Project

# Title Team Members TA Documents Sponsor
46 BioSteady
 Alisha Chakraborty
Asmita Pramanik
Pranav Nagarajan
 Surya Vasanth design_document1.pdf
final_paper1.pdf
grading_sheet1.pdf
presentation1.pdf
proposal1.pdf
video1.mp4
**Team Members:**

Alisha Chakraborty (alishac4)

Asmita Pramanik (asmitap2)

Pranav Nagarajan (pranavn6)


**PROBLEM**

The rigor of student life has not only contributed to our rising stress levels, but also to our dependence on stimulants like caffeine to rapidly increase overall productivity. Furthermore, the wide availability of coffee shops in our school and workplaces make it easy for us to turn to such stimulants, without considering the detrimental combined effects of caffeine and stress on our health. Heightened levels of stress cause various physiological changes such as increased heart rate and skin conductance. Current research suggests that caffeine intake also exhibits similar physiological changes which introduces us to the problem of not being able to differentiate between the two. The ability to differentiate between the two will allow students to make informed decisions about the frequency of their caffeine consumption, consequently contributing to better physical and mental health.


**SOLUTION**

Our proposed solution is to integrate data collected from heart rate and galvanic skin conductance sensors to estimate and notify the user whether they are most likely experiencing physiological changes under stress or caffeine. Doing so will make it easier for them to decide whether it is wise to drink coffee in moments of high stress.
When we are affected by stress, the adrenaline release in our body immediately triggers a ‘fight or flight’ response, which causes a spike in heart rate. An additional bodily response is also that there are sudden spikes and drops in skin conductance with a general decrease. Under the effects of caffeine, the heart rate does increase but gradually over a couple of minutes. Its effect on skin conductance is that it is normal to low for about 200 seconds and then exhibits a steep increase. Using these facts, we will determine whether the user should consume coffee or not based on their general state of mind.

**SOLUTION COMPONENTS**

**1. Subsystem 1: Biomedical Sensing**

This subsystem will collect the user's physiological data like heart rate, oxygen levels, and skin conductivity and will transmit it to the MCU for data processing.

Heart Rate and Oximeter Sensor
Sensor : MAX30102

Datasheet: https://www.analog.com/media/en/technical-documentation/data-sheets/max30102.pdf

Functionality : uses PPG (PhotoPlethysmoGraphy) to measure heart rate and oxygen saturation when processed through an MCU

Communication : I2C

Power Requirements : I2C pull-ups operate on 3.3 V and core operates on 1.8V

Galvanic Skin Response Sensor

Sensor: Elecbee GSR Skin sensor module

Datasheet : https://www.seeedstudio.com/Grove-GSR-sensor-p-1614.html?gad_source=1&gbraid=0AAAAACiAB45royCnyQi5xNgTS40BTYnFL&gclid=CjwKCAiAneK8BhAVEiwAoy2HYbC6TTsLlyUQMAoK6wCHRL13LKu2egu27oheSHQcOb3TPxl8o-h5IxoC6jQQAvD_BwE

Functionality : measures skin conductance to process physiological stress levels, higher voltage output = lower skin resistance which means more sweat

Communication : Analog output voltage changes based on the skin’s conductance
Power Requirements: 3.3V - 5V

**2. Subsystem 2 : MCU & Power management**

This subsystem will use the biometric data from the sensors for analysis as well as manage communication with external interfaces

Microcontroller : STM32L432KC

Datasheet : https://www.st.com/resource/en/datasheet/stm32l432kc.pdf

Interfaces : two I2C for MAX30102 and ADC for GSR sensor

Power Supply : 1.71 to 3.6 V for I/Os and 1.62V to 3.6V for ADCs

Functionality: This MCU will be used to collect the data from the sensors as well as use the USB to UART bridge for frontend web application

Voltage Regulators
LM39401-A : 5V to 3.3V Regulator for MCU and sensors
ASM1117-1.8 : 3.3V to 1.8V Regulator for MAX30102 Cores


**CRITERION FOR SUCCESS**

The system must reliably collect physiological data using the MAX30102 heart sensor and the GSR sensor, ensuring accurate measurement of heart rate, oxygen saturation and skin conductance.These values should be processed in real-time without errors or delays.

