Case Study

Facilitating Smarter and Safer Home Care with Computer Vision in Healthcare Solution

The global home healthcare market surpassed $424 billion in 2024 and is projected to reach $816 billion by 2033, driven largely by a 7–10 % CAGR fueled by aging populations and rising demand for in‑home clinical services.

At a Glance

This case study highlights how computer vision can make home healthcare simpler, safer, and more accessible.

After the pandemic, many people wanted to manage basic health needs, like injections and blood tests, from home. But without proper guidance, these tasks felt overwhelming and risky. The goal was clear: build a mobile app that could guide users step-by-step, using just their phone camera.

By combining technologies like visual recognition, pose detection, and real-time feedback, the solution turns complex medical steps into easy-to-follow actions, giving patients more confidence and freedom to care for themselves.

Key Highlights

Step-by-step self-injection guidance

Real-time pose and position detection

Visual recognition of medical devices

Enhanced Safety for Home Procedures

Reduced pressure on healthcare systems

Built with robust AI stack

Challenges

Detecting hand/device position in real-time with high precision
Managing false positives during pose or angle detection
Designing a smooth user experience for first-time users or aged users
Handling device readiness detection (e.g., decapping) reliably
Balancing AI complexity with mobile performance constraints

About Client

The client is a HealthTech innovator focused on making at-home care more accessible, safe, and tech-driven. They specialize in solutions that blend medical expertise with intelligent technologies to improve everyday patient experiences.

Technology

Home Care

Use Case

Location

Explore Our Computer Vision Development Services

How we helped?

We engineered a mobile app that turns a smartphone camera into a smart medical guide, designed specifically to support self-injection and blood testing at home. This computer vision in healthcare solution combines multiple layers of AI to offer real-time, interactive support and safety checks. Here’s a breakdown of the key features:

60%

Reduction in clinical support calls

40%

Faster completion of home procedures

Improvement in care delivery efficiency

The Solution

➡️ Visual Device Recognition

The first step was making the app smart enough to recognize what the user is holding. Using ML Kit, the app detects the type of medical device, whether it’s a specific injection pen or a blood collection tool. This ensures the app delivers the correct instructions tailored to that exact device.

➡️ Pose Detection

Correct body posture is critical for safe injections. We integrated MediaPipe, Google’s cross-platform framework, to detect the user’s overall pose, such as arm placement and angle, so the app can guide users to adjust their posture if needed before proceeding.

➡️ Holding Position Detection

To prevent misalignment or incorrect application, we used OpenCV to analyze the angle and contact point between the device and the user’s body. The system checks whether the device is positioned in the right spot before allowing the user to continue.

➡️ Holding Time Detection

Accuracy isn’t just about position, it’s also about timing. This feature tracks how long the device is held against the skin to make sure the injection or blood collection process is fully completed. It helps reduce errors from early removal or rushing.

➡️ De-capping Detection

Before use, injection pens or collection tools need to be uncapped. We trained the system using TensorFlow and Ultralytics YOLO to identify whether the cap is still on. If the device isn’t ready, the app gently alerts the user to correct it before proceeding.

➡️ Step-by-Step Visual Guidance

Every interaction is designed to feel intuitive. Clear visuals, animations, and voice prompts walk users through each step in real-time. Whether someone is using it for the first time or managing a routine, the app ensures they feel confident and supported throughout.