Real-Time Tool for Human Gait Detection from Lower Trunk Acceleration

  • Helena R. GonçalvesEmail author
  • Rui MoreiraEmail author
  • Ana RodriguesEmail author
  • Graça MinasEmail author
  • Luís Paulo ReisEmail author
  • Cristina P. SantosEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 747)


The continuous monitoring of human gait would allow to more objectively verify the abnormalities that arise from the most common pathologies. Therefore, this manuscript proposes a real-time tool for human gait detection from lower trunk acceleration. The vertical acceleration signal was acquired through an IMU mounted on a waistband, a wearable device. The proposed algorithm was based on a finite state machine (FSM) which includes a set of suitable decision rules and the detection of Heel-Strike (HS), Foot-flat (FF), Toe-off (TO), Mid-Stance (MS) and Heel-strike (HS) events for each leg. Results involved 7 healthy subjects which had to walk 20 m three times with a comfortable speed. The results showed that the proposed algorithm detects in real-time all the mentioned events with a high accuracy and time-effectiveness character. Also, the adaptability of the algorithm has also been verified, being easily adapted to some gait conditions, such as for different speeds and slopes. Further, the developed tool is modular and therefore can easily be integrated in another robotic control system for gait rehabilitation. These findings suggest that the proposed tool is suitable for the real-time gait analysis in real-life activities.


Gait detection Lower trunk Acceleration Real-time Wearable 



This work is supported by the FCT Fundação para a Ciência e Tecnologia - with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145-FEDER-006941; and the LIACC Project PEst/UID/CEC/00027/2013.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center for MicroElectroMechanical (CMEMS)University of MinhoBragaPortugal
  2. 2.Neurology ServiceHospital of BragaBragaPortugal
  3. 3.Information Systems DepartmentUniversity of MinhoBragaPortugal
  4. 4.LIACC - Artificial Intelligence and Computer Science LaboratoryPortoPortugal

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