Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 82 | Real-Time Form Correction Device |
Bhanu Kunam Ishank Pujari Sree Akkina |
Po-Jen Ko | design_document1.pdf final_paper1.pdf presentation1.pdf proposal1.pdf video |
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| Team Members: - Bhanuprakash Kunam (bkunam2) - Sree Akkina (sakkina2) - Ishank Pujari (ipuja2) # Problem Free weight exercises (dumbbells/barbells) require intense focus, and users often cannot safely look at visual displays while performing complex movements. Additionally, beginners frequently suffer from poor form - such as wobbling or using momentum rather than muscle control - which is difficult to self-diagnose without a personal trainer. # Solution This project proposes the “Smart-Clip,” an IoT attachment for barbells and free weights that utilizes auditory feedback to correct form in real-time. The system aims to use an ESP32 microcontroller and a 6-axis IMU to analyze the lift’s stability and trajectory. A piezoelectric buzzer provides sound cues: a “clean” tone confirms a stable, good-form repetition, while a dissonant alert signals excessive wobble or dangerous acceleration. This allows the user to maintain safe positioning while receiving instant coaching on their technique. All form data is logged to an app via Bluetooth for post-workout analysis. # Solution Components ## Data Acquisition (Sensing) The physical clip attaches securely to the dumbbell handle. Inside, a 6-axis Inertial Measurement Unit (IMU) continuously monitors the weight’s movement in 3D space. The Accelerometer measures the velocity of the lift (is the user moving too fast/jerking the weight?). The Gyroscope measures rotational stability (is the user’s wrist wobbling or tilting effectively?) ## On-Device Processing The ESP32 microcontroller acts as the central processing unit. Instead of sending raw, noisy data to the phone, the ESP32 performs Edge Computing. Noise Filtering applies a smoothing filter to ignore small hand tremors. The Form Analysis Algorithm compares the motion vector against a “Gold Standard” vertical path. If the vector deviates sideways (wobble) or accelerates beyond a safety threshold (momentum), the system flags the repetition as “Poor Form.” ## Feedback Generation (The Interface) The system employs a dual-loop feedback mechanism to provide both real-time coaching and long-term analytics. For immediate, a passive piezoelectric buzzer emits distinct auditory cues: a sharp, high-pitched beep confirms a valid repetition with proper form, whereas a low, dissonant buzz alerts the user to instability or dangerous acceleration. In parallel, the device utilizes Bluetooth Low Energy (BLE) to transmit detailed performance metrics, such as total count, lift tempo, and stability scores, to a companion mobile application, allowing users to review their workout history and track progress over time. ## App The companion mobile application serves as the centralized hub for workout analytics, receiving data from the Smart-Clip via Bluetooth Low Energy (BLE). It records all session metrics, including repetition counts, tempo, and stability scores, locally on the device, allowing users to track and analyze their long-term progress through historical graphs and trend reports. Beyond data storage, the app acts as a control interface, enabling users to customize the clip’s sensitivity thresholds and audio feedback settings to match their specific training regimen. # Criterion For Success 1. Repetition Accuracy: Counts bicep curls with = 90% accuracy; detects > 15 degrees wobble in >= 9/10 trials with no false alerts on clean reps. 3. Feedback Latency: Audio feedback occurs within 200 ms of IMU-detected rep completion. 4. Bluetooth Integrity: 100% of completed sets transmit correctly to the app within a 2 m range. 5. Mechanical Stability: Clip rotates less than 10 degrees on the handle during a 10-rep set. 6. Power Efficiency: Operates for at least 1 hour with average current draw under 100 mA. |
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