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:
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."
The video monitoring system for fall prevention we at Integra Sources are currently working on can be broken down into four parts:
Make some noise
The app uses computer vision algorithms for processing images of a mole and detecting malignant melanoma. We achieved 80% diagnostic accuracy without any machine learning methods
The device is a plastic bracelet designed for patients at military hospitals and clinics in the USA. It notifies hospital staff about an emergency with the patient's condition