Wireless wearable surface electromyography signal acquisition and monitoring system

1. Details of the work;     

The wireless wearable surface electromyography signal acquisition and monitoring system designed this time mainly includes a collection end, a receiving end and a monitoring host computer. The collection end is mainly responsible for collecting the surface electromyographic signals of the human body, converting them into quantified digital signals, and transmitting them to the receiving end. The receiving end is mainly responsible for transmitting the received surface electromyographic signals to the computer for processing through the serial port. The monitoring host computer can obtain the surface electromyography signal transmitted from the receiving end through the serial port, thereby calculating the characteristics of the signal, including: mean value (MEAN), absolute mean value (MAV), root mean square (RMS), variance (VAR), Zero-crossing rate (ZC) and waveform length (WL). Moreover, the original surface EMG signals and characteristics are visualized and dynamically monitored through a dynamic line chart. At the same time, the control of the robot hand can also be realized through the receiving end. The wireless wearable instant noodle EMG signal acquisition and monitoring system designed this time uses a surface EMG signal acquisition circuit based on the AD620 instrument amplifier and LM321 low-power operational amplifier. The surface EMG signal is processed through three medical electrodes. It is a quantifiable analog electrical signal. The collection end and receiving end use STM32F411CCU6 as the main control core, which is responsible for the collection, processing and transmission of surface electromyographic signals. The NRF24L01 2.4GHz wireless communication module is used between the collection end and the receiving end for data transmission, and can control the robot grip. The receiving end transmits the surface electromyographic signal to the computer for processing through the CH340T serial port to USB chip. The monitoring end uses the QT5 framework to write a visual interface to visualize the collected surface electromyographic signals and characteristics. The wireless wearable surface electromyography signal acquisition and monitoring system designed this time realizes the collection, filtering, visual monitoring of surface electromyography signals, and controls the robot hand based on the electromyography signals. At the same time, this design can also be used to help disabled people control prostheses, muscle fatigue or strength assessment, human-computer information interaction, and sign language translation. 

 2. Describe the challenges faced by the work and the problems it solves;

    The main challenges faced by the wireless wearable surface electromyography signal acquisition and monitoring system designed this time are as follows: (1) Surface electromyography signals are very weak bioelectric signals, with amplitudes generally around 0-5mv, and will Disturbed by a lot of noise. Therefore, how to collect and process surface EMG signals well is the first challenge. (2) The collection terminal designed this time needs to be worn on the arm of the person being collected, so how to design the PCB structure of the collection terminal well is the second challenge. (3) The surface electromyography signal is a one-dimensional random time series, so what kind of features can be extracted to make the signal more expressive is the third challenge. In response to the challenges raised above, we adopted the following solutions: (1) To address the problem of weak surface electromyography signals and accompanying large interference, we used the AD620 instrument amplifier to amplify them, and used the LM321 Low-power operational amplifiers are used to design high-pass filters and low-pass filters to filter surface EMG signals and retain surface EMG signals from 10Hz to 1000Hz. (2) In response to the problem of needing to be worn, we have designed a PCB structure that meets the wear requirements. The electrode buckle can be welded to the PCB board and then connected to the medical electrode. It is powered by a mobile power supply and can be easily worn on the human arm. (3) Regarding the problem of extracting features, we consulted the information, mainly extracting the average value (MEAN), absolute average value (MAV), root mean square (RMS), variance (VAR), zero-crossing rate (ZC) and waveform length. (WL) Visualization of these six highly expressive features. And the control of the robot hand is realized through the root mean square characteristics.

  3. Describe the key points involved in the hardware and software parts of the work;   

  1**. Hardware design of the work*    Figure 1 Hardware frame diagram

The hardware design part of the wireless wearable surface electromyography signal collection and monitoring system designed this time is mainly focused on the collection end and receiving end. Figure 1 is the overall framework diagram of the hardware. The acquisition end mainly includes the NRF24L01 module, STM32F411CCU6 and surface electromyography signal acquisition circuit. The receiving end mainly includes the NRF24L01 module, STM32F411CCU6, expansion pins and CH340T. 2.4GHz wireless transmission is used for data transmission between the two ends. The receiving end transmits the data to the computer for processing via USB. The following will give a detailed introduction to the main hardware circuit design of the acquisition end and the receiving end. In the appendix, we will give a specific circuit schematic diagram.                                                                                                                                                                                                                                                                                   Figure 2 Instrument amplifier circuit

This design uses AD620 instrument amplification to perform the first-level amplification of the surface electromyographic signal. Figure 2 is the schematic diagram of the instrument amplifier circuit. Its -IN and +IN pins input the negative terminal signal and positive terminal signal of the acquisition electrode respectively, and at the same time, the comparison electrode is connected to the ground terminal. The amplification factor of AD620 can be calculated by formula (1), where G is the gain and R_G is the resistance of the external resistor. The R_G we chose for this design is 82Ω, which means the amplification factor is about 600 times.                                                                                                                                                                                                                                                                                                        Figure 3 Filter circuit and adder circuit

