Modular Resistance Exoskeleton With Low-Friction Pivots
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Solution Overview
Problem
Conventional resistance training methods, such as using heavy weights or resistance bands, pose challenges for individuals with injuries or limitations, including the inability to lift heavy weights, difficulty in finding suitable alternatives that provide adequate resistance without compromising safety, and the challenge of accurately measuring and adjusting resistance levels.
Innovation Solution
Resistance exoskeletons that incorporate bands or springs within a protective shell, providing adjustable and targeted resistance while ensuring user safety through low-friction pivot points and modular design, allowing for a full range of motion and secure grip.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Force
If conventional resistance training with heavy weights is used, then adequate resistance for muscle development is provided, but safety is compromised for users with injuries or limitations
Solution Approach 1:
A machine learning model serves as an intermediary between the user's movement data and resistance control decisions. The model analyzes joint angles, movement velocity, and exercise type to dynamically adjust resistance levels, ensuring safe force application for users with injuries while maintaining adequate resistance for muscle development.
Solution Approach 2:
The resistance force is made dynamic rather than static. The system continuously adjusts resistance levels based on real-time movement data, exercise phase, and user-specific constraints. This allows the resistance to adapt throughout the range of motion, providing high resistance when safe and reducing it when injury risk is detected.
2Reliability
If alternative resistance methods like springs or bands are used, then safety is improved, but measurement precision of resistance levels deteriorates
Solution Approach 1:
The system implements continuous feedback loops where sensors monitor actual resistance forces, joint angles, and movement parameters. This data feeds back to the machine learning model, which adjusts resistance levels in real-time. The feedback mechanism enables precise measurement and control of resistance forces despite using flexible elements like springs and bands.
Solution Approach 2:
The patent replaces traditional mechanical resistance measurement systems with sensor-based electronic measurement and machine learning-based control. Instead of relying on fixed mechanical springs with known constants, the system uses sensors to directly measure forces and uses AI algorithms to calculate and adjust resistance levels, achieving higher precision.
3Reliability
If heavy mechanical equipment is used to provide support throughout the range of motion, then adequate mechanical support is provided, but device complexity and portability deteriorate
Solution Approach 1:
The support system is segmented into modular components: wearable sensors on the user's body, a portable control unit with machine learning capabilities, and adjustable resistance mechanisms. This segmentation allows the system to provide comprehensive mechanical support while maintaining portability and reducing overall complexity compared to fixed gym equipment.
Solution Approach 2:
The machine learning model enables the system to self-adjust and self-optimize without requiring constant manual intervention or complex mechanical linkages. The AI algorithm automatically determines appropriate resistance levels based on sensor data, replacing what would otherwise require complex mechanical adjustment mechanisms.
4Productivity
If conventional resistance training is used, then muscle development is achieved, but adaptability to individual user needs deteriorates
Solution Approach 1:
The training program is made dynamic and adaptive through machine learning. The system continuously learns from user performance data, injury history, and response to training stimuli, automatically adjusting resistance levels, exercise selection, and progression rates. This enables personalized adaptation to each user's specific needs while maintaining high effectiveness for muscle development.
Solution Approach 2:
The system dynamically changes multiple training parameters including resistance force magnitude, rate of loading, range of motion limits, and exercise duration based on real-time sensor data and machine learning predictions. These parameter adjustments enable the system to adapt to individual user needs while optimizing muscle development outcomes.
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 safety and effectiveness of strength training by allowing users to focus on specific muscle groups without joint strain, accommodating various exercises, and enabling precise resistance adjustments.
Implementation Method 1
a resistance band 212 coupled to the support arm 203
Implementation Method 2
resistance exoskeletons that incorporate bands or springs within a protective shell
Data Source
AI summary
Described herein are examples of resistance exoskeleton devices that include a modular rigid body configured to house resistance bands. The rigid body comprises a rigid member to support the exerciser's arm and house the resistance bands, an adjustable shoulder rest, and a support arm to connect the tricep rest to the shoulder rest. The device includes an interchangeable handle, a resistance band roller, and interchangeable resistance bands. The device features adjustment points, including support arm pins, quick-release locking pins, and thumb screws, to allow for customization and is configured to hold at least one low-friction pivot point to provide smooth motion between at least two modules during exercises.