AI Wellbeing Monitoring Solution

About

Falls among seniors are a critical safety concern. Each year, millions of older adults experience fall-related injuries, which often lead to hospitalization and loss of independence. There is a clear demand for an intelligent, reliable well-being monitoring solution that can detect falls in real time, provide remote monitoring, and integrate seamlessly with smart home systems. Wisdom.io is a New York-based healthcare and smart home company that provides innovative systems for improving the safety of older adults. The company specializes in real-time fall detection and routine monitoring technologies, helping seniors maintain independence while ensuring peace of mind for their families and caregivers. Wisdom.io collaborated with Softarex to develop a secure, scalable, computer vision-enabled platform. This platform enables caregivers and families to monitor the well-being of elderly relatives and receive timely alerts in case of emergencies.

Challenge

Real-time accuracy. The system had to be able to detect falls and prolonged inactivity with high precision while operating efficiently on edge devices with limited processing power.
Device compatibility. It had to work reliably with both standard and non-standard IP cameras to ensure seamless integration in diverse environments.
Continuous reliability. The solution had to guarantee stable, low-latency video streaming during continuous operation.
Scalability. The infrastructure must support future product launches and market expansion while maintaining high security and compliance standards.

Solution

For this project, we developed an AI-powered well-being monitoring platform designed to detect falls and prolonged inactivity. This solution integrates edge device software, cloud infrastructure, and user-facing applications.

Phase 1

During the MVP stage, we focused on delivering a computer vision–powered intelligence engine capable of real-time event detection, optimized for edge devices through TensorRT and quantization techniques. We also developed web and mobile applications that enable caregivers and family members to manage devices, view live video streams, and receive instant alerts about potential incidents. To ensure reliable performance, we set up a robust development and deployment pipeline using AWS infrastructure, Docker, Nginx, and Janus server configurations to enable low-latency streaming.

Phase 2

The project’s second phase focused on advancing the platform from an initial MVP to a commercially-ready product for in-home patient monitoring. We implemented robust 24/7 monitoring, logging, and automated scalability mechanisms. One of the key objectives was integration of mmWave radars for fall detection. This radar-based system ensures user privacy by detecting falls without using cameras, making it effective in environments where video is impractical due to privacy concerns, steam, or other visual interference. To ensure enterprise-grade reliability, our DevOps team implemented a scalable cloud infrastructure with robust 24/7 monitoring and automated scalability. We further prepared the product for market with streamlined customer onboarding tools, including HubSpot CRM integration. A major focus was placed on security and compliance, with strict data protection and access controls implemented to safeguard user information.

This is a custom solution

Does it look like it may be a good fit for you? Contact us, and we’ll build a custom solution tailored specifically to your business needs.

Project’s results

The delivered solution enabled Wisdom.io to:

Provide caregivers and families with real-time alerts about falls and routine activities.
Achieve high detection accuracy via Computer Vision and radars while maintaining efficiency on resource-limited devices.
Guarantee low-latency video streaming and stable, continuous operation.
Ensure adaptability through multi-camera support and non-standard device integration.
Establish a solid foundation for future market launch, with secure infrastructure, scalable architecture, and customer support tools.

Value delivered (KPIs)

The MVP was developed in six months.
The fall detection algorithm demonstrated a very high degree of accuracy during real-world testing.
The system processed video streams in real time on low-power Jetson devices.
It delivered stable 24/7 operation with optimized memory usage and continuous uptime.