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 importance of indoor air quality in schools and how HVAC and sensors help

For many, Covid-19 was an eye-opener for the importance of indoor air quality. Children spend a large portion of the day at school. American research shows, that children spend 1000 hours at school every year.It is therefore very important that students and teachers stay in a room with clean air. It is healthier and more pleasant. And poor air quality causes students to get worse grades. Why is good indoor air quality in schools and properly functioning HVAC so important? And how can sensors help monitor indoor air quality?

Lower grades, less fun

With stale air, students may find it hard to pay attention to the teacher. Or concentrate on tests or stay awake at all. Besides, poor indoor air quality may affect the ability to make decisions. So, without even realizing yourself, it can damage your productivity and your school results.

Research at K-12 education by Jacqueline M. Nowicki  (U.S. Government Accountability Office, K-12 Education: School Districts Frequently Identified Multiple Building Systems Needing Updates or Replacement., Jacqueline M. Nowicki, June 4, 2020) shows that: “compelling evidence…of an association of increased student performance with increased ventilation rates,” yet “ventilation rates in classrooms often fall far short of the minimum ventilation rates specified in standards.” 41% of U.S. school districts  need to update or replace their HVAC (heating, ventilation and air conditioning) systems in at least half of their schools. This means about 36,000 schools in the US.

In a survey of school buildings in the Netherlands, 7340 school buildings responded, but not always completely. Overall, 38% of the responding schools met the requested standards, that is 2789 schools. 807 schools (11%) indicated that they did not meet. The remaining schools could not (yet) say whether their building met the standards.

Further, a bad indoor air quality may lead to headaches and cause or worsen asthma and other respiratory illnesses. And, of course, it’s more pleasant to be in a classroom with clean air. Especially when you spend most of the day there.

How can you improve your indoor climate?

4 steps to improve Indoor Air Quality at schools

  1. Install and improve HVAC
  2. Filter and clean the air
  3. Measure indoor air quality with sensors
  4. Dashboard: monitor your indoor air quality

Install and improve HVAC

Due to poor ventilation, the ‘used’ air will not dilute enough with ‘new’, fresh air from outside. So, especially with many people in a closed room (like a class-room) and the ventilation is poor, the fresh air in this room gets more and more replaced by stale air. That’s why effective ventilation requires that it both brings fresh, oxygenated air from outdoors and removes stale indoor air.

sensors indoor air quality classroom Airguard

How to adjust HVAC within schools:

  • If you haven’t done already: install proper HVAC
  • A California study shows that 85% of the classrooms did not provide adequate ventilation
  • Purify the air in the building by extending the operating times of HVAC systems. Let the HVAC run before the first staff arrives and also after the last persons have gone home
  • Increase the rate of air exchanges to provide fresh air through natural of mechanical ventilation
  • Increase to 100% of fresh air intake or the maximum amount possible

Besides, regarding COVID-19, recent study (Centers for Disease Control and Prevention) shows that Covid-19 was 39% lower in schools by opening windows and doors, using fans, or those measurements in combination with air filtration methods.

Filter and Clean the Air

Air cleaners and HVAC filters filter pollutants or contaminants out of the air that passes thru them. They can help reduce airborne contaminants, including particles containing viruses. When ventilation with outdoor air is not possible or when outdoor air pollution is high, air purifiers (portable air cleaners) may be helpful without worsening comfort (temperature or humidity).

Sensors measure Indoor Air Quality

Children spend many hours indoors at school. Therefore, it is important to have a good indoor air quality. For the feeling of well-being for the children and teacher, but also for the children’s grades. You can measure the CO2 with a CO2 meter, or a sensor which combines the monitoring of CO2 with temperature, humidity and Volatile Organic Compounds, for instance ASN Airguard.

