3D gait assessment in young and elderly subjects using foot-worn inertial sensors

Mariani, Benoît; Hoskovec, C.; Rochat, Shékina; Büla, Christophe; Penders, Julien; Aminian, Kamiar

doi:10.1016/j.jbiomech.2010.07.003

Cited by 324 publications

(341 citation statements)

References 32 publications

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“…Nevertheless, some patients showed huge difficulties to walk (outlier in Figure 7), thereby creating a hardly automatically and reliably segmentable pattern, which does not look as a step anymore. In these cases, the identification pattern used by [13] is likely to be unusable. This underlines the influence of the segmentation process on the results, which is the most sensitive part of the algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…Meanwhile, due to an algorithm based on acceleration patterns identification and pelvis displacement estimation, the authors exposed some limitations regarding pathological gait. The second one is the solution proposed by Mariani et al [13], which presents a 3D gait assessment method validated on both young and elderly valid subjects. Using a foot-worn inertial sensor, it uses a complex de-drifting method based on sigmoid-like curve substraction modeled from a p-chip interpolation function (Carlson and Fritsch, 1985).…”

Section: Introductionmentioning

confidence: 99%

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Implementation and Validation of a Stride Length Estimation Algorithm, Using a Single Basic Inertial Sensor on Healthy Subjects and Patients Suffering from Parkinson’s Disease

Sijobert¹,

Benoussaad²,

Denys³

et al. 2015

Health

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As low cost and highly portable sensors, inertial measurements units (IMU) have become increasingly used in gait analysis, embodying an efficient alternative to motion capture systems. Meanwhile, being able to compute reliably accurate spatial gait parameters using few sensors remains a relatively complex problematic. Providing a clinical oriented solution, our study presents a gyrometer and accelerometer based algorithm for stride length estimation. Compared to most of the numerous existing works where only an averaged stride length is computed from several IMU, or where the use of the magnetometer is incompatible with everyday use, our challenge here has been to extract each individual stride length in an easy-to-use algorithm requiring only one inertial sensor attached to the subject shank. Our results were validated on healthy subjects and patients suffering from Parkinson's disease (PD). Estimated stride lengths were compared to GAITRite © walkway system data: the mean error over all the strides was less than 6% for healthy group and 10.3% for PD group. This method provides a reliable portable solution for monitoring the instantaneous stride length and opens the way to promising applications.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implementation and Validation of a Stride Length Estimation Algorithm, Using a Single Basic Inertial Sensor on Healthy Subjects and Patients Suffering from Parkinson’s Disease

Sijobert¹,

Benoussaad²,

Denys³

et al. 2015

Health

View full text Add to dashboard Cite

show abstract

“…This includes optimizing sampling frequency [31], sensor calibration (e.g., using in-field procedures [32,33]), eliminating irrelevant information from sensor data (e.g., non-human generated sensor noise) using filters [34], drift correction (e.g., using Fourier-based integration [35] or sequential drift correction [23]), and gravity cancellation (e.g., using Kalman filtering [35] or gradient descent-based algorithms [19]). These procedures usually produce gravity-free kinematic information of the sensing unit, such as the estimated orientation and position.…”

Section: Preprocessingmentioning

confidence: 99%

“…The obtained sensor data are typically stored locally on the acquisition system with limited capacity. Therefore, data acquisition systems often have cloud-based data transmission capabilities [23]. For maximum usability, wireless transmission is typically employed, e.g., Bluetooth, Wi-Fi, or proprietary solutions.…”

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confidence: 99%

An Overview of Smart Shoes in the Internet of Health Things: Gait and Mobility Assessment in Health Promotion and Disease Monitoring

et al. 2017

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New smart technologies and the internet of things increasingly play a key role in healthcare and wellness, contributing to the development of novel healthcare concepts. These technologies enable a comprehensive view of an individual's movement and mobility, potentially supporting healthy living as well as complementing medical diagnostics and the monitoring of therapeutic outcomes. This overview article specifically addresses smart shoes, which are becoming one such smart technology within the future internet of health things, since the ability to walk defines large aspects of quality of life in a wide range of health and disease conditions. Smart shoes offer the possibility to support prevention, diagnostic work-up, therapeutic decisions, and individual disease monitoring with a continuous assessment of gait and mobility. This overview article provides the technological as well as medical aspects of smart shoes within this rising area of digital health applications, and is designed especially for the novel reader in this specific field. It also stresses the need for closer interdisciplinary interactions between technological and medical experts to bridge the gap between research and practice. Smart shoes can be envisioned to serve as pervasive wearable computing systems that enable innovative solutions and services for the promotion of healthy living and the transformation of health care.

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“…Kevin [11] analyzed a theory, design, and evaluation of a miniature, wireless IMU (Initial Measurement Unit) that precisely measures the dynamics of a golf club used in putting. Mariani [12] described 3D gait assessment in young and elderly subjects using foot-worn inertial sensors. Yujin [13] suggested Upper Body Motion Tracking With Inertial Sensors.…”

Section: Introductionmentioning

confidence: 99%

A Three-Dimensional Motion Anlaysis of Horse Rider in Wireless Sensor Network Environments

Lee¹,

Kwak²

2014

ijacsa

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Abstract-This paper constructed a database of the national representative level of a professional horse-rider by wearing a motion-capture suit attached with 16 inertial sensors under an inertial sensor-based wireless network environment, then made a visual comparative analysis through a few methods (graphical and statistical) on the values of all motion features (elbow angle, knee angle, knee-elbow distance, backbone angle and hip position) classified depending on horse types (using two horses named Warm-blood and Thoroughbred) and footpace types (at a trot and a canter) and obtained by various methods of calculating Euclidean distance, the second cosine, maximum and minimum values, and made a comparative analysis depending on motion features of a horse-rider by using MVN studio software. In the study, the experimental results confirmed the validity of the proposed method of obtaining the motion feature database of a horse-rider in the wireless sensor network environment and making an analytical system.

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3D gait assessment in young and elderly subjects using foot-worn inertial sensors

Cited by 324 publications

References 32 publications

Implementation and Validation of a Stride Length Estimation Algorithm, Using a Single Basic Inertial Sensor on Healthy Subjects and Patients Suffering from Parkinson’s Disease

Implementation and Validation of a Stride Length Estimation Algorithm, Using a Single Basic Inertial Sensor on Healthy Subjects and Patients Suffering from Parkinson’s Disease

An Overview of Smart Shoes in the Internet of Health Things: Gait and Mobility Assessment in Health Promotion and Disease Monitoring

A Three-Dimensional Motion Anlaysis of Horse Rider in Wireless Sensor Network Environments

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