Project: Vortex Ring – Introduction and Theory

Introduction

Now that I have finally wrapped up EOGee/EOGlass I can focus on my next project. I decided to do something with magnetics because I wanted to build a little more applied intuition and understanding of magnetic sensors and actuators. My original plan was to investigate building my own electro-permanent magnets, because I can think of some interesting applications for a switchable magnet. However in the process of my research I came up with a new idea for a wearable communication technology.

Human Body Communication (HBC) is a catch-all term for communication methods that use the human body as a communication channel. This means that the signals are contained within the human body and do not travel through a wire or through the air. This is potentially useful in wearable devices that may need to communicate with each other, for example a smart-watch can communicate with a heart rate monitor on the user’s chest, or an accelerometer on their leg, or even a medical device like pacemaker or cochlea implant. In such a network, all devices can communicate with a central hub (e.g. smart-watch) that then communicates to the user’s phone or other device to deliver the data to the user. Advantages of this approach potentially include reduced power consumption (due to the body’s higher channel gain compared to air), reduced network congestion (as each human will constitute a separate network rather than all sharing the air), as well as increased security and/or privacy (since the signals are constrained to the user’s body).

There are two broad classes of HBC – galvanic (where current is used to transmit information) and capacitive coupling (where voltage is used to transmit information). These are described in the paper A Review on Human Body Communication: Signal Propagation Model, Communication Performance, and Experimental Issues by Zhao et al.

Galvanic and Capacitive Coupling Human Body Communication
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Project: Recreating ShArc – Testing Linearity

In the previous article I showed my successful implementation of ShArc, a novel bend/shape sensor using capacitive sensing. My video showed that the system could track the bend shape of the sensor but it was not quantitively analysed.

To take this a little further I wanted to show the relationship between the signal that the sensor generates and the radius of curvature. To begin with I 3D printed a flexible covering for the flex using TPU filament and the modified extruder I designed to enable TPU printing on my Monoprice 3D printer.

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Project: EOGee – Injecting Fake Signals with FakeEyes for Frequency Analysis

In order to further characterise and test the EOGee prototypes, it is important to be able to inject known signals and measure the output. This is not trivial, as the devices are configured to measure micro-volts of signal and a normal signal generator cannot synthesising a signal of such small magnitude. Secondly, the device takes a differential measurement, and so a differential signal is required rather than a single-ended one, as most signal generators produce.

To fix this issue I designed FakeEyes, a PCB to take a 3.3V peak single ended signal and convert it into a 1700uV peak differential signal.

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Project: EOGee – Hardware Noise Analysis

Having previously improved the drift performance of EOGlass, I decided to look a little into the sources of noise. It’s pretty clear that there is a lot of noise on the signal.

Noise on the signal

It’s actually pretty tricky to know exactly what is noise, given that I don’t really know exactly what my eyes were supposed to be doing and, even if I did, the eyes often make micro-saccades that the conscious brain isn’t aware of. If I zoom into a section that looks like I was keeping my eye relatively still, the noise turns out to be about 60 ADC counts peak-to-peak. This is equivalent to approximately 21863nVpkpk, when referred to the input (ie before amplification) (12-bit ADC, 3.3V range, gain of 2211).

I wanted to know how much of this noise was coming from the hardware, and how much was coming from the user. For this I decided to analyse the noise contributions from each stage of the signal chain. Texas Instruments have a good application note on noise analysis.

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Project: EOGee – EOGlass Drift Reduction

In a previous article I showed how the bias current of the AD8226 amplifier was causing a 20nV current to flow through the electrodes resulting in a large voltage offset due to the impedance mismatch between the left and right electrodes. This resulted in a large drift in the signal due to changes in impedance between the user and electrodes as they moved or sweated, which often saturated the ADC. I built a test board and showed how this could be reduced by a factor of about 1000x by using the AD8220 which has a bias current of less than 10pA.

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Project: Recreating ShArc

I first came across “ShArc” on a twitter post. ShArc is a novel bend/shape sensor by Fereshteh Shahmiri and Paul H. Dietz that they write about in their 2020 paper, “ShArc: A Geometric Technique for Multi-Bend/Shape Sensing“.

Figure 1 from the paper shows the bendable sensor made from a stack of flexible circuit boards and the reconstructed curve that is sensed.

The sensor is made of two flexible circuit boards (“flexes”) stacked either side of multiple layers of polyimide material, which is the same material used for the flex substrate. Both flexes are constrained together at one end, and when the flexes are bent, they slide relative to each other. The topmost flex (TX Flex) has a series of transmit electrodes along its length, while the bottom most flex (RX Flex) has a series of corresponding receive electrodes along its length. As the two flexes slide relative to each other, the capacitance between each TX and RX electrode pair changes. By measuring this capacitance the system can infer the relative slide between the two flexes and in turn infer the bend radius.

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Project: EOGee – Improving Drift via Reduced Bias Current

Problem: The previous prototypes of EOGlass that I have shown use wet electrodes to make good contact to the user. This results in a reliable, low-impedance connection but is also messy and inconvenient. Recently I’ve been trying out dry electrodes for convenience, but they suffer from two main issues: drift and noise.

EOG data over about an hour shows a significant drift effect. The EOG data is there beneath the drift.
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Project EOGee: EOGee2 and DC Coupling, Part 3

The main barrier to getting a DC coupled signal using EOGee1 was that the DC offset voltage of the EOGee signal was much larger than the actual signal itself. Because the signal itself is so small we need a large gain to amplify it, but this also amplifies the offset voltage which then saturates the amplifier.

My solution is to use a signal chain like this:

sigchain

EOGee2 Signal Chain

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Project: EOGee – Noise, Part 1

Previously we have focused on the mains interference component of noise in the EOG signal. This is because it has been the dominant source. However we have seen that this noise can be significantly reduced with appropriate shielding and we could reduce it further still with shorter leads.

Now that the 60Hz noise is reduced, there is a very clear spiking signal coming from somewhere. The signal does not have any obvious periodicity but it is significantly larger than the other noise and also generally affects only one sample. It is easier to see the noise if I remove R108 which means that the final gain stage is disconnected from previous gain stages so the ADC is driven to midrail and is not affected by 60Hz noise at the input.

Screenshot 2020-04-11 at 17.58.50

The signal spikes occasionally

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