Ultra-low Power Tiny AI Chip for Wearables

NeuroSense, a Tiny AI chip based on the Neuromorphic Analog Signal Processor, addresses the most annoying problems of mass-market wearables: power consumption, accuracy of heart rate measurements, cloud connectivity and data privacy issues

Mass market wearables could be smarter!

People need them always-on, low-power, accurate and cloud-independent

To support modern Neural Networks’ capabilities, a wearable’s hardware consumes a lot of electricity, because AI requires compute resources and cloud connection. That’s why mass market wearables, such as fitness trackers, smartwatches, health monitoring devices, and remote care wearables for seniors, have short battery life and low tracking accuracy.

Is underperformance annoying? Sure! And it can be even dangerous...

1Heart rate calculations, while in motion, have low accuracy due to inconsistent noise factors

2PPG and IMU calculations are resource-hungry

3Human activity learning requires loads of raw data transferred to  the cloud

Problems solved by NeuroSense

1Three times better heart rate accuracy than algorithm-based calculations

2Ultra-low power consumption below 100µW

3Human activity recognition on sensor level, cloud-independent operation

heart rate measurements

1 sec:  ECG reference, NeuroSense 

Heart rate measurement accuracy by NeuroSense is twice as accurate as algorithm-based calculations.

Wearable Device Application with NeuroSense
NASP for wearables

NeuroSense offers increased accuracy of heart rate monitoring and the advanced functionality of human activity recognition.
A simple PPG configuration with only two LEDs and one photodiode is enough for the heart rate measurement.
NeuroSense can inform the user about a weak PPG signal based on the heart rate confidence metric value, prompting the user  to adjust the wearable device.
Activity matrices allow the wearable device to offer classification-based features:

  • Learn and automatically detect new human activities
  • Compare the activity execution to a desired reference execution (for example, of the trainee to the trainer’s execution)
  • Classify human activity into the base classes like rest or moderate or intensive activity.
NeuroSense Competitive Edge

NeuroSense outperforms other solutions in the market of affordable wearables. 
In typical devices, power savings come at the expense of performance and accuracy limitations. Otherwise, high accuracy may result in low operating times below 20 hours on the battery charge.

In contrast, NeuroSense enables by orders of magnitude longer operation and continuous monitoring and recognition due to analog computations and additional power-saving methods. At the same time, the neural network approach makes biometrics extraction far more accurate than algorithmic methods.

NeuroSense saves the limited space inside a small wearable device. 

Power Consumption - mW
NASP: 0.1
Typical Sensor Unit: 10-12
Size - mm x mm
NASP: 6 x 6
Typical Sensor Unit: 9 x 9
Accuracy - MAPE %
NASP: up to 4%
Typical Sensor Unit: 9 - 13%

Neuromorphic Module instead of the sensor MCU

Longer battery life due to ultra-low power consumption

Small IC footprint

Low manufacturing costs

More parameters to measure

Oxygen saturation, arrhythmia, steps counter, sleep tracking, stress monitoring

Any human activity learning and recognition

Tunable for more applications

Providing short time-to-market