## Inspiration

As a team, we've all witnessed the devastation of muscular-degenerative diseases, such as Parkinson's, on the family members of the afflicted. Because we didn't have enough money or resources or time to research and develop a new drug or other treatment for the disease, we wanted to make the medicine already available as effective as possible. So, we decided to focus on detection; the early the victim can recognize the disease and report it to his/her physician, the more effective the treatments we have become.

## What it does

HandyTrack uses three tests: a Flex Test, which tests the ability of the user to bend their fingers into a fist, a Release Test, which tests the user's speed in releasing the fist, and a Tremor Test, which measures the user's hand stability. All three of these tests are stored and used to, over time, look for trends that may indicate symptoms of Parkinson's: a decrease in muscle strength and endurance (ability to make a fist), an increase in time spent releasing the fist (muscle stiffness), and an increase in hand tremors.

## How we built it

For the software, we built the entirety of the application in the Arduino IDE using C++. As for the hardware, we used 4 continuous rotation servo motors, an Arduino Uno, an accelerometer, a microSD card, a flex sensor, and an absolute abundance of wires. We also used a 3D printer to make some rings for the users to put their individual fingers in.
The 4 continuous rotation servos were used to provide resistance against the user's hands. The flex sensor, which is attached to the user's palm, is used to control the servos; the more bent the sensor is, the faster the servo rotation. The flex sensor is also used to measure the time it takes for the user to release the fist, a.k.a the time it takes for the sensor to return to the original position. The accelerometer is used to detect the changes in the user's hand's position, and changes in that position represent the user's hand tremors. All of this data is sent to the SD cards, which in turn allow us to review trends over time.

## Challenges we ran into

Calibration was a real pain in the butt. Every time we changed the circuit, the flex sensor values would change. Also, developing accurate algorithms for the functions we wanted to write was kind of difficult. Time was a challenge as well; we had to stay up all night to put out a finished product. Also, because the hack is so hardware intensive, we only had one person working on the code for most of the time, which really limited our options for front-end development. If we had an extra team member, we probably could have made a much more user-friendly application that looks quite a bit cleaner.

## Accomplishments that we're proud of

Honestly, we're happy that we got all of our functions running. It's kind of difficult only having one person code for most of the time. Also, we think our hardware is on-point. We mostly used cheap products and Arduino parts, yet we were able to make a device that can help users detect symptoms of muscular-degenerative diseases.

## What we learned

We learned that we should always have a person dedicated to front-end development, because no matter how functional a program is, it also needs to be easily navigable.

## What's next for HandyTrack

Well, we obviously need to make a much more user-friendly app. We would also want to create a database to store the values of multiple users, so that we can not only track individual users, but also to store data of our own and use the trends of different users to compare to the individuals, in order to create more accurate diagnostics.
## Inspiration

Sign language is already difficult to learn; adding on the difficulty of learning movements from static online pictures makes it next to impossible to do without help. We came up with an elegant robotic solution to remedy this problem.

## What it does

Handy Signbot is a tool that translates voice to sign language, displayed using a set of prosthetic arms. It is a multipurpose sign language device including uses such as: a teaching model for new students, a voice to sign translator for live events, or simply a communication device between voice and sign.

## How we built it

**Physical**: The hand is built from 3D printed parts and is controlled by several servos and pulleys. Those are in turn controlled by Arduinos, housing all the calculations that allow for finger control and semi-spherical XYZ movement in the arm. The entire setup is enclosed and protected by a wooden frame.

**Software**: The bulk of the movement control is written in NodeJS, using the Johnny-Five library for servo control. Voice to text is process using the Nuance API, and text to sign is created with our own database of sign movements.

## Challenges we ran into

The Nuance library was not something we have worked with before, and took plenty of trial and error before we could eventually implement it. Other difficulties included successfully developing a database, and learning to recycle movements to create more with higher efficiency.

## Accomplishments that we're proud of

From calculating inverse trigonometry to processing audio, several areas had to work together for anything to work at all. We are proud that we were able successfully combine so many different parts together for one big project.

