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A review of foot pose and trajectory estimation methods using inertial and auxiliary sensors for kinematic gait analysis

  • Nikiforos Okkalidis EMAIL logo , Kenneth P. Camilleri , Alfred Gatt , Marvin K. Bugeja and Owen Falzon
Published/Copyright: June 25, 2020
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Biomedical Engineering / Biomedizinische Technik
From the journal Biomedical Engineering / Biomedizinische Technik Volume 65 Issue 6

Abstract

The use of foot mounted inertial and other auxiliary sensors for kinematic gait analysis has been extensively investigated during the last years. Although, these sensors still yield less accurate results than those obtained employing optical motion capture systems, the miniaturization and their low cost have allowed the estimation of kinematic spatiotemporal parameters in laboratory conditions and real life scenarios. The aim of this work was to present a comprehensive approach of this scientific area through a systematic literature research, breaking down the state-of-the-art methods into three main parts: (1) zero velocity interval detection techniques; (2) assumptions and sensors’ utilization; (3) foot pose and trajectory estimation methods. Published articles from 1995 until December of 2018 were searched in the PubMed, IEEE Xplore and Google Scholar databases. The research was focused on two categories: (a) zero velocity interval detection methods; and (b) foot pose and trajectory estimation methods. The employed assumptions and the potential use of the sensors have been identified from the retrieved articles. Technical characteristics, categorized methodologies, application conditions, advantages and disadvantages have been provided, while, for the first time, assumptions and sensors’ utilization have been identified, categorized and are presented in this review. Considerable progress has been achieved in gait parameters estimation on constrained laboratory environments taking into account assumptions such as a person walking on a flat floor. On the contrary, methods that rely on less constraining assumptions, and are thus applicable in daily life, led to less accurate results. Rule based methods have been mainly used for the detection of the zero velocity intervals, while more complex techniques have been proposed, which may lead to more accurate gait parameters. The review process has shown that presently the best-performing methods for gait parameter estimation make use of inertial sensors combined with auxiliary sensors such as ultrasonic sensors, proximity sensors and cameras. However, the experimental evaluation protocol was much more thorough, when single inertial sensors were used. Finally, it has been highlighted that the accuracy of setups using auxiliary sensors may further be improved by collecting measurements during the whole foot movement and not only partially as is currently the practice. This review has identified the need for research and development of methods and setups that allow for the robust estimation of kinematic gait parameters in unconstrained environments and under various gait profiles.

Keywords: inertial sensors; kinematic gait analysis; wearable sensors; zero velocity interval
Corresponding author: Nikiforos Okkalidis, Centre for Biomedical Cybernetics, University of Malta, Msida, MSD 2080, Malta, E-mail: .

  1. Research funding: None declared.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

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Received: 2019-07-03
Accepted: 2020-03-09
Published Online: 2020-06-25
Published in Print: 2020-11-18

© 2020 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Reviews
  3. A review of foot pose and trajectory estimation methods using inertial and auxiliary sensors for kinematic gait analysis
  4. EEG Source Imaging (ESI) utility in clinical practice
  5. Research articles
  6. A multi-source co-frequency stimulus method for electroencephalogram (EEG) enhancement
  7. Analysis of statistical coefficients and autoregressive parameters over intrinsic mode functions (IMFs) for epileptic seizure detection
  8. Scalp electroencephalography (sEEG) based advanced prediction of epileptic seizure time and identification of epileptogenic region
  9. A simple model to detect atrial fibrillation via visual imaging
  10. Insertion torque/time integral as a measure of primary implant stability
  11. A microelectromechanical system (MEMS) capacitive accelerometer-based microphone with enhanced sensitivity for fully implantable hearing aid: a novel analytical approach
  12. Integrating artificial neural network and scoring systems to increase the prediction accuracy of patient mortality and organ dysfunction
  13. Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia
https://doi.org/10.1515/bmt-2019-0163
Keywords for this article
inertial sensors; kinematic gait analysis; wearable sensors; zero velocity interval

Articles in the same Issue

  1. Frontmatter
  2. Reviews
  3. A review of foot pose and trajectory estimation methods using inertial and auxiliary sensors for kinematic gait analysis
  4. EEG Source Imaging (ESI) utility in clinical practice
  5. Research articles
  6. A multi-source co-frequency stimulus method for electroencephalogram (EEG) enhancement
  7. Analysis of statistical coefficients and autoregressive parameters over intrinsic mode functions (IMFs) for epileptic seizure detection
  8. Scalp electroencephalography (sEEG) based advanced prediction of epileptic seizure time and identification of epileptogenic region
  9. A simple model to detect atrial fibrillation via visual imaging
  10. Insertion torque/time integral as a measure of primary implant stability
  11. A microelectromechanical system (MEMS) capacitive accelerometer-based microphone with enhanced sensitivity for fully implantable hearing aid: a novel analytical approach
  12. Integrating artificial neural network and scoring systems to increase the prediction accuracy of patient mortality and organ dysfunction
  13. Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia
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