An optoelectronic muscle contraction sensor for prosthetic hand application.

Surface electromyography (sEMG) is considered an established means for controlling prosthetic devices. sEMG suffers from serious issues such as electrical noise, motion artifact, complex acquisition circuitry, and high measuring costs because of which other techniques have gained attention. This work presents a new optoelectronic muscle (OM) sensor setup as an alternative to the EMG sensor for precise measurement of muscle activity.

Detection of COVID-19 Infection in CT and X-ray images using transfer learning approach.

Background: The infection caused by the SARS-CoV-2 (COVID-19) pandemic is a threat to human lives. An early and accurate diagnosis is necessary for treatment.

Objective: The study presents an efficient classification methodology for precise identification of infection caused by COVID-19 using CT and X-ray images.

A low-cost transradial prosthesis controlled by the intention of muscular contraction.

Persons with upper-limb amputations face severe problems due to a reduction in their ability to perform the activities of daily living. The prosthesis controlled by electromyography (EMG) or other signals from sensors, switches, accelerometers, etc., can somewhat regain the lost capability of such individuals.

Human Locomotion Classification for Different Terrains Using Machine Learning Techniques.

Gait analysis on healthy subjects was performed based on surface electromyographic and acceleration sensor signal, implemented through machine learning approaches. The surface EMG and 3-axes acceleration signals have been acquired for 5 different terrains: level ground, ramp ascent, ramp descent, stair ascent, and stair descent. These signals were acquired from the tibialis anterior and gastrocnemius medial head muscles that correspond to dorsiflexion and plantar flexion, respectively.

A compact-sized surface EMG sensor for myoelectric hand prosthesis.

Myoelectric prosthesis requires a sensor that can reliably capture surface electromyography (sEMG) signal from amputees for its controlled operation. The main problems with the presently available EMG devices are their extremely high cost, large response time, noise susceptibility, less amplitude sensitivity, and larger size. This paper proposes a compact and affordable EMG sensor for the prosthetic application.

A low-cost, wearable sEMG sensor for upper limb prosthetic application.

This paper proposes a low-cost and sensitive surface electromyography (sEMG) sensor for the myoelectric prosthesis. The sensor consists of a skin interface, signal conditioning circuitry and power supply unit all encased in a single package. The tuned RC parameters based envelope detection scheme employed in the sensor enables faster as well as reliable recognition of EMG signal patterns regardless of its strength and subject variability.

Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals.

In motor imagery (MI) based brain-computer interface (BCI) signal analysis, mu and beta rhythms of electroencephalograms (EEGs) are widely investigated due to their high temporal resolution and capability to define the different movement-related mental tasks separately. However, due to the high dimensions and subject-specific behaviour of EEG features, there is a need for a suitable feature selection algorithm that can select the optimal features to give the best classification performance along with increased computational efficiency. The present study proposes a feature selection algorithm based on neighbourhood component analysis (NCA) with modification of the regularization parameter.

Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network.

The objective of developing this software is to achieve auto-segmentation and tissue characterization. Therefore, the present algorithm has been designed and developed for analysis of medical images based on hybridization of syntactic and statistical approaches, using artificial neural network (ANN). This algorithm performs segmentation and classification as is done in human vision system, which recognizes objects; perceives depth; identifies different textures, curved surfaces, or a surface inclination by texture information and brightness.