## Inspiration

At **Happy Muscles**, we're driven by a simple yet profound idea – *everyone deserves to experience the joy of a pain-free, active life*. The inspiration behind our app stems from the desire to empower individuals to take control of their muscle health and well-being. We believe that AI technology can be a game-changer in this domain, and our journey to create *"Happy Muscles"* began with the vision of making wellness accessible, personalized, and delightful.

## What it does

Happy Muscles is not just another fitness app. It's your **personal wellness companion**. Our app caters to two distinct needs:

*For those experiencing muscle pain*, especially seniors, it offers a virtual pain map. Users can identify the specific muscle causing discomfort, and our advanced AI suggests personalized stretches and exercises to find relief.

*For fitness enthusiasts hitting the gym*, we provide guidance on effective warm-up and cooldown stretches for targeted muscle groups, reducing the risk of injuries and enhancing performance.

## How we built it

We harnessed the capabilities of modern technology to create **Happy Muscles**. Our tech stack includes React JS for the frontend, FastAPI for the backend, and we hosted it all on Replit.

**React JS**: We used React JS to design a user-friendly and responsive front-end interface, making it easy for users to navigate and interact with the app.

**FastAPI**: Our back-end was powered by FastAPI, which allowed us to efficiently handle user data, connect with our AI models, and provide real-time recommendations.

**Replit**: We chose Replit for its ease of collaboration and deployment capabilities, enabling us to work together seamlessly and bring **Happy Muscles** to life.

## Challenges we ran into

Creating an app that provides personalized wellness recommendations comes with its set of challenges. One of the key hurdles we faced was fine-tuning the AI to offer accurate and effective advice for various muscle-related issues. Ensuring user privacy and data security was another significant challenge, which we addressed with robust measures.

Additionally, real-time collaboration on a hackathon project can be demanding. Synchronizing our efforts and maintaining a smooth workflow was a challenge we had to overcome.

## Accomplishments that we're proud of

We're incredibly proud of what we've achieved in such a short time. Happy Muscles is not just an idea anymore; it's a reality. We successfully created an app that has the potential to positively impact people's lives by promoting better muscle health and fitness.

Our AI-powered muscle recognition and recommendation system is a major achievement. It's the result of hard work, dedication, and a shared vision to make wellness accessible to all.

## What we learned

In the course of building Happy Muscles, we learned that the fusion of technology and wellness can be a powerful force for good. It's amazing how a hackathon can bring out the best in a team, fostering creativity, collaboration, and innovation.

We also realized the importance of user-centered design. Making the app accessible and user-friendly was as critical as its functionality. (Still Working to make it responsive on all devices)

## What's next for Happy Muscles

Our journey doesn't end here. Happy Muscles is just the beginning. In the future, we aim to expand our AI's capabilities, add more exercises and stretches, and provide even more personalized guidance to users.
We are looking forward to make the app responsive to all devices.
We will be adding images for more visual instructions on the stretching
We will be looking more deeper into the muscles and providing more accurate stretching results to that specific model

We're also looking forward to gathering user feedback and refining the app based on their experiences and needs. Our goal is to continuously improve and innovate, all while keeping people's happiness and health at the core of our mission.

Join us in our mission to make muscles happy, one stretch at a time. Together, we can build a healthier, happier world.
## Inspiration

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Through extensive research into health management applications and a comprehensive competitor analysis, it became evident that these applications predominantly emphasize physical health and are often overly generalized, lacking broad applicability. Subsequent discussions between our development team and colleagues underscored the vulnerability of developers to an unhealthy work environment.

In light of these findings, we have undertaken the initiative to introduce a service that is holistic in its approach, encompassing three key dimensions of health: physical, mental, and overall well-being. Our objective is to offer developers constructive guidance and recommendations to enhance their health and well-being.

This initiative represents a tailored and enhanced iteration of wearable health applications, akin to industry-leading solutions such as Apple Health and Google Fit.

## What it does

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This application leverages data from the Terra API to furnish users with an accurate assessment of their current physical, mental, and overall well-being status. Moreover, it offers actionable recommendations designed to be seamlessly integrated into developers' everyday lives, with the overarching aim of inspiring and motivating users to adopt and sustain a healthier lifestyle.

## How we built it

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Node.js with the express library was used to build the Backend; React, Figma, HTML, and CSS were used to build the front end and design UIUX.

While MongoDB had not been completely integrated into the code at this stage, the application took a proactive approach by segregating data collection into two distinct components: one housing the user's raw data and the other storing the analyzed final status. This bifurcation was a deliberate measure aimed at safeguarding user privacy while still furnishing managers with the requisite information for effective team management.

## Challenges we ran into

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Within a constrained timeframe, the necessity to complete a product development project posed a significant challenge for our team. This was further exacerbated by the need to utilize tools and technologies with which we had limited prior experience, thereby compounding the time constraint issue.

## Accomplishments that we're proud of

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What we take the utmost pride in is the exceptional design of the service, a testament to the remarkable work of Atmiya and Maxwell. Equally, the project's value is derived from the successful utilization of previously unexplored skills for Min and Omar, underscoring our ability to tackle new challenges and achieve the project's completion.

## What we learned

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Through the hack Harvard, we could learn about the following

-the power of peer mentorship

-how teamwork can boost productivity

-the way to understand new skills in a short time period

## What's next for DevHealth

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Regrettably, due to the constraints of time, we were unable to realize all the desired functions we had planned for. Nevertheless, we remain hopeful for the opportunity to further develop this service. Our envisioned future enhancements include:

Building a web application tailored for team managers to streamline their interaction with the service.

Creating an iOS application to enhance accessibility and provide timely notifications to users.

Developing a watchOS application to gather a broader spectrum of data not typically collected by mainstream wearables like the Apple Watch and Fitbit, among others.
## Inspiration

Physiotherapy is expensive for what it provides you with, A therapist stepping you through simple exercises and giving feedback and evaluation? WE CAN TOTALLY AUTOMATE THAT! We are undergoing the 4th industrial revolution and technology exists to help people in need of medical aid despite not having the time and money to see a real therapist every week.

## What it does

IMU and muscle sensors strapped onto the arm accurately track the state of the patient's arm as they are performing simple arm exercises for recovery. A 3d interactive GUI is set up to direct patients to move their arm from one location to another by performing localization using IMU data. A classifier is run on this variable-length data stream to determine the status of the patient and how well the patient is recovering. This whole process can be initialized with the touch of a button on your very own mobile application.

## How WE built it

on the embedded system side of things, we used a single raspberry pi for all the sensor processing. The Pi is in charge of interfacing with the IMU while another Arduino interfaces with the other IMU and a muscle sensor. The Arduino then relays this info over a bridged connection to a central processing device where it displays the 3D interactive GUI and processes the ML data. all the data in the backend is relayed and managed using ROS. This data is then uploaded to firebase where the information is saved on the cloud and can be accessed anytime by a smartphone. The firebase also handles plotting data to give accurate numerical feedback of the many values orientation trajectory, and improvement over time.

## Challenges WE ran into

hooking up 2 IMU to the same rpy is very difficult. We attempted to create a multiplexer system with little luck.
To run the second IMU we had to hook it up to the Arduino. Setting up the library was also difficult.
Another challenge we ran into was creating training data that was general enough and creating a preprocessing script that was able to overcome the variable size input data issue.
The last one was setting up a firebase connection with the app that supported the high data volume that we were able to send over and to create a graphing mechanism that is meaningful.