Choosing the right Arm Cortex-M processor for your biomedical smart product: a practical guide

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Advances in telemedicine healthcare products over the past decades have been truly miraculous with ingenious little devices invented by start-ups as well as by larger corporations, .e.g Apple’s smart watch and the Fitbit. These advancements have been facilitated by the availability of low-cost microcontrollers offering algorithmic functionality, allowing developers to implement wearables with excellent battery life and edge based real-time data analysis.

Over 90% of the microcontrollers used in the smart product market are powered by so called Arm Cortex-M processors that offer a combination of high algorithmic performance, low-power and security. The Arm Cortex-M4 is a very popular choice with hundreds of silicon vendors (including ST, TI, NXP, ADI, Nordic, Microchip, Renesas), as it offers DSP (digital signal processing) functionality traditionally found in more expensive devices and is low-power. Arm and its rich eco system of partners provide developers with easy-to-use tooling and tried and tested software libraries, such as the CMSIS-DSP and CMSIS-NN frameworks for algorithm development and machine learning.

The choice is vast, and can be very confusing. Therefore, here are some practical hints and tips for both managers and developers to help you decide which Arm Cortex-M processor is best for your biomedical product.

Which Arm Cortex-M processor do I choose for my biomedical application?

The Arm Cortex-M0+ processor is an ultra-low power 32-bit processor designed for very low-cost IoT applications, such as simple wearable devices. The low price point is comparable with equivalent 8-bit devices, but with 32-bit performance. Microcontrollers built around the M0+ processor provide developers with excellent battery life (months to years), a rich peripheral set and a basic amount of connectivity and computational performance. The latter means that only simple algorithms can be implemented, such as algorithms for correcting baseline wander and minimizing the effects of motion artefacts using accelerometer data via an adaptive filter, such as the NLMS algorithm. Although for PPG pulse rate measurement applications, the sampling rate is typically 50Hz, leaving the processor plenty of time to perform various simpler algorithmic operations, such as digital filtering and zero-crossing detection.

For high performance PPG applications, sampling rates in the order of 500Hz are typically used. These types of applications usually look at more biomedical features, such as identifying the Systolic and Diastolic phases and finding the Dicrotic notch using feature extraction algorithms and ML models. These extra functionalities provide a significant strain on the processor’s abilities, and as such are beyond the abilities of the M0+.

The Cortex-M3 is a step up from the M0+, offering better computational performance but with less power efficiency. The extra processing power, rich hardware peripheral set for connecting other sensors and connectivity options makes the M3 a very good choice for developers looking to develop slightly more advanced wearable products, such as the Fitbit device that is based on ST’s low-power STM32L series of microcontrollers.

High performance wearables and beyond

The Arm Cortex-M4 processor and its more powerful bigger brother the Cortex-M7 are highly-efficient embedded processors designed for IoT applications that require decent real-time signal processing performance and memory. Depending on the flavour of the processor, the M4F/M7F processors implement DSP hardware accelerated instructions, as well hardware floating point support. This lends itself to the efficient implementation of much more computationally intensive biomedical DSP and ML algorithms needed for more advanced telemedicine products.

The hardware floating point support unit expedites RAD (rapid application development), as algorithms and functions developed in Matlab or Python can be ported to C for implementation without the need for a lengthy data arithmetic quantisation analysis. Microcontrollers based on the M4F or M7F, usually offer many of the hardware peripheral and connectivity advantages of the M3, providing developers with a very powerful, low power development platform for their telemedicine application.

The Arm Cortex-M33 is a step up from the M4 focusing on algorithms and hardware security via Arm’s TrustZone technology and memory-protection units. The Cortex-M33 processor attempts to achieve an optimal blend between real-time algorithmic performance, energy efficiency and system security.

State-of-the art AI microcontrollers

Released in 2020, the Arm Cortex-M55 processor and its bigger brother the Cortex-M85 are targeted for AI applications on microcontrollers. These processors feature Arm’s Helium vector processing technology, bringing energy-efficient digital signal processing (DSP) and machine learning (ML) capabilities to the Cortex-M family. In November 2023, Arm announced the release of Cortex-M52 processor for IoT applications. This processor looks to replace the older M33 processor, as it combines Helium technology with Arm TrustZone technology.

Although the IP for these processors is available for licencing, only a few IC vendors have developed a microcontroller, e.g. Samsung’s Exynos W920 SoC that has been specifically designed for the wearables market. The SoC packs two Arm Cortex-A55 processors, and the Arm Mali-G68 GPU using state-of-the art 5nm semiconductor technology. The chipset also features a dedicated low-power Cortex-M55 display processor for handling AoD (Always-on Display) tasks – although a little over the top for simple wearable devices, the Exynos processor family certainly seems like an excellent choice for building next generation AI capable low-power wearable products.

So, which one do I choose?

The compromise for biomedical product developers when choosing an M4, M7 or M33 based microcontroller over an M3 device usually comes down to a trade-off between algorithmic performance, security requirements and battery life. If good battery life and simple algorithms are key, then M3 devices are a good choice. However, if more computationally intensive analysis algorithms are required (such as ML models), then the M4 or M7 should be used.

