained, but the method is time-intensive and limited to monitored activity times
Solution Approach 1:
The system enables users to independently receive real-time gait feedback through wearable sensors and mobile devices without requiring a coach's continuous presence. Users self-monitor their motion characteristics and receive automated feedback, eliminating the time cost of manual coaching while maintaining personalized instruction.
Solution Approach 2:
The system implements continuous real-time feedback loops by monitoring motion characteristics through sensors and immediately processing data to provide gait correction guidance. This automated feedback mechanism replaces time-intensive manual coaching with instantaneous digital feedback that is always available during activity.
2Adaptability or versatility
If conventional coaching methods are used to provide gait instruction, then personalized feedback can be obtained, but it requires a coach's presence and is limited to particular coach's expertise
Solution Approach 1:
The patent replaces the mechanical system of human coaches with an automated electronic system comprising wearable sensors, processors, and communication devices. This substitution eliminates the need for a coach's physical presence and expertise while maintaining personalized feedback capabilities through algorithmic analysis of motion data.
Solution Approach 2:
The system is designed to be universally applicable to multiple users and activity types without requiring specialized human expertise for each case. The same sensor-based platform and processing algorithms can serve different users with varying gait needs, making the system adaptable and versatile while reducing complexity compared to human coaching.
3Measurement precision
If basic sensors are used to detect motion characteristics, then the system is simple, but the sensor range is inadequate to capture full range of accelerations
Solution Approach 1:
The system addresses limited sensor range by dynamically adjusting measurement parameters and using signal processing techniques to capture the full range of accelerations. The processor analyzes motion data within available sensor ranges and compensates for limitations through computational methods, maintaining measurement precision without requiring more complex hardware.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables users to improve their gait and performance by receiving immediate, personalized feedback that adjusts dynamically, allowing for real-time corrections and consistent instruction, enhancing efficiency and reducing the risk of injury.
Implementation Method 1
receiving, from a detection device, motion data comprising a motion characteristic value for a motion characteristic
Data Source
Figure 1A~1B
Figure 2~3
Figure 4A
AI summary
The present disclosure relates generally to a method for providing motion biofeedback to a user while the user performs an activity, where the biofeedback can occur in real time or after discrete points in time (e.g., after a drive phase or maximum velocity phase). The method includes determining an activity type performed by the user; determining a target value for one or more motion characteristics associated with the activity, wherein the target value is determined by historical collected motion data; receiving motion data related to the activity as the user performs the activity; comparing the motion data to the target value; and transmitting feedback to a feedback device based on the comparison to the target value, wherein the feedback corresponds to a motion adjustment proportional to a deviation of the motion data relative to the target value.