Pose-Tracking Robot Interaction for Accessible Motion Mimicry
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing user-robot interaction systems are complex and limited to industrial automation, lacking suitability for use as games or learning systems for children or disabled individuals with motor and cognitive difficulties, and are not reliable, accurate, or inexpensive.
Innovation Solution
A user-robot interaction system utilizing image detection means, neural networks for keypoint detection, and actuators to mimic user movements, supported by wireless communication, enabling a cost-effective and accurate interaction suitable for children or disabled users, with adaptable arm and wheel movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If complex user-robot interaction systems are used for industrial automation, then automation capability is improved, but device complexity increases and limits application to industrial settings only
Solution Approach 1:
The system segments the interaction into distinct components: image detection means captures user movements, processing unit analyzes the captured data, and robot actuators execute movements. This segmentation simplifies each component while maintaining overall automation capability, allowing application in diverse settings beyond industrial automation.
Solution Approach 2:
The processing unit acts as an intermediary between image detection means and robot actuators. It processes captured images to extract movement data and translates this into control signals for the robot, simplifying the interaction architecture and enabling broader applications including educational and therapeutic settings.
2Extent of automation
If complex interaction systems are implemented, then automation performance is improved, but ease of operation deteriorates for children and disabled users
Solution Approach 1:
The system uses image detection means to automatically capture and analyze user movements without requiring manual input or complex controls. The processing unit autonomously processes the captured images and generates appropriate robot control signals, making the system easily operable by children and disabled users who cannot operate complex interfaces.
Solution Approach 2:
The system replaces manual control mechanisms with optical detection and automated image processing. Instead of requiring users to manipulate physical controls or interfaces, the system captures movements visually and automatically translates them into robot actions, significantly improving accessibility for users with motor and cognitive difficulties.
3Measurement precision
If high-precision keypoint detection is implemented using neural networks, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The processing unit extracts only the essential key information from captured images - specifically the keypoints representing significant body positions. This extraction approach achieves high measurement precision for movement detection while avoiding the need to process and analyze entire images, thereby reducing computational complexity and resource requirements.
Solution Approach 2:
The system applies neural network-based keypoint detection selectively to specific regions and features of interest in the captured images rather than processing the entire image uniformly. This local quality approach concentrates computational resources on detecting critical movement points, achieving high precision where needed while minimizing overall processing complexity.
4Measurement precision
If advanced image processing and neural networks are used, then measurement precision is improved, but manufacturing cost increases
Solution Approach 1:
The system extracts only essential keypoint data from images rather than performing comprehensive image analysis. This extraction strategy achieves high measurement precision for movement detection while using computationally efficient algorithms that can be implemented on low-cost hardware, reducing manufacturing costs compared to systems using full-image processing or complex neural networks.
Data Source
AI summary
User-robot interaction system including a robot, a wireless communication module, an image detector, and a processing unit configured to implement a user pose algorithm configured to receive the images from the image detector, identify keypoints in the image and calculate a three-dimensional position of the keypoints, and a robot pose algorithm configured to receive the three-dimensional position of the keypoints calculated by the user pose algorithm and process control signals suitable for driving the actuators of the robot in order to move the robot in accordance with the user's movements.