## Inspiration

"Every day in Canada, more than 350 people develop dementia"

* Alzheimer Society of Canada
Dementia and memory loss is a worldwide problem and we witnessed the challenges faced by dementia patients and their caregivers on a daily basis. This drove us to create a solution that could enhance the quality of life for dementia patients while providing much-needed support to their caregivers.

## What it does

NURO is a comprehensive wearable assistant designed specifically for individuals living with dementia. It serves multiple functions to address various aspects of their daily life challenges. Firstly, NURO provides personalized reminders for medications, appointments, and daily routines, helping to mitigate memory lapses and maintain a sense of normalcy. Secondly, it offers real-time location tracking with geo-fencing capabilities to prevent dangerous wandering behaviors, ensuring the safety of the wearer. Additionally, NURO incorporates adaptive learning technology, which analyzes the wearer's behaviors, routines, and preferences to tailor its reminders and interactions over time, making it more effective and personalized. Last but definitely not the least, if the user forgets something, the AI voice will answer and tell the user the answer.

## How we built it

NURO was built using a combination of hardware and software components. For the hardware, we utilized a Raspberry Pi as the central processing unit, which was made wearable through a clip-on design. Additionally, we integrated a microphone for voice input and wireless speakers for audible reminders and communication. On the software side, we developed a frontend interface that allows users to manage reminders, appointments, and other settings. We also implemented algorithms for real-time location tracking, adaptive learning, and data analysis.

## Challenges we ran into

We ran into a few challenges during the development of this project. The first one being live speech diarization. Current online integrations do not support live speaker diarization, so we had to implement a pipeline with the help of FFMPEG.

## Accomplishments that we're proud of

We're very proud of our accomplishments such as integrating the Solace Event Broker into our application. Furthermore, we're proud of integrating hardware components within our application such as the Raspberry Pi and a microphone. We're very proud of speech-to-text conversion which was able to recognize different voices ( speech diarisation).

## What we learned

We learned about the process of multithreading in a Python script. In terms of hardware, we learned how to configure a Raspberry Pi and integrate this computer with other components. Additionally, we became proficient in using LLM models to manipulate our data and learned about the usage of a VectorDB database. Our team also learned about the best practices to integrate the Solace Event Broker into our application.

## What's next for Nuro

In the future, we could try to implement visual feed along with the auditory feed. This visual feed can be very useful when it comes to remembering where you put things. A lot of dementia patients forget where they last placed their object such as their keys. This visual feed can capture where the items were last placed and remind the patient where their item is. This visual feed can just be a camera in the patient's glasses.
## Inspiration

This project was inspired by the rising issue of people with dementia. Symptoms of dementia can be temporarily improved by regularly taking medication, but one of the core symptoms of dementia is forgetfulness. Moreover, patients with dementia often need a caregiver, who is often a family member, to manage their daily tasks. This takes a great toll on both the caregiver, who is at higher risk for depression, high stress levels, and burnout. To alleviate some of these problems, we wanted to create an easy way for patients to take their medication, while providing ease and reassurance for family members, even from afar.

## Purpose

The project we have created connects a smart pillbox to a progressive app. Using the app, caregivers are able to create profiles for multiple patients, set and edit alarms for different medications, and view if patients have taken their medication as necessary. On the patient's side, the pillbox is not only used as an organizer, but also as an alarm to remind the patient exactly when and which pills to take. This is made possible with a blinking light indicator in each compartment of the box.

## How It's Built

Design: UX Research: We looked into the core problem of Alzheimer's disease and the prevalence of it. It is estimated that half of the older population do not take their medication as intended. It is a common misconception that Alzheimer's and other forms of dementia are synonymous with memory loss, but the condition is much more complex. Patients experience behavioural changes and slower cognitive processes that often require them to have a caretaker. This is where we saw a pain point that could be tackled.
Front-end: NodeJS, Firebase
Back-end: We used azure to host a nodeJS server and postgres database that dealt with the core scheduling functionality. The server would read write and edit all the schedules and pillboxes. It would also decide when the next reminder was and ask the raspberry pi to check it. The pi also hosted its own nodeJS server that would respond to the azure server for requests to check if the pill had been taken by executing a python script that directly interfaced with the general purpose input-output pins.
Hardware: Raspberry Pi: Circuited a microswitch to control an LED that was engineered into the pillbox and programmed with Python to blink at a specified date and time, and to stop blinking either after approx 5 seconds (recorded as a pill not taken) or when the pillbox is opened and the microswitch opens (recorded as a pill taken).

## Challenges

* Most of us are new with Hackathons, and we have different coding languages abilities. This caused our collaboration to be difficult due to our differences in skills.
* Like many others, we have time constraints, regarding our ideas, design and what was feasible within the 24 hours.
* Figuring out how to work with raspberry pi, how to connect it with nodeJS and React App.
* Automatically schedule notifications from the database.
* Setting up API endpoints
* Coming up with unique designs of the usage of the app.

