Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 78 | Carpal Tunnel Wrist Glove |
Deepika Batra Li Padilla Rawnie Singh |
John Li | design_document1.pdf final_paper1.pdf grading_sheet1.pdf presentation1.pdf proposal1.pdf video |
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| # SMART CARPAL TUNNEL WRIST GLOVE Team Members: * Deepika Batra (dbatra3) * Li Padilla (jpadi4) * Rawnie Singh (rawnies2) # Problem Digital artists often experience fatigue and discomfort in the wrist, knuckles, and fingers after prolonged drawing sessions. This strain typically goes unnoticed until pain develops. Continued stress on the hand muscles can lead to more serious conditions, such as carpal tunnel syndrome, which can cause hand/wrist pain, burning/numbness in fingers, and overall weakness in the wrist and hand. The repetitive motions of digital art that come with brush strokes, sketching, and rendering, can cause significant swelling around the tendons in the carpal tunnel, resulting in pressure on the median nerve. # Solution Many use compression gloves to alleviate symptoms related to carpal tunnel syndrome, but there is an opportunity to introduce a similar product with a technological component. This product would use strain gauge sensors to monitor the user’s grip and monitor joints/muscles that undergo prolonged repetitive motion. Through these sensors, a software application could assess the level of stress on the hand and wrist, provide notifications to prompt breaks, and suggest targeted stretches aimed at alleviating tension in the specific regions of the hand and wrist. Since this device aims to promote good wrist/hand health and muscle strain prevention, it aims to point the user to healthier stretching and break unhealthy habits of prolonged muscle stress. # Solution Components ## Subsystem 1: Sensor Layer **Strain Gauges** Strain gauges can measure deformation or mechanical strain within a material by changing its electrical resistance when stretched or compressed, which works well for applications related to structural load analysis. Strain gauges used in tandem with IMUs allow for a fuller picture of mechanical movement within the hand, i.e. wrist angle and flexion detection (which correlates to potential nerve compression risks). A strain gauge rosette can measure wrist angles and analyze strains that occur during wrist flexion/extension/radial and ulnar deviation, and may be placed near key ligaments such as the dorsal wrist area.. A suitable strain gauge that may be used is the Vishay CEA-06-062UR-350, as it can measure multi-axial strains. This approach adds a biomechanical analysis layer to the glove, which may detect harmful wrist postures even when muscles are not active. **Inertial Measurement Unit** IMUs can track repetitive motion by measuring linear acceleration and angular velocity, which makes them useful to detect wrist and hand movements associated with repetitive strain which can then contribute to nerve compression. An option for an IMU would be a ICM-20948, which is a low-power sensor making it suitable for wearable applications such as our glove. These IMUs would be placed in specific positions of the wrist (such as the dorsal side to capture radial/ulnar deviation), just above the wrist to track forearm rotation, and the back of the hand (near the metacarpals) to monitor fine motor motion such as finger extension/flexion dynamics. ## Subsystem 2: MCU The microcontroller will take the signals from the sensor layer (from the sensor gauges and IMUs) as inputs and perform amplification, filtering, and analog-to-digital conversion that were outlined in subsystem 1 above. We are thinking of utilizing a microcontroller with a built-in ADC and programmable amplifier/band-pass filters such as the TI-MSP430FR5994. ## Subsystem 3: Amplifying/Filtering Signal Processing (live input) Strain Gauge Signal Processing Strain gauges measure strain by changing its electrical resistance; to convert these tiny resistance changes into measurable voltages, a Wheatstone bridge is used to amplify changes in resistance caused by strain (we aim to use a full-bridge with four gauges to maximize sensitivity and thermal stability). The output of resistance changes into voltage is usually too small; the signal will first be amplified and filtered (will likely use low-pass filtering to remove high-frequency noise since strain gauge signals are typically maximum 20 Hz for human motion) prior to analog-to-digital conversion. Then, the microcontroller will calculate wrist angle and stress based on the digital signal and trigger any feedback (i.e. user notification or display system). **Inertial Measurement Unit Signal Processing** The raw data from the IMUs will likely involve filtering to remove noise unrelated to actual motion. Useful data related to motion frequency, range of motion, and angular velocity can be derived with FFT for orientation tracking (the IMU collects time-series data and would include periodic signals if repetitive tasks are being performed). Processed IMU data can then be correlated with strain gauge outputs to identify patterns of repetitive motion and grip. ## Subsystem 4: Power Subsystem (signal analysis) As shown in the block diagram, the main power systems are transferring the required amount of voltage from the PCB to the battery, communication module, and MCU. The board design is explained in subsection 6, but the biggest component to discuss is the rechargeable battery which will connect to the glove. We will most likely use a lithium ion battery. Smart Carpal Tunnel Wrist Glove RFA - Block Diagram https://docs.google.com/document/d/1LMrlfA7iYeF-7hu9MXWzzaYylBaH1Q71_ltEKfnskbM/edit ## Subsystem 5: Communication Protocol/Display System This subsystem will receive signals from Subsystem 3 and compare them with threshold values we set to assess whether the user is applying prolonged stress that may lead to harmful muscle activity. The output voltage readings (vout) across the strain gauge is proportional to the change in electrical resistance. We can calculate the strain by dividing the ratio of vout/vin by the gauge factor (the ratio of the relative change in electrical resistance and relative change in length). Force is then calculated by multiplying the strain, young’s modulus of the material, and the cross-sectional area of the stressed material. We can then use this force value for our Maximum Voluntary Contraction (MVC) comparison with a threshold value of 20%. The stress readings from the strain gauge, along with their duration, will be compared to the 4% threshold. The wrist flexion/extension will be compared to the value 30º and radial deviation will be compared to the value of 15°. We will have an application that communicates the result of this comparison. If any of the readings exceed the threshold, the system will suggest the user take breaks every 20-30 minutes (through an LED/text display system on the glove). Additionally, strain gauge readings will provide insights into which joints undergo repetitive motion and, subsequently, relevant muscles. The external app will also display various stretches to help reduce stress and tension in those muscles and joints. We will explore the possibility of exporting real-time signal data from the MCU to a PC via UART through implementing a very simple program that interprets the data and intelligently suggests a stretch out of a database. ## Subsystem 6: Board Design The PCB will be designed on KiCad and will be the ‘brain’ of the system. The design will route the microcontroller signals to the notification system, ensuring that power will be supplied to both portions of the design (live input & user-facing). Both subsystems will likely have varying current limits and required voltage inputs, which will be taken care of on the PCB using voltage regulation and power conversion techniques - possibly a buck converter or linear regulator. # Criterion For Success Accurately measures repetitive motion and where (i.e. Accurately measures angle of wrist flexion and extension Notifies user of prolonged muscle strain and proposes stretches that targets the users’ muscles that were under prolonged use with 80% accuracy System properly works on 2 different group members, with different grips to show detection can be used on unique users in |
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