WORLD CLASS FALL PREVENTION
Infonomy develops biokinetic algorithms to promote a healthy living. Drawing on a deep knowledge of physics, physiology and IT, the algorithms are the result of more than seven years of R&D. The algorithms optimize energy consumption, processing capabilities and storage capacity of sensor systems.
Infonomy products such as the Snubblometer® set out to change the game of fall prevention. Infonomy’s iCore platform delivers cloud services based on information from sensor data to a wide variety of customers.
Infonomy fight the battle against the increasing number of fall injuries. Our strategies are:
Fall Detection aims to detect and inform that a fall has occurred. In this area, our “Fall Hunter” algorithms are licensed to leading international partners.
Fall Protection aims to either passively or actively protect against fall injuries as they occur. In this area, our algorithms analyze sensor data and activate protection when fall injury is imminent. We cooperate with world leading industrial partners and license our algorithms.
Fall prevention aims to prevent falls altogether by means of timely intervention. In this area, our algorithms collect and analyze data from a large number of sensors, establish when the risk of falling changes. and assess the effectiveness of interventions such as personalized training programs. In this area, we are a leading partner of “MoTFall” – a cutting edge project sponsored by the Swedish Innovation Agency (Vinnova).
The Snubblometer® is a wearable device that has been scientifically proven to measure and analyze changes in gait, movement patterns, balance and near fall events. The solution consists of hardware and software that have been developed in close cooperation with end-users, which has resulted in a perfectly ergonomic and user-friendly solution. It cannot be sensed by the wearer and is thus completely unobtrusive and non-stigmatizing.
The product reports and analyses aggregates that have a well-documented diagnostic relevance. The analyses include:
- Gait analysis – continual analysis of step size, step frequency, posture, balance and sway parameters and more over periods of sustained movement.
- Detection of discrete events – the tool recognizes predefined features in movement such as “rise out of bed”, stumbling and fall detection. Measurement of activity levels and sleep quality.
Infonomy have developed a portfolio of proprietary algorithms that transform sensor data into valuable information. We optimize our algorithms for specific needs of the customer and for the device that they are integrated into. Most of our algorithms track and analyze human movement, but some are used to track movement of machines and vehicles as well. Furthermore, we have a long history of developing and managing image processing algorithms for e.g. automated meter reading.
Examples of Infonomy’s proprietary algorithms are:
- Biometric and Bio-kinetic algorithms
- Algorithms for fall detection, -protection and -prevention
- Activity classification and analysis
- Measurement of balance
- Gait analysis
- HR/HRV, stress analysis
- Classification and analysis of Activities of Daily Living
- Classification of meditation states
- Algorithms for logistic and industrial applications
- Analysis of machinery in the packaging industry
- Image processing algorithms for automated meter reading
- Other algorithms
- Analysis of rhythm in music
Infonomy’s iCore is an end-to-end platform for sensor networks. iCore allows you to extract, structure and visualize your business critical information. Information blind spots disappear as you begin to see in real time what is happening in your business. iCore truly enables machine to machine communication and the internet of things.
iCore is built on the philosophy that the world is changing and so are your information needs. iCore has been designed as a universal platform that is adaptable and future safe. Because iCore is information and application driven, it supports your business as your information and application needs grow and change; iCore adapts as sensors are added to your network, new sensor types are added, and as you want to do more with your sensor data.