Real name: 
Congratulations!  You have been automatically subscribed to IEEE DataPort and can access all datasets on IEEE DataPort!
First Name: 
Anish
Last Name: 
Turlapaty
Affiliation: 
IIIT Sri City
Job Title: 
Associate Professor
Expertise: 
Signal Analysis, Machine learning
Short Bio: 
Anish C. Turlapaty born in India, received the B.Tech degree in electronics and communication engineering from Nagarjuna University, India, in 2002, the M.Sc in engineering from the Chalmers University, Sweden in 2006, and the Ph.D in electrical engineering from the Mississippi State University, Mississippi State, MS in 2010. From July 2010 to January 2012, he was at Mississippi State as a postdoctoral associate. From February 2012 to January 2015, he was a postdoctoral associate in the engineering and aviation sciences at University of Maryland Eastern Shore. From March 2015 to July 2017, he was a faculty at the ECE department of VR Siddhartha Engineering College, Vijayawada, India. From July 2017 to August 2021, he was an assistant professor at Indian Institute of Information Technology Sri City, Chittoor, Andhra Pradesh, India. He is currently an associate professor at IIIT Sri City. His current research interests are applications of statistical signal processing and pattern recognition. He is an active member of the IEEE and three of its societies. He has received graduate student of the year awards for outstanding research from both the Mississippi State University Centers & Institutes and the Geosystems Research Institute at MSU for the year 2010.

Datasets & Competitions

We present a sEMG signal database corresponding to the Indian population named “ElectroMyography Analysis of Human Activities - DataBase -2 (EMAHA-DB2).” This data set consists of two different weight training activities which involve isotonic and isometric contractions. Weight training activities are effective for improving muscle strength, overall health, and regaining limb functionality for people undergoing rehabilitation post stroke-related episodes. The EMG signals acquired during weight training can be used for muscle recruitment analysis.

Categories:
494 Views

Surface EMG (sEMG) signals collected during activities of daily life (ADL) provide better insights toward understanding neuromuscular disorders, persons with limb disabilities, aging adults and neuromotor deficits. Hand movement and control mechanism analysis may improve the design of prosthetic devices, realistic biomechanical hands, and rehabilitation therapy. We present a sEMG signal database corresponding to the Indian population.

Categories:
1296 Views