Project

# Title Team Members TA Documents Sponsor
1 Sound Asleep
Adam Tsouchlos
Ambika Mohapatra
presentation1.pdf
# **Sound Asleep**
**Team Members:**
- Adam Tsouchlos (adamtt2)
- Ambika Mohapatra (ambikam2)

# **Problem**

Poor sleep can have serious effects on your health, increasing chances of conditions like poor mental health, kidney failure, diabetes, and more. It was found that slow wave sleep declines with age and that it is considered the most restorative stage of sleep. It is important for improving immune function, memory consolidation, and emotional regulation. Recent literature discusses using auditory stimulation during sleep to increase longevity of slow wave sleep for better overall physical and mental health. There are other devices that use EEG technology, but most have no auditory stimulation and the others were said to be very uncomfortable.

# Solution
**Sound Asleep**: a non-invasive wearable that transmits EEG data to a companion app. This then interacts with the user’s Bluetooth device to deliver precisely timed auditory stimulation.
The user can choose their own bluetooth device for increased comfort during sleep.

# Solution Components
# Subsystem 1 – EEG Acquisition and Wearable Hardware
This subsystem is responsible for acquiring the EEG signals.

- EEG leads optimized for overnight use.

- Wearable headband or soft cap to keep electrodes in place throughout the night.

- Low-noise amplification and filtering circuitry to ensure signals are usable for real-time processing.

- Small rechargeable battery to power sensors and wireless transmission.

# Subsystem 2 – Wireless Transmission and Power
This subsystem ensures EEG data can be reliably sent to the processing unit.

- Bluetooth Low Energy (BLE) or Wi-Fi module for continuous data transfer.

- Onboard microcontroller to digitize EEG signals and handle wireless protocols.

- Battery management system for safe charging and overnight operation.
# Subsystem 3 – Sleep Stage Classification and Signal Processing
This subsystem processes EEG data in real-time to detect sleep stages and identify slow wave activity.

- Algorithms for sleep staging (NREM, REM, wake) using EEG features.

- Slow wave detection algorithms trained/tested on pre-labeled EEG datasets.

- Closed-loop timing logic to sync auditory stimulation with ongoing slow waves.

- Possible algorithms to be used:

**YASA Slow-waves detection.**

https://github.com/raphaelvallat/yasa/blob/master/notebooks/05_sw_detection.ipynb

**CoSleep GitHub project.**

https://github.com/Frederik-D-Weber/cosleep

# Subsystem 4 – Auditory Stimulation Delivery (and App User Interface)
This subsystem delivers pink noise bursts at intervals during SWS.

- Mobile (or desktop) app triggers sound output through the user’s paired Bluetooth device (primary option as of now).


- Sound customization features via app for intensity, duration, frequency, and comfort.

- Sleep session dashboard showing nightly summaries (total sleep, time in slow wave sleep, stimulation events delivered).

# Criterion for Success
# ****Hardware****

- Wearing the EEG device is considered comfortable by users.

- EEG device stays attached during full night of sleep

- EEG readings are accurately transmitted to the software.

# Software

- EEG readings are correctly detected and processed by the app.

- Slow wave sleep stage is accurately identified.

- Auditory stimulation is transmitted to user’s bluetooth device.

# Outcomes
- User has increased slow wave sleep duration and amplitude.
- Improvement in memory test after sleeping with the device compared to without it.

# References
- Ngo et al. (2013). Auditory closed-loop stimulation of the sleep slow oscillation enhances memory. https://pubmed.ncbi.nlm.nih.gov/23583623/


- Bo-Lin Su et al. (2015). Detecting slow wave sleep using a single EEG signal channel. https://pubmed.ncbi.nlm.nih.gov/25637866/



WHEELED-LEGGED BALANCING ROBOT

Gabriel Gao, Jerry Wang, Zehao Yuan

WHEELED-LEGGED BALANCING ROBOT

Featured Project

# WHEELED-LEGGED BALANCING ROBOT

## Team Members:

- Gabriel Gao (ngao4)

- Zehao Yuan (zehaoy2)

- Jerry Wang (runxuan6)

# Problem

The motivation for this project arises from the limitations inherent in conventional wheeled delivery robots, which predominantly feature a four-wheel chassis. This design restricts their ability to navigate terrains with obstacles, bumps, and stairs—common features in urban environments. A wheel-legged balancing robot, on the other hand, can effortlessly overcome such challenges, making it a particularly promising solution for delivery services.

