Optical sensors shine a spotlight into our health, highlighting observations on which we can act to guide our routines and habits.
Optical sensing is important because it's extremely versatile. We shine some light into a region that we want to analyze, the light will interact with the analyte, and we look at how that light changes. This is very non-intrusive and non-destructive.
Ian Chen, Executive Director, Maxim’s Industrial & Healthcare Business Unit.
During his speech at the Sensors Expo and Conference in San Jose, California, Ian Chen, an executive director in Maxim’s Industrial & Healthcare Business Unit, discussed how optical methods are being applied in bioanalytical functions.
He noted that when using optical sensing, we look closely at areas such as light intensity, whether fluorescence is detected, varying light behaviors and interference patterns to determine how the optical path has changed
An instance of a frequently encountered sensing system would be a standard, domestic smoke detector. These operate by estimating particles which block out light in an optical chamber. However, as these systems classify anything blocking the light as smoke, they can be vulnerable to false positives.
In contrast to this, a smart optical-sensing smoke detector capitalizes on the fact that light scatters differently according to color, and thus uses two colors of light within the sensor, offering a quicker reaction, as smoke is caught in open air. This type of equipment is also durable, as the detector can be sealed.
When used in health-related functions, optical heart-rate monitoring is achieved by shining a light into human tissue, before analyzing the light coming through or back to ascertain what is occurring within the tissue. A green LED is the most commonly used light source for this method.
The quantity of light absorbed or reflected can vary depending on the amount of blood that enters the arteries as the heart pulsates. When light is detected, a signal determines what is consistent and synchronized with the human heart beat. The difficulty with this technique is that skin is extremely variable, with its composition varying from person to person.
Each of the many layers of tissue of which our skin is comprised has its own reflective and transmissive indices. As such, optical design software combines optical heart-rate monitor data gathered with CAD models of tissue to investigate.
Sensing is about how we distill signals we want from all the other stuff affecting the signal received. So it’s not just about sensing the signal. We need to understand the biology.
Ian Chen, Executive Director, Maxim’s Industrial & Healthcare Business Unit.
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Optical heart-rate monitoring involves shining an LED light source into human tissue to collect insights.
Beyond Heartbeats
A PPG is an optically obtained plethysmogram, which offers a volumetric measurement that is often used in wearable devices, such as fitness trackers, to monitor vital signs The strength and quality of photoplethysmogram (PPG) signals detected will be affected by variations in optical properties of the skin.
As such, developers of such devices must take into account factors which may affect the strength of the received signal, such as movement of the device, air gaps or glass covering the signal.
Pulses hold richer data than heartbeats, and further information is available in every feature when one examines the shape of their signals. A blood capillary is a multi-dimensional curiosity, and using light to examine it offers only a 1D representation. Machine learning allows us to examine the shape of the pulse, paying attention to the height and latency of the peak, before using this information to gain other insights on patients.
Of course, a designer has no control over the properties of skin; however, they can manipulate the profusion index (PI) through the system design. PI refers to the ratio between the AC and DC parts of the PPG signal. Chen elaborated to his audience that PI can be maximized within the mechanical design of devices such as wearables.
To illustrate, a wearable could be made less susceptible to the effects of non-significant movement through the use of multiple LEDs or photodetectors. In addition to this, the effects of movement could also be minimized by utilizing a patch or in-ear design, rather than a watch format.
Examining heart rate or PPG signals through machine learning offers an additional method to learn more about, and subsequently account for, the noise in the system.
Use of multiple optical sensors can also offer benefits. Chen noted that ICs should consider a number of factors when appraising optical sensors. LED driver noise and linearity are key areas to note on the transmit path, as any noise in the LED power signal would have an impact on the power of the LED signal.
On the other hand, ambient light cancellation, a high signal-to-noise ratio and a wide signal range must be considered when examining the receive path. Chen explained that Maxim’s ambient light cancellation is signification due to its dual approach:
- Analog coarse cancellation. This is where ambient light levels are detected while the LED is switched off, and then subtracted from the photodetector output ahead of sampling for the PPG signal. A coarse DC signal is also withdrawn before sampling, in order to avoid saturating the converter.
- Digital fine cancellation. This is where the LED is off and residual DC, AC and 1/F noise are withdrawn when sampling.
Chen then elaborated on the continuing advancements of optical sensing, noting how each generation of devices developed offers improved levels of power consumption. With lower power use comes the capacity for features such as sensor fusion.
"When we look at sensor fusion, there are a couple of ideas to bring up. Wearable sensors can be used to monitor, let's say heartbeat or blood pressure, or they can be used to provide a continuous set of information for the user," Chen stated, noting that both uses are acceptable, but require greatly different levels of power.
Information to Empower a Healthier World
To close his talk, Chen discussed the non-visible nature of our greatest technology, noting how we have come to view the vastly complex system of weather prediction technology, which requires a great number of sensors, as a simple set of data informing us how best to dress on that day.
He expanded on his hopes to see wearable sensors used to improve human life in this way, creating a system in which we could develop wearable wellness sensors which could interact with. For example, Alexa, your running app, or other health applications; allowing us to use health data as practical information on which we can act, in the same way we would a weather report.
Chen finished his talk by inviting his audience to consider what types of innovation they would want to work on together, and how next steps could be taken collaboratively. It is clear to see that enriched data on our health and wellness could be beneficial to all.
This information has been sourced, reviewed and adapted from materials provided by Maxim Integrated.
For more information on this source, please visit Maxim Integrated.