Maxim shows three ways to speed up health wearables design
January 29, 2020
Californian electronics company Maxim Integrated says there are three ways its bio-algorithm sensor hub can speed up the design of healthcare wearables.
Say a company has an idea for a new type of wearable, something that will accurately measure parameters such as blood pressure, heart rate and blood-oxygen levels (SpO2). The market for these devices is hot, so the firm wants to time the launch of the device to the most opportune market window.
One way to streamline the development process is by using an integrated bio-algorithm sensor hub. Such a hub consists of an IC with installed algorithms that seamlessly connect to a device's optical sensor and host microcontroller. With the right integrated hub, designers won't have to worry about acquiring the expertise needed to develop sensor-based algorithms. They can also reduce schedule and validation risks.
Convenient and regular monitoring of certain health parameters can contribute to better overall wellbeing, enabling people to be more proactive about their care. Consider blood pressure. Too high or too low, and there could be serious health consequences. Blood pressure is vital, yet invisible. The market need for blood pressure monitoring is clear and, considering the statistics, rather urgent.
According to the Centers for Disease Control & Prevention, about one in every three American adults has high blood pressure and the same number has pre-hypertension. Having high blood pressure increases the risk for heart disease and stroke, and high blood pressure costs the USA $48.6bn a year in health care services, medication, missed days of work and related factors. Low blood pressure can trigger dizziness and fainting.
By knowing blood-pressure numbers, people are equipped to take the steps needed to prevent it from getting too high or too low, or remedying an unhealthy situation. Familiar blood-pressure monitoring systems include cuff-based devices that measure absolute blood pressure, as well as optical devices.
An example of an optical device is a smartphone that measures blood-pressure trending via a photoplethysmography (PPG) sensor integrated on the phone. Both of these examples are resting-state monitoring methods, the only approach that the clinical community recognises. The optical approach can tap into the traditional method as part of its calibration process. An example use case can take these steps:
- Step 1: The user measures blood pressure using a traditional monitoring device and enters the value into the optical device's GUI at resting state.
- Step 2: The user places his or her finger on the PPG sensor to complete calibration at resting state.
- Step 3: The user places his or her finger on the PPG sensor to monitor blood-pressure trending at resting state.
The firm’s Max 32664 version A bio-algorithm sensor hub can streamline the development of health-monitoring applications that meet medical-grade requirements in body-worn form factors. It supports fingertip-based applications that monitor heart rate, heart-rate variability and blood oxygen (SpO2).
For example, the sensor hub can be integrated into an application such as a biometric smartwatch that tracks vital signs and is capable of sharing the user's data in real time with healthcare professionals. This type of wearable application can be useful for, say, remote patient monitoring.
Here are three ways that an integrated biosensor algorithm hub such as the Max 32664 can streamline the development of a health monitoring wearable:
- Reduce design time and validation risks by ensuring that disparate components will work together effectively, thanks to embedded firmware and algorithms for health and fitness wearables. The algorithms are also customised to specific body locations and applications.
- Simplify the design process by taking advantage of complete optical system design guidance and ready-to-use reference designs. Also, field updates can be made to the sensor data processing algorithms.
- Accelerate time to market. Since the algorithm code runs on a small, dedicated sensor hub microcontroller, designers won't have to integrate into an existing application processor. And they won't have to spend time writing their own algorithms.