Launching: C. Northcote Parkinson Disease Wearable Tech

More than 10 million people cosmopolitan are living with Parkinson's disease (Atomic number 46). A progressive systema nervosum distract that causes stiffness and affects the movement of the patient. In simpler terms, many people suffered from Parkinson's disease but it is not curable. If inscrutable brain stimulation (DBS) is mellowed sufficient and then there's a encounter for Atomic number 46 to comprise curable.

By addressing this problem, I volition be creating a tech device that could possibly help hospitals to offer PD patients more accurate and possible medications.

I created a wearable tech device – Nung. It can accurately appropriate patient's vibration value throughout the day. Trailing and analyzing recurring pattern to help hospitals make better medication decisions for for each one patient.
Not only does it leave accurate data to hospitals, it also brings conveniences to PD patients when they revisit their doctors. Commonly, patients leave recall their past symptoms and ask Dr. for further medication adjustment. However, it is difficult to recall every man-to-man detail, thence making the medication adjustment inaccurate, and inefficient. But with the use of this wearable technical school device, hospitals can identify the vibration pattern with ease.

Step 1: Electronics

- ESP8266 (wireless local area network module)

- SW420 (vibration sensor)

- Breadboard

- Jumper wires

Gradation 2: Vibration Monitor Website

By graphing this out, hospitals can visualize patient's condition live.

1. SW420 captures the vibration data from the substance abuser

2. Save the time and vibration information to a database (Firebase)

3. The website will get the data stored in the database

4. Output a graph (x-axis - time, y-axis - vibration value)

Step 3: Machine Learning Simulate

I've decided to manipulation Polynomial Regression toward the mean role model to identify the drug user's greatest medium vibration value from different period of time. Reason being my data points do not show an obvious correlation betwixt the x and y-axis, polynomial fits wider range of curvature and more faithful prediction. However, they are very nociceptive to outliers, if there are extraordinary or two unusual person data points, it will involve the result of the graph.

x_axis = numpy.linspace(x[0], x, 50) # range, generation y_axis = numpy.poly1d(numpy.polyfit(x, y, 5)) # draw x y, 5 nth terms

Step 4: Assembly

At the end, I modify a few electronics and decided to apply lithium polymer battery to power the wearable technical school. This is because information technology is rechargeable, phosphorescent weight, small and can travel freely.

I've solder all the electronics together, fashioned the case along Fusion 360 and printed it out in black to make the whole ware look arrow-shaped and minimal.

if you want to understand more about this project, feel free to check out my web site.

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