## What we learned

We learned about the importance of teamwork and friendship :)

## What's next for Handy Signbot

-Creating a smaller scale model that is more realistic for a home environment, and significantly reducing cost at the same time.

-Reimplement the LeapMotion to train the model for an increased vocabulary, and different accents (did you know you can have an accent in sign language too?).
## Inspiration

Stroke costs the United States an estimated $33 billion each year, reducing mobility in more than half of stroke survivors age 65 and over, or approximately 225,000 people a year. Stroke motor rehabilitation is a challenging process, both for patients and for their care providers. On the patient-side, occupational and physical therapy often involves hours of training patients to perform functional movements. This process can be mundane and tedious for patients. On the physician’s side, medical care providers need quantitative data and metrics on joint motion while stroke patients perform rehabilitative tasks.

To learn more about the stroke motor skill rehabilitation process and pertinent needs in the area, our team interviewed Dr. Kara Flavin, a physician and clinical assistant professor of Orthopaedic Surgery and Neurology & Neurological Sciences at the Stanford University School of Medicine, who helps stroke patients with recovery. We were inspired by her thoughts on the role of technology in the stroke recovery process to learn more about this area, and ultimately design our own technology to meet this need.

## What it does

Our product, Kinesis, consists of an interconnected suite of three core technologies: an Arduino-based wearable device that measures the range of motion exhibited by the joints in users’ hands and transmits the data via Bluetooth; a corresponding virtual reality experience in which patients engage with a virtual environment with their hands, during which the wearable transmits range of motion data; and a Bluetooth to Android application to MongoDB data collection and visualization mechanism to store and provide this information to health care professionals.

## How we built it

Our glove uses variable-resistance flex sensors to measure joint flexion of the fingers. We built circuits powered by the Arduino microcontroller to generate range-of-motion data from the sensor output, and transmit the information via a Bluetooth module to an external data collector (our Android application.) We sought a simple yet elegant design when mounting our hardware onto our glove.
Next, we built an Android application to collect the data transmitted over Bluetooth by our wearable. The data is collected and sent to a remote server for storage using MongoDB and Node.js. Finally, we the data is saved in .csv files, which are convenient for processing and would allow for accessible and descriptive visuals for medical professionals.
Our virtual-reality experience was built in the Unity engine and was constructed for Google Cardboard, deployable to any smartphone device that supports Google Cardboard.

## Challenges

Our project proved to be an exciting yet difficult journey. We quickly found that the various aspects of our project - the Arduino-based hardware, Android application, and VR with Unity/Google Cardboard - were a challenging application of internet of things (IoT) and hard to integrate. Sending data via Bluetooth from the hardware (Arduino) to the Android app and parsing that data to useful graphs was an example of how we had to combine different technologies. Another major challenge was that none of our team members had prior Unity/VR-development/Google Cardboard experience, so we had to learn these frameworks from scratch at the hackathon.

## Accomplishments that we’re proud of

We hope that our product will make motor rehabilitation a more engaging and immersive process for stroke patients while also providing insightful data and analytics for physicians. We’re proud of learning new technologies to put our hack together, building a cost-effective end-to-end suite of technologies, and blending together software as well as hardware to make a product with incredible social impact.

## What we learned

We had a very well-balanced team and were able to effectively utilize our diverse skill sets to create Kinesis. Through helping each other with coding and debugging, we familiarized ourselves with new ways of thinking. We also learned new technologies (VR/ Unity/Google Cardboard, MongoDB, Node.js), and at the same time learnt something new about the ones that we are familiar with (Android, hardware/Arduino). We learnt that the design process is crucial in bringing the right tools together to address social causes in an innovative way.

## What's next for Kinesis

We would like to have patients use Kinesis and productize our work. As more patients use Kinesis, we hope to add more interactive virtual reality games and to use machine learning to derive better analytics from the increasing amount of data on motor rehabilitation patterns. We would also like to extend the applications of this integrated model to other body parts and health tracking issues.