Project

# Title Team Members TA Documents Sponsor
36 Slow Wave Sleep Enhancement System RFA
Aidan Stahl
Kavin Bharathi
Vikram Chakravarthi
Hossein Ataee design_document1.pdf
final_paper1.pdf
presentation1.pdf
proposal1.pdf
proposal2.pdf
proposal3.pdf
video
Sound Sleep
# Slow Wave Sleep Enhancement System

## Disclaimer:

We are assisting Team 05 - Acoustic Stimulation to Improve Sleep who presented during the first class lecture with this project

# Team Members:
- Kavin Bharathi (kavinrb2)
- Aidan Stahl (ahstahl2)
- Vikram Chakravarthi (vikram5)

# Problem:

Many common neurological conditions like Alzheimer’s disease, depression, and memory issues are associated with patients receiving lower quality of sleep. Specifically, these issues often stem from a lack of a specific type of sleep known as slow wave sleep (SWS). As individuals age, sleep disorders and other sleep-related issues lead to a lack of overall sleep. As a result, the amount of time an individual spends in SWS and the quality of SWS they experience typically declines with age, contributing to many of the issues mentioned above.

# Solution:

Describe your design at a high-level, how it solves the problem, and introduce the subsystems of your project.
Our team is trying to improve sleep quality using a wearable device that is non-invasive and cost effective. This device will record EEG waves and then detect when the user is in Slow Wave Sleep (SWS) using the aid of specialized software. Once the user enters SWS, the system emits carefully timed bursts of pink noise through an auditory interface to enhance slow wave activity and extend its duration. This project is being done for the “Team 05 - Acoustic Stimulation to Improve Sleep” proposal by Maggie Li, Nafisa Mostofa, Blake Mosher, Presanna Raman. Currently, our sponsors have a wearable headset that measures how much time is spent in SWS and a “Cyton + Daisy Biosensing PCB” to process incoming signals. This board costs $2,500, and we are aiming to design an alternative, cheaper PCB within the class budget of $150. Providing a cheaper alternative that offers similar functionality is what makes our project unique and patentable.

# Solution Components:

## EEG Leads

- EEG Leads are conductive electrodes, small metal disks, that are placed on the scalp. These electrodes measure small voltage differences generated by electrical activity produced by neurons in the brain.

## MCU/EEG Wave Detection System

- The MCU/EEG wave detection system is used to detect the analog EEG waves from the EEG headband, amplify the signal (the EEG waves are very low voltage, so amplification will be necessary), digitize them, and transmit those signals to a computer for further processing to detect SWS.

## Computer/Software

- Utilize YASA, open-source command-line tool, to analyze EEG signals
- Python script to utilize command-line tool while EEG data is being collected
- Script also starts the process of playing pink noise once SWS is detected
- Interactive UI that allows user to visualize EEG data

## Audio Source

- An audio source will be used to play pink noise after the user enters SWS.

# Criterion For Success:

- Playing pink noise after detecting SWS signal with minimal delay
- Correctly classify SWS with good accuracy
- Ensure wearable device is comfortable for user through survey metrics

CHARM: CHeap Accessible Resilient Mesh for Remote Locations and Disaster Relief

Martin Michalski, Melissa Pai, Trevor Wong

Featured Project

# CHARM: CHeap Accessible Resilient Mesh for Remote Locations and Disaster Relief

Team Members:

- Martin Michalski (martinm6)

- Trevor Wong (txwong2)

- Melissa Pai (mepai2)

# Problem

There are many situations in which it is difficult to access communicative networks. In disaster areas, internet connectivity is critical for communication and organization of rescue efforts. In remote areas, a single internet connection point often does not cover an area large enough to be of practical use for institutions such as schools and large businesses.

# Solution

To solve these problems, we would like to create a set of meshing, cheap, lightweight, and self-contained wireless access points, deployable via drone. After being placed by drone or administrator, these access points form a WiFi network, usable by rescuers, survivors, and civilians. Our network will have QoS features to prioritize network traffic originating from rescuers. Having nodes/access points deployable by drone ensures we are able to establish timely connectivity in areas where search and rescue operations are still unable to reach.