在表面肌电信号经过第一级放大之后，我们采用低通滤波器和高通滤波器所组成的带通滤波对表面肌电信号进行滤波，并且采用加法器将滤波后的交流信号转换为直流信号。图3为我们设计的低通滤波电路、高通滤波电路和加法电路的电路原理图。其中我们设计低通滤波器的截止频率约为1000Hz，高通滤波器的截止频率为10Hz，加法器的偏置电压为1.5V。                                                                                                                                                                                                                                                                 图4 STM32F411CCU6最小系统电路

本次设计的采用STM32F411CCU6作为主控芯片，图4为该芯片的最小系统电路。本次设计采用的是芯片的内部振荡电路作为时钟。采用SWD方式进行程序的下载和调试。采用3.3V进行供电。                                                                                                                                                                                                                                                                                           图5 NRF24L01模块电路

本次设计采用NRF24L01 2.4GHz无线传输模块进行数据传输，图5为该模块的电路。NRF24L01采用3.3V供电，采用SPI方式与单片机进行通信，并且支持中断方式读取。                                                                                                                                                                                                                                                                           图6 串口转USB电路

本次设计的接收端通过串口转USB的方式，将接收到的表面肌电信号传输给电脑端处理。图6为串口转USB电路原理图。我们采用了CH340T作为串口转USB的功能芯片，该芯片采用3.3V供电，外部需要提供12MHz的时钟。

                                                                                                                                                                                                       图7 预留引脚电路

在接收端我们预留出了一些功能引脚，以供扩展使用。图6为我们预留的引脚的电路。我们将串口的TXD、RXD引脚引出，提供给外部扩展设备来获取原始表面肌电信号。我们留出了4个PWM输出端口，可以直接用于舵机等设备的控制。在本次设计中，我们将使用PMWA端口对机械手抓进行控制。 2**、作品软件设计**     本次设计的无线可穿戴式表面肌电信号采集与监测系统的软件设计主要包含下位机（采集端和接收端）软件设计和上位机（监测端）软件设计，以下将分别介绍。 （a）下位机软件设计                                                                                                                                                                                                                                                                                                                图8 下位机软件流程

图8为本次设计的系统的下位机软件流程图。在采集端，主要为使用单片机内部的ADC对表面肌电信号进行采集。我们将STM32F411中的ADC配置为定时器触发模式，并且将定时器的出发时间间隔设置为500us，即采用2000Hz的采样率对表面肌电信号进行采集。因为，我们设计的低通滤波器的截止频率为1000Hz，根据奈奎斯特采样定理需要两倍的采样频率。在每次量化采集完成后，打包通过NRF24L01传输给接收端。在接收端，主要使用NRF24L01接收采集端传输过来的表面肌电信号信息，然后将其打包通过串口转USB传输给电脑端处理。同时，我们在接收端内部采用滑动窗口的方法进行RMS特征的计算，并且通过RMS的大小来控制机械手抓的张开合并。本次设计中采用滑动窗长度为400个采样点，增量为200个采样点。 （b）上位机软件设计                                                                                                                                                                                                                                                                                                                        图9 上位机软件框架

The wireless wearable surface electromyographic signal acquisition and monitoring system designed this time is mainly written using the QT framework. Sub-thread 1 uses the serial port to receive and analyze the raw surface electromyographic signal data uploaded by the receiving end. Sub-thread 2 performs feature extraction on the original data of surface electromyographic signals, and performs visual dynamic monitoring of the original signals and features. The characteristics of six of the signals are calculated using formulas (2)-(7). Among them, x_mean, x_mav, x_rms, x_var, x_zc and x_wl are the average value, absolute average value, root mean square, variance, zero-crossing rate and waveform length respectively. x_i is the value of the electromyographic signal at each point. N is the length of the sliding window. T is the noise correction coefficient.                                                                                                                                                                                                           Figure 10 Host computer interface                                                                                                                                        

Figure 10 shows the host computer interface designed this time. This interface allows you to select the serial port of the receiving device for connection. Then the received surface electromyographic signal raw data and six characteristic data are combined. The length of the sliding window displayed for the surface electromyographic signal raw data is 200 sampling points, and the length of the sliding window displayed for the six features is 20 sampling points.

* 4. List of materials for the work; Table 1 List of materials at the collection end

Table 2 Receiver material list

Table 3 Robot grasping control material list

* 5. Upload pictures of the work; (the competition logo must be on the PCB and photos must be uploaded. If not, the contest will be considered as giving up the competition) The following is a display of the actual work of the wireless wearable surface electromyography signal acquisition and monitoring system designed this time. Figure 11 is a physical diagram of the designed receiving end. Figure 12 is a physical diagram of the designed collection end. Figure 13 is the overall physical diagram of the system. The logo icons required for PCB are all marked with red boxes.

                                                                                                                                                                                           Figure 11 Physical picture of the receiving end

                                                                                                                                                           Figure 12 Actual picture of the collection end

                                                                                                                                                                  Figure 13 Overall physical picture

Attachment 1. Schematic diagram of the collection end. Attachment 2. PCB of the collection end. Attachment 3. Schematic diagram of the receiving end. Attachment 2. PCB of the receiving end.

* 6. Demonstrate your work and record it as a video for upload; (The content of the video must include: introduction of the work; functional demonstration; performance test; close-up shot of the competition logo on the PCB. If not, it will be deemed as giving up the competition) Note: See the attachment for the demonstration video. Video compression may cause trouble for you to watch, sorry!    7. Open source documents.