Bevilacqua, AntonioAntonioBevilacquaBrennan, LouiseLouiseBrennanArgent, RobRobArgentCaulfield, BrianBrianCaulfieldKechadi, TaharTaharKechadi2019-08-072019-08-072019 IEEE2019-06-27978-1-5386-1311-5/19/1558-4615http://hdl.handle.net/10197/10958EMBC '19: 41st International Engineering in Medicine and Biology Conference, Berlin, Germany, 23-27 July 2019Segmenting physical movements is a key step for any accelerometry-based autonomous biofeedback system oriented to rehabilitation and physiotherapy activities. Fundamentally, this can be reduced to the detection of recurrent patterns, also called motion primitives, in longer inertial signals. Most of the solutions developed in the literature require extensive domain knowledge, or are incapable of scaling to complex motion patterns and new exercises. In this paper, we explore the capabilities of inertial measurement units for the segmentation of upper limb rehabilitation exercises. To do so, we introduce a novel segmentation technique based on Convolutional Neural Networks and Finite State Machines, called ConvFSM. ConvFSM is able to isolate motion primitives from raw streaming data, using very little domain knowledge. We also investigate different combinations of sensors, in order to identify the most effective and flexible setup that could fit a home-based rehabilitation feedback system. Experimental results are presented, based on a dataset obtained from a combination of common upper limb and lower limb exercises.en© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Machine Learning & StatisticsRehabilitationUpper limb rehabilitationExercisesConvolutional Neural Networks and Finite State MachinesConvFSMLower limb rehabilitationConference Publication10.1109/EMBC.2019.88564282019-08-06https://creativecommons.org/licenses/by-nc-nd/3.0/ie/