Project

# Title Team Members TA Documents Sponsor
89 Screentime Habit Correction Headband
Colin Moy
Jake Chen
Zhiyuan Chen
Weijie Liang proposal1.pdf
# Screentime Habit Correction Headband

Team Members:
- Jake Chen (jakezc2)
- Colin Moy (colincm2)
- Zhiyuan Chen (zc67)

# Problem

With the majority of people having more and more access to screens, many people spend a large amount of time in front of a desktop computer. After some time, their posture deteriorates into slouching and they can end up sitting too close to the screen. With poor posture, the neck and back can be strained and can be detrimental to long term health. Additionally, when sitting too close to the screen, the eyes can get dry from not blinking enough and get strained. Even if you have good posture and distance, sitting at the screen for too long can also strain your eyes and back.

# Solution

Our Screentime Habit Correction Headband will allow the user to track their habits during screentime and correct bad habits. By using a headband with two sensors, the device will be able to track the posture of the user based on the calibration done when the device is powered on, as well as the distance between the user and the screen they are looking at. The device will send feedback to the user using vibrations, a speaker, and a LED when the user’s posture deteriorates or they get too close to the screen. In addition, the device will also send feedback to the user if they have been sitting in front of the screen for too long. The headband will be lightweight and will be wired to a box that contains the bulk of the electronics as well as the rechargeable battery for the device. In addition to the physical device, there will also be an app that can track screentime and posture data from the device using Bluetooth.

# Solution Components

## Power

Our power subsystem will contain a Lithium-Polymer battery with a TP4056 charging module. It will also be able to regulate and step down voltages using an LDO and buck converters and send them to all the other components in the device.

Lithium Polymer battery,
TP4056,
LDL1117-3.3


## Sensors

There are two sensors on the device. The first sensor is the ICM-42670-P, which is an IMU that is able to sense position and orientation in order to tell the MCU to send feedback when the user’s posture is bad. The second sensor is the VL53L0X Time-of-Flight Sensor, which is able to detect the distance from the user to a screen. This sensor will tell the MCU to send feedback when the user is too close to their screen.

ICM-42670-P,
VL53L0X


## Feedback

The feedback subsystem consists of a vibration motor (Mini ERM), speaker (Piezoelectric Buzzer), and two LEDs. There are two cases when the feedback subsystem will activate. One case is when the user is either slouching or too close to the screen. The other case is when the user has been sitting in front of the screen for too long. Each case will have their own dedicated LED, while both cases will activate the vibration motor and speaker.

Coin vibration motor,
Piezoelectric Buzzer,
2 LEDs


## Processing

The processing system consists of the microcontroller. The MCU that we will be using is the ESP32. It will use sensor data as well as its own timer to determine when to send feedback to the user based on time of exposure to a screen, distance to a screen, and posture. The MCU will also manipulate the sensor data so the two cases won’t interfere with each other. In addition, the MCU will have Bluetooth capabilities that will be able to communicate with the app and allow it to track data.

ESP32-S3


## App

The app will measure a lot of data from the sensors using Bluetooth. The app will display the time it takes before the user’s posture deteriorates or the screen gets too close to the user, the amount of times this occurs, and the general data such as daily screentime. The app will also have a graph of all these statistics that it can track over the course of a week.


## Design
The headband will have a switch that is used to turn the device on and off, with device calibration when switched on. The headband also will only contain the two sensors and the vibration motor, and the headband will be wired to a separate box, meant to be placed on the desk. The box will hold everything else, from the LEDs, speaker, microcontroller, and power subsystem.


# Criterion For Success

## Headband:


Accurate distance measurements from headband to screen transmitted to stationary module (±0.5 in)

Lightweight (weight limit of 100g)

Alarm activates when distance to screen is less than 12 inches

Alarm activates when IMU detects the user’s head looking down at an angle of over 15 degrees for 3 seconds or when IMU detects it has been lowered by at least 2 inches for 3 seconds

Alarm activates when user has been sitting for at least 60 minutes

Alarm is turned off when user fixes posture to ±0.5 inches of normal position and is further than 12 inches from the screen

Fast calibration for posture (Under 15 seconds)

Switch can power the device off and on, as well as calibrate when switched on

Device operates for at least 2 hours on a single battery charge

## App:


Values displayed on the app match the values output by the microcontroller (average time from initial screen exposure to unsafe screen distance, average time from initially sitting down to bad posture)

Previous recorded values can be displayed in a graph

## Box:

Battery is chargeable by USB-C

Decentralized Systems for Ground & Arial Vehicles (DSGAV)

Mingda Ma, Alvin Sun, Jialiang Zhang

Featured Project

# Team Members

* Yixiao Sun (yixiaos3)

* Mingda Ma (mingdam2)

* Jialiang Zhang (jz23)

# Problem Statement

Autonomous delivery over drone networks has become one of the new trends which can save a tremendous amount of labor. However, it is very difficult to scale things up due to the inefficiency of multi-rotors collaboration especially when they are carrying payload. In order to actually have it deployed in big cities, we could take advantage of the large ground vehicle network which already exists with rideshare companies like Uber and Lyft. The roof of an automobile has plenty of spaces to hold regular size packages with magnets, and the drone network can then optimize for flight time and efficiency while factoring in ground vehicle plans. While dramatically increasing delivery coverage and efficiency, such strategy raises a challenging problem of drone docking onto moving ground vehicles.

# Solution

We aim at tackling a particular component of this project given the scope and time limitation. We will implement a decentralized multi-agent control system that involves synchronizing a ground vehicle and a drone when in close proximity. Assumptions such as knowledge of vehicle states will be made, as this project is aiming towards a proof of concepts of a core challenge to this project. However, as we progress, we aim at lifting as many of those assumptions as possible. The infrastructure of the lab, drone and ground vehicle will be provided by our kind sponsor Professor Naira Hovakimyan. When the drone approaches the target and starts to have visuals on the ground vehicle, it will automatically send a docking request through an RF module. The RF receiver on the vehicle will then automatically turn on its assistant devices such as specific LED light patterns which aids motion synchronization between ground and areo vehicles. The ground vehicle will also periodically send out locally planned paths to the drone for it to predict the ground vehicle’s trajectory a couple of seconds into the future. This prediction can help the drone to stay within close proximity to the ground vehicle by optimizing with a reference trajectory.

### The hardware components include:

Provided by Research Platforms

* A drone

* A ground vehicle

* A camera

Developed by our team

* An LED based docking indicator

* RF communication modules (xbee)

* Onboard compute and communication microprocessor (STM32F4)

* Standalone power source for RF module and processor

# Required Circuit Design

We will integrate the power source, RF communication module and the LED tracking assistant together with our microcontroller within our PCB. The circuit will also automatically trigger the tracking assistant to facilitate its further operations. This special circuit is designed particularly to demonstrate the ability for the drone to precisely track and dock onto the ground vehicle.

# Criterion for Success -- Stages

1. When the ground vehicle is moving slowly in a straight line, the drone can autonomously take off from an arbitrary location and end up following it within close proximity.

2. Drones remains in close proximity when the ground vehicle is slowly turning (or navigating arbitrarily in slow speed)

3. Drone can dock autonomously onto the ground vehicle that is moving slowly in straight line

4. Drone can dock autonomously onto the ground vehicle that is slowly turning

5. Increase the speed of the ground vehicle and successfully perform tracking and / or docking

6. Drone can pick up packages while flying synchronously to the ground vehicle

We consider project completion on stage 3. The stages after that are considered advanced features depending on actual progress.

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