Sensors 2014, 14, 2776-2794; doi:10.3390/s140202776 OPEN ACCESS sensors ISSN 1424-8220 www.mdpi.com/journal/sensors Article Online Phase Detection Using Wearable Sensors for Walking with a Robotic Prosthesis Maja Gorˇ siˇ c 1 , Roman Kamnik 1, *, Luka Ambroˇ ziˇ c 1 , Nicola Vitiello 2 , Dirk Lefeber 3 , Guido Pasquini 4 and Marko Munih 1 1 Faculty of Electrical Engineering, University of Ljubljana, Trˇ zaˇ ska 25, Ljubljana 1000, Slovenia; E-Mails: maja.gorsic@robo.fe.uni-lj.si (M.G.); luka.ambrozic@robo.fe.uni-lj.si (L.A.); marko.munih@robo.fe.uni-lj.si (M.M.) 2 The BioRobotics Institute, Scuola Superiore Sant’Anna, viale Rinaldo Piaggio 34, Pontedera 56025, Pisa, Italy; E-Mail: n.vitiello@sssup.it 3 Vrije Universiteit Brussel, Faculty of Applied Sciences, Pleinlaan 2, Brussels B-1050, Belgium; E-Mail: dlefeber@vub.ac.be 4 Fondazione don Carlo Gnocchi, Florence 50018, Italy; E-Mail: gupasquini@dongnocchi.it * Author to whom correspondence should be addressed; E-Mail: roman.kamnik@robo.fe.uni-lj.si; Tel.: +386-1-4768-355; Fax: +386-1-4768-239. Received: 19 November 2013; in revised form: 19 January 2014 / Accepted: 23 January 2014 / Published: 11 February 2014 Abstract: This paper presents a gait phase detection algorithm for providing feedback in walking with a robotic prosthesis. The algorithm utilizes the output signals of a wearable wireless sensory system incorporating sensorized shoe insoles and inertial measurement units attached to body segments. The principle of detecting transitions between gait phases is based on heuristic threshold rules, dividing a steady-state walking stride into four phases. For the evaluation of the algorithm, experiments with three amputees, walking with the robotic prosthesis and wearable sensors, were performed. Results show a high rate of successful detection for all four phases (the average success rate across all subjects >90%). A comparison of the proposed method to an off-line trained algorithm using hidden Markov models reveals a similar performance achieved without the need for learning dataset acquisition and previous model training. Keywords: wearable sensory system; inertial sensors; instrumented insoles; gait phase detection; robotic prosthesis