Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 7 | STORM RFA |
Abhee Jani Dev Patel Vikram Battalapalli |
Angquan Yu | design_document1.pdf proposal1.pdf |
|
| # STORM: Sprint Training Optimization and Real-time Monitoring **Team Members:** - Abhee Jani (abheej2) - Trivikram Battalapalli (tb17) - Dev Patel (devdp2) # Problem During most sprint training and high intensity cardiovascular activities, we see a lack of proper monitoring for biomechanical metrics including heart rate, VO2max, ground contact time, and stride cadence. Current solutions, including force-detecting treadmills and coaches, are not only very costly but also inaccessible to the average athlete trying to better their performance. In addition, these solutions are not all-inclusive and omit more specific data such as thigh angular velocity which is one of the most impactful metrics on sprint speed. Other solutions, such as fitness wearables, can only track average speeds over long distances but not over short sprints such as 100m. There is a need for a system that’s not only affordable but can help the user optimize their training in real-time. # Solution Our solution is a multi-sensor monitoring system that tracks various biomechanical metrics and interfaces with a mobile app to provide the user with analysis of their sprinting form. The first component is a chest strap sensor that tracks heart rate, VO2max, overall speed, and steps. It also uses haptic feedback to notify the user to stay in their desired heart rate zones during training. The second component is a knee strap sensor that tracks leg movement and ground contact time to evaluate the user’s sprint form and mechanics. The data from both sensors is wirelessly transmitted to our mobile app using Bluetooth, where the user can visualize their performance metrics and track their progress over time. This integrated solution will give the athletes actionable insights that will enhance their training regimen, sprint technique, and cardiovascular performance. # Solution Components ## Subsystem 1: Chest Strap Monitoring System **Function:** Tracks heart rate, VO2 max, tilt, speed, and steps; provides haptic feedback **Components:** - **Heart Rate Sensor:** Maxim Integrated MAX30102 - **Microcontroller:** STM32H7 for data processing and wireless communication - **Haptic Feedback:** Precision Microdrives 306-109 for vibration notifications. - **Bluetooth Module:** HC05 Bluetooth Module - **Inertial Measurement Unit:** STM iNEMO LSM6D032X for pedometer, gyroscope, and accelerometer to calculate steps, tilt, and speed - **Flash Memory:** W25Q32JVSSIQ TR to log data collected from the sensors - **Rechargeable battery:** 3.7V 500mAh Li-ion Rechargeable Battery - **Battery Power Regulator:** MCP1702 3.3V Linear Regulator ## Subsystem 2: Knee Sensor for Sprint Analysis **Function:** Monitors leg movement and ground contact time. **Components:** - **Microcontroller:** STM32H7 for data processing and wireless communication. - **Bluetooth Module:** HC05 Bluetooth Module - **Inertial Measurement Unit:** STM iNEMO LSM6D032X for pedometer, gyroscope, and accelerometer to calculate steps, tilt, and speed - **Flash Memory:** W25Q32JVSSIQ TR to log data collected from the sensors - **Rechargeable battery:** 3.7V 500mAh Li-ion Rechargeable Battery - **Battery Power Regulator:** MCP1702 3.3V Linear Regulator ## Subsystem 3: Mobile Application **Function:** Retrieve sprint related data from subsystem 1’s bluetooth sensor transfer this data to the cloud and develop a frontend analysis for sprint metrics. **Components:** - **React Native Framework:** For developing the mobile application with cross-platform compatibility - **AWS Cloud Services:** For securely storing and processing data in the cloud - **Kinesis:** Data streaming and analytics from the mobile app to the cloud - **Lambda:** For data processing after its been streamed with Kinesis - **S3:** Storing the sensor data of user metrics - **DocumentDB:** Database management system in a noSQL format - **REST API:** For data transfer between the mobile app and cloud services # Criterion For Success ### Data Accuracy and Reliability: - **High-Accuracy Biomechanical Metrics:** Track ground contact time stride cadence and thigh angular velocity with a margin of error within 10% compared to high-speed video analysis or industry-standard equipment - **Precision in Cardiovascular Monitoring:** Maintain heart rate zone tracking with a 95% confidence level across different levels of exertion - **System Reliability and Durability:** Ensure sensors are resilient to sweat impact and environmental conditions typical in high-intensity training ### Software and User Experience: - **Real-Time Feedback and Responsiveness:** Ensure the chest strap's haptic feedback system responds within 200 milliseconds to changes in heart rate zones - **User-Friendly Data Visualization:** Provide an intuitive UI with features like color-coded performance indicators and trend graphs easily interpretable by athletes without a technical background - **Seamless Data Integration and Cloud Connectivity:** Complete cloud processing and retrieval within 5 seconds for a full training session’s data |
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