Project

# Title Team Members TA Documents Sponsor
12 heat exhaustion device for construction workers
Danny Schaub
Tongli Zhou
Zackary Haycraft
Prannoy Kathiresan design_document1.pdf
final_paper1.pdf
photo1.jpg
photo2.jpg
presentation1.pptx
proposal1.pdf
video
# Heat Exhaustion Detection
Team Members:
- Zack Haycraft (Zackary3)
- Tongli Zhou (tongliz2)
- Danny Schaub (dschaub2)
# Problem
When working in certain industries such as factory production lines, construction and power plants high heat environments increases the risk of heat stroke or heat exhaustion, which puts the safety of the workers at risk. Generally, by the time the person is aware of the symptoms of heat exhaustion, it is too late. We have talked to some construction workers near campus, and most don’t possess any wearable device that tracks their health information. Besides, most smart watches on the market are more suitable for heartrate and calorie tracking during exercise, rather than tracking and extracting reliable information critical to construction workers in more extreme environment.
# Findings
There are scientific findings that indicate the relationship between sweat chloride concentration and whether the individual is experiencing heat stroke: HS patients have sweat chloride concentration of around 5.3 mmol/L, while it is higher than 20 mmol/L for normal people. (https://www.sciencedirect.com/science/article/pii/S1658361213001029)
# Solution
To provide this extra layer of safety the device will be a wearable device that the individual will be able to wrap around their arm and as the individual sweats, the sweat will pass through a duct and the conductivity of the sweat will be measured. A temperature sensor will also be used for temperature correction of the sample. This will provide a measurement of the electrolytes present in the individuals sweat and if the electrolyte concentration reaches below an established limit the device will light LEDs to indicate to the individual and other workers in the area that the individual needs to be removed from the environment and replace electrolytes. As an extra precaution a gyroscope sensor will be used to measure the individual’s activity level as well as a temperature and humidity sensor to monitor the hazardous level of the outside conditions.
# Solution Components
## Subsystem 1- Power Supply
-Battery powered, 2 AA batteries
## Subsystem 2-Conductivity Sensor
-The conductivity sensor will be made using two electrodes in conjunction with an AC signal and the voltage drop across the probes will be proportional to electrolyte concentration. Stainless steel electrodes should suffice
-H bridge using switching MOSFET for AC signal (VO617A )

## Subsystem 3- Microcontroller
-The microcontroller will take in the temperature and conductivity data to calculate salt concentration and actuate the indicator light when the threshold is crossed. (Raspberry Pi pico)
## Subsystem 4- gyroscope
-The gyroscope will estimate the work done by the worker and provide supplemental data to the raspberry Pi for analysis.
-accelerometer and gyroscope sensor (MPU-6050)
## Subsystem 5- IOT device
-Use an IOT device to collect information from the sensors on the wearable band, use them to determine when to allow the worker to take a break for rest and get electrolytes.
## Subsystem 6- Temperature and humidity sensor
-This will provide the user with a visual indication of the danger level of the environment utilizing the heat index
-heat index information (https://www.nalc.org/workplace-issues/body/OSHA-All-in-One-Heat-Guide.pdf)
-sensor (DHT11)
# Criterion For Success
-The device can accurately measure the electrolyte concentration of a sample within +/- 10% error
-the device can accurately measure the temperature of a solution sample within +/- 10% error
-The device will illuminate when presented with a solution below the concentration threshold

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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