## Accomplishments

* We got through our first Hackaton, Wohoo!
* Improving skills that we are strong at, as well as learning our areas of improvement.
* With the obstacles we faced, we still managed to pull out thorough research, come up with ideas and concrete products.
* Actually managed to connect raspberry pi hardware to back-end and front-end servers.
* Push beyond our comfort zones, mentally and physically

## What's Next For nudge:

* Improve on the physical design of the pillbox itself – such as customizing our own pillbox so that the electrical pieces would not come in contact with the pills.
* Maybe adding other sensory cues for the user, such as a buzzer, so that even when the user is located a room away from the pillbox, they would still be alerted in terms of taking their medicines at the scheduled time.
* Review the codes and features of our mobile app, conduct a user test to ensure that it meets the needs of our users.
* Rest and Reflect
## Inspiration:

People living with dementia often lose access to core memories about their loved ones. Having to ask repeatedly for reminders about who their family members are and key stories they shared can be dehumanizing and painful for their family. As students with grandparents who suffer from memory loss, we wanted to create an accessible tool that lets elderly family members remain connected to their most treasured memories and access key details about their loved ones, all without worrying about becoming a burden to their caretakers or family. We aim to preserve cherished memories and human dignity through old age.

## What it does:

To address these challenges, we built Memory. This cutting-edge tech with a human-centered approach allows seniors living with dementia to converse verbally with our Amazon Alexa add-on. Our app consists of multiple features as outlined below:
Memory: Prompt our solution with simple questions like “Remind me about my son” or “Where does my daughter live.” Going beyond simple facts, seniors can ask for reminders such as “Tell me a story about my son” or even something as nostalgic as “I want to hear my childhood song.” These facts, stories, and pieces of music come from user input, which can be custom-added by family members.
Neuro-intervention: Decrease a patient’s agitation and improve communication and caregiver relationships - for instance, with music prompts delaying cognitive decline and promoting brain plasticity in the elderly brain.

## How we built it:

Our application consists of a front-end (conversation) built with Amazon Alexa Developer Tools and a back-end built with Python and Javascript with AWS as our Cloud Server. We built our conversational recall assistant by designing and coding our own custom Amazon Alexa add-on and DynamoDB database within the Alexa developer console and AWS, enabling speech-to-text with accessible transcription of patients’ prompts, and comprehensive queryable insights allowing near-human-like dialogues between patients and Memory.

## Challenges we ran into:

Creating the database from which memories and stories can be pulled was a major challenge. Learning to build our back-end on the Alexa Developer Tool, AWS, and DynamoDB was more difficult than expected, with limited documentation and experience building on the platform. Yet, considering the additional benefits of this platform and device - being easily accessible for dementia patients with its always-on capability - we considered building on this platform essential to enhance accessibility, thus tying our previous knowledge of Python and Javascript to Alexa Developer Tool extensions.

Another main challenge for us was our desire to learn deeply about the medical industry with a specific focus on dementia even with our 36-hour time crunch, meaning that we spent hours researching pain points in the industry. Despite the time pressures this introduced, it ultimately allowed us to refine our idea into a viable product.

## Accomplishments that we're proud of:

As novice coders and first-time hackers, we’re proud of successfully learning and navigating an entirely new interface, creating a product focused on accessibility, and making a significant advancement for dementia patients.
We are also proud of tackling a problem that is meaningful to us both on a deeply human level and that has broad-reaching socioeconomic implications. According to a study by Mayo Clinic, dementia impacts over 55 million elders across the world, costing caregivers and society over 1.3 trillion USD per year. While more than 60-70% of elderly people are affected by dementia today, only 30% have their condition under control. In a time of aging populations and shortages of young workers, it will become increasingly critical to elders to be able to recall memories independently and without reliance on caretakers. Harnessing the increasingly widespread technology of home conversational assistants like Alexa is, we believe, an important first step towards tackling this problem.

## What we learned:

Technical: Building this app, we learned about new programming tools (Amazon Alexa Developer Tools and AWS), collaborating as a team, and creatively solving problems.
Industrial: Throughout this hackathon, we also had the opportunity to learn about a significant industry and to learn from other developers and mentors who provided valuable industrial and individual pain points.

## What's next for Alexa Memory

Beyond these 36 hours, we hope to explore several paths:
We hope to allow Alexa Memory to record memories when elderly users do remember them and decide to tell their stories orally, so that when the user loses access to these memories, Alexa Memory can play it back to them. This feature would benefit not only the elderly but also their younger family members. The recording function can help to preserve oral histories that are often lost, so that they can be passed down through generations.

We need to conduct deeper research on effective behavioral treatments of dementia and the specific needs of dementia patients. We’ll do this through more interviews with professors and researchers, physicians, and patients and their families. These conversations will help us build product features that truly solve our users’ pain points and help us better empathize with their experience.
Lastly, we aim to finish building out our user input system, in order to allow family members to input memories and facts that the elderly user can then ask Memory to recall.