A broad range of sensors are already widely available that can capture measurements that combined with sophisticated machine learning algorithms can be presented to the user as actionable health and activity data. Beyond accelerometers and gyroscopes for tracking our movement, modern wearables can, for example, be embedded with biosensors to track brain and heart activity, galvanic skin response sensors to measure sweat gland activity and therefore an individual’s stress levels, as well as heart rate and oximetry monitors to measure fitness or identify a poor respiratory response to exercise.
As the sensors have become more sophisticated, so by necessity have the System-on-Chips (SoCs) that both power them and provide them with smartphone connectivity. The sensors continuously churn data and demand the services of a processor capable of rapid Digital Signal Processing (DSP) and Floating-Point (FP) arithmetic to transform it into accurate information for the user. For example, Nordic’s high-end nRF5340 SoC features a dual-processor hardware architecture with a dedicated application processor to meet the complex computational requirements of the next generation of sophisticated wearables, as well as a network processor optimized for low power radio operation to look after the wireless connectivity.
While wireless SoC makers such as Nordic have extended the capabilities of their solutions, developers of wearables for health and wellness have not stood still either. UK-based DnaNudge launched its DnaBand that uses a combination of the user’s DNA and wireless technology to help people make healthier food choices. With the user’s genetic profile loaded onto the wearable, they can then scan the barcodes of approximately half a million food products and instantly