Motorized Footwear Lacing With Multi-Stage Gesture Recognition
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Solution Overview
Problem
Conventional motorized lacing systems in footwear lack sensitivity to wearer gestures, failing to detect and respond to deliberate commands for adjusting lace tension, as they do not utilize activity sensors that can differentiate between motion and intended gestures.
Innovation Solution
A sensor system integrated into the footwear that detects foot activity and gestures, such as heel clicks or toe taps, to enable gesture-based control of the lacing mechanism, using multiple stages to differentiate between enabling and command gestures, thereby accurately interpreting user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional motorized lacing systems are used, then lacing automation is achieved, but gesture detection capability is lost
Solution Approach 1:
The patent combines the motorized lacing system with an activity sensor system into a single integrated footwear device. The sensor system includes sensors that detect foot movements and gestures, which are then processed by a controller to automatically adjust lace tension. This merging allows the system to provide both automated lacing and gesture detection capabilities simultaneously.
Solution Approach 2:
The activity sensor system serves multiple functions: it detects gestures for lacing control, monitors foot movement, and provides data for various performance metrics. The same sensor system that enables gesture recognition also supports other footwear functions, making the device multi-functional and adaptable to different user needs.
2Adaptability or versatility
If activity sensors are added to detect gestures, then gesture control is enabled, but device complexity increases
Solution Approach 1:
The sensor system is divided into multiple independent sensors positioned at different locations within the footwear (e.g., heel, toe, midfoot). Each sensor detects specific types of movements or gestures independently. The controller then processes signals from these segmented sensors to determine user intent, reducing the complexity of any single sensor while maintaining overall system capability.
Solution Approach 2:
A controller or processing unit acts as an intermediary between the multiple sensors and the motorized lacing system. This intermediary component consolidates the complex task of interpreting multiple sensor signals into a unified gesture recognition algorithm, simplifying the overall system architecture by centralizing the decision-making logic.
3Measurement precision
If multiple stages are used to differentiate gestures, then gesture recognition accuracy is improved, but response time increases
Solution Approach 1:
The system enters a low-power listening mode where sensors continuously monitor for gesture initiation. When a potential gesture is detected, the system pre-activates the gesture recognition algorithm and prepares the motorized lacing system for immediate response. This preliminary action reduces the effective response time by having components ready before a full gesture sequence is completed.
Solution Approach 2:
The gesture recognition system uses periodic sampling of sensor data at optimized intervals. Instead of continuously processing all sensor signals at high frequency, the system samples at periodic intervals that are sufficient to detect gesture patterns while minimizing processing time. This periodic action maintains accuracy by capturing essential gesture information without the computational overhead of continuous high-frequency processing.
Data Source
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AI summary
An article of footwear includes a motorized tensioning system, sensors, and a gesture control system. Based on information received from one or more sensors the gesture control system may detect a prompting gesture and enters an enabled mode for receiving further instructions. In the enabled mode the system may detect a variety of different control gestures that correspond to different tensioning commands.