In third-world countries and in remote areas, there are few doctors available to diagnose disease. Among these diseases are strokes, which require immediate medical attention to avoid serious injury. Unfortunately, many do not receive treatment in time, simply because they cannot identify whether they are indeed having a stroke or not.
Telemedicine offers a solution to this problem. However, it is only a partial solution. Doctors can gain insight through visual and audio cues, but much is left out in regards to sensory and musculoskeletal information. Nervetelligence bridges this knowledge gap by providing physicians with data which could previously only be gathered by direct interaction with patients.
What it does
Nervetelligence is a machine a patient inserts their arm into, along with an accompanying web application. The machine gathers three different data types: the existence of proprioception; light touch sensation in the palm and in the forearm; and musculoskeletal strength. The web application allows physicians to video chat with patients and record patient response to stimuli for further analysis.
In order to detect the existence of proprioception, patients extend their arm into the Nervetelligence box until their finger fits snugly in far most compartment. Then, a rotating wheel with two spokes will either push the phalanges up or down. Patients will be asked to answer about the directionality of the given push. Since stroke victims have difficulty recognizing their joint's position in space, correct answers would be a good sign.
Light touch sensation will be solicited through two hanging servo motors, one fixed with a pencil and the other with a leaf. These two objects will be dragged across skin as they oscillate back and forth. Patients will respond to whether the object was felt on the skin or not. Missing or only a partial sensation can indicate a stroke.
Finally, musculoskeletal strength will be measured with a Myo Armband. The Nervetelligence box supports the testing of arm vitality through two distinct arm movements.
How I built it
Nervetelligence was prototyped in a cardboard box. There are two breadboards, three servo 180-degree motors, two Arduino Uno's, and one force sensor attached. The force sensor detects when a finger is inserted into the proprioception cavity. The Arduino Uno's control the servo motors responsible for light touch sensation and are triggered at the will of the doctor.
Challenges I ran into
We faced challenges when trying to control the servo motors in real-time. This proved difficult since they were controlled by Arduino's, which are microcontrollers and require re-upload to change behavior. Furthermore, we faced obstacles when implementing video messaging functionality on the web application.
Accomplishments that I'm proud of
We are proud of all of the hardware we used and its analog software. Most of us worked with hardware for the first time ever, so it was a tremendous learning experience. Also, I am proud of our exceptional teamwork. We were all strangers when we met through Facebook—now we can say we are all good friends!
What I learned
We learned about breadboards, resistors, jump wires, soldering, Arduinos, servos, and force sensors, web development, the Myo armband, and the diagnosis of stroke.
What's next for Nervetelligence
We want to expand the capabilities of Nervetelligence to gather information about other diseases, such as diabetes and rheumatoid arthritis. A host of illnesses require physicians to probe the arm. This information, including pulse-oximetry, peripheral nervous system response, blood glucose levels, respiratory rates, facial gestures, and temperature is valuable to assess the progression of the disease. We want Nervetelligence to grow into an all-in-one stop for arm sensitivity tests. But we don't intend to stop there! Similar diseases afflict the foot and other body parts, which can be interacted with the same as arms.