STMicroelectronics Rolls Out Biosensor for Health and Fitness Wearables

ST’s new solution has a highly integrated analog front end for advanced wearable features.

STMicroelectronics has recently introduced the ST1VAFE3BX, an advanced biosensor designed to enhance wearable technology in healthcare and fitness. 

The ST1VAFE3BX fuses input channels for cardio and neurological sensing, motion tracking, and an embedded AI core.

The wearables market can deliver sophisticated health monitoring to both consumers and medical professionals. Continuously tracking metrics such as heart rate, brain activity, and movement is no longer limited to single-function devices. By simultaneously tracking physiological signals and physical movement, ST’s latest biosensor addresses a growing demand in the wearable industry for compact, multi-functional devices that deliver continuous health insights with minimal power requirements.

A Biosensor With a Low Power Profile

ST's new ST1VAFE3BX biosensor (datasheet linked) integrates a vAFE and a three-axis accelerometer to provide detailed biopotential and motion analysis for wearables and portable health devices. 

The vAFE channel features a differential input amplifier with programmable gain and selectable input impedance (ranging from 100 MΩ to 1 GΩ) optimized to capture subtle biopotential signals such as ECG and EEG at up to 3,200 Hz. This architecture enables fine-tuning of the analog signal processing directly within the sensor while an embedded 12-bit ADC digitizes the signal for real-time analysis.

Block diagram of the ST1VAFE3BX. 

The accelerometer has a configurable full-scale range (±2 g to ±16 g) and low noise levels down to 220 µg/√Hz, offering output data rates from 1.6 Hz to 800 Hz. Integration with the vAFE allows synchronized biopotential and motion data measurements. A machine learning core and a programmable finite state machine (FSM) also enable on-device pattern recognition and processing to reduce microcontroller demands and power consumption.

Communication options include I²C, SPI, and MIPI I3C interfaces, with an embedded 128-level FIFO buffer that supports data batching and enhances power efficiency by reducing frequent host processor polling. The device is housed in a compact 2.0 mm x 2.0 mm x 0.74 mm LGA package and operates within a temperature range of -40°C to +85°C. It also has advanced features such as adaptive self-configuration, high-shock survivability (up to 10,000 g), and built-in pedometer functions.

A Primer on Biopotential Sensing

Biopotential sensing detects electrical signals generated by the body’s physiological processes, typically measured across the skin’s surface or within specific tissues. Produced by ion fluxes across cellular membranes, these signals are instrumental in monitoring physiological functions such as cardiac rhythm (ECG), brain activity (EEG), and muscle movement (EMG). When ions move within cells, they create minute voltage changes that biopotential sensors detect. The sensors amplify these weak signals to make them analyzable.

The equivalent circuit of an electrode placed on the skin.

A biopotential sensor captures these high-sensitivity voltage fluctuations using electrodes in contact with the skin or tissue. To measure biopotentials accurately, a sensor system typically includes an analog front-end designed to filter, amplify, and digitize the signals for processing. 

Since biopotential signals are often less than a millivolt, they are highly susceptible to noise interference, including electromagnetic noise from nearby electronic devices and motion artifacts. For this reason, the initial analog input stage features a differential amplifier with programmable gain to increase signal amplitude while rejecting noise. Anti-aliasing filters within the AFE help eliminate higher-frequency noise.

A Step Toward Low-Power, Continuous Monitoring

The ST1VAFE3BX biosensor integrates high-performance biopotential sensing and motion tracking to serve advanced applications in wearable health technology. By reducing reliance on external processors and achieving lower power consumption, such solutions could drive wearable devices toward truly continuous monitoring without compromising battery life.