Overview / Usage

As per the WHO Global Report on Falls Prevention in Older Age

•Approx. 35% of people aged 65 or more fall each year. •Older people staying in nursing homes fall more often than those living in community housing

•Between **30 to 50% **people living in long-term care institutions fall each year.

•Injuries tend to worsen in cases of people unattended during a fall

Existing solutions being wearables suffer in terms of scalability, proactive adoption and cost-effectiveness. Especially if the solution needs to be adopted by Elderly homes.

Methodology / Approach

Utilize existing video surveillance infrastructure at elderly homes and provided AI driven notifications when high-risk areas are accessed or a habitant is found to have fallen.

Technologies Used

The prototype is built using

• Raspberry Pi
• Intel Movidius
• PoseNet + Tensorflow