Impact of Activity Pace and Arm Position on Classification of ADLs.

This study focuses on EMAHA-DB7, a new set of multi-channel surface electromyography (sEMG) signals designed to assess daily activities in different conditions. The dataset comprises sEMG signals from 10 subjects engaging in 10 distinct ADLs. Each activity was executed at two different paces and in three different arm positions, ensuring a diverse set of measurement conditions.

Regression Analysis for COVID-19 Infections and Deaths Based on Food Access and Health Issues.

COVID-19, or SARS-CoV-2, is considered as one of the greatest pandemics in our modern time. It affected people's health, education, employment, the economy, tourism, and transportation systems. It will take a long time to recover from these effects and return people's lives back to normal.

A Laplacian-Gaussian Mixture Model for Surface EMG Signals from Upper Limbs.

Existing literature suggests that the probability density function (pdf) of surface Electromyography (sEMG) signals follows either a Gaussian or Laplacian model. In this paper, a Laplacian-Gaussian mixture model is proposed for the EMG signals extracted from the upper limbs. The model is validated using both quantitative and qualitative perspectives.