If you have installed HVAC, in some cases this doesn’t work properly. This may be caused by:

•             Problems with installation of HVAC systems

•             Incorrect HVAC systems purchased

•             Incorrect controls and thermostats

•             No follow-up testing after installation

•             Poorly-maintained filters

Besides, when you’re busy, keeping an eye on the air quality may easily be ignored. Sensors warn you that the indoor quality has worsened. And they help you to maintain your indoor air quality such, that the risk of spreading the viruses is as least as possible. These warn you with a signal on the sensor and an alert on your app. So, you can take action, adjust your HVAC or just open a window.

Indoor air qualities sensors monitor your indoor climate. They monitor CO2, TOVC, humidity and temperature.

Dashboard: monitor your indoor air quality

Monitor the indoor air quality of your school with a dashboard. You can monitor humidity, temperature, TVOC and CO2 in real time. And it shows how the school performs over time: are there any locations where the indoor air quality easily drops to an unwanted level? So, you can find out the causes and improve air quality.

It is not only schools themselves that are increasingly recognizing the importance of a good indoor climate for students and teachers.  But more and more governments (and parents) are also aware of the importance of air quality within schools. Through monitoring, schools can show authorities that they are meeting air quality standards. And also show parents that they provide a healthy and pleasant learning environment for their children.

Further, facility managers can use their reports by optimizing and save on energy costs by use of energy based on occupation levels and other factors.

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AIoT optimised DSP filtering library for Arm, RISC-V and MIPS microcontrollers

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ASN Filter Designer’s new ANSI C SDK framework, provides developers with a comprehensive automatic C code generator for microcontrollers and embedded platforms. This allows developers to directly deploy their AIoT filtering application from within the tool to any STM32, Arduino, ESP32, PIC32, Beagle Bone and other Arm, RISC-V, MIPS microcontrollers for direct use.

Arm’s CMSIS-DSP library vs. ASN’s C SDK Framework

Thanks to our close collaboration with Arm’s architecture team, our new ultra-compact, highly optimised ANSI C based framework provides outstanding performance compared to other commercial DSP libraries, including Arm’s optimised CMSIS-DSP library.

Benchmarks for STM32: M3, M4F and M7F microcontrollers running an 8th order IIR biquad lowpass filter for 1024 samples

As seen, using o1 complier optimisation, our framework is able to surpass Arm’s CMSIS-DSP library’s performance on an M4F and M7F. Although notice that performance of both libraries is worse on the Cortex-M3, as it doesn’t have an FPU. Despite the difference, both libraries perform equally well, but the ASN DSP library has the added advantage of extra functionality and being platform agnostic, making it ideal for variety of biomedical (ECG, EMG, PPG), audio (sound effects, equalisers) , IoT (temperature, gas, pressure) and I4.0 (flow measurement, vibration analysis, CbM) applications.

AIoT applications designed on the newer Cortex-M33F and Cortex-M55F cores can also take advantage of extra filtering blocks, double precision arithmetic support, providing a simple way of implementing high performance AI on the Edge applications within hours.

Advantages for developers

  • A developer can now develop, test and deploy a complete DSP filtering application within the ASN Filter Designer within a few hours. This is very different from a traditional R&D approach that assigns a team of developers for several days in order to achieve the same level of accuracy required for the application.
  • Open source and agnostic code base: In order to allow developers to get the maximum performance for their applications, the ASN-DSP SDK is provided as open source and is written in ANSI C. This means that any embedded processor and any level of compiler optimisation can be used.
  • Memory size required for the ASN-DSP SDK is relativity lower than other standard DSP libraries, which makes the ASN-DSP SDK extremely suitable for microcontrollers that have memory constrains.
  • Using the ASN Filter Designer’s signal analyser tool, developers now can test the performance, accuracy and assess the frequency response of their designed filter and get optimised C code which they can directly use in their application.
  • The SDK also supports some extra filtering functions, such as: a median filter, a moving average filter, all-pass, single section IIR filters, a TKEO biomedical filter, and various non-linear functions, including RMS, Abs, Log and Sqrt.  These functions form the filter cascade within the tool, and can be used to build signal processing applications, such as EMG and ECG biomedical applications.
  • The ASN-DSP SDK supports both single and double precision floating point arithmetic, providing excellent numerical accuracy and wide dynamic range. The library is unique in the sense that it supports double precision arithmetic, which although is not the most optimal for microcontrollers, allows for the implementation of high-fidelity filtering applications.

