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Sensitivity Analysis Of Artificial Neural Neteworks: Case Study In Clinical Electromyography

C. Pattichis; C. Charalambous; L. Middletton Engineering in Medicine and Biology Society, 1991. Vol.13: 1991., Proceedings of the Annual International Conference of the IEEE, 1991

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Exploiting accelerometers to improve movement classification for prosthetics

Arjan Gijsberts; Barbara Caputo Rehabilitation Robotics (ICORR), 2013 IEEE International Conference on, 2013

Recent studies have explored the integration of additional input modalities to improve myoelectric control of prostheses. Arm dynamics in particular are an interesting option, as these can be measured easily by means of accelerometers. In this work, the benefit of accelerometer signals is demonstrated on a large scale movement classification task, consisting of 40 hand and wrist movements obtained from ...

An Analysis of Facial Morphology for the Robot Assisted Smile Recovery

Dushyantha Jayatilake; Anna Gruebler; Kenji Suzuki 2008 4th International Conference on Information and Automation for Sustainability, 2008

Expression based non verbal communication accounts for a significant amount of the information exchange in human communication. In order to recreate facial expressions artificially for conveying an exact message it is necessary to make them as natural as possible since human beings posses a remarkable ability to recognize the emotion from an expression. In this paper we try to analyze ...

Pupil dilation and motor response elicitation by ultrasound neuromodulation

H. Kamimura; S. Wang; H. Chen; Q. Wang; C. Aurup; C. Acosta; A. Carneiro; E. Konofagou Ultrasonics Symposium (IUS), 2015 IEEE International, 2015

Focused ultrasound (FUS) neuromodulation has been previously proposed as a promising technique to drive neuronal activity. Here, we explored motor- and cognitive-related brain regions of mice by targeting specific brain structures using FUS neuromodulation in the mega-Hz range under a specific type of anesthesia. Contralateral motor responses were observed showing successful target specificity of the FUS neuromodulation achieved with 1.9 ...

Proposal of a real-time computational tool for the measurement of spasticity in stroke patients

A. N. Silva; M. B. Silva; I. A. Marques; E. L. M. Naves; A. B. Soares 5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC), 2014

Spasticity is a motor disorder that affects over 12 million people worldwide. Its pathophysiology is rather complex and might be caused by cortical problems or spinal cord injury, for example, and involves different circuitries and networks. The measurement of spasticity is very important for providing better support for the patients, both in diagnosis and treatment. However, there's not an agreement ...

IEEE-USA E-Books

• Biofeedback Applications

  This chapter contains sections titled: Introduction Biofeedback Application to Impairment Syndromes SEMG Biofeedback Techniques Summary References

• Neural Networks and TimeFrequency Analysis of Surface Electromyographic Signals for Muscle Cerebral Control

  This chapter contains sections titled: Introduction Data Acquisition: Surface Electromyography Methods Employed: Spectral and Coherence Analysis Artifact Removal: Neural Networks and Time-Frequency Analysis Simulations: Filter Performance Application: Common Drive Detection Conclusions References

• Control of Powered Upper Limb Prostheses

  This chapter contains sections titled: Introduction Myoelectric Signal as a Control Input Conventional Myoelectric Control Emerging MEC Strategies Summary References

• Index

  No abstract.

• Needle and Wire Detection Techniques

  This chapter contains sections titled: Anatomical and Physiological Background of Intramuscular Recording Recording Characteristics of Needle Electrodes Conventional Needle EMG Special Needle Recording Techniques Physical Characteristics of Needle EMG Signals Recording Equipment References

• Biophysics of the Generation of EMG Signals

  This chapter contains sections titled: Introduction EMG Signal Generation Crosstalk Relationships between Surface EMG Features and Developed Force Conclusions References

• Myoelectric Manifestations of Muscle Fatigue

  This chapter contains sections titled: Introduction Definitions and Sites of Neuromuscular Fatigue Assessment of Muscle Fatigue How Fatigue Is Reflected in Surface EMG Variables Myoelectric Manifestations of Muscle Fatigue in Isometric Voluntary Contractions Fiber Typing and Myoelectric Manifestations of Muscle Fatigue Factors Affecting Surface EMG Variables Repeatability of Estimates of EMG Variables and Fatigue Indexes Conclusions References

• SingleChannel Techniques for Information Extraction from the Surface EMG Signal

  This chapter contains sections titled: Introduction Spectral Estimation of Deterministic Signals and Stochastic Processes Basic Surface EMG Signal Models Surface EMG Amplitude Estimation Extraction of Information in Frequency Domain from Surface EMG Signals Joint Analysis of EMG Spectrum and Amplitude (JASA) Recurrence Quantification Analysis of Surface EMG Signals Conclusions References

• Surface Mechanomyogram

  This chapter contains sections titled: The Mechanomyogram (MMG): General Aspects during Stimulated and Voluntary Contraction Detection Techniques and Sensors Comparison Comparison between Different Detectors Simulation MMG Versus Force: Joint and Adjunct Information Content MMG Versus EMG: Joint and Adjunct Information Content Area of Application References

• Rehabilitation Robotic Prostheses for Upper Extremity

  One of the most valuable applications for medical mechatronics is the robotic prosthesis for it not only plays a crucial role in rehabilitation but also helps much in improving life quality of patients. Recently, various rehabilitation robots have been developed. The method to reduce the complexity is first introduced in this chapter. The method is based on a robust control law and an independent joint model, and is derived in detail in this chapter. The chapter emphasizes the mechanical mechanisms of the rehabilitation robot hand and the associated power systems. It focuses on robust control law design based on the independent joint model to reduce the complexity of the design process. The chapter also introduces a method which can improve the stability of neuroprostheses, and nontarget electromyography (EMG) pattern filtering (NTPF) scheme. The results have shown that the introduced NTPF scheme is able to maintain a high EMG classification rate.