GLASS Thesis
Wearable Torsional Sensor for Motion Monitoring
Mission Statement
I wish to explore feasible and scalable engineering solutions to problems related to robotics and medical technologies.
The project should focus on ways of combining hardware (mechanical engineering) and software (computer science) solutions, which closely follow the ideologies of my past projects.
I wish to demonstrate the potential impacts of my project through small-scale validation and proofs-of-concept.
It does not have to be cutting-edge or advanced but should bring innovative ideas, conceptually or practically, into its field.
Concepts Breakdown
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Sensing of Complex Joint Movements
Human joints like the wrist don't just bend—they twist, slide, and rotate in complex ways. Accurately sensing these motions is essential for building wearable tech that can monitor joint health or assist in rehabilitation. Our research introduces a soft, textile-based torsional sensor that can track joint rotations up to 360° with high precision. It uses a structure called a supercoiled polymer, made by twisting together conductive threads into a spring-like form. This structure is soft, like human skin and muscle, and it detects motion by measuring small changes in electrical resistance as it twists. Unlike bulky or rigid alternatives, this wearable sensor is lightweight, flexible, and can be embedded in everyday fabrics for continuous motion tracking.
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From Fishing Line to Wearable Sensor
At the heart of our wearable sensor is an innovative use of materials: nylon fishing line and conductive thread. When tightly twisted and coiled, these materials create a spring-like structure that becomes sensitive to torsion—like a muscle sensing when it twists. We tested three designs and found the supercoiled version to be the most effective. It balanced high sensitivity, low stiffness (comparable to soft tissue), and minimal energy loss during movement. This design had a gauge factor of 8.97 (a measure of how much resistance changes with strain) and responded within 0.67 seconds, fast enough to track daily movements. The sensor’s softness and reliability make it ideal for wearable applications like monitoring wrist rotations or developing smart garments.
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Smart Modeling for Real-Time Tracking
To interpret the data from our sensor, we developed a physics-based model that links how much the thread twists to how much the sensor’s resistance changes. This allows us to calculate the exact angle of joint rotation, even when under high stretch. The model accounts for subtleties like asymmetric stiffness—the fact that the sensor behaves slightly differently when twisting forward versus backward. We demonstrated this in a smart textile prototype, where the sensor was woven into fabric and used to reconstruct five wrist poses (like supination or radial deviation) in 3D. The result shows accurate motion tracking with less than 11% error, using a soft material that moves naturally with the body. This approach opens doors for wearable health monitoring, physical therapy, and more intuitive human-machine interfaces.
Contributions
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My paper presents a novel soft-material-based torsional sensor designed for wearable joint monitoring, with applications in personalized healthcare and human-machine interaction.
Health– This work supports NYU Tandon’s Health area by advancing low-cost, non-invasive wearable technologies that enable continuous, data-driven joint monitoring. The sensor's soft, tissue-like structure ensures comfort and compliance, while its high-resolution sensing supports early detection and personalized treatment strategies for musculoskeletal and neurological conditions.
Robotics – By developing a physics-informed model and integrating it with textile-based sensing, this research contributes to Tandon’s Robotics area of human-centered systems. The sensor’s ability to reconstruct complex joint motion opens the door for more seamless rehabilitation wearables, responsive assistive devices, and adaptive robotic exosuits.
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This research contributes to the advancement of health informatics by introducing a soft, textile-integrated torsional sensor capable of capturing high-resolution joint movement data in real time. Designed for wearable use, the sensor enables continuous monitoring of complex motions like wrist rotation, making it ideal for rehabilitation and biomechanical assessment outside of clinical settings. By combining soft materials with physics-based modeling, the system delivers interpretable, high-fidelity data that can be integrated into digital health platforms. This supports more personalized, remote care and equips clinicians with the insights needed to track progress, adjust therapy, and improve patient outcomes.
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Goal 3 – Good Health and Well-Being
This SDG directly connects to the global challenge of advancing health informatics by addressing the need for equitable and effective healthcare solutions. Soft robotics, as explored in my research, enhances telerehabilitation systems by providing detailed biomechanical data through wearable sensors. These advancements enable remote monitoring and personalized treatments, improving access to high-quality healthcare for individuals in underserved areas. By integrating innovative sensing and control technologies, this work supports SDG 3’s goal to ensure healthy lives and promote well-being for all, particularly through affordable and accessible rehabilitation resources for post-stroke and injury recovery.Goal 10 – Reduced Inequalities
This research addresses SDG 10 by reducing the cost and complexity of healthcare technologies, making rehabilitation more accessible to underserved and low-resource populations. The wearable torsional sensor, made from inexpensive materials like nylon fishing line and silver-coated conductive threads, costs approximately $2.06 per unit to fabricate. This affordability, combined with the sensor’s ability to capture high-resolution joint movement data, makes it a viable solution for communities with limited access to clinical care. By offering scalable, low-cost solutions for biomechanical monitoring and rehabilitation, this work helps bridge the gap in healthcare access and promotes social and economic inclusion through technological equity.