To test the device, I set up four trials for running and biking with and without the exoskeleton and recorded 30 seconds of data after 5 minutes of running or biking. To collect data, I used an MPU-6050 IMU and FSR in the circuit shown to the right. The IMU, attached to the shin with the z-axis aligned vertically, measured acceleration and angular velocity by estimating stride frequency. The FSR measured force to assess the impact on the ground or pedals at the ball of the foot.  Using an Arduino UNO, I was able to collect real-time data. From here, I completed MATLAB-based signal processing, filtering, and sensor calibration. This allowed me to store the data and plot the force, z-acceleration, and gait phase.

Results and Conclusions

From the four trials, it was determined that the exoskeleton was successful in reducing the impact force for running and enhanced the stability for biking. This is seen in the graphs below, where the running trial shows a smaller force range with the exoskeleton, and the biking trial shows a smaller standard deviation in the force, reflecting a more constant force on the pedal and increased stability in the cycle. These results show the exoskeleton preserved normal gait mechanics, reduced impact force, and improved force consistency, suggesting potential for rehabilitation and injury prevention. For future improvements, it would be useful to make a device that is adjustable for various heights and leg sizes and test different torsion springs to optimize force reduction performance. Overall, this project gave me valuable insights into passive wearable robotics and lays the foundation for more advanced rehabilitation exoskeletons.

Trial 1 for Running: Without exoskeleton on left and with exoskeleton on right