As mentioned earlier, the Armv7E-M architecture used in M4/M7 processors supports a DSP extension that implements an SIMD (single instruction, multiple data) architecture extension that can significantly improve the performance of an algorithm. The hardware floating point unit is very good for expediting MAC (multiply and accumulate) operations used in digital filtering, requiring just three cycles to complete. Other DSP operations such as add, subtract, multiply and divide require just one cycle to complete.

The M7 out performs its M4 little brother by offering approximately twice the computational performance and some devices even offer hardware double precision floating point support which make M4/M7 processors attractive for high accuracy algorithms needed for medical analysis.

If data security is paramount, for example protecting and securing transferring patient data to a cloud service, then the M33 or the M52 (when avalaible) are good choices. These devices also offer a high level of protection against tampering and running of authorised code via TrustZone’s trusted execution environment.

Some IC vendors now offer hybrid micro-controllers that implement multi-processors on chip, such as ST’s ST32Wx family that combine the M0+ and M4 in order to get the advantages of each processor and maximise battery life. 

Finally, advances in semiconductor technology means that a modern M4F processor produced with 40nm process technology may match or even surpass the energy efficiency of an M3 produced with 90nm technology from several years ago. As such, higher performance processors that were until several years too costly and energy inefficient for low-cost wearables products are rapidly becoming a viable solution to this exciting marketplace.

Author

  • Dr. Sanjeev Sarpal

    Sanjeev is an AIoT visionary and expert in signals and systems with a track record of successfully developing over 25 commercial products. He is a Distinguished Arm Ambassador and advises top international blue chip companies on their AIoT solutions and strategies for I4.0, telemedicine, smart healthcare, smart grids and smart buildings.

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Implementing IoT algorithms on Arm Cortex-M processors: a unified view

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We live in a time where wearable/mobile products comprised of sensors, apps, AI and IoT (AIoT) technology are part of everyday life. Every year we hear about amazing advances in processor technology and AI algorithms for all aspects of life from industrial automation to futuristic biomedical products.

For developers, the requirement to design low-cost products with better battery life, higher computational performance and analytical accuracy, requires access to a suite of affordable processor technology, algorithmic libraries, design tooling and support.

This article aims to provide developers with an overview of all salient points required for algorithm implementation on Arm Cortex-M processors.

Can you give me a concrete example?

Almost all IoT sensor applications require some level of signal processing to enhance data and extract features of interest. This could be temperature, humidity, gas, current, voltage, audio/sound, accelerometer data or even biomedical data.  

Consider the following application for gas concentration measurement from an Infra-red gas sensor. The requirement is to determine the amplitude of the sinusoid in order to get an estimate of gas concentration – where the bigger amplitude is the higher the gas concentration will be.

Analysing the figure, it can be seen that the sinusoid is corrupted with measurement noise (shown in blue), and any estimate based on the blue signal will have a high degree of uncertainty about it – which is not very useful for getting an accurate reading of gas concentration!

After cleaning the sinusoid with a digital filter (red line), we obtain a much more accurate and usable signal for our gas concentration estimation challenge. But how do we obtain the amplitude?

Knowing that the gradient at the peaks is zero, a relativity easy and robust way of finding the peaks of the sinusoid is via numerical differentiation, i.e. computing the difference between sample values and then looking for the zero-crossing points in the differentiated data. Armed with the positions and amplitudes of the peaks, we can take the average and easily obtain the amplitude and frequency.  Notice that any DC offsets and low-frequency baseline wander will be removed via the differentiation operation.

This is just a simple example of how to extract the properties of a sinusoid in real-time using various algorithmic IP blocks. There are of course a number of other methods that may be used, such as complex filters (analytic signals), Kalman filters and the FFT (Fast Fourier Transform).

Arm Cortex-M processor technology

Although a few processor technologies exist for microcontrollers (e.g. RISC-V, Xtensa, MIPS), over 90% of the microcontrollers used in the smart product market are powered by so-called Arm Cortex-M processors that offer a combination of high algorithmic performance, low-power and security. The Arm Cortex-M4 is a very popular choice with several silicon vendors (including ST, TI, NXP, ADI, Nordic, Microchip, Renesas), as it offers DSP (digital signal processing) functionality traditionally found in more expensive devices and is low-power.

Algorithmic libraries and support

An obvious hurdle for many developers is how to port their algorithmic concept or methods from Python/Matlab into embedded C for real-time operation? This is easier said than done, as many software engineers are not well-versed in understanding the mathematical concepts needed to implement algorithms. This is further complicated by the challenge of how to implement algorithms developed by researchers that are not interested/experienced in developing real-time embedded applications.

A possible solution offered by the Mathworks (Embedded Coder) automatically translates Matlab algorithms and functions into C for Arm processors, but its high price tag and steep learning curve make it unattractive for many.

