MyoGym: introducing an open gym data set for activity recognition collected using myo armband

The activity recognition research has remained popular although the first steps were taken almost two decades ago. While the first ideas were more like a-proof-of-concept studies the area has become a fruitful soil to novel methods of machine learning, to adaptive modeling, signal fusion and several different types of application areas. Nevertheless, one of the slowing aspects in methodology development is the burden in collecting and labeling enough versatile data sets. In this article, a MyoGym data set is introduced to be used in activity recognition classifier development, in development of models for unseen activities, in signal fusion, and many other areas not yet known. The data set includes 6D motion signals and 8 channel electromyogram data from 10 persons and from 30 different gym exercises, each of them consisting a set of ten repetitions. The benchmark results provided, in this article, are in purpose made straightforward that their repetitiveness should be easy for any newcomer in the area.

[1]  Mi Zhang,et al.  USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors , 2012, UbiComp.

[2]  A. Häkkinen,et al.  Effects of strength, endurance and combined training on muscle strength, walking speed and dynamic balance in aging men , 2011, European Journal of Applied Physiology.

[3]  Didier Stricker,et al.  Introducing a New Benchmarked Dataset for Activity Monitoring , 2012, 2012 16th International Symposium on Wearable Computers.

[4]  Gernot Bahle,et al.  What Can an Arm Holster Worn Smart Phone Do for Activity Recognition? , 2011, 2011 15th Annual International Symposium on Wearable Computers.

[5]  Pekka Siirtola,et al.  Accelerometer vs. Electromyogram in Activity Recognition , 2016 .

[6]  Héctor Pomares,et al.  A benchmark dataset to evaluate sensor displacement in activity recognition , 2012, UbiComp.

[7]  Paul Lukowicz,et al.  Measuring muscle activities during gym exercises with textile pressure mapping sensors , 2017, Pervasive Mob. Comput..

[8]  David G. Stork,et al.  Pattern Classification , 1973 .

[9]  Paul J. M. Havinga,et al.  Fusion of Smartphone Motion Sensors for Physical Activity Recognition , 2014, Sensors.

[10]  Mi Zhang,et al.  Human Daily Activity Recognition With Sparse Representation Using Wearable Sensors , 2013, IEEE Journal of Biomedical and Health Informatics.

[11]  Heli Koskimäki,et al.  Avoiding Bias in Classification Accuracy - A Case Study for Activity Recognition , 2015, 2015 IEEE Symposium Series on Computational Intelligence.

[12]  Héctor Pomares,et al.  Daily living activity recognition based on statistical feature quality group selection , 2012, Expert Syst. Appl..

[13]  Tullio Vernazza,et al.  Analysis of human behavior recognition algorithms based on acceleration data , 2013, 2013 IEEE International Conference on Robotics and Automation.

[14]  Bernt Schiele,et al.  A tutorial on human activity recognition using body-worn inertial sensors , 2014, CSUR.

[15]  Paul Lukowicz,et al.  Wearable Activity Tracking in Car Manufacturing , 2008, IEEE Pervasive Computing.

[16]  Ignacio Rojas,et al.  Design, implementation and validation of a novel open framework for agile development of mobile health applications , 2015, BioMedical Engineering OnLine.

[17]  Billur Barshan,et al.  Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units , 2014, Comput. J..

[18]  Pekka Siirtola,et al.  Activity recognition using a wrist-worn inertial measurement unit: A case study for industrial assembly lines , 2009, 2009 17th Mediterranean Conference on Control and Automation.

[19]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[20]  Daniela Micucci,et al.  UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones , 2016, ArXiv.

[21]  Ricardo Chavarriaga,et al.  The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition , 2013, Pattern Recognit. Lett..

[22]  Mike Y. Chen,et al.  Tracking Free-Weight Exercises , 2007, UbiComp.

[23]  Gary M. Weiss,et al.  Activity recognition using cell phone accelerometers , 2011, SKDD.

[24]  Dan Morris,et al.  RecoFit: using a wearable sensor to find, recognize, and count repetitive exercises , 2014, CHI.

[25]  Pekka Siirtola,et al.  Recognizing Unseen Gym Activities from Streaming Data - Accelerometer Vs. Electromyogram , 2016, DCAI.

[26]  Davide Anguita,et al.  A Public Domain Dataset for Human Activity Recognition using Smartphones , 2013, ESANN.