Abstract

Capture and utility of neurophysical signals through non-invasive techniques can provide valuable insight into properties of the nervous system and physical response to external stimuli. Electromyography (EMG) is an electrophysiologic test that measures muscle response and electrical activity. This project aims to utilize real-time processing of EMG signals from the wrist and translate captured features into controls. A potential application is replacing the remote control of an electric skateboards, which could be attached to a smartwatch and is more intuitive to use. This project addresses the design and fabrication of a circuit board with ideal amplification and filter values to capture EMG, a wristband to house the system in a consumer wearable device, software to process and classify features within the signal, and a smartphone app for managing the device and connecting it to external peripherals.

Wristband Design

The design constraints included: lightweight, comfortable, houses all electronics, and maintains constant electrode positioning. Other factors such as cost and maintenance and upgrade ease were also considered in the design process. Several iterations were tested throughout the process including the materials used, thickness, 3D printing methods, mounting and clasping methods, and use of aesthetic and protective coverings.

For the initial prototype, the band was printed in one thin, flexible piece of polyactic acid (PLA) that was plastic welded together to form a permanent band that could be slipped over the hand. This was too rigid and inconvenient to put on so the band was instead printed out of thermoplastic polyurethane (TPU) which was much more flexible and elastic allowing tolerance in the user’s arm diameter.

Circuit Design

Our current circuit utilizes gold-plated cup electrodes to measure the underlying signal. The electrodes feed in a very small voltage signal (50 - 3500 uV) into an instrumentation amplifier (AD620) with a gain of 23.5 V/V. The output is then fed into a 1st order RC high-pass filter with a cutoff frequency of 0.04 Hz, which is used to remove baseline drift from our measured waveform. Baseline drift indicates non-periodic DC-level fluctuations that are mostly unrelated to arm movement, such as inconsistent electrode contact, changes in skin potential, etc.

After this drift removal stage, we pass the signal into a Twin-T notch filter with a resonant frequency of 61.2 Hz. This is to attenuate any 60 Hz components that may result from power-line interference. This is followed by a 2nd order Sallen-Key low-pass filter with a cutoff frequency of 212 Hz in order to attenuate higher frequency noise that's outside of the relevant EMG frequency band.

Finally, the signal is passed through a non-inverting amplifier circuit with a gain of 668 V/V. We then use a voltage divider to shift the signal from -5V->5V to a 0V->5V domain so that it can be read by the ESP-32's ADC, which has 12 bit resolution and a 200 Hz sampling frequency. The entire analog front-end provides an overall gain of approximately 7800 V/V and has a passband from 0.4 - 212 Hz.

Results

The wristband was manufactured and tested on a person’s wrist. For interacting and receiving data from the device, a central dashboard was developed. This allows users to plot the data from either a bluetooth or serial connection with the device, as well as save it for later analysis. There is confirmation both visually and using thresholding that simple motions such as grasping (open-closed) can be classified, verifying the quality of the signal received and processed by the system.

Conclusion

The circuit board were successfully designed and manufactured, and the wristband was designed, printed, and tested. The bluetooth connection with the device was established, and a dashboard for collecting and saving data was created. A preliminary thresholding-based classifier for open/closed hands was also developed, concluding phase 1 of the project.

Future Directions

The future directions of the project include adding an IMU and developing a finer-grained classification system to allow for determining the full range of motion of the hand. On the hardware side, the long term goal is to integrate the board into a smartwatch band. This system would enable a lightweight method for a wide variety of controls and applications.

Team

Inga Zhuravleva - Electrical Engineer
Venkata Alapati - Software Engineer
Riley Hernandez - Mechanical Engineer

NeuroWrist