TXT

This repository aims to publish a sEMG database for hand gesture recongnition, which is suitable for intra-session, inter-session, inter-day and inter-subject tests. Six subjects were involved in data collection on ten days, and two sessions a day with the interval of half an hour. In each session, one trial (10 secondes) for each geature was conducted. The electrode sleeve did not reweared between two sessions in a day. The utilised sEMG device was customised by the Intelligent System and Biomedical Robotics Group, which was discussed in [1]. 

# ISRMyo-I: A Database for sEMG-based Hand Gesture Recognition

## Introduction

Machine learning is becoming increasingly important for companies and the scientific community. It allows us to generate solutions for several problems faced by society. In this study, we perform a science mapping analysis on the machine learning research, in order to provide an overview of the scientific work during the last decade in this area and to show trends that could be the basis for future developments in the field of computer science. This study was carried out using the CiteSpace and SciMAT tools based on results from Scopus and Clarivate Web of Science.

The dataset is an extensive collection of labeled high-frequency Wi-Fi Radio Signal Strength (RSS) measurements corresponding to multiple hand gestures made near a smartphone under different spatial and data traffic scenarios. We open source the software code and an Android app (Winiff) to create this dataset, which is available at Github (https://github.com/mohaseeb/wisture). The dataset is created using an artificial traffic induction (between the phone and the access point) approach to enable useful and meaningful RSS values.