Algodroid R&D Project: Building a Computer Vision System for Preventing Falls in the Elderly

Machine learning
Computer vision
Smart home


When people grow old, falls can be extremely dangerous. They are often the result of a sudden medical condition, such as stroke, seizure, or heart attack. The problem is especially acute for people who live alone: once they fall, a significant amount of time can pass before they receive assistance.


To reduce the risk of falls, numerous companies are working on technology solutions for fall prevention. These solutions include wearable devices, such as medical bracelets, reaction-based alarms, virtual sitters, video monitoring systems, and even smart shoes.


One of our clients, a Belgium-based startup called Algodroid is working on technology that uses cameras to detect falls in the homes of the elderly. They turned to us to help implement their solution.

The problem Algodroid intended to solve with technology wasn't a trivial one. To build a system for fall detection we needed to implement a set of intelligent algorithms that would be able to:

  • Recognize a human posture (sitting, walking, lying down, falling)
  • Detect a human in a frame
  • Detect the occurrence of a fall
  • Differentiate between an incident of falling and the process of lying down


Because of the complexity of the project, we offered Algodroid our Research & Development collaboration model that entails scientific research and project feasibility evaluation. We did thorough research in the area of computer vision and machine learning and are currently working on the implementation of artificial vision algorithms that enable video data collection and analysis.

"INTEGRA maintains a patient and helpful approach, and there have been no communication issues amidst time differences. They’ve provided regular and comprehensive progress updates to ensure accuracy, as well as constant resource availability, throughout the ongoing project."

Philippe Laxan, founder of Algodroid

Technologies Used

C++ language in combination with OpenCV library has been used for algorithms prototyping and receiving PoC.
We used JeVois smart machine vision camera as a compact and cheap solution which could be integrated into an MVP product.
Depth cameras, such as Intel RealSense and Orbbec Astra have been used for distance estimation to an object under observation.
We used Nuitracl library for working with depth channel.
BodyTracking algorithms have been used for obtaining information related to a human's silhouette.
Banana Pi Media Board Computer has been used as an RTSP server for streaming frames received from Astra camera.
RTSP server has been implemented with the help of C++ language in combination with live555 library.
Qt Framework has been used for developing a cross-platform GUI application for further interaction with Banana Pi board.


The video monitoring system for fall prevention we at Integra Sources are currently working on can be broken down into four parts:

  • 3D cameras to track older people's activities throughout the day
  • Single board PC for processing data from the cameras.
  • Artificial vision algorithms to recognize human postures and detect if a person has fallen
  • A communication system that sends an alarm message to caregivers along with a picture once a fall is detected.

Make some noise

Scope of work

Picked out a 3D camera with depth sensing to identify the object's state in the dark

Developed computer vision algorithms in C/C++ using OpenCV

Built a communication system that gathers information from all the cameras installed in the house

Make some noise

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