That being said, Arm and its rich ecosystem of partners provide developers with extensive easy-to-use tooling and tried and tested software libraries. Arm’s CMSIS-DSP and CMSIS-NN frameworks for algorithm development and machine learning (ML) are two very popular examples that are open source and are used internationally by tens of thousands of developers.

The Arm CMSIS-DSP software framework is particularly interesting as it provides IoT developers with a rich collection of fast mathematical and vector functions, interpolation functions, digital filtering (FIR/IIR) and adaptive filtering (LMS) functions, motor control functions (e.g. PID controller), complex math functions and supports various data types, including fixed and floating point. The important point to make here is that all of these functions have been optimised for Arm Cortex-M processors, allowing you to focus on your application rather than worrying about optimisation.  

The Arm-CMSIS framework solutions are strengthened by Arm partners ASN and Qeexo who provide developers with easy-to-use real-time filtering, feature extraction and ML tooling (AutoML) and reference designs, expediting the development of IoT applications, including industrial, audio and biomedical. These solutions have been optimised for Arm processors with the help of Arm’s architecture experts and insider knowledge of compiler workings.

A benchmark of ASN’s floating point application-specific DSP filtering library versus Arm’s CMSIS-DSP library is shown below for three types of Arm cores.

Framework Benchmarks: lower number of clock cycles means higher performance.

As seen, the performance of the ASN library is slightly faster by virtue of the application-specific nature of the implementation. The C code is automatically generated from the ASN Filter Designer tool.

Cortex-M4 and Cortex-M7

The Arm Cortex-M4 processor and its more powerful bigger brother the Cortex-M7 are highly-efficient embedded processors designed for IoT applications that require decent real-time signal processing performance and memory.

Both the Cortex-M4 and M7 core benefit from the Armv7E-M architecture that offers additional DSP extensions. Depending on the flavour of the processor, the M4F/M7F processors implement DSP hardware accelerated instructions (SIMD), as well as hardware floating point support via an FPU (floating point unit), giving them a significant performance boost over the Cortex-M3. The ‘F’ suffix signifies that the device has an FPU.

This lends itself to the efficient implementation of much more computationally intensive DSP and ML algorithms needed for more advanced IoT products and real-time control applications requiring highly deterministic operations.

Microcontrollers based on the M4F or M7F, usually offer many of the hardware peripheral and connectivity advantages of the simpler M3, providing developers with a very powerful, low-power development platform for their IoT application. The Cortex-M7F typically offers much higher performance than its Cortex-M4F little brother, doubling the performance on FFT, digital filters and other critical algorithms.

Floating point or fixed point?

The hardware floating point support unit expedites RAD (rapid application development), as algorithms and functions developed in Matlab or Python can be ported to C for implementation without the need for a lengthy data arithmetic quantisation analysis. Although floating point comes with its own problems, such as numeric swamping, whereby adding a large number to a small number ignores the smaller component. This can become troublesome in digital filtering applications using the standard Direct Form structure. It is for this reason that all floating-point filters should be implemented using the Direct Form Transposed structure, as discussed in the following article.

Correctly designing and implementing these tricks requires specialist knowledge of signal processing and C programming, which may not always be available within an organisation. This becomes even more frustrating when implementing new algorithms and concepts, where the effects of the arithmetic are yet to be determined.

Single vs double precision floating point

For a majority of IoT applications single precision (32-bit) floating point arithmetic will be sufficient, providing approximately 7 significant digits of precision. Double precision (64-bit) floating point provides approximately 15 significant digits of precision, but in truth should only be used in applications that require more than 7 significant digits of precision. Some examples include: FFT based noise cancellation, CIC correction filters and Rogowski coil compensation filters. 

Some Cortex-M7F’s (e.g. STM32F769) implement a Double precision FPU providing an extra performance boost to high numerical accuracy IoT applications.

Fixed point

Fixed point is not necessarily less accurate than floating point, but requires much more quantisation analysis, which becomes tricky for signals with a wide dynamic range. As with floating point careful analysis is required, as weird effects can appear due to the level of quantisation used, leading to unreliable behaviour if not properly investigated. It is this challenge that can slow down a development cycle significantly, in some cases taking months to validate a new algorithm.

Many developers have traditionally considered devices without an FPU (e.g., Cortex-M0/M3) as the best choice for low-power battery applications. However, when comparing a modern Cortex-M7 device manufactured using 40nm semiconductor process technology, to that of a ten-year-old Cortex-M3 using 180nm process technology, the Cortex-M7 device will likely have a lower power profile.

Acceleration of DSP calculations

The Armv7E-M architecture supports a DSP extension that implements an SIMD (single instruction, multiple data) architecture extension that can significantly improve the performance of an algorithm. The basic idea behind SIMD involves parallel execution of an instruction (eg. Add, Subtract, Multiply, Divide, Abs etc) on multiple data elements via the use of 64 or 128-bit registers. These DSP extension intrinsics (SIMD optimised instruction) support a variety of data types, such as integers, floating and fixed-point.

