During the final week of the project, I conducted comprehensive testing and fine-tuning to ensure the Emotibit and mBot Neo integration was working smoothly and reliably. I gathered feedback from users, including Dr. Crawford and others, to assess the project’s effectiveness and usability.

In the ninth week, I focused on improving the user interface and interaction aspects of the project. I created a simple graphical interface on the computer using Python libraries to visualize the Emotibit data in real-time. This interface could display heart rate, skin temperature, and motion data as graphs or numerical values. I also implemented user-friendly controls to adjust the threshold values for triggering color changes on the mBot Neo. This enhancement aimed to make the project more accessible and user-friendly.

During the eighth week, I continued to work on the integration of Emotibit data with the mBot Neo. Building upon the progress from the previous week, I refined the code to ensure that the Cyberpi’s color changes dynamically in response to the Emotibit data. I also experimented with different threshold values and conditions to make the response more sensitive and accurate. Additionally, I documented the code and results for reference and troubleshooting purposes.

During the seventh week, I was tasked with taking the data that the Emotibit produces and making it perform a specific action on the Cyberpi for the mBot Neo. In order to this I tried creating a variable that would store whatever the current reading is being outputted at that current moment, and then used a sequences of if/else statements that would compare the current number being read and another value. So for example if the current reading was less than 80, the cyberpi would appear blue. This process was semi-successful as the cyberpi would be set to the color that came from the emotibit value, but it wouldn’t change if the value was increased or decreased. For my next week I plan to continue trying to work out this problem.

During the sixth week, I was tasked with setting up an Emotibit and configuring it to collect real-time data, as well as have the Emotibit be able to take the data and output it to Visual Studio Code via OSC. An Emotibit is a wearable sensor device that captures physiological data for health monitoring and research. It measures heart rate, skin temperature, motion, and more. It is open-source and compatible with various software platforms. Setting up the Emotbit wasn’t too difficult as there is a GitHub that explains the setup process. Once this was done, I tried to set up a simple client and simple server using python-osc, but was not able to produce an output. I moved onto trying to connect the Emotibit to Visual Studio Code, using a sample code but also was unsuccessful with this. When I met with Dr. Crawford this week, he was able to explain the errors that I had made when trying to do these tasks and I was able to go back, make changes, and get the results that were expected which included a successful sending of packages between the client and server, and the Emotibit data being able to be read through Visual Studio Code.

During the fifth week, I presented some sample scripts I created in Visual Studio Code, as well as the PID Wall Following script. The sample scripts included scripts to control the LED colors that can appear on the mBot Neo, as well as the text that appears. I used this to create a reaction time game, that prompts the user to click on a button on the mBot Neo as soon as it displays a color, to gauge their reaction time in seconds. After showing Dr. Crawford these scripts, I was given the task of configuring and setting up an EmotiBit so we can begin to understand how to gauge engagement of using the mBot Neo.

During the fourth week, I was tasked with finding out a way to program the makeBlock mBot Neo, outside of the makeblock software. This was a slightly difficult task due to the lack of information that is out right now on the makeBlock. After going through some forums and tutorials with Dr. Crawford, we eventually found documentation on commands that can be used to control the mBot Neo. After this, my next task was to come up with some example programs to control the mBot as well as recreate the wall-following script inside Visual Studio Code.

During the third week, I was tasked with creating a script that would allow the mBot Neo to follow a wall using the ultrasonic sensor 2 that comes with it. This proved to be difficult at first since we had to disassemble the robot in order to move where the sensor was originally, to the side. After some reconfiguring, I was able to come up with a script that followed the wall, but as soon as the wall was lost the robot would zoom straight off. After showing this to Dr. Crawford he assisted me by breaking down the process to create a new script by incorporating variables and a PiD controller. Once this was completed, I was able to make a script for the robot to accurately follow the wall.

During the second week I was tasked with putting together the mBot Neo, downloading the mBlock software, and figuring out how to control the mBot. Downloading the mBlock software was quite simple as they have their own website and connecting the mBot neo to my computer was also simple since it came with a USB to Tpe C cable. Utilizing various Youtube tutorials, I was able to create a script that allowed he mBot neo to avoid obstacles using the ultrasonic sensor that comes with it. After presenting this to Dr. Crawford, I was tasked with creating a script that would allow the mBot to follow a wall.

During the first week, I met with Dr. Crawford and my mentors to discuss the project that I will be working on and gained an understanding of what Dr. Crawford will be looking for. Dr. Crawford also explained to me what, “Physiological Computing Education”, is along with the differences between EEG and EMG.