Superhumachines are superhuman humachines (human-machine intelligence), i.e. a nexus of humans and machines that gives rise to superintelligence in a true mind-machine interface.

Our goal is to make Superhumachines: Superhuman Machine Intelligence for those with sleep difficulties or on the spectrum or with mobility difficulties...

Here we teach really simple examples like "Mind Over Motor" and "Jobbing on the Sleep" (getting useful work done while sleeping which is the reciprocal of "sleeping on the job").

It all starts with machine intelligence (smart electric machines capable of cognition) and fine motor control (closed-loop motor control), using a BCI (Brain-Computer Interface).

Read this paper, "Electrical Engineering Design with the Subconscious Mind" (if you have trouble getting it from IEEE it is also available from the Library of Kazakhstan), and also read this paper, "Smart paddleboard and other assistive veyances" and in particular, the section on PIIIDDD controllers. This project is for the "Freehicle" which is a vehicle or more generally a "veyance" for freedom of mobility for persons with disabilities, as the freehicle operates on land (indoors, outdoors on sidewalks, bike paths, and roads), on water, and in air. The Superhumachine thus forms a BCI (Brain-Computer Interface) that can work during sleep and prior (to improve sleep quality) as well as wakeful states, either as its own stationary (mounted in a fixed location) or mobile (e.g. self-driving) context.

For this work we use the Muse-S brain-sensing headband made by InteraXon, a company formed and founded in the principle residence of Muse co-founder S. Mann in downtown Toronto, Ontario, Canada.

We shall begin by looking at closed-loop motor feedback-based control, using a simple motor that has a Hall-effect sensor built-in. To make this project easy, affordable, and tractable by the average hobbyist or student, we found a very low-cost motor as the subject motor, namely the GA25-370 Gear Motor DC with Speed Encoder, Stainless Steel High Torque Electric Micro Speed Reduction Geared Motor Brush DC Motors Reducer Copper (DC12V 1200RPM) purchased for $13.12 Canadian on Amazon and widely available at even lower prices if ordered directly from the manufacturer. Nearly any motor with a sensor will work to demonstrate the simple aspects of fine motor control. Here we will use the SWIMotor™ Metavision™ kit from the previous Instructable, namely https://www.canadarobotix.com/products/3096

Mochoid for Mental Health and Better Sleep Quality

Another fun activity with the Mind-Over-Motor project is Mochoid which is an art installation using a spinning string of lights connected to a similar motor, and this may also be used for Mind-Over-Motor meditation and the like. See for example the Mochoid explained in the Proceedings of the Mersivity Symposium 2023 or a previous Instructable on Mochoid.

Ultimately we wish to learn how to build Superhumachines to help people on the spectrum or with sleep difficulty improve their quality of sleep, achieve mindfulness, get in a better mental zone for studying, etc., at the home or office or school, as well as in applying these same ideas to improve mobility.

Begin with the lab kit from the previous Instructable and simply add to it a motor that has a sensor such as a Hall-effect sensor. Typically these sensors provide either a 3-phase output (though digital, i.e. binarized or thresholded) or a complex-valued output so that we can still discern direction (i.e. that we can distinguish a positive frequency from a negative frequency).

Drill out the central hole to fit the larger motor; in the example shown we drill to 5/16 inch.

Drill new holes for the new motor mount screws. In the example, we choose a perpendicular axis so as to preserve the original holes so you can swap back to the original motor if you wish.

Drill two 9/64th inch holes 17mm apart, centered on the center 5/16th hole.

The motor is attached with two screws, M3 thread size.

Connect the real part of the output (the in-phase output of the quadrature detector) which appears on the yellow wire 3rd from the top (if colors and positions disagree, go based on position since the occasional connector has the wrong colors), through a voltage divider made from two 10k ohm resistors in series. Connect this to 36 of the ESP32 (upper leftmost analog input).

Connect the imaginary part of the output (the quadrature output of the quadrature detector) which appears on the green wore 4th from the top, through a voltage divider made from another two 10k ohm resistors in series. Connect this to 39 on the ESP 32 (second from the top of the 4 analog inputs near the upper left).

Connect the motor + output itself (top position, red wire) to the 34 of the ESP32 (the 3rd from the top of the 4 analog inputs).

The 4th from the top of the 4 analog inputs is not used here at this time but you can find another use for it if you like.

Connect the motor - output to a rather "stiff" voltage divider made of two 470 ohm resistors in series between ground and +3.3 volts.

Power the encoder by connecting the blue wire (2nd from the bottom) to +5 volts on the breadboard. The +5 volts comes from USB in pin on the ESP32 by way of the breadboard connection.

Spin by hand to plot out the Hall-effect sensors and understand the way that motor sensors work. Here we can see how many effective lobes (i.e. including the gear train) are present in the sensing apparatus.

Try spinning forward and backward and notice that the pattern is the same. This is the wonder of quadrature encoders: it doesn't matter whether forward or backward the pattern's the same, and conversely, we can of course therefore tell which way the motor is turning by sensing the encoder outputs, giving us speed and direction (i.e. angular velocity).

The next step is to spatialize some other information such as axis labels, text, images, graphics, etc., and then to plot something of interest such as brainwaves, or other material, and finally to implement SSVEP and Mind-Over-Motor meditation.

Once we have fine control over the position of the motor we can train with it using a brain sensor such as the Muse or Muse-S for biofedback, in accordance with previous Instructables on the use of SWIM.

See also https://ieeexplore.ieee.org/abstract/document/9209380

Another useful thing to try is propulsion of a simple research prototype of vehicle for mobility, using bioefeedback.