# Solution

The primary objective of this phase of the project is to demonstrate that a single leg of the robot can successfully bear weight and function as an electronic suspension system. Achieving this will lay the foundation for the subsequent development of the full robot.

# Solution Components

## Subsystem 1. Hybrid Mobility Module:

Actuated Legs: Four actuator motors (DM-J4310-2EC) power the legged system, enabling the robot to navigate uneven surfaces, obstacles, and stairs. The legs also functions as an advanced electromagnetic suspension system, quickly adjusting damping and stiffness to ensure a stable and level platform.

Wheeled Drive: Two direct drive BLDC (M3508) motors propel the wheels, enabling efficient travel on flat terrains.

**Note: 4xDM4310s and 2xM3508 motor can be borrow from RSO: Illini Robomaster** - [Image of Motors on campus](https://github.com/ngao4/Wheel_Legged_Robot/blob/main/image/motors.jpg)

The DM4310 has a built in ESC with CAN bus and double absolute encoder, able to provide 4 nm continuous torque. This torque allows the robot or the leg system to act as suspension system and carry enough weight for further application. M3508 also has ESC available in the lab, it is an FOC ESC with CAN bus communication. So in this project we are not focusing on motor driver parts. The motors would communicate with STM32 through CAN bus with about 1 kHz rate.

## Subsystem 2. Central Control Unit and PCB:

An STM32F103 microcontroller acts as the brain of the robot, processing input from the IMU through SPI signal, directing the motors through CAN bus. The pcb includes STM32F103 chip, BMI088 imu, power supply parts and also sbus remote control signal inverter.

Might further upgrade to STM32F407 if needed.

Attitude Sensing: A 6-axis IMU (BMI088) continuously monitors the robot's orientation and motion, facilitating real-time adjustments to ensure stability and correct navigation. The BMI088 would be part of the PCB component.

## Subsystem 3. Testing Platform

The leg will be connected to a harness as shown in this [sketch](https://github.com/ngao4/Wheel_Legged_Robot/blob/main/image/sketch.jpg). The harness simplifies the model by restricting the robot’s motion in the Y-axis, while retaining the freedom for the robot to move on the X-axis and jump in the Z-axis. The harness also guarantees safety as it prevents the robot from moving outside its limit.

## Subsystem 4. Payload Compartment (3D-printed):

A designated section to securely hold and transport items, ensuring that they are protected from disturbances during transit. We will add weights to test the maximum payload of the robot.

## Subsystem 5. Remote Controller:

A 2.4 GHz RC sbus remote controller will be used to control the robot. This hand-held device provides real-time control, making it simple for us to operate the robot at various distances. Safety is ensured as we can set a switch as a kill switch to shutdown the robot in emergency conditions.

**Note: Remote controller model: DJI DT7, can be borrow from RSO: Illini Robomaster**

The remote controller set comes with a receiver, the output is sbus signal which is commonly used in RC control. We would add an inverter circuit on pcb allowing the sbus signal to be read by STM32.

Note: When only demoing the leg function, the RC controller may not be used.

## Subsystem 6. Power System

We are considering a 6s (24V) Lithium Battery to power the robot. An alternative solution is to power the robot through a power supply using a pair of long wires.

# Criterion For Success

**Stable Balancing:** The robot (leg) should maintain its balance in a variety of situations, both static (when stationary) and dynamic (when moving).

**Cargo Carriage:** The robot(leg) can be able to carry a specified weight (like 1lb) without compromising its balance or ability to move.

_________________________________________________________________________

**If we are able to test the leg and function normally before midterm, we would try to build the whole wheel legged balancing robot out. It would be able to complete the following :**

**Directional Movement:** Via remote control, the robot should move precisely in the desired direction(up and down), showcasing smooth accelerations, decelerations, and turns.

**Platform Leveling:** Even when navigating slopes or uneven terrains, the robot should consistently ensure that its platform remains flat, preserving the integrity of the cargo it carries. Any tilt should be minimized, ideally maintaining a platform angle variation within a range of 10 degrees or less from the horizontal.

**Position Retention:** In the event of disruptions like pushes or kicks, the robot should make efforts to return to its original location or at least resist being moved too far off its original position.

**Safety:** During its operations, the robot should not pose a danger to its surroundings, ensuring controlled movements, especially when correcting its balance or position. The robot should be able to shut down (safety mode) by remote control.

Project Videos