An inventive product named iStick, specifically designed for the visually impaired,  was recently developed by a team of HD in Computer and Electronic Engineering students at IVE (Haking Wong). Despite recent calls by the Hong Kong government for the establishment of “barrier-free passages”, there are still many obstacles to achieving completely barrier-free access for all. The team deployed innovation to transform white canes into unconventional iStick,  a multifunctional guide stick to provide users with a friendlier and safer experience through the following features:

AI Object Read-out – Names of obstacles are identified using the YOLOv4 AI algorithm and read out loud using a speech system.

Fall Detection – When a fall occurs, nearby pedestrians are alerted for help through light and sound effects; an emergency message is sent to a family member’s smartphone; and the user’s current location and health status are recorded, along with the road slope.

Road Slope Feedback – The slope of the road is calculated by a microcontroller through a combination of LIDAR and a Gimbal system with an MEMS motion sensor. If the slope value indicates that the user is going downhill or uphill, the angle of the stick is adjusted for balance by a feedback servo mounted on the handle. Through this feature, the user is alerted to potential hazards and accidents can be prevented.

Health-Monitoring System – When the users presses the health sensor mounted on the handle, their current heart rate and blood oxygen level are read out by a speech system. The user’s health readings are also uploaded to the cloud, enabling family members to view their health history through a smartphone app.

IoT Data Logging, Monitoring, and Analysis – Through a combination of cloud services and a smartphone app, family members are sent emergency notifications (e.g., following falls) and can view the user’s current real-time location and health status, the locations of past falls, the context of each fall (health status and road slope at the time), and fall statistics (e.g. number of falls for each slope level).