Over the course of the semester, we will produce a couple of prototypes of these network nodes, with built in power management and environmental sensing. We aim to demonstrate our limited network’s mesh capabilities by setting up a mock network on one of the campus quads, and connecting at various locations.

# Solution Components

## Router and Wireless Access Point

Wireless Access for users and traffic routing will be the responsibility of an Omega2 board, with onboard Mediatek MT7688 CPU. For increased signal strength, the board will connect to a RP-SMA antenna via U.FL connector.

The Omega2 will be running OpenWRT, an Linux-based OS for routing devices. We will develop processes for the Omega2 to support our desired QoS features.

## Battery Management System

This module is responsible for charging the lithium-ion battery and ensuring battery health. Specifically, we will ensure the battery management system has the following features:

- Short circuit and overcurrent protection

- Over- and under-voltage protection

- An ADC to provide battery status data to the microcontroller

- 3.3v voltage regulation for the microcontroller and other sensors

In addition to miscellaneous capacitors and resistors, we intend to use the following components to implement the battery management system:

- The MT2492 step-down converter will be used to step down the output voltage of the battery to 3.3 volts. Between the GPS and extra power the microcontroller might consume with an upgraded Wifi antenna, low-dropout regulators would not provide sufficient power in an efficient manner. Instead, we will implement a 2 amp buck converter to improve efficiency and ensure there are no current bottlenecks.

- We will utilize two button-top protected 18650 3400 mAh lithium ion batteries in series to power each node. Placing two of these batteries in series will ensure their combined voltage never falls below the minimum voltage input of the buck converter, and accounting for the buck converter’s inefficiency these batteries should give us about 21 Wh of capacity. The cells we plan on using include a Ricoh R5478N101CD protection IC that provides over-voltage, under-voltage, and over-current protection. Using a standard battery form factor will make them easy to replace in the future as needed.

- A USB-C port with two pulldown resistors will provide 5 volt charging input with up to 3 amps of current, depending on the charger.

- The MT3608 step-up converter will boost the input voltage from the usb-c port and feed it into the charging controller.

- The MCP73844 Charge Management Controller will be used to charge the batteries. This controller supports CC/CV charging and a configurable current limit for safe and effective battery charging.

- The TI ADS1115 ADC will be used for battery voltage monitoring. This chip is used in the official Omega2 expansion board, so it should be easy to integrate in software. We will use a voltage divider to reduce the battery voltage to a range this chip can measure, and this chip will communicate over an I2C bus.

## Sensor Suite

Each node will have a battery voltage sensor and GPS sensor, providing the system with health information for each node. On top of the Wifi-connectivity, each module would have a series of sensors to detect the status of the physical node and helpful environment variables. This sensor suit will have the following features and components to implement it

- Ultimate GPS Module PA1616D will be used for positioning information. This chip utilizes 3.3V which is supplied through our battery management system.

Battery Voltage Monitor

- The TI ADS1115 ADC (mentioned in the BMS section) is for battery voltage monitoring. It interfaces via I2C to the Omega2.

## System Monitor

A system monitor which provides visibility of the overall system status for deployed network nodes. Information that we will show includes: last known location, battery health, and network statistics (e.g. packets per second) from the physical devices.

We plan on using React to provide an intuitive UI, using google-map-react and other React packages to create an interactive map showing the last known location and status of each node.

The backend will be hosted on a server in the cloud. Nodes will continually update the server with their status via POST requests.

# Criterion For Success

We aim to achieve the following performance metrics:

- 1.5 kg maximum mass

- Cover 7500 m^2 (North Quad) with 4 nodes

- Display the last known location, time connected, and battery voltage for all nodes via our system monitor

- 3 hour battery life

- 5 Mb/s WiFi available to laptops and smartphones in the coverage area

[*Link*](https://courses.engr.illinois.edu/ece445/pace/view-topic.asp?id=71252) *to assciated WebBoard discussion*