Problem Statement:
In urban environments, with growing urbanization and increasing population, the risks of mishaps due to slip and falls imposes a significant health concern. If not properly addressed, falls can lead to severe injuries and in some cases even lead to fatal accidents especially among the aged population. Traditional fall detection methods rely heavily on manual observation and human intervention. These methods may not always be effective in detecting falls. There is urgent need for an innovative and novel solution for Slip-and-Fall accident detection which would detect falls accurately in real-time real-time, providing prompt alerts to emergency responders and medics for timely assistance, thus reducing the impacts of severe such accidents.

Solution:
The smart solution incorporates IoT devices like cameras and sensors along with video feed installed in important crowded places like commercial places, transit hubs, public places, etc. and AI-driven technology to detect falls in real time. Leveraging Computer Vision and AI/ML algorithms, the system can differentiate between normal activities like standing or walking and potential anomalies like falls with higher accuracy. Urgent alerts can be sent to emergency services once an anomaly is detected, potentially providing immediate assistance and thus avoiding severe accidents.

Aimpact Value:

1. Slip-and-Fall Detection – Anomaly Detection to
2. Predictive Analysis – Behavioral Analysis to identify falls
3. Data Integration – Consolidating audio and video feed from all IoT devices
4. Continuous Feedback – Real-time feedback for all IoT sources for continuous monitoring
5. Automatic Alerts – Prompt alerts to concerned authorities for quick action

Industries:

1. Travel & Logistics
2. Public Services
3. Healthcare
4. Retail