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.

The semgdaq board 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 feature extracting them then transmitting the feature data as vectors to an external AI accelerated board through an SM12B-SRSS IDC connector using 12C and UART communication protocals where AI models are run for various applications including robotic control, muscle signals medical assessment and gesture recognition. The feature vectors are comprised of onset detection, slope sign changes, autoregression coefficients and Short Time Fourier Transform magnitude spectrum data for each segment or window of the signals in real time. This vectors can be used as the basis for further feature extraction on more computationally resourceful hardware where machine learning algorthms can be employed for descision making in the applications mentioned earlier. The board leverages INA125P instrumentation amplifiers together with filter stages utilizing LM324QT op-amps for conditioning and an STM32G4A1VET6 microcontroller for the digitization, processing, feature extraction and data transmission. 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 feature data. The board also has USB-FS and JTAG to cater for debugging and external flash memory to extend its data storage and processing capability. 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.

USB-Powered ESP32-C5 Indoor Air Quality Monitor with STCC4 CO2/SHT41 (I2C), PMS7003 (UART), AP2112K 3.3V LDO, Polyfuse Protection on 55×45 mm 2-Layer PCB

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.

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.

The LTC3109 from Linear Technology is a highly integrated DC/DC converter specifically designed for energy harvesting applications. It can operate from ultra-low input voltages as low as 30mV, utilizing a unique, proprietary auto-polarity architecture to function regardless of input polarity. The component is ideal for harvesting energy from thermoelectric generators (TEGs) and thermopiles, efficiently converting this energy to power remote sensors, wireless transmitters, and low-power devices. Key features include selectable output voltages of 2.35V, 3.3V, 4.1V, or 5V, a 2.2V low-dropout (LDO) regulator, a logic-controlled output, and an energy storage system to maintain operation during power interruptions. The LTC3109 is encapsulated in a small, 20-lead (4mm × 4mm) QFN or SSOP package, making it compact and suitable for space-constrained applications in HVAC systems, building automation, and industrial wireless sensing. Additionally, the power good indicator and the ability to use compact step-up transformers further enhance its suitability for low power, energy-harvesting systems.

ESP32 /eMMC Integration with Bidirectional Level Shifting Project Overview: This project aims to integrate an ESP32 microcontroller with an eMMC (embedded Multi Media Card) storage module to create a robust data processing and storage solution. The system utilizes bidirectional level shifting to ensure seamless communication between the 3.3V logic of the ESP32 and the 1.8V logic of the eMMC, enabling efficient data handling and processing. Objectives: Data Storage and Processing: Leverage the high-speed capabilities of the eMMC for data storage while offloading processing tasks from the ESP32 to enhance overall system performance. Voltage Level Compatibility: Implement a bidirectional level shifting solution to facilitate communication between the ESP32 and eMMC, ensuring signal integrity and compatibility across different voltage levels. Modular Design: Create a modular and scalable design that can be easily adapted for various applications, including IoT devices, data logging systems, and embedded applications. Key Components: ESP32 Microcontroller: A powerful microcontroller with integrated Wi-Fi and Bluetooth capabilities, ideal for IoT applications. eMMC Storage Module: A high-speed storage solution that provides ample memory for data-intensive applications. Bidirectional Level Shifter: A 20-channel level shifter (74LVC4245 and TXB0104D) to convert signals between 1.8V and 3.3V, ensuring reliable communication between the ESP32 and eMMC. Power Management: Utilize a MIC5205 LDO voltage regulator to step down the 3.3V supply to 1.8V for the eMMC, ensuring stable power delivery. Implementation Steps: Circuit Design: Design the circuit schematic, including connections for the ESP32, eMMC, level shifter, and power management components. PCB Layout: Create a PCB layout that optimizes trace lengths for high-speed signals, ensuring proper length matching and minimizing noise. Firmware Development: Develop firmware for the ESP32 to handle data reading, writing, and processing tasks, as well as managing communication with the eMMC. Testing and Validation: Conduct thorough testing to validate the functionality of the system, ensuring reliable data transfer and processing capabilities. Expected Outcomes: A fully functional system that demonstrates the integration of the ESP32 with eMMC storage, showcasing efficient data handling and processing. A modular design that can be adapted for various applications, providing a foundation for future projects in IoT and embedded systems.