sEMG-DAQ is a wearable 6 channel data acquisition unit for capturing surface electromyographic (sEMG) signals from human arm muscles using SJ2-3593D jack connectors while conditioning, digitizing, processing and transmitting them as sEMG data to an external AI accelerated board through an SM12B-SRSS IDC connector where AI models are run for various applications including robotic control, muscle signals medical assessment and gesture recognition. The board leverages an INA125P instrumentation amplifier together with filter stages utilizing LM324QT op-amps for conditioning and an STM32G4A1VET6 microcontroller for the digitization, processing and data transmission of the signals. Since AI models can only be as good as the data, the design of such a DAQ is necessary to ensure clean, reliable and real-time data for AI applications requiring sEMG data. The board also has USB-FS and JTAG to cater for debugging. The power (5V) is fed through a screw terminal and is regulated by two LDK320AM LDO regulators to offer 5V, 3.3V and 1.8V to meet the requirements of various components on the board.

Compact ESP32-S3 wearable environmental monitor with BME688 sensing, LiPo charging, MAX17043 fuel gauge, USB-C power, and JLCPCB-ready 4-layer layout constraints.

Wearable Industrial Safety Monitor — STM32C011 + ESP8266, IMU/environment/microphone sensing, RGB/vibration alerts, LiPo/AP7361 power, 70 mm × 50 mm 2-layer PCB with bottom GND plane.

NFC-harvested Omni-Touch wearable board with PN7160 frontend, STM32L041 host MCU, SE050 secure element, palm-grid matrix interface, fingerprint UART connector, and status LED on a flex smart-card form factor.

Wearable EMG acquisition PCB with 8 differential biopotential channels, BLE/USB MCU, USB-C LiPo charging, 3.3V buck regulation, and 2.4GHz chip antenna path.

This is a smart watch project based on E73-2G4M04S1A module with SoC nRF52810 on board. OLED display and coin battery #wearable #nRF52810 #E73 #Ebyte #BLE #referenceDesign

BLE wearable flex PCB with LiPo charging, PPG plus skin temperature, 3-channel EMG, IMU, and piezo impact sensing for a smart compression sleeve

ESP32 Wearable Health & GPS Tracker with Biometric Security, TFT Display, and Enhanced Power & Data Protection

Audio-first ESP32-S3 wearable PCB. 44 × 26 × 1.0 mm. ESP32-S3-MINI-1 + INMP441 + TP4056 + DW01A/FS8205A + ME6211C33. 800mAh 802545 LiPo. Native USB on GPIO19/20. BLE audio wearable.

Cow health-monitoring wearable PCB for a cow-mounted device. Rear strap electronics target 20-25 g and front sensing assembly target about 15 g. Uses an ESP32-based sensor controller, 200 mAh LiPo battery, battery charging circuitry, and interfaces for ammonia, methane, IR temperature, and saliva sensing. Design must tolerate moisture, mucus, debris, vibration, fur contact, and tongue/lick exposure while remaining compact and lightweight for wearable use.

Wearable closed-loop ultrasonic monitoring and transcutaneous neuromodulation system based on ESP32-C3, powered from a 7.4 V 1000 mAh LiPo battery with TP4056 charging, MT3608 boost conversion, AMS1117-3.3 regulation, JSN-SR04T ultrasonic sensing, LED and buzzer alerts through a BC547 driver, and a reserved neuromodulation interface for future integration.

Compact wearable smart cat collar PCB focused on ultra-low power, miniaturized size, curved-edge waterproof-friendly mechanical fit, GNSS tracking, motion sensing, RFID compatibility, Li-ion charging, and robust mass-production readiness.

Wearable health device prototype schematic with ECG sensing via AD8232, motion sensing via BNO085 or BMI270 IMU breakout, on-board processing using TI TM4C123G LaunchPad or MSPM0G3507 LaunchPad, optional ESP8266 Wi-Fi, LiPo battery charging with MCP73831, regulated 3.3 V power from TPS63031 buck-boost or AP2112 LDO prototype option, and user interfaces including electrode connector, JST battery connector, programming header, and optional on/off switch.

ESP32-S3 Wearable Audio Streaming Pendant (USB-C + LiPo + I2S Mic + microSD + OLED)

8-Channel Wearable BLE5 Brain–Computer Interface with 24-bit Biopotential AFE and Cross-Platform SDKs

Silent Gaude: 10 mm 4‑Layer ESP32-S3 LTE/GNSS Wearable Bracelet

Wearable Multi-Sensor ESP32 Platform

Ultra-Compact 20x10 mm Wearable BLE Finger Gesture PCB with ESP32-C3, IMU, Li-Po Charging, and Flex Sensor Support

ESP32-WROOM-32 Wearable Health Monitor with MAX30102, MPU6050, OLED, Camera Interface, and Advanced Power Management

Biskuit is a compact, ESP32-S3-powered wearable device designed for real-time transcription and effortless note-taking. Featuring a 3-microphone array and wireless communication to sync to to the cloud instantly.

