title: Recognizing the intensity of strength training exercises with wearable sensors.
abstract: In this paper we propose a system based on a network of wearable accelerometers and an off-the-shelf smartphone to recognize the intensity of stationary activities, such as strength training exercises. The system uses a hierarchical algorithm, consisting of two layers of Support Vector Machines (SVMs), to first recognize the type of exercise being performed, followed by recognition of exercise intensity. The first layer uses a single SVM to recognize the type of the performed exercise. Based on the recognized type a corresponding intensity prediction SVM is selected on the second layer, specializing in intensity prediction for the recognized type of exercise. We evaluate the system for a set of upper-body exercises using different weight loads. Additionally, we compare the most important features for exercise and intensity recognition tasks and investigate how different sliding window combinations, sensor configurations and number of training subjects impact the algorithm performance. We perform all of the experiments for two different types of features to evaluate the feasibility of implementation on resource constrained hardware. The results show the algorithm is able to recognize exercise types with approximately 85% accuracy and 6% intensity prediction error. Furthermore, due to similar performance using different types of features, the algorithm offers potential for implementation on resource constrained hardware.
authors.affiliation: Pervasive Computing Applications, Research Studios Austria, Vienna, Austria. Electronic address: igor.pernek@researchstudio.at.
authors.forename: Igor
authors.initials: I
authors.lastname: Pernek
authors.affiliation: EECS, University of California, Berkeley, CA, USA. Electronic address: gregorij@eecs.berkeley.edu.
authors.forename: Gregorij
authors.initials: G
authors.lastname: Kurillo
authors.affiliation: Faculty of Health Sciences, University of Maribor, Maribor, Slovenia; Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia. Electronic address: gregor.stiglic@um.si.
authors.forename: Gregor
authors.initials: G
authors.lastname: Stiglic
authors.affiliation: EECS, University of California, Berkeley, CA, USA. Electronic address: bajcsy@eecs.berkeley.edu.
authors.forename: Ruzena
authors.initials: R
authors.lastname: Bajcsy
mesh_terms: D000328:Adult; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D060388:Support Vector Machine; D014891:Weight Lifting
keywords: Accelerometers; Machine learning; Strength training; Werable sensors