Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Instrumentation Amplifier01:25

Instrumentation Amplifier

411
An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
411
Classification of Signals01:30

Classification of Signals

365
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
365

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Sensory-Cognitive Profiles in Children with ADHD: Exploring Perceptual-Motor, Auditory, and Oculomotor Function.

Bioengineering (Basel, Switzerland)·2025
Same author

The Integration of Artificial Intelligence with Micro-Nano-Systems: Perspectives, Challenges and Future Prospects.

Micromachines·2025
Same author

Model Parametrization-Based Genetic Algorithms Using Velocity Signal and Steady State of the Dynamic Response of a Motor.

Biomimetics (Basel, Switzerland)·2025
Same author

Perceptual-Motor Abilities and Reversal Frequency of Letters and Numbers in Children Diagnosed with Poor Reading Skills.

Bioengineering (Basel, Switzerland)·2025
Same author

Deciphering the Physical Characteristics of Ophthalmic Filters Used in Optometric Vision Therapy.

Healthcare (Basel, Switzerland)·2024
Same author

Pose Estimation of a Cobot Implemented on a Small AI-Powered Computing System and a Stereo Camera for Precision Evaluation.

Biomimetics (Basel, Switzerland)·2024

Related Experiment Video

Updated: May 1, 2026

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

44.0K

Electromyography Signals in Embedded Systems: A Review of Processing and Classification Techniques.

José Félix Castruita-López1, Marcos Aviles1, Diana C Toledo-Pérez1

  • 1Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro 76240, Mexico.

Biomimetics (Basel, Switzerland)
|March 26, 2025
PubMed
Summary

This study compares embedded systems for electromyography (EMG) signal classification, finding device choice depends on application needs like precision and power for wearables. It guides selecting technologies for embedded biomedical solutions using EMG data.

Keywords:
EMGFPGASoCartificial intelligenceclassification algorithmsembedded systems

More Related Videos

A Real-Time Wearable Electromyography Measurement System for Small Animals
05:00

A Real-Time Wearable Electromyography Measurement System for Small Animals

Published on: November 15, 2024

1.5K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

1.5K

Related Experiment Videos

Last Updated: May 1, 2026

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

44.0K
A Real-Time Wearable Electromyography Measurement System for Small Animals
05:00

A Real-Time Wearable Electromyography Measurement System for Small Animals

Published on: November 15, 2024

1.5K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

1.5K

Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Electromyography (EMG) signal classification is crucial for real-time wearable applications.
  • Implementing EMG algorithms on embedded systems presents challenges in performance and resource constraints.

Purpose of the Study:

  • To provide an overview of implementing EMG signal classification algorithms across diverse embedded system architectures.
  • To analyze various architectures (microcontrollers, DSP, FPGA, SoC, neuromorphic) for their suitability in wearable EMG applications.

Main Methods:

  • Analysis of embedded system architectures based on specifications like movement count and classification type.
  • Evaluation of architectures considering precision, processing time, energy consumption, and cost.
  • Focus on local inference for artificial intelligence models to optimize execution and resource usage.

Main Results:

  • Different embedded architectures offer varying trade-offs in performance and cost for EMG classification.
  • Microcontrollers, DSP, FPGAs, SoCs, and neuromorphic chips present unique advantages for specific real-time wearable needs.
  • Device selection is contingent upon system specifications, model robustness, classification complexity, and budget.

Conclusions:

  • The optimal embedded system for EMG signal classification depends on specific application requirements and constraints.
  • This work serves as a reference for selecting appropriate technologies for developing embedded biomedical solutions utilizing EMG.
  • Understanding the capabilities of each architecture is key for efficient and effective wearable device development.