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This week we made adjustments due to the COVID-19 Situation. The work we did this week can be found in the group posting.

This week I mostly worked on figuring out how to proceed with the rest of our team.

It turns out that there isn’t an easy way to get the parts for another setup — Adafruit and similar companies are shut down and Amazon has shipping dates for late April at the soonest. Because of this, we’ll have to keep working with the one device.

I also spent some time this week troubleshooting some issues Nick was having with connecting to it over different networks.

For our SOW, we decided to split the major parts of the design into separate goals to avoid needing access to the device, but we didn’t fully specify the end products. This week I’ll decide on concrete goals for demonstrating the hardware side’s functionality (and probably get Nick to mail the rpi since the Bluetooth interface will be on the hardware end).

This week, we looked at further design trade studies and decided to shift from focusing on RNN’s and SVM’s to working entirely on SVM’s due to power requirements of RNN’s on a Raspberry Pi chip.

Because the SVM design doesn’t have frequency domain features such as STFT and wavelet transform, I started working on feeding and tuning the paramaters of those features into the pipeline.

As well, SVM can sometimes benefit from PCA compression at the beginning of the SVM, so I started working on implementing and tuning the PCA compression.

This week, we decided a good way to collect preliminary data for designing our machine learning pipeline would be to use our smartphones. I took some falls in the hallway to collect some preliminary accelerometer data. I also began the design process for the RNN.

We also took our design phase discussions and fleshed them out into a visual presentation.

This week we narrowed down our hardware platform to a specific model RPi (Zero W) and picked out a plug-in accelerometer from the 18-500 inventory. I picked up the parts at the end of the week. Still need to get some components (portable power source, some means of attaching it to a person), but those won’t be necessary until we have a system working. Next task is to setup an OS and figure out how to talk to the IMU over I2C.

This week, we determined possible ways to process the input data into features. We determined that we will break the time series data into overlapping segments which we will manually label and classify with a machine learning method.

We determined we would try the following machine learning approaches :
RNN , SVM

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