The ANSI C SDK framework is further extended by our new C# .NET framework, allowing .NET developers to build high performance desktop applications with signal processing capabilities.

Find out more and try it yourself

Benchmarks on a variety of 32-bit embedded platforms, including a biomedical EMG filtering example, are covered in the following application note.

The both framework SDKs are available in ASNFD v5.0, which may be downloaded here.

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Save time and energy in the design of the algorithms for Drones and DC motor control with ASN Filter Designer

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Drones are one of the golden nuggets in AIoT. No wonder, they can play a pivotal role in congested cities and faraway areas for delivery. Further, they can be a great help to give an overview of a large area or for places which are difficult or dangerous to reach. Advanced Solutions did some research how the companies producing drones has solved some questions regarding their sensor technology. And in drones, there are a lot of sensors- and especial the DC motor control. We found out that with ASN Filter Designer, producers could have saved time and energy in the design of their algorithms with ASN Filter Designer.

Until now: hard-by found solutions

We found out that most producers had come very hard-by to their solutions. And that, when solutions are found, they are far from near perfect.

Probably, this producer has spent weeks or even months on finding these solutions. With ASN Filter Designer, he could have come to a solution within days or maybe hours. Besides, we expect that the measurement would be better too.

The most important issue is that algorithms were developed by handwork: developed in a ‘lab’ environment and then tried in real-life. With the result of the test, the algorithm would be adjusted again. Because a ‘lab’ environment where testing circumstances are stable, it’s very hard work to make the models work in ‘real’ life. For this, rounds and rounds of ‘lab development’ and ‘real life testing’ have to be made.

How ASN Filter Designer could have saved a lot of time and energy

ASN Filter Designer could have saved a lot of time in the design of the algorithms the following ways:

  • Design, analyze and implement filters for Drone senor applications 
  • Filters for speed and positioning control using sensorless BLDC motors
  • Speed up deployment

Real-time feedback and powerful signal analyzer

One of the key benefits of the ASN Filter Designer and signal analyzer is that it gives real-time feedback. Once an algorithm is developed, it can easily be tested on real-life data. To capture the real-life data, the ASN Filter Designer has a powerful signal analyzer in place. The tool’s signal analyzer implements a robust zero-crossings detector, allowing engineers to evaluate and fine-tune a complete sensorless BLDC control algorithm quickly and simply.

Design and analyze filters the easy way                         

You can easily design, analyze and implement filters for drone sensor applications. Including: loadcells, strain gauges, torque, pressure, temperature, vibration and ultrasonic sensors. And assess their dynamic performance in real-time with different input conditions.  With the ASN Filter Designer, no algorithms are needed: you just have to drag the filter design. The tool calculates the coordinates itself.

For speed and position control using sensorless BLDC (brushless DC) motors based on back-EMF filtering you can easily experiment with the ASN Filter Designer. See the results in real-time for various IIR, FIR and median (majority filtering) digital filtering schemes. The tool’s signal analyzer implements a robust zero-crossings detector. So you can evaluate and fine-tune a complete sensorless BLDC control algorithm quickly and simply.

Speed up deployment

Perform detailed time/frequency analysis on captured test datasets and fine-tune your design. Our Arm CMSIS-DSP and C/C++ code generators and software frameworks speed up deployment to a DSP, FPGA or micro-controller.

Drones use lots of sensors, and most challenges will be solved with them! ASN Filter Designer provides you with a simple way of improving your sensor measurement performance with its interactive design interface.

So, if you have a measurement problem, ask yourself: will I have a lot of frustrating and costs (maybe not ‘out of pocket’, but still: costs) of creating a filter by hand? Or would I create my filter within days or even hours and save a lot of headache and money. Because: it’s already possible to have a full 3-month license for only 140 euro!

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