MGait
            : Model-Based Gait Analysis Using Wearable Bend and Inertial Sensors

Movement disorders, such as Parkinson’s disease, affect more than 10 million people worldwide. Gait analysis is a critical step in the diagnosis and rehabilitation of these disorders. Specifically, step and stride lengths provide valuable insights into the gait quality and rehabilitation process. However, traditional approaches for estimating step length are not suitable for continuous daily monitoring since they rely on special mats and clinical environments. To address this limitation, this article presents a novel and practical step-length estimation technique using low-power wearable bend and inertial sensors. Experimental results show that the proposed model estimates step length with 5.49% mean absolute percentage error and provides accurate real-time feedback to the user.

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Inertial Sensor-Based Step Length Estimation Model by Means of Principal Component Analysis

Sensors ◽

10.3390/s21103527 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3527

Author(s):

Melanija Vezočnik ◽

Roman Kamnik ◽

Matjaz B. Juric

Keyword(s):

Principal Component Analysis ◽

Human Body ◽

Inertial Sensor ◽

Principal Component ◽

Step Length ◽

Component Analysis ◽

Indoor Positioning ◽

Estimation Model ◽

Length Estimation ◽

Proposed Model

Inertial sensor-based step length estimation has become increasingly important with the emergence of pedestrian-dead-reckoning-based (PDR-based) indoor positioning. So far, many refined step length estimation models have been proposed to overcome the inaccuracy in estimating distance walked. Both the kinematics associated with the human body during walking and actual step lengths are rarely used in their derivation. Our paper presents a new step length estimation model that utilizes acceleration magnitude. To the best of our knowledge, we are the first to employ principal component analysis (PCA) to characterize the experimental data for the derivation of the model. These data were collected from anatomical landmarks on the human body during walking using a highly accurate optical measurement system. We evaluated the performance of the proposed model for four typical smartphone positions for long-term human walking and obtained promising results: the proposed model outperformed all acceleration-based models selected for the comparison producing an overall mean absolute stride length estimation error of 6.44 cm. The proposed model was also least affected by walking speed and smartphone position among acceleration-based models and is unaffected by smartphone orientation. Therefore, the proposed model can be used in the PDR-based indoor positioning with an important advantage that no special care regarding orientation is needed in attaching the smartphone to a particular body segment. All the sensory data acquired by smartphones that we utilized for evaluation are publicly available and include more than 10 h of walking measurements.

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Adaptive Step Length Estimation Algorithm Using Low-Cost MEMS Inertial Sensors

2007 IEEE Sensors Applications Symposium ◽

10.1109/sas.2007.374406 ◽

2007 ◽

Cited By ~ 93

Author(s):

S. H. Shin ◽

C. G. Park ◽

J. W. Kim ◽

H. S. Hong ◽

J. M. Lee

Keyword(s):

Inertial Sensors ◽

Low Cost ◽

Estimation Algorithm ◽

Step Length ◽

Length Estimation ◽

Adaptive Step

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Step length estimation using inertial sensors: Evaluation study

Gait & Posture ◽

10.1016/j.gaitpost.2012.12.056 ◽

2013 ◽

Vol 37 ◽

pp. S27 ◽

Cited By ~ 1

Author(s):

A. Ferrari ◽

L. Rocchi ◽

J. Van den Noort ◽

J. Harlaar

Keyword(s):

Inertial Sensors ◽

Step Length ◽

Evaluation Study ◽

Length Estimation

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Step Length Estimation Methods Based on Inertial Sensors: A Review

IEEE Sensors Journal ◽

10.1109/jsen.2018.2857502 ◽

2018 ◽

Vol 18 (17) ◽

pp. 6908-6926 ◽

Cited By ~ 27

Author(s):

Luis Enrique Diez ◽

Alfonso Bahillo ◽

Jon Otegui ◽

Timothy Otim

Keyword(s):

Inertial Sensors ◽

Step Length ◽

Estimation Methods ◽

Length Estimation

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Walking Step Length Estimation using Waist-mounted Inertial Sensors with Known Total Walking Distance

IEEE Access ◽

10.1109/access.2021.3087721 ◽

2021 ◽

pp. 1-1

Author(s):

Thanh Tuan Pham ◽

Young Soo Suh

Keyword(s):

Inertial Sensors ◽

Step Length ◽

Walking Distance ◽

Length Estimation

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Step Length Estimation and Activity Detection in a PDR System Based on a Fuzzy Model with Inertial Sensors

Studies in Computational Intelligence - Recent Advances on Hybrid Approaches for Designing Intelligent Systems ◽

10.1007/978-3-319-05170-3_45 ◽

2014 ◽

pp. 631-645

Author(s):

Mariana Natalia Ibarra-Bonilla ◽

Ponciano Jorge Escamilla-Ambrosio ◽

Juan Manuel Ramirez-Cortes ◽

Jose Rangel-Magdaleno ◽

Pilar Gomez-Gil

Keyword(s):

Inertial Sensors ◽

Fuzzy Model ◽

Step Length ◽

Activity Detection ◽

Length Estimation

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Step Length Estimation Using Handheld Inertial Sensors

Sensors ◽

10.3390/s120708507 ◽

2012 ◽

Vol 12 (7) ◽

pp. 8507-8525 ◽

Cited By ~ 130

Author(s):

Valérie Renaudin ◽

Melania Susi ◽

Gérard Lachapelle

Keyword(s):

Inertial Sensors ◽

Step Length ◽

Length Estimation

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Step Length Estimation with Wearable Sensors Using a Switched-Gain Nonlinear Observer

10.31224/osf.io/u9vmr ◽

2021 ◽

Author(s):

Ali Nouriani ◽

Robert A McGovern ◽

Rajesh Rajamani

Keyword(s):

Inertial Sensors ◽

Wearable Sensors ◽

Infrared Camera ◽

Step Length ◽

The Body ◽

Estimation Problem ◽

Nonlinear Observer ◽

Length Estimation ◽

Minimum Number ◽

Sensor Configuration

This paper focuses on step length estimation using inertial measurement units. Accurate step length estimation has a number of useful health applications, including its use in characterizing the postural instability of Parkinson’s disease patients. Three different sensor configurations are studied using sensors on the shank and/or thigh of a human subject. The estimation problem has several challenges due to unknown measurement bias, misalignment of the sensors on the body and the desire to use a minimum number of sensors. A nonlinear estimation problem is formulated that aims to estimate shank angle, thigh angle, bias parameters of the inertial sensors and step lengths. A nonlinear observer is designed using Lyapunov analysis and requires solving an LMI to find a stabilizing observer gain. It turns out that global stability over the entire operating region can only be obtained by using switched gains, one gain for each piecewise monotonic region of the nonlinear output function. Experimental results are presented on the performance of the nonlinear observer and compared with gold standard reference measurements from an infrared camera capture system. An innovative technique that utilizes three sensors is shown to provide a step length accuracy nearly equal to that of the four-sensor configuration.

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