Industrial Design and Mechanical Engineering student driven by a passion for designing products that combine thoughtful aesthetics with practical function.
Drawing on mechanical intuition, manufacturing knowledge, and user-centred design principles, he seeks to create products that work as well as they look.
- +61 416 984 667
- naruncheng@gmail.com
- Melbourne, Australia
Client Project
TechTrainer
2026
- — Engineering
- — Client Project
- — Concept to Delivery
TechTrainer is an industry co-op project with venture capital firm Hampton Capital. The brief was to design and prototype a wearable smart garment that tracks biomechanics to help athletes improve form and reduce injury risk. The first product is a pair of sensor embedded compression pants focused on the squat exercise.
The system is split into three subsystems: hardware, sensing (wireless data streaming and sensor fusion), and vision (multi-camera AI skeleton tracking to validate sensor data). I was responsible for hardware design and prototyping, including PCB design, sensor integration, and integration with the garment, while also contributing to sensing and vision work.
A functional prototype was delivered to the client, and the project has now moved into its next phase of development.
A key objective of the project’s first phase was to define an expandable system architecture for TechTrainer, laying the foundation for future expansion into additional muscle groups and exercise movements.
The architecture is built on three subsystems: wearable, sensing and vision. The wearable subsystem covers the smart garment and all its embedded hardware. The sensing subsystem handles wireless data streaming, fusion and processing. The vision subsystem is a modular add-on that uses multi-camera AI tracking to validate the sensing data.
Physical layout diagram of the smart garment. The hardware is separated into three separate housings. The top housing mounts at the waistband and houses the MCU, battery and antenna. The middle housing straps above the knee, and houses IMU1 and two EMG modules. The bottom housing straps below the knee and houses IMU2. The two EMG electrodes are placed according to SENIAM (Surface ElectroMyoGraphy for the Non-Invasive Assessment of Muscles) convention at the Vastus Lateralis and Medialis.
CAD models of the three modules. Each was designed to be as low-profile as possible, with geometry contoured to the curvature of its position on the body and informed by anthropometric data on leg dimensions. The modules house the sensors alongside custom PCBs that distribute power and signals through the garment.
A custom through-hole PCB was designed for each housing to provide interfacing between sensors and the ESP32-C3 XIAO MCU from Seeed Studio. Given the 3.3V architecture and the power demand of the ESP32 based MCU, a 1100mAh Li-Po battery was chosen. JST-SH connectors were used. SMD designs or flexible PCBs should be explored in the future for a more compact form factor.
Hardware assembly of the prototype.
Strap based design for easy attachment & removal of modules.
Magnetic POGO connectors between each module for easy setup.
Cable management slots designed into the plastic housings.
Transparent enclosures make troubleshooting easier and look great.
The assembled garment sits close to the skin and the modules and embedded cable harness remain comfortable and unobtrusive. It allows full, unrestricted movement through the squat’s entire range of motion.
On the software side, the TechTrainer app evolved from a basic ESP32-hosted sensor readout into a full squat analysis platform. It guides the user through connecting, calibrating, recording and reviewing each session, fuses IMU data with ZED camera pose estimation in real time, and tracks metrics like knee flexion, depth, tempo and symmetry over time. A built-in demo mode simulates both data streams, so the system can be shown without hardware.
Dashboard of the working GUI for TechTrainer. This gives an at-a-glance view of progress. It charts peak knee flexion per session (toggle between peak and range of motion), alongside cards for the latest session’s depth, rep counts, tempo and left–right symmetry, filterable over 7, 30 or 90 days.
The live session tab walks the user through connecting the garment, calibrating in a standing pose, then recording and reviewing a set. Knee angle from the IMU streams alongside ZED camera pose estimation overlaid on the video feed, so both data sources can be compared in real time.
The sessions tab stores every recorded session, letting users revisit past squats, review their metrics and compare performance over time rather than treating each workout as a one-off.
With the first functional prototype complete, TechTrainer has moved into user testing. We’re now working with gyms, personal trainers and physiotherapists to collect real-world squat data and gather feedback on the garment, the app and the usefulness of the metrics themselves. This testing will validate the system outside the lab, build a dataset to refine our inference models, and shape the next iteration of both the hardware and software before expanding to new muscle groups and movements.
More updates to come along with video demos of the system.
- +61 416 984 667
- naruncheng@gmail.com
- Melbourne, Australia
Industrial Design and Mechanical Engineering student driven by a passion for designing products that combine thoughtful aesthetics with practical function. Drawing on mechanical intuition, manufacturing knowledge, and user-centred design principles, he seeks to create products that work as well as they look.