Weekly Status Reports

Product Pitch

Product Pitch

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It’s estimated that 42 million people in the U.S. suffer from some form of movement disorder [1]. Whether this requires rehabilitation of the nervous system or a form of prosthetic, a common challenge that follows is proper muscle rehabilitation. We created the game MyoRun that combats deterioration of muscles and improves muscle control through continuous play. This game is an electromyography (EMG) controlled endless-runner style video game. With innovative EMG technology and an easy-to-use interface, this game is interactive, fun,…

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Alex’s Status Report for December 5th

Alex’s Status Report for December 5th

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This past 2 weeks ha been a huge progress. At this point the game side of things are finished. All the previously mentioned bugs, that potentially prevented from having a playable and robust game, are gone, and the game can be played “endlessly”. I solved the issue with switching lanes by moving the positions based on a fixed time loop instead of a velocity dependent positioning. Because it is a position dependent, when the position gets to a certain region,…

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Kayla’s Status Update for December 5th

Kayla’s Status Update for December 5th

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This week, and including last week, I worked on narrowing down the feature extraction and classification pipeline to be used for classifying EMG signals in our project. After discussing with Tarana to understand the expected data input to the SVM classifier, I created a 1×15 matrix for the feature vector to which included the max value for each electrode, five binary signals about whether or not a particular electrode fired during the recorded signal, and the peak frequency from the…

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Team Status Report for December 5th

Team Status Report for December 5th

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In the past two weeks, our team has made a lot of progress on our project. During the Thanksgiving break, we achieved the milestone set in Phase III of our schedule by completing our final integration test. As the TAs had warned us early in the semester that integration would be the most challenging part, we made sure that we kept the separate parts of our project on the right trajectory by frequently integrating everything throughout the semester. This has…

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Tarana’s Status Report for December 5th

Tarana’s Status Report for December 5th

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In the past two weeks, a lot of progress was made towards our final product. During the Thanksgiving break I worked with Kayla to build the classifier that would be used to interpret the muscle input signals and classify which movement it belonged to. We integrated together the feature extraction that Kayla had been working on along with the SVM model that I had been working on. Afterwards, we integrated our classifier with the game that Alex had been developing…

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Alex’s Status Report for November 21st

Alex’s Status Report for November 21st

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This week I created more obstacle sets and randomization for the gifts. The coins(gift boxes) are placed to look more random to improve playability and enjoyability. I also have been working on creating a UI to indicate number of gift boxes collected – like a scoring system. This is important because this will be a metric used to measure player’s performance over time to ultimately measure playability of the game. Later this week I found a serious bug in the…

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Team Status Update for November 21st

Team Status Update for November 21st

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This week as a team we are putting pieces together before our third integration test. Early next week we plan to do this final integration test in order to be set to work on the rest of our deliverables for the final submission and demo of this project. We are all moving this/next week because of the holiday so that has been taking up some of our time. Major things that happened this week on the signal side, feature extraction…

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Tarana’s Status Report for November 21st

Tarana’s Status Report for November 21st

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This week, I worked on improving the base models for making simulated data. I’ve found that the hyperbolic secant function is a great representation of the peaks in muscle movements, so I’ve been using linear combinations of that to make a good prior distribution function. I have also been familiarizing myself with scikit, a python package that has SVM support, and could help us with our classifier.  Part of our project involves classification of signals, and to better aid that…

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Kayla’s Status Update for November 21st

Kayla’s Status Update for November 21st

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This week I have been continuing work on the machine learning aspect of the signal processing. I obtained my first accuracy result from splitting our 42 trials of data between training, validation, and testing data with an accuracy of 35% using a Long Short-Term Memory (LSTM) algorithm classifier. This definitely does not meet the accuracy we are aiming for so there is still work to be done in the adjustment of hyperparameters and the input feature vector. In my further…

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Kayla’s Status Update for November 14th

Kayla’s Status Update for November 14th

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This week I dove into applying different data dimensionality reduction and classification methods to our data. First, with the primary data that we have collected, I created a visualization of the peaks of the recorded signal data in order to see if there might be any obvious trends in the correlation between movements and the peak of their signals. This graph shows the max value for the voltage recorded by each electrode for each sample of each movement from the…

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