Shashwat Pradhan – Founder & CEOシャシュワット プラダーン

Problems around the Quantified Self1 Manual tracking

2 Too many apps/wearables to get data

3 Insufficient value from tracked data

Mobile enabling automatic- With new sensors in mobile and wearables

- Machine learning/AI algorithms

- Self-tracking can be automatic for everyone

Mobile enabling automatic- Google Fit & Apple Health helping consolidate fitness

data

- This extends to smart watches too

But data is not enough- Numbers are not enough to improve/implement

lifestyle change

- E.g. 50% users stop using their Fitbit in one year

But data is not enough- Users are not getting enough value from their data

- No improvements being made (eg. Weight loss)

Getting Value from data- Real-life/AI Coaches to help users take action on their

data

- Reports using aggregates, trends with graphs and story-telling with data

Privacy is key- Users conscious of where their personal data is going

- Data breaches can be risky for location or health data

Instant- Instant automatically tracks phone/app usage, travel,

sleep, fitness & places- Data- Phone sensor + Healthkit/Google Fit + O.S.- Machine learning/AI algorithms on top of data

Insights on your data- Apart from dashboard- Daily & weekly reports using

story-telling and graphs- Chatbot coach to query data and gives alerts from

trends and correlations

Insights on your data e.g. You have been spending more time on your phone since the last few days (35%), also you have been sleeping lesser (-20%)

Private- No user registration needed

- All tracked data stays only the smartphone

Future- Integrating with more apps, services & wearables

- Working with more sets of data HR, weight, etc

What Quantified Self users are looking for- Better insights on their data to make apps/services

more sticky- Making tracking simpler, cheaper & less battery

intensive

For more

- Instant – http://instantapp.today

- For Quantified Self news & updates, Emberify blog: http://emberify.com/blog

- Contact me at shashwat@emberify.com

Thank youありがとうございました