Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 78 | Wearable Basketball Jumpshot Mechanics Analyzer |
Aiden Zack Arjun Vyas Tanmay Nair |
Mingrui Liu | design_document1.pdf final_paper1.pdf proposal1.pdf video |
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| Tanmay Nair (netid: tanmayn2 ) Arjun Vyas (netid: avyas9) Aiden Zack (netid: aidenrz2) Problem: A basketball jumpshot involves a chain of body mechanics that requires coordination from your feet to your wrist to achieve a simple goal that is much more complicated than what the average person sees: Making the ball go in the hoop. So many players across the world have exhibited different mechanics in their jumpshot, so when they reach out to coaching for help, they tend to hear subjective advice that is often inconsistent, difficult to put into numbers, and, more importantly, harder to fit into the player’s perspective. Existing resolutions utilize shot trajectory and do not tap into the biomechanics that reside in the shooter. In essence, this leads to players lacking reliable, repeatable data to identify points of improvement in their mechanics, address consistency issues, and record progress. Solution: This project will implement a system dedicated to quantifying a user’s basketball jumpshot by analyzing the consistency and timing of the “kinetic chain”. It starts with node sensors that will be worn on the user's shooting wrist and the knee of the user’s shooting side. These sensors will hold an IMU, microcontroller, and wireless (or wired, tbd) communication. The knee sensor will focus on lower-body motion and take measures related to shot success, such as the timing of the jump and how much the knee flexes to determine the dip. The wrist sensor will look at the upper-body mechanics that finish out the shot, like the angular velocity and release timing of the wrist, along with how high it sits for the follow-through. These 2 data nodes will be synchronized in our system, extracted for timing measures like jump-to-release, and then processed for evaluation and feedback. This will focus on the repeatability and timing of the user’s body mechanics, providing user-oriented assistance that adjusts as the user progresses. Solution Components: PCB, Li-Po Battery Pack, USB-C Charging Port, SPI/I2C Communication Bus, IMU Sensors (3-axis accelerometer + 3-axis gyroscope), FPGA*, PCB Chest Harness. *FPGA may not be needed if we decide to use specific types of IMU sensors with FSYNC/SYNC capability to trigger sampling on the same external edge. Subsystem 1: IMU Sensor on the Knee This IMU sensor will be worn on the user’s shooting leg, right above the knee, along the side of the femur. The important metrics to grab from here will be the displacement and angular rotation with respect to the zero-calibration (standing straight up). This IMU will be synchronized with the other IMU sensor on the wrist, being sampled under a SPI/I2C communication bus that will carry data from the sensors to the PCB, which will then be processed and sent to the FPGA via USB/UART. Subsystem 2: IMU Sensor on the Wrist This IMU sensor will be positioned on the back of the user's thumb to accurately record the motion of the wrist. The key metrics we are looking for are angular velocity, physical displacement, and the timing between each of the 3 phases between the movements. The angular velocity can be determined by seeing the physical start and end positions of the wrist motion during phase 3 of the shot, divided by the elapsed time. The 3 phases of the shot are Raising the ball (Shoulder Movement) Pushing the ball forward (Chest Movement + Elbow Extension) Releasing the ball (Wrist Movement) Subsystem 3: PCB The PCB is the centerpiece of all external component communications. The 2 IMU sensors will communicate with the MCU on the PCB via I2C/SPI. The MCU will then send the data to the computer over USB/UART. The data will be interpreted in Python using closed-loop feedback communications with the user. Criterion For Success: Wrist and knee IMU sensors accurately record motion data Communication buses accurately read the data off the IMU sensors with low latency and send it to the MCU on the PCB The MCU can communicate with the computer via USB/UART We can see the telemetry data, observe significant changes (edge detection/triggers) in behavior via measurements, and quantify these changes in order to provide feedback to the user based on their input. |
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