Tiny wrist-worn sensor that collects data for over 22 weeks with one charge.
Automatic data transfer to secure cloud for hassle free remote data collection.
Transparent, validated open-source algorithms to analyse the raw tri-axial acceleration signal.
Fibion’s innovative cloud-based actigraphy monitoring system delivers 24-hour, Real-World Data information about sleep/wake behavior, circadian rhythms, and daytime physical activity.
With its industry-leading 22 weeks measurement time with the initial charge, Fibion Sleep opens up totally new possibilities to research designs and clinical use settings.
Fibion Sleep records raw tri-axial acceleration, so you can choose the algorithm that best fits your needs and participant group. Full access to “future-proof” raw sensor data means that data can be analysed indefinitely, and new and more sophisticated sleep algorithms and analysis techniques can be applied to data as they emerge.
The Fibion Sleep sensor makes your data collection as hassle-free as possible. No charging, no cables, no buttons just smooth workflow to get accurate actigraphy data. Unrivalled 22 weeks measurement time with the initial charge.
The Fibion Sleep sensor fits in different wristband styles. Let your participant decide on the most comfortable solution.
Getting research data has never been this easy. Any smart device with Fibion App will draw data on all nearby sensors securely to the cloud service. Simple Fibion App works basically on any Android and iOS smartphone or tablet.
You can start and stop measurements remotely with a tap of a finger. The cloud platform is accessible from anywhere with a smartphone, tablet, or computer.
As data is located in the secure cloud, anyone in your research team can access it instantaneously anywhere in the world. They can check how measurements are progressing, check quickly that everything is correct in data, and even start analysing the data when participants still have the devices. The new level of convenience.
Fibion records raw triaxial acceleration data, so you can use any open-source algorithms developed by the scientific community to generate meaningful, reliable measures.
Raw data makes data also “future-proof” as you can apply new and more sophisticated sleep algorithms to collected data as they emerge. For example, the DORMI algorithm, which uses neural networks to give you relevant sleep metrics, such as total sleep time (TST), sleep eficiency and wake after sleep onset (WASO).