Project :: ECE 445 - Senior Design Laboratory

Project

#	Title	Team Members	TA	Documents	Sponsor
36	Slow Wave Sleep Enhancement System RFA	Aidan Stahl Kavin Bharathi Vikram Chakravarthi
	# 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

Title

Team Members

Documents

Sponsor

Slow Wave Sleep Enhancement System RFA

Aidan Stahl
Kavin Bharathi
Vikram Chakravarthi

# 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

Featured Project

# People

Scott Liu - sliu125

Connor Lake - crlake2

Aashish Kapur - askapur2

# Problem

Buses are scheduled inefficiently. Traditionally buses are scheduled in 10-30 minute intervals with no regard the the actual load of people at any given stop at a given time. This results in some buses being packed, and others empty.

# Solution Overview

Introducing the _BusPlan_: A network of smart detectors that actively survey the amount of people waiting at a bus stop to determine the ideal amount of buses at any given time and location.

To technically achieve this, the device will use a wifi chip to listen for probe requests from nearby wifi-devices (we assume to be closely correlated with the number of people). It will use a radio chip to mesh network with other nearby devices at other bus stops. For power the device will use a solar cell and Li-Ion battery.

With the existing mesh network, we also are considering hosting wifi at each deployed location. This might include media, advertisements, localized wifi (restricted to bus stops), weather forecasts, and much more.

# Solution Components

## Wifi Chip

- esp8266 to wake periodically and listen for wifi probe requests.

## Radio chip

- NRF24L01 chip to connect to nearby devices and send/receive data.

## Microcontroller

- Microcontroller (Atmel atmega328) to control the RF chip and the wifi chip. It also manages the caching and sending of data. After further research we may not need this microcontroller. We will attempt to use just the ens86606 chip and if we cannot successfully use the SPI interface, we will use the atmega as a middleman.

## Power Subsystem

- Solar panel that will convert solar power to electrical power

- Power regulator chip in charge of taking the power from the solar panel and charging a small battery with it

- Small Li-Ion battery to act as a buffer for shady moments and rainy days

## Software and Server

- Backend api to receive and store data in mongodb or mysql database

- Data visualization frontend

- Machine learning predictions (using LSTM model)

# Criteria for Success

- Successfully collect an accurate measurement of number of people at bus stops

- Use data to determine optimized bus deployment schedules.

- Use data to provide useful visualizations.

# Ethics and Safety

It is important to take into consideration the privacy aspect of users when collecting unique device tokens. We will make sure to follow the existing ethics guidelines established by IEEE and ACM.

There are several potential issues that might arise under very specific conditions: High temperature and harsh environment factors may make the Li-Ion batteries explode. Rainy or moist environments may lead to short-circuiting of the device.

We plan to address all these issues upon our project proposal.

# Competitors

https://www.accuware.com/products/locate-wifi-devices/

Accuware currently has a device that helps locate wifi devices. However our devices will be tailored for bus stops and the data will be formatted in a the most productive ways from the perspective of bus companies.