Project

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11 Wristband that keeps you awake
 Bum Jun Kim
Jaime Ontiveros
Junfei Gu
 Jack Li appendix1.JPG
appendix2.JPG
appendix3.zip
design_document3.pdf
final_paper1.pdf
presentation1.pdf
proposal1.pdf
Team Members:
Names: Bum “Jun” Kim, Jaime Ontiveros
Net IDs: bkim102 jontive2

Problem:

Sleep is a basic and mandatory function of the human body, however there are often times when people want to fight urge the sleep and need to stay awake. The range of people affected by this problem is large and diverse:
Students who need to stay awake in class and while studying
Drivers or operators of vehicles or robust machinery who need to stay awake during work
Anyone who takes public transportation who can’t afford to miss their stop
Patients of narcolepsy who can spontaneously fall asleep at any time
The effects of dozing off can range from small minor inconveniences to major life threatening ones and while in most cases, getting a good night's sleep seems to be the obvious solution, there are many people who struggle to do so.

Where it differs from existing solutions:

At first this project may just seem to be as simple as adding a vibrating function to one of the popular smartwatches or wearable sleep monitors but there are clear differences. The aforementioned devices’ main goal is to track a person's sleep, using an accelerometer to record the movements of the wearer and determine the wearer’s sleep status by the stillness of his/her body. The problem with this is twofold. Firstly, the movement of a body during sleep will only completely stop after stage 2 of NREM sleep, meaning that by the time the user has been detected sleeping, he/she has already been asleep for 10-20 minutes and the damage will have already been done. The second problem is that it won’t be able to detect people falling asleep in other settings besides laying down on a bed making it useless for drivers and the like.

On the other end of the spectrum there are products that can prevent a driver from falling asleep using a camera to detect facial relaxation. However the problem with this is that it requires a camera to be mounted meaning it can’t be used in other settings and is limited only for that use.

Solution Overview:

Our proposed solution is a wearable (ideally a wristband) that can detect when a wearer start falling asleep during stages 1 and 2 of NREM sleep cycle and can keep that person awake. There are many different technologies that can detect sleep and while sleep monitors may use one or two, we will use as many as possible to detect sleep as soon as it starts happening.
We will wake up/keep the user awake through a mix of features that will stimulate the senses such as vibrations and electric shocks.
Both of these main functions will have customizable features which can be adjusted via an app on a smartphone. The user will be able to set which stage of sleep they want to be woken up during, as well as set a delay where the device will wake the user up after a set amount of time.
The user will also be able to control the intensity of vibration and shock to account for deeper sleepers.
A very important feature will also be its form factor. The device needs to be wearable casually so any user can use it anywhere at anytime. Another focal point is battery life. The device needs to be on for nearly all day for patients with narcolepsy.

Solution Components:

Sleep detecting subsystem:

This will be physically a part of the device with a variety of sensors:
A heart beat/pulse sensor can detect sleep by detecting a decrease in heart rate during the first 2 stages of NREM sleep. A typical adult can have a heart rate of anywhere between 60-100 BPM so we will need the user to calibrate a standard heart rate when the user is doing his/her daily activities. The pulse sensor works by sensing the variations in light in a blood stream illuminated by an LED to know when blood is flowing.
An average pulse sensor costs around $15-25 and is fairly inexpensive.
While an accelerometer by itself may not be enough, using it in tangent with other sensors can provide us with more information to detect sleep as quickly and accurately as possible. These are inexpensive and should cost around 10-15 a piece.
During the first 2 stages of NREM sleep, there is also a significant decrease if muscle activity, therefore we can also use a muscle sensor such as the Myoware muscle sensor to detect a decrease or loss of muscle activity. The Myoware muscle sensor costs $37
-This will most likely be physically connected to the PCB
-This will send information to the control subsystem.

Stimulating Subsystem:

This subsystem will be able to make vibrations with an off-balanced motor or servo, as well as output a weak electric shock. This can be dangerous for some users so it will be a feature that can be disabled or adjusted. It will also have to be on the device itself and the PCB and receive information from the control subsystem.

Wireless Subsystem:
This subsystem will communicate with a smartphone app to determine how strong the vibrations and shocks are as well as how long the delay will be. It may also be able to toggle to power on the device to allow for a smaller form factor. It will also send information to the control subsystem.

Control Subsystem:

This will be the main part of the PCB that will receive the information from the sleep detecting subsystem and determine whether or not the user has fallen asleep. It will then use the information received from the wireless subsystem to determine the output of the stimulating subsystem.

Criterion For Success:

Our goal is to create a wearable device that can be worn casually by anyone in any place at anytime. It will be able to detect when a user sleeps or starts sleeping fast enough to avoid any inconveniences that would be caused from sleeping in that situation. The user will be able to reliable stay awake with adjustable levels of vibrations and electric shocks that are strong enough to keep him/her awake and will be able to do it reliably without having to worry about battery life in case of constant use.

Additional Ideas:

If the mentioned sensors are not enough then there is also the option of making a headband with EEG technology to detect alpha and beta brain waves for the most accurate measurements possible. This is not ideal however, because it is not as casual and wearable as a wristband.

Smart Frisbee

Ryan Moser, Blake Yerkes, James Younce

Smart Frisbee

Featured Project

The idea of this project would be to improve upon the 395 project ‘Smart Frisbee’ done by a group that included James Younce. The improvements would be to create a wristband with low power / short range RF capabilities that would be able to transmit a user ID to the frisbee, allowing the frisbee to know what player is holding it. Furthermore, the PCB from the 395 course would be used as a point of reference, but significantly redesigned in order to introduce the transceiver, a high accuracy GPS module, and any other parts that could be modified to decrease power consumption. The frisbee’s current sensors are a GPS module, and an MPU 6050, which houses an accelerometer and gyroscope.

The software of the system on the frisbee would be redesigned and optimized to record various statistics as well as improve gameplay tracking features for teams and individual players. These statistics could be player specific events such as the number of throws, number of catches, longest throw, fastest throw, most goals, etc.

The new hardware would improve the frisbee’s ability to properly moderate gameplay and improve “housekeeping”, such as ensuring that an interception by the other team in the end zone would not be counted as a score. Further improvements would be seen on the software side, as the frisbee in it’s current iteration will score as long as the frisbee was thrown over the endzone, and the only way to eliminate false goals is to press a button within a 10 second window after the goal.