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Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users
PLAN Group, Schulich School of Engineering, The University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada
Present address: Nottingham Geospatial Institute, University of Nottingham, Nottingham, NG8 1BB, UK.
Present address: GEOLOC Lab, IFSTTAR, 44344 Bouguenais, France.
* Author to whom correspondence should be addressed.
Received: 1 December 2012; in revised form: 18 January 2013 / Accepted: 22 January 2013 / Published: 24 January 2013
Abstract: Microelectromechanical Systems (MEMS) technology is playing a key role in the design of the new generation of smartphones. Thanks to their reduced size, reduced power consumption, MEMS sensors can be embedded in above mobile devices for increasing their functionalities. However, MEMS cannot allow accurate autonomous location without external updates, e.g., from GPS signals, since their signals are degraded by various errors. When these sensors are fixed on the user’s foot, the stance phases of the foot can easily be determined and periodic Zero velocity UPdaTes (ZUPTs) are performed to bound the position error. When the sensor is in the hand, the situation becomes much more complex. First of all, the hand motion can be decoupled from the general motion of the user. Second, the characteristics of the inertial signals can differ depending on the carrying modes. Therefore, algorithms for characterizing the gait cycle of a pedestrian using a handheld device have been developed. A classifier able to detect motion modes typical for mobile phone users has been designed and implemented. According to the detected motion mode, adaptive step detection algorithms are applied. Success of the step detection process is found to be higher than 97% in all motion modes.
Keywords: pedestrian navigation; handheld MEMS; step detection; mobile phone; gait analysis
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Cite This Article
MDPI and ACS Style
Susi, M.; Renaudin, V.; Lachapelle, G. Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users. Sensors 2013, 13, 1539-1562.
Susi M, Renaudin V, Lachapelle G. Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users. Sensors. 2013; 13(2):1539-1562.
Susi, Melania; Renaudin, Valérie; Lachapelle, Gérard. 2013. "Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users." Sensors 13, no. 2: 1539-1562.