Wikipedia has a full explanation showing how feedback makes System output in the diagram below lock in on a reference point. With that as background, we will focus on how this Biofeedback app works to put you on a "Glide Path" to land your BMI on your goal following a natural Decay Curve that avoids rebounds. At the right hand side, System output is body mass,¹ as determined by the System (our body) piling upⁱⁱ calories (System input) that it does not burn.

The Sensor is a combination² of two mathematical An EMA, rejecting some of the noise while keeping 100% of the slope.

Then Least Squares Fit of a line through the remaining noise.

filters built into this program and an ordinary Notice the feedback - you want to control its reading.

To do so, you respond to a Red/Green warning signal. scale.

The Measured output is a projection³ of the Granted, you do see the scale weight before recording it.

The secret ingredient is to focus on the projection.

scale reading and the rate ofⁱⁱ descent toward the Reference point (goal Type it in at the bottom to tell the program when to switch back and forth between red and green.

you set ).

This app is an Also called Bang-Bang control.

This was chosen to allow mental recovery time.

On/Off control system - like a The program tells you when you need a new surge of will-power.

thermostat and furnace. The Measured error is presented as "turn on the will power" or "dropping

fast enough ".

The remaining item is you⁴ - the Controller - who closes the loop. Tackling one Burn-out is less of a problem.

Habits allow periods of low effort.

habit at a time will keep the BMI Like the furnace, will-power shuts down periodically.

Like a house, the body and the habits smooth out the result.

heading toward the goal if we keep the signal mostly green. Control system jargon calls that "locking in".

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This method has a subtlety - the rate of descent is what the filters extract (the "m" in Y=mX+b of linear regression), and if one "diets" too vigorously, the signal turns red again. Thus the speed with which one heads toward goal is controlled to follow a natural decay curve; getting to goal faster than a harsh diet would.

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FOOTNOTES: ¹ The output of a system is what you want to control, which in this case is the scale reading. The System Input, to its left, is calories in food. For the body, there is a delay of about a year in losing calories that have been consumed.

A comparable system would be a furnace, house and thermostat, in which case the system output would be temperature and the system input would be energy in e.g. natural gas. For a furnace, the walls and interior of the house slow the loss of heat. The temperature stays comfortable after the furnace shuts off and before the thermostat turns it back on.

² The sensor is an ordinary scale, but functionally, it is only part of a sensor subsystem which includes two filters in the app and the involvement of the user to type in data. Note that the input of the sensor is the output of the system.

The first mathematical filter is an EMA, which rejects 97% of the scale fluctuations while keeping 100% of the slope. Then a Least Squares Fit of a line through the remaining noise reduces it and extracts the slope, which is the rate of drop in scale readings.

These filters are in the program, and rely on the user to read a scale and type in the data. One more step is required to make the user an integral part of the feedback loop; the extracted slope is used to calculate the projected weight and display it.

³ The user is shown a two-state comparison of the future weight and the goal - either green for "no new habit needed" or orange-red for "time to re-focus".

Diets cause burn-out by demanding extra effort until the goal is met. Then the user must put more effort into developing a maintenance routine to keep the weight off. People naturally relax until things get really bad again and "yo - yo dieting" rsults.

This sensor design guides one's habits from the initial fast drop smoothly to staying on the goal.

⁴ Feedback control systems deal with information, collected by the sensor and presented to a comparator (shown by the plus and minus by the arrows to the left above). In this case the information processing is not done by a thermostat, but rather by the user of the app. Normally one focuses on the day-to-day changes in scale weight, but the use of heavy filtering and the shift from actual weight to rate of descent leads one to avoid discouragement by showing success all the way down.

ⁱⁱ ASIDE: For those who set up PID controllers, the body's accumulation of unburned calories is an integral process. The derivative from Linear regression changes the loop gain back to Proportional. In the case of heating a house there is no derivative feedback so there is no overshoot like this app exhibits.