The Biskuit pendant is a WiFi/BLE Enabled wearable AI Diary which captures Images and sounds periodically and creates an audio visual timeline of your day so that you don't forget anything.

Product Type: Wearable AI pendant Primary Function: Records audio, generates transcripts, and organizes information about daily interactions User Interaction: Input: Activation button Output: RGB LED ring, Bluetooth link to phone Key Features: Audio Recording: Activated by button press Transcription: Converts audio to text Sentiment Analysis: Embedded AI evaluates sentiment Information Management: Filters essential information and action items Technical Specifications Form Factor: Wearable pendant Display: RGB LED ring around the edge Sensors: 2 Microphones 1 Button Connectivity: Bluetooth for phone linkage Wi-Fi USB-C for charging Wireless Protocol: Wi-Fi, Bluetooth Battery Type: LiPo 2000 mAh Battery Life: 6 hours of continuous use Charging Method: USB-C Operating Voltage: 3.3V Operating Conditions: Temperature Range: -10°C to 70°C Humidity: 10 to 90% Software: Python for AI and processing Compliance: RoHS, FCC, CE Reliability: 20,000 hrs Life Cycle Expectancy: 10 years AI Capabilities Speech to Text Recognition: Converts audio input to written text Embedded AI Sentiment Analysis: Evaluates the mood or sentiment expressed in the text Essential Information Filtering: Identifies and segregates crucial data and actionable items Power Consumption and Efficiency Power consumption must align with battery capacity to ensure 6 hours of continuous operational use.

Biskuit is a compact, ESP32-S3-powered wearable device designed for real-time transcription and effortless note-taking. Featuring a 3-microphone array and wireless communication to sync to to the cloud instantly.

Product Type: Wearable AI pendant Primary Function: Records audio, generates transcripts, and organizes information about daily interactions User Interaction: Input: Activation button Output: RGB LED ring, Bluetooth link to phone Key Features: Audio Recording: Activated by button press Transcription: Converts audio to text Sentiment Analysis: Embedded AI evaluates sentiment Information Management: Filters essential information and action items Technical Specifications Form Factor: Wearable pendant Display: RGB LED ring around the edge Sensors: 2 Microphones 1 Button Connectivity: Bluetooth for phone linkage Wi-Fi USB-C for charging Wireless Protocol: Wi-Fi, Bluetooth Battery Type: LiPo 2000 mAh Battery Life: 6 hours of continuous use Charging Method: USB-C Operating Voltage: 3.3V Operating Conditions: Temperature Range: -10°C to 70°C Humidity: 10 to 90% Software: Python for AI and processing Compliance: RoHS, FCC, CE Reliability: 20,000 hrs Life Cycle Expectancy: 10 years AI Capabilities Speech to Text Recognition: Converts audio input to written text Embedded AI Sentiment Analysis: Evaluates the mood or sentiment expressed in the text Essential Information Filtering: Identifies and segregates crucial data and actionable items Power Consumption and Efficiency Power consumption must align with battery capacity to ensure 6 hours of continuous operational use.

Product Type: Wearable AI pendant Primary Function: Records audio, generates transcripts, and organizes information about daily interactions User Interaction: Input: Activation button Output: RGB LED ring, Bluetooth link to phone Key Features: Audio Recording: Activated by button press Transcription: Converts audio to text Sentiment Analysis: Embedded AI evaluates sentiment Information Management: Filters essential information and action items Technical Specifications Form Factor: Wearable pendant Display: RGB LED ring around the edge Sensors: 2 Microphones 1 Button Connectivity: Bluetooth for phone linkage Wi-Fi USB-C for charging Wireless Protocol: Wi-Fi, Bluetooth Battery Type: LiPo 2000 mAh Battery Life: 6 hours of continuous use Charging Method: USB-C Operating Voltage: 3.3V Operating Conditions: Temperature Range: -10°C to 70°C Humidity: 10 to 90% Software: Python for AI and processing Compliance: RoHS, FCC, CE Reliability: 20,000 hrs Life Cycle Expectancy: 10 years AI Capabilities Speech to Text Recognition: Converts audio input to written text Embedded AI Sentiment Analysis: Evaluates the mood or sentiment expressed in the text Essential Information Filtering: Identifies and segregates crucial data and actionable items Power Consumption and Efficiency Power consumption must align with battery capacity to ensure 6 hours of continuous operational use.