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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

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.

The both framework SDKs will be released with ASNFD v5.0 available for licensed users in September 2021.

Find out more

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

Need more information or just want to be notified when release v5.0 is available? Please register your interest here.