Wearable Motion Feedback for Hand-Object Collision Prevention
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Solution Overview
Problem
Current AI-enabled feedback systems for task assistance do not effectively detect spatial positioning between a user's hands and objects, posing risks of injury during tasks like cutting or scraping, particularly with sharp utensils.
Innovation Solution
A wearable device and computer system that uses sensors to monitor body motions, models them using AI, determines acceptable motion parameters, and initiates haptic feedback when thresholds are exceeded to prevent injuries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a wearable device with sensors is used to monitor body motions, then user safety is improved through real-time feedback, but device complexity increases
Solution Approach 1:
The system continuously monitors body motions using sensors and provides real-time feedback to the user when risky motions are detected. The computer receives motion data, compares it against acceptable motion parameters, and triggers feedback notifications to guide users toward safer movements, directly improving user safety through closed-loop control.
Solution Approach 2:
The wearable device integrates multiple functions including motion sensing, data processing, risk assessment, and feedback delivery into a single system. The computer system serves multiple purposes by modeling body motions, determining acceptable parameters for different actions, assessing risks, and coordinating feedback, reducing the need for separate specialized devices.
2Measurement precision
If AI modeling is used to determine acceptable motion parameters, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system pre-establishes acceptable motion parameters for various actions before actual use. The computer stores predefined motion thresholds and acceptable ranges for different tasks (e.g., cutting, scraping), allowing rapid comparison against real-time sensor data without requiring complex real-time calculations, thus improving measurement precision while managing complexity.
Solution Approach 2:
The computer acts as an intermediary between the simple sensors on the wearable device and the complex AI modeling requirements. It receives raw motion data, applies preprocessing, compares against stored parameters, and generates feedback signals, bridging the gap between simple hardware and sophisticated analysis without requiring the wearable itself to be overly complex.
3Reliability
If real-time feedback is initiated when motion thresholds are exceeded, then user safety is improved, but loss of time occurs due to feedback processing
Solution Approach 1:
The system proactively prevents unsafe actions by providing feedback before injuries occur. By continuously monitoring motion parameters and comparing them against acceptable thresholds, the system warns users of impending risky movements, allowing them to correct their motion before harm occurs, thus preventing rather than reacting to safety issues.
Solution Approach 2:
The feedback mechanism is designed to trigger immediately when motion thresholds are exceeded, skipping unnecessary processing delays. The computer compares sensor data against predetermined parameters in real-time and initiates feedback notifications without delay, ensuring safety interventions occur at the critical moment when they are most effective.
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
Enhances user safety by providing timely feedback to avoid hand-object collisions and injuries, adapting to individual risk factors and updating models with new data.
Implementation Method 1
The motion data including sensor data from sensors at a location, and the sensors detecting the person's body motion, and the sensors including a wearable device on the person's body
Implementation Method 2
The feedback can include haptic feedback generated using the wearable device
Data Source
AI summary
Detecting spatial positioning between a user's hands and an object, using a wearable device, to provide feedback to a user. Motion data of a person's body motion while performing an action can be received at a computer. The motion data can include sensor data from sensors at a location where the sensors detect the person's body motion. The sensors can include a wearable device on the person's body. The computer can be used to model the person's body motion using the motion data. A set of parameters for acceptable motions is determined based on an action risk assessment of the action. Feedback can be initiated to the person, using the wearable device, based on the person's body motion exceeding a body motion threshold based on the set of parameters for the acceptable motions and the model.