The microcontroller (STM32) must integrate the sensor data and differentiate between stress-induced changes (characterized by rapid spikes in heart rate and increased skin conductance) and caffeine-induced changes (characterized by gradual increases in heart rate with stable skin conductance).

Data transmission from the microcontroller to the web application should be a seamless process without any data-loss, ensuring real-time visualization of physiological states.

The web application must display the processed results in a clear, user-friendly format, allowing users to quickly interpret whether their physiological changes are stress- or caffeine-related.

The system must work reliably, right from the collection of data through the sensors to being able to display the results on a web application, ensuring it functions effectively in different scenarios.

The project should be able to let its users make informed decisions about their caffeine intake based on clear, actionable feedback provided by the system.

**REFERENCES**

Villarejo, María Viqueira, Begoña García Zapirain, and Amaia Méndez Zorrilla. 2012. "A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee." Sensors 12 (5): 6075-6101.
https://doi.org/10.3390/s120506075.

Iron Man Mouse

Jeff Chang, Yayati Pahuja, Zhiyuan Yang

Featured Project

# Problem:

Being an ECE student means that there is a high chance we are gonna sit in front of a computer for the majority of the day, especially during COVID times. This situation may lead to neck and lower back issues due to a long time of sedentary lifestyle. Therefore, it would be beneficial for us to get up and stretch for a while every now and then. However, exercising for a bit may distract us from working or studying and it might take some time to refocus. To control mice using our arm movements or hand gestures would be a way to enable us to get up and work at the same time. It is similar to the movie Iron Man when Tony Stark is working but without the hologram.

# Solution Overview:

The device would have a wrist band portion that acts as the tracker of the mouse pointer (implemented by accelerometer and perhaps optical sensors). A set of 3 finger cots with gyroscope or accelerometer are attached to the wrist band. These sensors as a whole would send data to a black box device (connected to the computer by USB) via bluetooth. The box would contain circuits to compute these translational/rotational data to imitate a mouse or trackpad movements with possible custom operation. Alternatively, we could have the wristband connected to a PC by bluetooth. In this case, a device driver on the OS is needed for the project to work.

# Solution Components:

Sensors (finger cots and wrist band):

1. 3-axis accelerometer attached to the wrist band portion of the device to collect translational movement (for mouse cursor tracking)

2. gyroscope attached to 3 finger cots portion to collect angular motion when user bend their fingers in different angles (for different clicking/zoom-in/etc operations)

3. (optional) optical sensors to help with accuracy if the accelerometer is not accurate enough. We could have infrared emitters set up around the screen and optical sensors on the wristband to help pinpoint cursor location.

4. (optional) flex sensors could also be used for finger cots to perform clicks in case the gyroscope proves to be inaccurate.

Power:

Lithium-ion battery with USB charging

Transmitter component:

1. A microcontroller to pre-process the data received from the 4 sensors. It can sort of integrate and synchronize the data before transmitting it.

2. A bluetooth chip that transmits the data to either the blackbox or the PC directly.

Receiver component:

1. Plan A: A box plugged into USB-A on PC. It has a bluetooth chip to receive data from the wristband, and a microcontroller to process the data into USB human interface device signals.

2. Plan B: the wristband is directly connected to the PC and we develop a device driver on the PC to process the data.

# Criterion for Success:

1. Basic Functionalities supported (left click, right click, scroll, cursor movement)

2. Advanced Functionalities supported(zoom in/out, custom operations eg. volume control)

3. Performance (accuracy & response time)

4. Physical qualities (easy to wear, durable, and battery life)