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Arseni Aliakseichyk

Embedded and Infrastructure Engineer

arseni.aliakseichyk@gmail.com • LinkedIn • GitHub • Portfolio • Słupsk, Poland

CyberGlove - IoT Smart Glove Platform

ESP32 Wearable for Gesture Control

Personal • 2025 – Present
In Active Development
CyberGlove Phase 1 - ESP32 + MPU-6050 sensor node on perfboard, hand-soldered, powered via LiPo with LED indicator active
ESP32-C3 + MPU-6050 IMU sensor node on perfboard, hand-soldered, green power LED active, held in hand against blue silicone mat
SPI debug session: KingstVIS logic analyzer software on monitor showing decoded MOSI/MISO/SCLK channels, LA1010 logic analyzer connected to breadboard with IMU module
Perfboard bottom side showing hand-soldered wire routing — red, yellow, and black wires connecting power and signal traces
KiCad 8 schematic: ESP32-C3-Zero + MPU-9250 IMU (SPI) + TP4056 USB-C LiPo charger + S9V11F3S5C3 buck-boost 3.3V regulator + SPDT power switch
CyberGlove Phase 1 sensor node demo — ESP32 IMU tilt controller in action
Phase 2 progress (May 2026): self-designed OpenSCAD 3D-printed optical fixture — split-clamshell housing aligning IR emitter LED, plastic optical fiber, and phototransistor with zero play for repeatable bend-sensor readings

CyberGlove is my flagship personal project - an IoT smart glove for real-time gesture recognition and control, in active development. Phase 1 (complete): firmware on ESP32 (C, ESP-IDF, FreeRTOS) with MPU-6050 (photo) → MPU-9250 → ICM-20948 (on order) IMU over SPI and custom fiber optic bend sensor via 12-bit ADC, streaming sensor data at 50 Hz over WebSocket to a live web dashboard. Hardware drafted schematically in KiCad 8 Schematic Editor, with perfboard layout planned in KiCad PCB Editor to keep hand-soldering clean and trace routing deliberate. Assembled on perfboard with hand-soldered SMD and through-hole components for comfortable iteration: ESP32-C3-Zero + IMU sensor node + full LiPo power chain (TP4056 USB-C charger → S9V11F3S5C3 buck-boost 3.3V regulator + SPDT power switch). KiCad here is used as a drafting and layout-planning tool - full multilayer PCB fabrication is an experiment I’m still exploring. The Phase 1 sensor node was validated in a real application - integrated into the robot platform as an IMU-based tilt controller, proving the hardware and firmware stack before moving to Phase 2. Phase 2 (in progress): adding fiber optic bend sensors on all five fingers via ADC for full finger curl detection, combined with IMU orientation - enabling full gesture recognition with planned TinyML inference on-device.

Key Features - Phase 1 (complete)

  • MPU-9250 9-DoF IMU over SPI (accelerometer + gyroscope + magnetometer)
  • Custom fiber optic bend sensor via ADC oneshot driver (12-bit resolution)
  • Real-time WebSocket telemetry at 50 Hz (JSON sensor data)
  • Schematic + perfboard layout in KiCad 8 (Schematic + PCB Editor), assembled on perfboard: ESP32-C3-Zero + MPU-9250 + LiPo power chain
  • LiPo power management: TP4056 USB-C charger + S9V11F3S5C3 buck-boost 3.3V
  • Wi-Fi STA mode with event-driven automatic reconnection
  • FreeRTOS task management for concurrent sensor reading and network I/O
  • Validated on robot platform as IMU tilt controller (UDP → Raspberry Pi)

Phase 2 (in progress) Update May 2026

  • Fiber optic bend sensors on 5 fingers via ADC
  • Self-designed OpenSCAD 3D-printed optical fixture: split-clamshell housing aligning IR emitter LED, plastic optical fiber, and phototransistor with zero play for repeatable bend-sensor readings (printed in-house)
  • Full gesture recognition combining finger curl + IMU orientation
  • Planned TinyML on-device inference for custom gesture classification
  • C
  • ESP-IDF
  • FreeRTOS
  • SPI
  • ADC
  • ICM-20948
  • WebSocket
  • Wi-Fi
  • KiCad (Schematic + PCB)
  • Perfboard
  • LiPo Power
  • OpenSCAD
  • 3D Printing