I independently built a low-fidelity prototype to monitor the improvement in gait of cerebral palsy patients post surgery. Cerebral palsy patients walk with an Equinas gait characterized by hyperflexion of the hip and knees as well as toe walking. In simpler terms, they walk on their toes in a crouched position. The goal of the GaitSense was to allow doctors and physiotherapists to monitor a patient’s gait remotely by analyzing their hip flexion, knee flexion, toe pressure and heel pressure.

I independently built GaitSense using an arduino, two BNO055 sensors and two force sensitive resistors (FSR). The design consists of a band around the upper thigh with a housing for the arduino and power source. Another band with the first BNO055 sensor is placed just below to measure hip flexion in Euler coordinates. A third band is placed at the knee with the second BNO055 sensor to measure knee flexion. There is a foot sole with a strap to attach to the foot the way one would wear a sandal. There is an FSR where the big toe would come in contact with the sole and an FSR at the heel to measure the force exerted by the toes and the heel. The data from the sensors is communicated from the Arduino via bluetooth serial communication to a Python GUI that graphs the live data.

My team and I developed the winning low-fidelity prototype in 6 hours that simulates the disposal of nuclear waste into waste bins at the McMaster Engineering Competition (MEC). Flicky 2.0 is an autonomous device that can load three ping pong balls and launch at three waste bins with specified locations. The firing mechanism consists of a spinning sledgehammer-styled paddle powered by 2 wheel motors rotating in opposite directions to produce a large torque that causes the ball to launch after it falls onto the paddle. We used a servo motor in conjunction with an Arduino to rotate the device for autonomous loading of the ping pong balls and simultaneously aim at the waste bin.

The video below demonstrates Flicky 2.0's functionality and quick launch.

The goal of this project was to design a digital circuit that displays my student number (400307169) on loop using sequential logic design. I began by converting the decimal numbers to Boolean form and generated a state transition table to determine the inputs for the JK flipflops. I then created K-maps based off the state transition table to design Boolean logic expressions for the inputs to each JK flipflop. I proceeded to use Multisim to build a simulation of the circuit using the logic expressions with a combination of OR/AND gates. I attached a seven segment display to verify that the logic correctly produces the student number.

Finally, I built a physical model of my design using a SoulBay power supply and a transistor to make a clock signal. Throughout the simulation and physical build processes I encountered many challenges due to the complexity of the circuit. I troubleshooted these challenges mainly using the oscilloscope of a Hantek to check for incorrect or failed connections.

Using AutoDesk Inventor, my team designed an intraocular syringe that pediatric ophthalmologists can use to administer anti-VEGF medication to premature infants suffering from retinopathy. The design incorporates an ocular extension that is placed around the infant's eyelid to stabilize the syringe. In addition, an adjustable pedal is used to depress the plunger which reduces the movement of the thumb while administering medication and increases overall accuracy.

Below is my team's presentation that dives into the problem, explains our design iteration process, some challenges we faced and limitations in our design.

Fall D-Tech is a clip-on device that can be attached to a patient's belt to track how often they fall and alert a caregiver when they have fallen. The device uses an orientation sensor to measure the patient's acceleration towards the ground and determine if they are falling. My team coded the algorithm using Python and we designed the device using AutoDesk Inventor.

Check out the CAD model below!

In 36 hours, my team and I designed Rescue Band and placed first at the Ontario Tech University's Designathon using SolidWorks. Rescue Band emits low-intensity infrared light through the thin layer of skin on the wrist to the blood vessels. Photodetectors on the band will detect the light intensity in the bloodstream and will use frequency demodulation to calculate respiratory frequency.

If the heart rate is within 150-250 bpm or the respiratory rate is within 20-40 breaths/min, the band will determine that the user may be experiencing a panic attack and will ask the user if they need assistance through a mini speaker. The user can then press the SOS button to contact 911 or the guardian button to contact an immediate family member for help.

Arth-GROW-Plasty is a custom-made hip implant that replaces the femoral bone in a patient suffering from Legg-Calve-Perthes and promotes bone regeneration. My team modelled this assembly model in AutoDesk Inventor and we conducted a materials selection procedure to identify the most effective materials for the implant.

Using Simulink and Python, my team designed the software to be used by a circuit board based on an FRDM K-64F to simulate a pacemaker. The back end was developed using Simulink, and the front end GUI was built in Python. In the Simulink environment, we designed the functionality of 8 pacing modes using Stateflows, including inhibiting modes which prohibit the pacemaker from pacing if a natural pulse is sensed in that chamber of the heart.

In addition, we designed rate adaptive modes which adjust the pacing rate based on an onboard accelerometer’s activity level, simulating physical activities such as walking, jogging, and running. The GUI allows the practitioner to save and track the parameters of up to 10 patients’ pacemakers, using a user-based login screen. Once a user is logged in, they may dynamically adjust programmable parameters such as rate limit, pulse width, and pacing mode in real time and send those changes to the pacemaker using serial communication. We then verify that the parameters are properly set by echoing them back to the DCM and monitoring them.

My team and I represented McMaster and placed 4th in the 2023 Ontario Engineering Competition. In 6 hours we developed a low-fidelity prototype of an organization robot that can pick up two different sized blocks and place them into the same colored bin. We designed the 3D model of the robot using AutoDesk Inventor. The robot consists of an RGB sensor that detects the color of the block and then searches for the same colored bin to place the block in. The grabbing mechanism consists of an arm that moves up and down using a servo motor and has an adhesive at the edge to pick up the block. The block is dropped by increasing the frequency of the arm's motion such that it vigorously shakes and drops the block.

Next steps include implementing an ultrasonic sensor to detect the location of a block and bin autonomously. We also aim to add a servo motor to change the grabbing mechanism to that of a gripper for a more practical solution. The gripper will consist of two popsicle stick fingers with one side attached to the servo motor that will pinch and release the other popsicle stick finger to grab/release the block.