Browsing by Subject "Wearable"

Now showing 1 - 3 of 3
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    Design, Development, and Evaluation of Wearable Length Fastening Devices for Use with Twisted Coiled Actuators
    (2023-04) Dorn, Timothy
    Artificial muscles and compliant, large stroke linear actuators have enabled new classes of wearable robotics. However, these actuators are inefficient, needing constant power to maintain force and displacement, decreasing their utility in wearable systems. Variable length latching mechanisms alleviate this problem, matching actuator displacement, and holding force and displacement constant when the actuator is powered off. However, most existing latching designs are either not wearable, or must be disengaged manually, limiting their robotic applications. In this research, three wearable and remotely releasable latching mechanisms were designed for use in wearable robotic systems: a stepper motor with a belt and pulley; a linear ratchet; and a cam cleat. The designs were manufactured and tested, with all three designs maintaining force and displacement values up to 15N of cable tension and releasable up to 5N of cable tension. These results demonstrate the viability of integrating latches into soft wearable robotic systems.
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    Instrumented Socks with Novel Sensors for Fluid Accumulation Monitoring
    (2018-08) Zhang, Song
    The overarching goal of this dissertation is to develop wearable sensors that can be integrated onto an instrumented sock for home-based monitoring of lower leg fluid accumulation. Swelling in lower extremities is an early indicator of disease deterioration in cardiac failure, chronic venous insufficiency and lymphedema. At-home wearable monitoring and early detection of fluid accumulation can potentially lead to prompt medical intervention and avoidance of hospitalization. Three types of inexpensive and noninvasive wearable sensors are developed: leg size sensor, tissue elasticity sensor and water content sensor. The innovative leg size sensor developed has unique features of being drift-free, and capable of misalignment-rejection. It has an accuracy of being able to differentiate 1mm changes in diameter, much smaller than any changes that can be detected by the human eye. These features were achieved by using dual magnetic sensors, an inductor for generating alternating magnetic fields, and an unscented Kalman filter estimation algorithm. Elasticity is also an important indicator of fluid accumulation and defines how soft the leg is. The novel elasticity sensor has a simple architecture of two thin-film force transducers and two 3D-printed components, which form a cantilever mechanism. Mathematical models were established for the sensor to estimate tissue elasticity. Lab tests conducted on rubber samples with slightly different softness and human body showed promising results. Several generations of instrumented socks with the leg size sensor and the tissue elasticity sensor were fabricated in the lab. These socks were tested and validated to be accurate and useful in an IRB-approved study on healthy volunteers at Mayo Clinic. The leg size sensor was also integrated into a commercially available pneumatic compression medical device for treating lymphedema. A redesigned and miniaturized leg size sensor was sewed onto a wearable band, which was then attached to the pneumatic pump-based wearable system for monitoring lymphedema treatment progress. Finally, a compact water content sensor was developed. Ultrasound velocity in animal and human tissue has been found to change with water content. A novel integration of magnetic sensing and ultrasonic sensing was utilized to measure ultrasound velocity, and renders the previous bulky device wearable.
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    Wearable Automatic Pain Assessment For Olfaction-Based Chronic Pain Management
    (2024-03) Guan, Quan
    More than 67 million Americans -- about 20% of the U.S. population -- suffer from chronic pain. In addition to the suffering that it induces, chronic pain also leads to higher prevalence of depression, insomnia, and physical and mental impairment. A study showed the estimated cost for chronic pain management was between $560-635 billion in 2010 dollars. Despite the significant problems that chronic pain presents, there is a lack of effective pain management solutions. Medication-based treatments are not completely effective, can lead to drug overdoses, and play a major role in the current opioid crisis. Mind and body interventions (MBIs) -- activities that change mental and physical state -- have been shown to be an effective pain management solution without the negative side effects associated with typical approaches. One particular MBI is using a scent to help patients develop and maintain a mental association between the scent and a lower pain state that can later be used to manage or prevent a pain episode. This approach relies on an automated pain assessment and scent delivery device that can accompany a patient wherever they go. The most logical implementation involves a wearable device to monitor physiological signals related to pain and dispense scents as necessary. In this work, we describe a wearable device that monitors various physiological signals and processes them to predict a user's pain state. Various machine-learning models are trained with the data collected from clinical trials using a prototype of the wearable device. The correlation between physiological signals and pain agrees with results from literature, but the performance of machine learning models is not ideal due to limited dataset size. Results suggest that collection of additional physiological data for model training will increase performance. This work represents an important step toward novel, safe, and effective MBI-based chronic pain management.