The high efficiency of the Arm compiler allows for the automatic dissemination of your C code in order to break it up into SIMD intrinsics, so explicit definition of any DSP extension intrinsics in your code is usually unnecessary. The net result for your application is much faster code, leading to better power consumption and for wearables, better battery life.

What algorithmic operations would use this?

The following examples give an idea of operations that can be significantly speeded up with SIMD intrinsics:

  • vadd can be used to expedite the calculation of a dataset’s mean. Typical applications include average temperature/humidity readings over a week, or even removing the DC offset from a dataset.
  • vsub can be used to expedite numerical differentiation in peak finding, as discussed in the example above.
  • vabs can be used for expediting the calculation of an envelope of a fullwave rectified signal in EMG biomedical and smartgrid applications.
  • vmul can be used for windowing a frame of data prior to FFT analysis. This is also useful in audio applications using the overlap-and-add method.

The hardware floating point unit is very good for expediting MAC (multiply and accumulate) operations used in digital filtering, requiring just three cycles to complete. Other DSP operations such as add, subtract, multiply and divide require just one cycle to complete.

Combining DSP, low-power and security: The Cortex-M33

The Arm Cortex-M33 is based on the Armv8-M architecture and is a step up from the Cortex-M4 focusing on algorithms and hardware security via Arm’s TrustZone technology and memory-protection units. The Cortex-M33 processor attempts to achieve an optimal blend between real-time algorithmic performance, energy efficiency and system security.

TrustZone technology

Arm TrustZone implements a security paradigm that discriminates between the running and access of untrusted applications running in a Rich Execution Environment (REE) and trusted applications (TAs) running in a secure Trusted Execution Environment (TEE).  The basic idea behind a TEE is that all TAs and associated data are secure as they are completely isolated from the REE and its applications.  As such, this security model provides a high level of security against hacking, stealing of encryption keys, counterfeiting, and provides an elegant way of protecting sensitive client information.

State-of-the art AI microcontrollers

Released in 2020, the Arm Cortex-M55 processor and its bigger brother the Cortex-M85 are targeted for AI applications on microcontrollers. These processors feature Arm’s new Helium vector processing technology based on the Armv8.1-M architecture that brings significant performance improvements to DSP and ML applications. However, as only a few IC vendors (Alif, Samsung, Renesas, HiMax, Bestechnic, Qualcomm) have currently released or are planning to release any devices, Helium processors remain a gem for the future. 

Key takeaways

Arm and its rich ecosystem of partners provide IoT developers with extensive easy-to-use tooling and tried and tested software libraries for designing an implementing IoT algorithms for their smart products. Arm Cortex-MxF processors expedite RAD by virtue of their ease of use and hardware floating-point support, and modern semiconductor technology ensures low-power profiles making the technology an excellent fit for IoT/AIoT mobile/wearables applications.

Author

  • Dr. Sanjeev Sarpal

    Sanjeev is an AIoT visionary and expert in signals and systems with a track record of successfully developing over 25 commercial products. He is a Distinguished Arm Ambassador and advises top international blue chip companies on their AIoT solutions and strategies for I4.0, telemedicine, smart healthcare, smart grids and smart buildings.

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What do you want to achieve with AIOT?: To Optimize

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“Improve your existing resources”

In a previous blog, we talked about how IoT can help in taking control. There is another step further: to optimize your processes with AIOT.

Benefits

  • Better use of existing resources
  • Take the right decisions at the right time
  • Optimal circumstances

Better use of existing resources

Control means you have a clear overview how assets are being used. Such as:

  • How long does each step in a process take?
  • What are the whereabouts of my assets (trucks, cranes, forklifts, containers…)?
  • What is the state of maintenance?

The next step is of course, to optimize your business.

First of all, many blogs write about total-new situations. In fact, most AIOT is needed in companies which are already established. With their inventory, processes, customers and all the responsibilities which come with them. Large investments have been made to reach the business today. And so, their processes may not be optimal, at least they work. So how to benefit from AIOT, without throwing away all these investments? And: how to be sure processes are at least working as they do know? Smart sensors help to bring the whole process at today’s level, without throwing away resources which are working fine. Besides, companies can choose to implement AIOT piecemeal.

This is especially the case when it’s about highly essential functions such as infrastructure, sluices and installations. Here, the asset is not just an asset, but a part of a total infrastructure. Downtime of such an asset has large implications for society as a whole.

Many processes are still monitored piecemeal. A further optimization is to connect systems with each other. Get 1 overview in 1 dashboard. Learn how your processes are doing, and where are the optimizations are required.

Take the right decisions at the right time

To measure is to know, to know is to be able to improve.

One most mentioned benefits of AIOT is preventive maintenance. Preventive maintenance means that something is repaired or replace, before it is breaks. Or at least, to maintain while the damage is still small. In normal situations there would be downtime, now repairs can be made scheduled. And if downtime is needed for repairs, then it can be scheduled at times the least inconvenient.

