Data-sensing Glove
Data-sensing Glove
Sep 2024 - April 2025 | HaptE
Designed and built a minimum viable product (MVP) for the student-led startup HaptE, developing a functional prototype that used a range of electronic components to capture and interpret hand motion data during physical labor tasks.
Market Research and Experimentation
Building on insights from over 50 stakeholder interviews conducted by the team before I joined, we identified a core opportunity: a wearable glove that could track hand movement data for labor-intensive tasks. I led research into system integration for the MVP, consulting with experts including PhD students from Northwestern’s HaND Robotics Lab to determine the most effective sensing technology and visualization application. After evaluating various options, we selected Inertial Measurement Units (IMUs) for their balance of precision and integration flexibility. I prototyped an initial system using two IMUs, which transmitted data to a custom MATLAB script that visually interpreted the raw sensor output for hand motion analysis.
MVP Design and Testing
Wireless Glove with 1 IMU
With a better understanding of quaternion and matrix outputs from IMUs, we transitioned to a fully wearable prototype. We integrated the electrical components into a glove form factor by soldering and sewing the sensors, along with a compact battery and charging circuit for wireless operation. The data was fed into a new Python pipeline I developed, using PyBullet to simulate a 3D model of the hand. We tested several sensor configurations, including 1-, 2-, and 3-IMU setups. After extensive experimentation, we optimized the glove around a single IMU, using inverse kinematics algorithms to estimate full-hand motion from a single input point.
Testing Glove with 3 IMUs
Further Research
After building a functioning MVP focused on our perceived use cases in manufacturing, logistics, and rehabilitation, our focus shifted toward exploring how a data-generating wearable could enhance human-robot interaction in industrial environments. I conducted a deep dive into automation trends and robotic workflows, supported by field visits to various warehouse environments and attendance at industry conferences such as ProMat. In parallel, we iterated on the hardware, testing alternative sensors including flex sensors to improve power efficiency and reliability for continuous use.