Anti-Tremor Technology for Parkinson's Disease Patients' Cutlery

Professor Introduction

716 Robotics Team | Specializes in intelligent robotics

Project Description

The research focuses on developing technology to mitigate tremors in utensils used by Parkinson's disease patients. Parkinson's disease leads to involuntary hand and body tremors, significantly impacting daily activities such as eating. The goal is to design and innovate engineering solutions to stabilize utensils, thereby reducing the interference of tremors during meals. This involves ensuring utensil stability, designing user-friendly interfaces, and implementing tremor detection and control algorithms. The project will leverage computer motor control systems for precise control and motion planning of mechanical arms to stabilize utensils. Advanced control algorithms and sensor technologies will be employed to ensure quick and accurate responses to hand tremors. Additionally, machine learning techniques will be used to learn and adapt to individual tremor patterns, providing personalized tremor compensation.

Project Keywords

Project Outline

Part 1 :  Introduction to  Parkinson's Disease and Assistive Technologies
• Overview of Parkinson's Disease and Its Impact on Daily Life
• Importance of Assistive Technologies for Enhancing Quality of Life
• Current Solutions and Their Limitations

Part 2 : Research Objectives and Hypotheses
• Investigating the Feasibility of Tremor Mitigation in Utensils
• Developing and Optimizing the Tremor Mitigation Technology

Part 3 :  Review of Current Research and Methods
• Review of Existing Assistive Technologies and Tremor Mitigation Techniques
• Identification of Gaps and Limitations in Current Solutions

Part 4:  Design and Simulation of Tremor Mitigation Technology
• Analyzing the Requirements for Utensil Stability
• Designing the Mechanical Arm and Control System Components
• Simulating Tremor Mitigation and Stability Enhancements

Part 5 :   Development of Control Systems and Sensor Integration
• Developing Control Algorithms for Tremor Detection and Compensation
• Integrating Sensors for Real-Time Tremor Monitoring
• Implementing Machine Learning Models for Personalized Tremor Adaptation

Part 6 :  Prototype Manufacturing and Testing
• Manufacturing the Mechanical Arm and Utensil Prototype
• Debugging and Initial Testing of the Prototype
• Evaluating the Prototype's Performance in Simulated and Real-World Scenarios

Part 7: Optimization and Parameter Tuning
• Identifying Optimal Parameters for Tremor Compensation
• Iteratively Optimizing the Control System for Better Performance
• Quantitative Analysis of Tremor Mitigation Efficiency

Part 8: Results and Discussion
• Graphical Representation of Experimental Results and Performance Metrics
• Interpretation of Results and Discussion of Implications for Assistive Technologies
• Comparison with Existing Solutions and Discussion of Advantages and Limitations

Part 9: Conclusion and Future Directions
• Summary of Key Findings and Their Significance
• Identification of Research Limitations and Suggestions for Future Research
• Recommendations for Practical Applications in Assistive Technologies

Part 10: Reporting and Presentation
• Writing a Detailed Research Report with Clear Structure, Concise Language, and Accurate Data Presentation
• Preparing and Delivering a Clear and Engaging Oral Presentation of Research Background, Methods, Results, and Conclusions

Suitable for

High School Students:  
• Interest in Robotics and Biomedical Engineering: Students with a keen interest in robotics, biomedical engineering, and assistive technologies.
• Basic Knowledge : Students with foundational knowledge in programming, mechanics, and electronics.

University Students:
• Relevant Major : Students majoring in mechanical engineering, electrical engineering, computer science, or biomedical engineering.
• Proficiency in Software Tools : Students with skills in programming languages (Python, C++), and familiarity with machine learning frameworks (TensorFlow, PyTorch).

Researchers and Educators:
• Advanced Knowledge: Professionals with a deep understanding of robotics, AI, and control systems.
• Teaching Integration: Educators who can integrate research findings and methods into their courses and teaching practices.