It’s already been said: to be able to schedule repairs. Take the right decisions at the right time. Besides, in the old situation, a foreman has to do his round, where he gives each machine the same attention. With AIOT, the quality of the assets can be guarded with sensors. So, at his round, a foreman can give most attention to the machines which mostly need it.

The same applies to a sector as biomedical: ‘to prevent is better then to cure’. So, help your clients and/or yourself to stay healthy. An example is fall detection. And does the elderly take his medicine?

Help your patients with therapy, to make use of knowledge from all previous patients: is therapy going on track? Also: give the patients who need it the right amount of attention. Instead of seeing all your patients with a standard scheduled time-frame, and as a consequence, give none of them enough time really.  If therapy is lagging, you probably want to give those patient attentions. Is therapy going faster then expected: what are the reasons? How can this knowledge be used to improve therapy in the future? Besides, if people can do therapy and appointments at home, they don’t have to spend their precious time; where the actual time needed for treatment is shorter then the time spent on travelling and waiting.

Optimal circumstances

Sensors can guard that product are made or kept in optimal circumstances. E.g., if cutting parts of a machine are still sharp enough, and in their right precision. Or guard the temperature of cooling or keep an eye on the indoor air quality. This may also make guarantees possible, and thus creating added value to your products or service.

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Proud member KPN Fieldlab Brainport Industries Campus (BIC) in Eindhoven

Connecting society with 5G

In 2020, KPN opened a 5G Fieldlab on the Brainport Industries Campus (BIC) in Eindhoven. This fieldlab focusses on smart industry, where different use cases show the possibilities of 5G for manufacturing. With its BIC partners, KPN has set up Smart Industry project with the new generation of mobile network technology. The aim of a Fieldlab is to experiment with the benefits of 5G in a representative environment. How can this new network technology be used in combination with developments such as artificial intelligence for numerous smart applications.  To explore how 5G can optimize business processes and improve customer experience.

3.5 GHz

This includes the 3.5GHz frequency, which is ideal for testing high-bandwidth use cases. At this moment. In 2022, frequencies in the 3.5 GHz band will be distributed during the new 5G auction. From 2022-2023, 3.5 GHz can be used outdoors.

The Netherlands is a leader in Europe when it comes to the quality and speed of mobile networks. A position that contributes to the economic success of the Netherlands. To maintain this position, it is essential that the necessary 3.5GHz frequency can be used in these test environments. The availability of this frequency is crucial for the successful testing of 5G applications and the introduction of 5G in the Netherlands. KPN is actively trying to contribute to a suitable solution for this problem.

Ultrawideband (UWB)

The 5G indoors is combined with Ultrawideband. UWB is very useful to track objects on the factory  floor and in warehouses in real time. Objects, assets and people can be determined with up to thirty centimeters accuracy, and often even more accurately.

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The roll-out to 5G: enabler for Industry 4.0

Since the end of July 2020, KPN has renewed its mobile network which enables 5G. KPN is rapidly expanding coverage throughout the Netherlands. Business customers and entrepreneurs can already make use of special 5G services. To see tomorrow’s digital highway in action, since 5G is one of the enablers for smart industry

3.5 GHz frequency auction

Starting in 2022, KPN will auction G5 frequencies. Meanwhile, together with customers and technology partners, telecom and ICT service provider KPN has launched 5G field labs to discover the value of 5G applications. Thanks in part to 5G technology, these types of applications will become a reality.

During the new 5G auction, frequencies in the 3.5 GHz band will be distributed. These will enable connections at much higher speeds. At the auction in the first quarter of 2022, at least three parties must obtain licenses for the frequencies. No single party may acquire more than 40 percent of the available frequencies, says the proposal for the course of the auction.

A total of 300 megahertz of bandwidth is to be distributed. This consists of three blocks of 60 megahertz and twelve blocks of 10 megahertz. The auction will be held in three phases. Prior to and during the auction the Ministry of Economic Affairs will not provide information about the total number of participants. At the end, the winning parties will be announced and the State Secretary will make the entire bidding process public.

Tomorrow’s digital highway

Thanks to the capacity and reliability of our network, new applications such as innovations in security, healthcare, mobility, logistics and the manufacturing industry become possible.  Unlike 4G, 5G is expected to become an ecosystem from which many business sectors, industries and areas can benefit. Innovations from which the whole of society benefits. In addition to higher speed, 5G focuses explicitly on flexibility in the network to support very short response times and higher reliability. This will enable a wide range of new applications for customers and industries.

5G is expected to provide a huge boost in business for augmented and virtual reality, robotics, drones, intelligent assets, wearables, AI-based video analytics and Internet of Things (IoT), among others.

In addition to 5G, Internet of Things also requires edge computing. This involves placing a small cloud with computing power, storage and network capacity at the edge of the network, as it were, close to applications, devices and users. Because data no longer has to travel all the way up and down to the cloud or a data center, time-critical applications such as self-driving cars and augmented reality become possible.

