Gait Partitioning Methods: A Systematic Review

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ID: 272530
2016
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Abstract
In the last years, gait phase partitioning has come to be a challenging research topic due to its impact on several applications related to gait technologies. A variety of sensors can be used to feed algorithms for gait phase partitioning, mainly classifiable as wearable or non-wearable. Among wearable sensors, footswitches or foot pressure insoles are generally considered as the gold standard; however, to overcome some inherent limitations of the former, inertial measurement units have become popular in recent decades. Valuable results have been achieved also though electromyography, electroneurography, and ultrasonic sensors. Non-wearable sensors, such as opto-electronic systems along with force platforms, remain the most accurate system to perform gait analysis in an indoor environment. In the present paper we identify, select, and categorize the available methodologies for gait phase detection, analyzing advantages and disadvantages of each solution. Finally, we comparatively examine the obtainable gait phase granularities, the usable computational methodologies and the optimal sensor placements on the targeted body segments.
Reference Key
taborri2016sensorsgait Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Juri Taborri;Eduardo Palermo;Stefano Rossi;Paolo Cappa;Taborri, Juri;Palermo, Eduardo;Rossi, Stefano;Cappa, Paolo;
Journal sensors
Year 2016
DOI
10.3390/s16010066
URL
https://dx.doi.org/10.3390/s16010066
Keywords
electromyography (emg) wearable sensors gait phase partitioning gait pattern footswitches inertial measurements units (imu) opto-electronic system force platform systematic review Monitoring National Center for Biotechnology Information NCBI NLM MEDLINE review humans pubmed abstract nih national institutes of health national library of medicine research support non-u.s. gov't electromyography computer-assisted* signal processing gait / physiology* juri taborri clothing pmid:26751449 pmc4732099 doi:10.3390/s16010066 eduardo palermo paolo cappa accelerometry ambulatory*

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