## Inspiration

Nicotine addiction is an epidemic that is taking the nation in millions due to the growing popularity of "E-Cigarettes." This has affected over 50 million people in the U.S. alone including our friends, family, and loved ones. 90% of these users started smoking before reaching 20 years old. Since the outbreak of trendy E-Cig companies like JUUL and Suorin, the number of high schoolers smoking spiked by 71% in just 2 years. With this problem heavily affecting our generation and generations to come, we want to propose, HelpingHand, a solution that could help mitigate the detrimental effects we noticed.

## What it does

HelpingHand aims to combat this problem by utilizing FitBit's to detect when drug abusers experience withdrawal symptoms throughout their rehabilitation process. HelpingHand emphasizes the importance of positive reinforcement, peer support, and community in helping those struggling to quit, actually quit. Our advanced technology detects when the drug abuser is experiencing stress, therefore a high probability of nicotine relapse and notify their loved ones to provide a helping hand.

## How we built it

Fitbit SDK for the Fitbit app and sending REST calls to the iOS app.
Flutter and Dart for the iOS app and to receive REST calls.
Express.JS for Twilio API, communication between the Fitbit data on the phone and Firebase.
Firebase for an intermediary server through which our platforms can communicate.
Java, org.json to create mock JSON containing timestamps, heartbeat rate, number of steps taken.
And lots and lots of coffee!

## Challenges we ran into

Due to our unfamiliarity with the Fitbit API, an initial challenge we ran into was figuring out how to collect the intraday time-series heart rate data from the Fitbit. Cross-platform communication between the Fitbit and our iOS Flutter app proved to be rather difficult, but once we overcame that hurdle things became much easier.

Our team also spent a significant amount of time with research on smoking and its health effects, particularly the effect it has on heart beat rate and heart beat variability. Unfortunately, there is no publicly available dataset containing heart rate for individuals attempting to quit smoking.

After interviewing people who have experienced nicotine withdrawals in the past, we discovered that high levels of anxiety will greatly increase a smoker's urge to relapse. It was from here that we began to think of the core aspects of HelpingHand and to start building the app.

## Accomplishments that we're proud of

We're surprised at ourselves as to how much all learned this weekend! There were many different API's, frameworks, and hardware we weren't entirely comfortable with and having the opportunity to challenge ourselves was a ton of fun. We're especially proud of the fact that we have been able to finish -- and get working -- the core, defining aspects of HelpingHand.

Every one of the team members personally knows someone who has suffered or is currently suffering from nicotine addiction. *By leveraging existing technologies such as the Fitbit, we sincerely hope that HelpingHand will positively impact the substance abuse epidemic in the United States.*

**However, HelpingHand's mission doesn't stop at PennApps. We're committed to extending the app to other substances such as opioids, as well as mental health with depression-related anxiety hacks.**

## What we learned

We fiddled a ton with the Fitbit SDK, and learned how to make Fitbit apps! Communication across the different platforms -- between Fitbit, our mobile app, Firebase, and Twilio was challenging but incredibly rewarding. In addition to the technology, a big takeaway from this is the immense knowledge we gained about drug abuse, rehabilitation, and recovery. From the personal stories we shared, to the case studies we discovered, this was truly a great opportunity to learn through real-world application.

## What's next for HelpingHand

We would like to scale our app so that people across the world can get help coping with nicotine addiction. It would also enable for an expansive, online support community. We know "machine learning" gets tossed around a lot, but utilizing it to minimize false positives would be super promising.

Moreover, only a portion of smart watch wearers use FitBit. An integration of HelpingHand to more health watches such as the Apple Watch would make our app more accessible.

We also spent time debating on whether to tackle nicotine addiction or depression-related anxiety attacks during this weekend. While we opted for nicotine addiction, we believe extending HelpingHand to depression and other abused substances will open the door for many more applications.
## Inspiration

EV vehicles are environment friendly and yet it does not receive the recognition it deserves. Even today we do not find many users driving electric vehicles and we believe this must change. Our project aims to provide EV users with a travel route showcasing optimal (and functioning) charging stations to enhance the use of Electric Vehicles by resolving a major concern, range anxiety. We also believe that this will inherently promote the usage of electric vehicles amongst other technological advancements in the car industry.

## What it does

The primary aim of our project is to display the **ideal route** to the user for the electric vehicle to take along with the **optimal (and functional) charging stations** using markers based on the source and destination.

## How we built it

Primarily, in the backend, we integrated two APIs. The **first API** call is used to fetch the longitude as well as latitude coordinates of the start and destination addresses while the **second API** was used to locate stations within a **specific radius** along the journey route. This computation required the start and destination addresses leading to the display of the ideal route containing optimal (and functioning) charging points along the way. Along with CSS, the frontend utilizes **Leaflet (SDK/API)** to render the map which not only recommends the ideal route showing the source, destination, and optimal charging stations as markers but also provides a **side panel** displaying route details and turn-by-turn directions.

## Challenges we ran into

* Most of the APIs available to help develop our application were paid
* We found a **scarcity of reliable data sources** for EV charging stations
* It was difficult to understand the documentation for the Maps API
* Java Script

## Accomplishments that we're proud of

* We developed a **fully functioning app in < 24 hours**
* Understood as well as **integrated 3 APIs**

## What we learned

* Team work makes the dream work: we not only played off each others strengths but also individually tried things that are out of our comfort zone
* How Ford works (from the workshop) as well as more about EVs and charging stations
* We learnt about new APIs
* If we have a strong will to learn and develop something new, we can no matter how hard it is; We just have to keep at it

## What's next for ChargeRoute Navigator: Enhancing the EV Journey

* **Profile** | User Account: Display the user's profile picture or account details
* **Accessibility** features (e.g., alternative text)
* **Autocomplete** Suggestions: Provide autocomplete suggestions as users type, utilizing geolocation services for accuracy
* **Details on Clicking the Charging Station (on map)**: Provide additional information about each charging station, such as charging speed, availability, and user ratings
* **Save Routes**: Allow users to save frequently used routes for quick access.
* **Traffic Information (integration with GMaps API)**: Integrate real-time traffic data to optimize routes
* **User feedback** about (charging station recommendations and experience) to improve user experience
## Inspiration

Every year hundreds of thousands of preventable deaths occur due to the lack of first aid knowledge in our societies. Many lives could be saved if the right people are in the right places at the right times. We aim towards connecting people by giving them the opportunity to help each other in times of medical need.

## What it does

It is a mobile application that is aimed towards connecting members of our society together in times of urgent medical need. Users can sign up as respondents which will allow them to be notified when people within a 300 meter radius are having a medical emergency. This can help users receive first aid prior to the arrival of an ambulance or healthcare professional greatly increasing their chances of survival. This application fills the gap between making the 911 call and having the ambulance arrive.

## How we built it

The app is Android native and relies heavily on the Google Cloud Platform. User registration and authentication is done through the use of Fireauth. Additionally, user data, locations, help requests and responses are all communicated through the Firebase Realtime Database. Lastly, the Firebase ML Kit was also used to provide text recognition for the app's registration page. Users could take a picture of their ID and their information can be retracted.

## Challenges we ran into

There were numerous challenges in terms of handling the flow of data through the Firebase Realtime Database and providing the correct data to authorized users.

## Accomplishments that we're proud of

We were able to build a functioning prototype! Additionally we were able to track and update user locations in a MapFragment and ended up doing/implementing things that we had never done before.