5G: enabler for Industry 4.0

5G is also a key enabler for Industry 4.0. This involves using Internet of Things, cloud computing and data integration, among other things, to make the production process fully computer-controlled and remote. The human thought process is thereby partially or completely taken over. Due to its high speed and reliability and short latency, 5G is essential within Industry 4.0 for, for example, controlling production lines, facilitating self-driving vehicles and connecting large numbers of IoT devices.

Field Labs

KPN Eindhoven 5G Fieldlab

The national rollout of 5G has only just started, but KPN has been testing 5G for useful applications in its Field Labs for some time. 

The 5G field lab for the manufacturing industry shows everyone which 5G indoor use cases are possible in a factory environment. Besides speed, 5G also enables larger reliability and very low network latency. A large number of wireless sensors also plays an important role in the further rollout of the IoT. Thus, the 5G Field Labs shows that 5G can be used for very different applications simultaneously.

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How Humidity affects Asthma

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Humidity is a measure of how much water or moisture there is in the air. Many people with asthma have more complaints when the air is humid. When people speak about humidity, they actually talk about ‘relative humidity’. This is the percentage water in the air, compared to the maximum amount of water the air can hold given the current temperature. When the weather is hot, the air can contain more water than cold air. So, the same relative humidity of say 60% might feel more wet on hot days than on cold days. How does humdity affect asthma?

Importance of humidity

Many people find a humidity of 30-60 percent comfortable. During the hot summer months, many people feel that a humidity level of 55% is comfortable. Above this level, the air is considered humid. Because sweat doesn’t evaporate enough to cool you off, you feel hot and sticky when the air is humid. Above the level 65% is felt as oppressive.

But also: humid air is harder to breath. That can be a problem if you have asthma. Many people with asthma feel that a humidity level above 65% may worsen their symptoms of asthma. When you have asthma, it is more difficult to pull enough air into the lungs, because your airways become narrow. This may cause feeling of shortage of breath or wheezing and coughing.

3 Ways Humidity worsens Asthma

Allergens, chemicals and strong scents are common triggers for people who suffer from asthma. But high humidity can be also just as troublesome.

People with asthma have inflamed airways that are sensitive to things that may not bother other people. That’s why humidity, and all that comes with it, can be a problem for people with asthma. Here are some reasons why.

  • Humid air feels harder to breathe in
  • Humidity may worsen air quality
  • Humidity can mean very high temperatures

Humid air feels harder to breathe in

Hot, humid air may feel heavier and denser and thus harder to breathe in. Besides, humidity may activate sensory nerve fibers in the airways. These C-fibers may narrow the airways and stimulate coughing, which makes it difficult to breathe. Besides, when heat and humidity make the air harder to breathe, the body temperature can go up. This causes sweat, which can lead to dehydration. This can lead to make you breathe faster. These factors may trigger asthma symptoms.

On the other hand, when the air is very dry -a relative humidity is less than 15%- it may also lead to coughing when you’re asthmatic. When the air is very dry, the mucous membranes of the respiratory system may dry out. These membranes line your lungs and respiratory system. This leads to an increased risk for infections from viruses: due to the decreasing natural defense from influenza or the common cold virus. Dry mucous membranes may aggravate allergy symptoms and worsen asthma symptoms (most asthmatics have also allergies).

Humidity may worsen air quality

Humidity can also trigger asthma because moist increase levels of mold, dust mites, ground-level ozone. Those are known as asthma triggers.

When the humidity level is higher than 50 percent, mold might begin to grow. Mold is often found at damp places. If you are sensitive for mold, it may trigger your asthma.

Dust mites are also a problem inside when humidity is high. Dust mites live in furniture, carpets, etc.  If the humidity in your home is higher than 50 percent, dust mites thrive and multiply themselves. Their dead bodies and waste may trigger asthma.

Heat and Humidity may also lead to stagnant air from pollutants (like ozone), allergens (dust, mold, dust mites, pollen) and smoke. This may also trigger asthma symptoms.

Asthma worsens feelings of well-being and productivity

For people suffering from asthma, poor humidity levels don’t affect only the feeling of well-being. It has effects on your productivity. An international study in the Journal of Asthma and Allergy shows: “The average percentage of work hours missed in a single week due to asthma symptoms was 9.3%, ranging from 3.5% (UK) to 17.4% (Brazil). Nearly three-quarters of patients reported an impact on their productivity at work caused by asthma. Overall work productivity loss (both time off and productivity whilst at work) due to asthma was 36%, ranging from 21% (UK) to 59% (Brazil). When asked how asthma made participants feel at work, many respondents highlighted how their respiratory symptoms affect them. Tiredness, weakness and mental strain were also identified as particular challenges, with respondents describing concerns about the perception of colleagues and feelings of inferiority”

Control your humidity level

Humidity levels can worsen asthma in 3 ways. It doesn’t only affect the feeling of well-being, but also productivity. Thus, it is very important for offices, schools, institutions etc. to assure that humidity levels are being kept on levels where children, employees, visitors feel most comfortable. Smart sensor solution like the AirGuard help to monitor the indoor air quality.

