SensorBeat offers a number of advantages compared to cloud-based AI solutions
SensorBeat operates in real time on the device, which means that we can create a system with no/low latency between data creation by the sensor, decision and action.
SensorBeat is intelligent and autonomous, which means that once installed in a hardware device it can think for itself and it does not have to be connected to a network all the time. It can be disconnected and connect when a certain situation or trigger occurs.
SensorBeat AI learning is easy and fast and can be done with small datasets. Many other AI systems are slow to learn and require large datasets.
Having the intelligence on the device lowers the amount of sent data, because it will only send curated data. Sending less data lowers power consumption, because running the software on the device consumes much less power than transmitting all sensor data over Bluetooth LE (BLE) or any other communication method.
SensorBeat Lib can run on low-cost microprocessors, such as ARM Cortex M0. Cost is very important when you want to roll out millions of devices. SensorBeat Lib requires around 10 kB of RAM on the device.
Sending less data will lower the costs for data communication.
SensorBeat Lib is communication-agnostic, ie it works with all communication technologies in the market, eg Bluetooth LE, 2G/3G/4G/5G and WiFi. Using SensorBeat together with Low Power Wide Area Networks (LPWAN), such as NB-IoT, Sigfox or LORA is an ideal fit, since they share the same qualities that are important for IoT networks, low power and low cost devices.
Streaming and storing data in the cloud makes you vulnerable from a privacy perspective.
SensorBeat takes sensor data or any input signal and translates into useful information
The SensorBeat AI Core is the core component in all our products. It includes generic functions for classification and AI learning.
Feature extraction is specific for each application (Motion, Gesture, Signal, etc).
Data collection is specific for each use case.
SensorBeat Capture comprises a mobile app and a capture device. It’s used to capture synchronised motion sensor data and video during the data capture phase. Once the data capture is ready, it is sent to SensorBeat Analyser for analysis.
SensorBeat Analyser is a software package for analyzing and collecting action from motion sensors and video. This is where you do the AI learning. You can playback video with synchronised sensor data, in order to identify important patterns. The result of the analysis is the motion pattern database for SensorBeat Lib.
SensorBeat Lib is a embedded software package that includes the SensorBeat software and the pattern database. It can be easily integrated into 3rd party hardware platforms. SensorBeat Lib is fast, accurate and has a very small footprint, which means that it can run on low-cost and low-power hardware devices. For OEM devices SensorBeat Lib is a source code / pre-compiled library written in ANSI C. SensorBeat Lib is also available on iOS, Android and Apple watchOS.
Download this fact sheet that explains the actual power consumption for SensorBeat Lib for a specific application.