The objective of this project is to create a device capable of detecting stress in its user. The wearable stress detection device will have 4 primary methods of determining if the user feels stressed: galvanic skin response, heart rate, temperature, and a switch. The switch can be flipped by the user if they feel stressed. All the sensors will be connected to a microcontroller housed in an enclosure the size of a person’s wrist. The data collected by the microcontroller is then sent to a website where the information is graphed and can be exported as a CSV. This project is intended to be used in research as the exported CSV file was originally meant to use machine learning. The algorithm over time would learn to identify what sensor readings are indicators of stress in the user to quickly identify what the stressor in the user is. Some of the main challenges associated with the project were data accuracy and device footprint. As the machine learning program would learn based on the data given, feeding accurate data is a critical aspect. Additionally, since the device is to be worn on a person's wrist for hours, it ideally should be as small as possible, both of which are still areas for improvement.