Using Adaptable Sensors for Data Driven Applications

Everything from the rate your heart beats to the rate at which crops grow can be recorded, analyzed, and actioned by computers. The rush to instrument and optimize everything is fuelled by the proliferation of connected devices.

By the time global mobile phone sales hit one billion units per year, the world was already changing quickly. A massive, globally distributed network of connected cameras was acquired which have already altered society profoundly after the handsets evolved into smartphones. Today, the introduction of the Internet of Things (IoT) ecosystems is generating other widely distributed networks of highly instrumented devices.

How the emergence of these networks will change the world is yet to be seen. However, we do know that most of their utility relies upon utilizing one or more sensors to turn a real world change, such as the evolving color of maturing leaves, into an electrical signal which can be calculated.

If they are to meet the requirements of IoT devices then these sensors need to draw little power, be highly integrated, robust, small, and stable over the long term, and service the trend of portability in markets like medical diagnosis. They must also be adaptable – it is probable that the measurements demanded of these devices will evolve whilst they are being utilized.

Optical Sensors for IoT Devices

The optical sensor is one of the most versatile tools in these situations because it can look through things (such as liquids flowing through tubes), at things (such as products on a manufacturing line), or at reflections off things (such as surfaces that have been processed in some way).

To attain accurate, repeatable, measurements that make later analysis meaningful can be a challenge. For example, there are an increasing amount of materials available with low reflectance, as anyone who has fitted a screen protector to their phone will know.

Many of these materials are being pressed into service in medical devices, such as in the windows through which sensors detect contamination or bubbles in flowing liquids. Deciphering the resultant signals requires a sensor whose sensitivity levels and output are programmable, to combat fluctuating levels of reflectance and low-contrast situations.

Additionally, it is also worth noting that sensor signals can regularly be very ‘delicate.’ They are usually small voltages or currents which are measured in electrically noisy situations from sensing devices whose performance alters with environmental factors, such as temperature or ambient light, and over their operational lifetime.

Managing the conditioning and extraction of these signals into stable, valid, and repeatable measurements requires analog support circuitry that itself can be subject to drift and aging.

One clear solution to a lot of these challenges is greater integration; to convert a standalone sensor to a low-power sensing module which has programmable sensitivity and thresholds, incorporates all of its supporting circuitry, and whose flexibility permits it to serve many markets and therefore to be sold at a lower price than the alternative.

OPB9000 Reflective Optical Sensor from TT Electronics

One such device is the OPB9000 reflective optical sensor from TT Electronics. This device integrates a photodetector and an infrared (IR) emitter in one package, plus the analog front-end circuitry, on-chip processing, and a digital interface in a surface-mount package of 4.0 × 2.2 × 1.5 mm. The circuitry is encapsulated in industrial resin, meaning it is robust and able to operate at temperatures between –40 to +85 °C.

Integration brings a number of advantages, the integrated sensor uses up to 80% less space and less power than the discrete alternative. This increases the portability and/or operating lifetime of the devices in which it is utilized by decreasing the sensor’s current draw and making room for a larger battery.

The integrated signal-conditioning circuitry handles temperature compensation to confirm consistent performance in different environments, and calibrates and adjusts the sensor’s output as components automatically, such as the IR emitter and age.

The integrated circuitry also means it is simpler to attain useful levels of immunity to ambient light, which is crucial in optical sensing. One way to achieve this is to filter out any DC and ultra-low frequency components from the sensor’s photocurrent, on the basis that the intensity of ambient light varies slowly in comparison to the sensing signal desired.

To deal with challenges such as low-reflectance materials, the integrated circuitry can also be utilized to calibrate the sensor. To achieve this, the current to the IR emitter is ramped up at a steady pace until the reference level for the photodetector is attained. Next, that LED drive current value is stored in on-chip memory to represent recognition of that specific surface in a particular situation.


Dynamically adaptable optical sensing is becoming increasingly crucial in a data-driven world. Integration, as exemplified by the TT Electronics OPB9000 module, aids in overcoming a number of the issues involved in using discrete optosensors. Its programmability makes it suitable for numerous end markets, permitting volume cost savings that make it more accessible.

This information has been sourced, reviewed and adapted from materials provided by TT Electronics plc.

For more information on this source, please visit TT Electronics plc.


Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    TT Electronics plc. (2019, October 29). Using Adaptable Sensors for Data Driven Applications. AZoSensors. Retrieved on June 24, 2022 from

  • MLA

    TT Electronics plc. "Using Adaptable Sensors for Data Driven Applications". AZoSensors. 24 June 2022. <>.

  • Chicago

    TT Electronics plc. "Using Adaptable Sensors for Data Driven Applications". AZoSensors. (accessed June 24, 2022).

  • Harvard

    TT Electronics plc. 2019. Using Adaptable Sensors for Data Driven Applications. AZoSensors, viewed 24 June 2022,

Tell Us What You Think

Do you have a review, update or anything you would like to add to this article?

Leave your feedback
Your comment type