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What do you want to achieve with AIOT?: Get in control of your processes

AIoT has many benefits. Those benefits can be summarized as: to save, to control, tot optimize and to innovate. How does AIOT gives you control over your processes? Some examples:

  • To measure is to know
  • Preventive Maintenance
  • Prevent Costs and nuisance in the event of a breakdown or bad functioning
  • Take the right decisions at the right time
  • Track & Trace

 To measure is to know

First, ‘to measure is to know’. You can only be in control of your processes, when you know how they are doing. Therefore, you need to measure.

However, AIOT is booming, still, many companies are in the blind how their processes are functioning.  Or insights are only piecemeal, insight in a machine here, a process there. Which means a company isn’t in control of their whole process.

Preventive Maintenance

Know the extent to which your machines are wearing out. Prevent them from breaking down and schedule maintenance at the least bad time. In doing so, your employees can pay attention to the parts that need their attention the most: sensors keep an eye on the ‘normally’ running parts.

Prevent Costs and nuisance in the event of a breakdown or bad functioning

In many cases, a machine is a part of chain of a whole production process. Thus, a breakdown of one machine means downtime for your whole process.

With IoT, you can create new ways and do more with the same budget. Many industries are working in heavy circumstances because of dust, wind, heat, pressure, etcetera. So, it’s important to recognize if the equipment is still working properly.  With IoT, you can predict and prevent equipment failure by monitoring product wear and replacement rates.  As such, you improve the reliability of your assets and reduce downtime. And if you recognize little faults, you can solve them easily before they have become big and expensive problems.

Take the right decisions at the right time

Having control means that you can take the right decision at the right time. E.g.:

  • To replace a motor of a machine
  • Adjust the circumstances, e.g., the indoor quality in your office, the temperature in your cooling
  • For both doctor and patient: Is the healing process going well?  Give the proper attention to patients who needs it. Instead of giving attention to every patient, without enough time. And even better:  You can optimize the healing process
  • Does the patient follow the medical instructions? Examples: is he doing his therapy on time and in the right way. Does he take his medications?  Especially groups of risk can be monitored so that timely action can be taken if necessary

Track & Trace

  • Keep grip on the presence of all your assets at all times
  • Keep grip on the production process, by knowing at any moment where parts are in the phase of a production process
  • Know where employees and visitors are on your premises, e.g., to avoid them entering hazardous areas. Or to warn them in case of calamities. And avoid unwanted visitors enter your premises

Optimal circumstances

  • Create optimal conditions for your employees. For example in the office by regulating a good indoor climate, which fortunately is getting more and more attention. Poor air quality can worsen the well-being of employees and visitors. It can also lead to lower productivity, for example because people feel lazy when the temperature is too high.
  • Some products and services are highly dependent on maintaining optimum conditions, such as cooling, for example, perishable products. When these conditions are no longer met, all products in a batch may have to be destroyed. Through IoT you can monitor whether these optimal conditions still apply. This allows you to intervene immediately when these conditions deteriorate.

See also:

The Benefits of AIOT: “Lower Costs or More Efficiency”

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Sensors in Biomedical AIOT

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Biomedical devices are at the forefront of AI and IOT (more often called AIOT). What is your most important reason to use sensors for biomedical devices?

Biomedical sensors for ai, iot and aiot to optimize

To control

  • Does the patient follow the medical instructions? Examples: is he doing his therapy on time and in the right way. Does he take his medications?  Especially groups of risk can be monitored so that timely action can be taken if necessary
  • Is treatment going well?  For both doctor and client alike. And even better:  You can optimize the healing process
  • Do medically devices still give the right measurement?
sensors biomedical devices optimize ai iot aiot

To optimize

  • Optimize your treatment: Compare the treatment results from your client with your other clients. And thus, find out point of improvement
  • Give attention for those who need it. Nobody wants to spend time unnecessary in a waiting room
  • Better use of existing resources
  • Connect systems with each other
  • Take the right decisions at the right time
  • Preventive maintenance Security

To innovate

  • Better serve your clients
  • Be at the forefront of medical developments
  • Track & trace
  • Create optimal circumstances with modern technology
sensors for biomedical devices iot ai aiot

To save

  • Give the client the best care
  • Spend your budget where most needed
  • To prevent is better than to cure
  • Prevent greater suffering, avoid extra high costs
  • Nobody is waiting for unnecessary treatment
  • Preventive maintenance on medical devices prevents higher repair costs and downtime
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The Benefits of AIOT: “Lower Costs or More Efficiency”

AIoT has many benefits. Those benefits can be summarized as: saving, controlling, optimizing and innovation. How does AIOT reduce costs and provide more efficiency?

How AIoT can help to save:

  • Preventive maintenance
  • Efficient use of time, equipment and money
  • Lesser costs of energy
  • Don´t throw away infrastructure which is working fine

Preventive maintenance

Purchasing new machinery involves high costs. The assets of public infrastructure exist of expensive equipment. So, there are high costs of replacing equipment which is failing. To reduce these costs, Preventive maintenance comes in. With Preventive maintenance, you can repair or replace parts from which you know that they will not be working properly in a short time. Or on the moment they are not working properly anymore.  With this maintenance program, you can act because an (expected) little failure has caused damage.

