QCare - Menopause care app

What is menopause?

Menopause is signalled by 12 months since last menstruation. Common symptoms include hot flashes and vaginal dryness. There may also be sleep disturbances. The combination of these symptoms can cause anxiety or depression.

Menopause is a natural process with treatments that focus on symptomatic relief. Vaginal dryness is treated with topical lubricants or oestrogen. Medications can reduce the severity and frequency of hot flushes. In special circumstances, oral hormone therapy may be used.

First of all Thanks to AARP innovation lap for doing research on a problem which is very helpful for every woman. I'm very excited about research this project and did some of my own reasearch on this. But the sad part is I'm not able to make anything. Because i can't order my components due to lockdown. So, I'm just saying my idea and I will update the design process once arrive my packages.

My Idea

There are 35 Symptoms for menopause. So, here we are taking the main 2 symptoms which are Hot flash and mood swings. We have a wearable device and supporting android app.

In wearable device consist of Temperature sensor, Microcontroller, Bluetooth and Heartbeat sensor.

Microcontroller collects all data and sends into the associated app via Bluetooth. using these data app will predict is there any chance for hot-flash or mood swings.

For predicting mood swings app will collect all the voice and do sentiment analysis. If there are any sudden mood swings find. The app will give a trigger to the vibrator in the wearable device and send some push notification as some suggestions to get rid off from the symptoms. If the app detects hot-flash via data from temperature sensor it will send push notification to user for drinking maximum water, washing sensitive area and all.

How to do sentiment analysis?

Sentimental analysis easily was done by using google NLP toolkit. Activate NLP API and then collect all voice > Do Speech to text > send into API these operation loops in every 3 minutes and if there is any sudden change in mood. Through this app will find the mood swings. Also, it uses heart-beat sensor data to increase the accuracy of prediction.

How to take into the next level

I would like to add nerve simulations to control mood swings. Also, want to add a cooling mechanism during hot-flash through wearble device. Now,I'm looking for a cooler which less in size, cheap in power usage.