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

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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Average Step Length Estimation Models’ Evaluation Using Inertial Sensors: A Review

IEEE Sensors Journal ◽

10.1109/jsen.2018.2878646 ◽

2019 ◽

Vol 19 (2) ◽

pp. 396-403 ◽

Cited By ~ 10

Author(s):

Melanija Vezocnik ◽

Matjaz B. Juric

Keyword(s):

Inertial Sensors ◽

Step Length ◽

Length Estimation ◽

Estimation Models

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An Improved Method of Step Length Estimation with Inertial Sensors

2018 IEEE 87th Vehicular Technology Conference (VTC Spring) ◽

10.1109/vtcspring.2018.8417729 ◽

2018 ◽

Author(s):

Qian Zhao ◽

Jun Tian ◽

Genming Ding ◽

Lili Xie

Keyword(s):

Inertial Sensors ◽

Step Length ◽

Improved Method ◽

Length Estimation

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Axis-Exchanged Compensation and Gait Parameters Analysis for High Accuracy Indoor Pedestrian Dead Reckoning

Journal of Sensors ◽

10.1155/2015/915837 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 4

Author(s):

Honghui Zhang ◽

Jinyi Zhang ◽

Duo Zhou ◽

Wei Wang ◽

Jianyu Li ◽

...

Keyword(s):

Dead Reckoning ◽

Step Length ◽

Walking Distance ◽

Analysis Algorithm ◽

Indoor Location ◽

Length Estimation ◽

Gait Parameters ◽

Parameters Analysis ◽

Heading Estimation ◽

Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is an effective way for navigation coupled with GNSS (Global Navigation Satellite System) or weak GNSS signal environment like indoor scenario. However, indoor location with an accuracy of 1 to 2 meters determined by PDR based on MEMS-IMU is still very challenging. For one thing, heading estimation is an important problem in PDR because of the singularities. For another thing, walking distance estimation is also a critical problem for pedestrian walking with randomness. Based on the above two problems, this paper proposed axis-exchanged compensation and gait parameters analysis algorithm to improve the navigation accuracy. In detail, an axis-exchanged compensation factored quaternion algorithm is put forward first to overcome the singularities in heading estimation without increasing the amount of computation. Besides, real-time heading is updated by R-adaptive Kalman filter. Moreover, gait parameters analysis algorithm can be divided into two steps: cadence detection and step length estimation. Thus, a method of cadence classification and interval symmetry is proposed to detect the cadence accurately. Furthermore, a step length model adjusted by cadence is established for step length estimation. Compared to the traditional PDR navigation, experimental results showed that the error of navigation reduces 32.6%.

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

ACM Transactions on Internet of Things ◽

10.1145/3485434 ◽

2022 ◽

Vol 3 (1) ◽

pp. 1-24

Author(s):

Sizhe An ◽

Yigit Tuncel ◽

Toygun Basaklar ◽

Gokul K. Krishnakumar ◽

Ganapati Bhat ◽

...

Keyword(s):

Gait Analysis ◽

Inertial Sensors ◽

Step Length ◽

Percentage Error ◽

Daily Monitoring ◽

Length Estimation ◽

Proposed Model ◽

Gait Quality ◽

Clinical Environments ◽

Traditional Approaches

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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