And, in many cases such as public infrastructure, a not working device isn’t just a not working device!  A failure of a sluice or railroad switch causes disruption for the infrastructure as a whole: Ships and trains can’t deliver their goods anymore on time. Customers are standing literary in the cold due to not working train infrastructure. With preventive maintenance you can spare them (or yourself) high costs and much annoyance.

Efficient use of time, equipment and money

Use your time, equipment or money? As efficient as possible. In a time of growing economies, employees are scarce and hard to find. So you want to make use of your employee’s time as efficient and effective as possible. This means that employees have to to be able give attention to things… really needed. IoT makes this possible. Some examples:

  • For offices: cleaners have to clean only the places of the office which have actually used instead of cleaning the whole building. Non-used offices can even be shut down.
  • Logistics: more efficienct planning of cranes, further transport
  • Already mentioned: the benefit of preventive maintenance

Lesser costs of energy

Another savings IoT makes possible is saving of energy.

And of course, this benefits the user but also the planet as a whole! And that makes your customers and employees even more satisfied. Which makes that they will stay customer or employer longer… Besides, if you rent offices, they will be longer and easier hired.

Don’t throw infrastructure which is working fine

In most buildings and logistics, the infrastructure has been built years ago with huge efforts and costs. The infrastructure is mission critical, so owners often still accept that their infrastructure isn’t the most efficient, as long as it works. Now sensors come in: they bring an extra layer upon the already existing devices, be it such different devices as hvac in office buildings or cranes in ports.

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How Indoor Humidity effects your feeling of well-being and health

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For feeling comfortable indoor, humidity is one of the most important factors, both physical and mentally. Where temperature is immediately perceived (‘cold in here’), humidity is also one of the most important factors for feeling comfortable indoors. Besides, temperature and humidity go hand-in-hand. Besides, humidity plays a factor in the growth of molds and other allergens.

Indoor air humidity

Humidity is the concentration of water vapor present in the air. Humidity depends on the temperature and pressure. Warm air is able to bind more water than cold. The same amount of water vapor results in higher humidity in cool air than warm air. So, humidity is also important how we experience the temperature. Many measurements of humidity consist of relative humidity: how much water there is in the air relative to the maximum of water it can contain given the same temperature. Regulation the indoor humidity and temperature go together.

Effect of humidity on well-being and health

Humans are more sensitive to changes in temperature than in relative humidity. However, humidity is an important factor in thermal comfort: the condition of mind that expresses satisfaction with the thermal environment. Outdoor, humidity has a much stronger influence at higher than at low temperatures.

Human bodies use evaporative cooling to regulate temperate as primary mechanism. The rate of which perspiration evaporates on the skin is under humid conditions lower than in arid ones. Humans feel warmer at a relative high humidity, because humans perceive the rate of heat transfer from the body rather than the temperature itself.

High humidity (‘humid air’) or low humidity (‘dry air’) can have negative effects on well-being and health. You can feel some effects immediately and they disappear when the humidity is adjusted (or when you leave the room), some effects may rise years later.

Effects of dry air

Dry air may cause:

  • Dry eyes
  • Chapped lips
  • Bloody nose
  • Itching of the nose
  • Irritation of the skin
  • Allergy problems and asthma

Tissue lining of the nasal passages may dry and crack due to low humidity. Besides, it may become more susceptible to penetration of the rhinovirus cold viruses. Very low humidity not only may create discomfort, but respiratory problems and aggravate allergies.

When humidity drops below 20%, it may cause eye irritation.

Dry air during winter

You have probably experienced yourself: at winter, indoor air quality is often rather dry. When temperature decreases under 0°C, relative humidity can drop to 20%. However, ‘good’ indoor humidity should be between 20 and 40%. Especially in winter, a humidity above 30% is preferred to reduce the change that the nasal passages dry out.

The cause of dry air is often the room temperature. That’s why room temperature should be kept under 22°C (72°F).

Humid air

Some effects of humid air indoor:

  • Fatigue
  • Frizzy hair
  • Feeling hot or sweaty
  • Sleep interruptions
  • Respiratory problems
  • Allergy problems and asthma

As said above, some people may suffer respiratory problems. Some of these problems may be related to conditions as asthma or may be caused due to anxiety. Many people hyperventilate as response. This causes feelings such as loss of concentration, numbness or faintness.

Humid air during summer

During summer, the ideal indoor humidity is between 30% to 50%, following the high humidity outside. In any case, constant humidity must be kept under 60%, to prevent the growth of microbes.

Humid air during winter

In some cases, the indoor humidity may rise above 45% during winter. Mostly this is caused by human activity with poor ventilation. The most immediate visible effect is condensing on cold surfaces as windows. When there is often the case of humid air, condense may affect the structure of the building and can cause health problems.

Solutions like Airmex can help you to monitor your humidity, for a comfortable, safe and healthy working environment.

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