Most of the safety software for women on the market today can be used only to alert the police by contacting family members in case of an emergency, which is often not enough to stop an incident.
Our team was inspired by the star-seeker project (which was invited to the mobileHCI conference), which uses real-time location data, combined with machine learning, about the location of an assault to design safe routes for women and to forecast possible dangerous locations. We will also use the community to build a broadcast system so that if a woman is in danger, she can immediately broadcast information to surrounding users to get help.
Needy will also build a personal credit system with the help of web3 technologies, such as decentralized autonomous organisation (DAO) and soulbound tokens (SBT). The Needy project will help reduce the number of fraudulent accounts by protecting users' privacy and ensuring the authenticity and timeliness of community help information.
By dividing the infringement into three stages – incident prevention, broadcasting during an incident, and collecting evidence after an incident – we were able to design targeted features for each of the three phases, from a security map to a warden system and broadcast feature, and we received good feedback in focus groups and mock user tests.
Team members
Mr Sun Xinjie (Zhejiang University)
Mr Chen Yipeng (Zhejiang University)
Mr Yu Fengyuan (Zhejiang University)
* Person-in-charge
(Info based on the team's application form)
- CityU HK Tech 300 Seed Fund (2022)
- Golden Glasses Award for Best Creativity, 2022 Summer bootcamp of future interaction for smart glasses