Inspiration

There are nearly 1 million patients suffering from Parkinson’s disease (PD) in the US today and the number is increasing by approximately 60,000 every year. One of the most debilitating motor symptoms of PD is freezing of gait (FOG), which is a patients' inability to walk or move their feet despite a full intention to do so. This may lead to falls, a loss of independence, and reduced quality of life. Around 50% of PD patients experience FOG and freezers are around 35% more likely to experience falls than non-freezers.

What it does

We have developed a device which can monitor the leg movements of a PD patient and send data over to a healthcare professional or a researcher for further analysis.

How we built it

We used Arduino along with a 3-axis accelerometer to sense the motion of the leg of a PD patient. We also used a Wifi module to provide wireless access to the data.

Challenges we ran into

We struggled to get the Wifi module to work with Arduino due to some technical difficulty. We also struggled to determine the best route of filtering the noisy signals generated by the accelerometer. We had trouble deciding between a Fourier transform and a Low Pass Filter (we ended up using a LPF).

Accomplishments that we're proud of

We cleaned up the noisy signal using a low pass filter. Despite this, the resulting signal still had a lot of noise. We tried multiple methods of distinguishing between freezing of gait and regular movement, and were able to accurately do so by writing a MATLAB program. Another accomplishment was creating a lightweight, user- friendly, and ergonomic wearable device despite the size constraint of the arduino uno and grove shield being quite large compared to the ankle.

What we learned

We learned about sampling rate, signal processing and filtering, Arduino and its coding, time management and team work.

What's next for Antifreeze

The data generated by our medical device can be used to study multiple things including the frequency of FOG episodes in a PD patient, the potential triggers of the episodes and the most favorable solutions for their particular case. This device can also be used to monitor the leg movements of other patients suffering from any kind of leg injury which might hamper their normal movement.

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