Heterogeneous data fusion for three-dimensional gait analysis using wearable MARG sensors

Qiu, Sen; Wang, Zhelong; Zhao, Hongyu; Hu, osheng

doi:10.1504/ijcse.2017.084154

Cited by 7 publications

(6 citation statements)

References 15 publications

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“…In terms of assessing different gait types, we identified a few studies that discussed both walking and running [38], [51], [91], [96]. The analysis of walking on different terrains such as ramp walkways, walking up-and downstairs, or outdoors was discussed in [68], [76], [96], [104], [111]. Temporal, spatial, and spatiotemporal gait features were found to be the most commonly analyzed features within the studies.…”

Section: A General Application Of Wearable Gait Analysismentioning

confidence: 99%

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

2021

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The current gold standard for gait analysis involves performing the gait experiments in a laboratory environment with a constrained space. However, there is growing interest in using flexible, efficient, and inexpensive wearable sensors as tools to perform gait analysis. This review aimed to identify and summarize the current advances in wearable sensors for various aspects of gait analysis, such as the application of wearable gait analysis systems, sensor systems and their attachment locations, and the algorithms used for the analysis. The PRISMA guideline was adopted to find relevant studies from the period 2011 to 2020 from several scientific databases. A total of 76 articles were selected based on the inclusion and exclusion criteria. A wearable inertial measurement unit (IMU) attached to the lower limb region was found to be the most common approach for gait analysis. Temporal, spatial, and spatiotemporal features were the most common quantitative gait features extracted from the wearable sensors. The proposed frameworks showed varying performances, and an increased number of sensors did not necessarily improve the estimation performance metrics. A few studies have integrated various machine learning techniques for classification problems, correction algorithms, crosschecking functions, and scoring functions. Finally, this review paper discusses the challenges and future direction of the research on quantitative gait analysis.

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Section: A General Application Of Wearable Gait Analysismentioning

confidence: 99%

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

2021

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“…Figure 4 a demonstrates the calculation principle and Figure 4 b,c show the calculated swing angles and knee flexion based on the gyroscope observations. In addition, the calculation method of knee flexion angle is as follows:

where

and

are initial angles between lower limbs (thigh and shank, respectively) and the gravity direction when the subjects are standing still at ease, which can be calculated by the measurements of the accelerometer as proposed in the previous study [ 29 , 30 ].

and

are angular velocity values of thigh fixed and shank fixed sensors.…”

Section: Methodsmentioning

confidence: 99%

“…Such system may also serve as a good supplement of the gold standard including optical system and plantar pressure monitoring system. In previous studies, we have adopted a wearable inertial sensor in a walking distance calculation and walking pattern classification [ 29 , 30 , 31 ]. Ambulatory measurement of the participant’s trunk inclination using inertial measurement unit (IMU) was carried out by Farris et al [ 3 ].…”

Section: Related Workmentioning

confidence: 99%

MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion

et al. 2018

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Gait and posture are regular activities which are fully controlled by the sensorimotor cortex. In this study, fluctuations of joint angle and asymmetry of foot elevation in human walking stride records are analyzed to assess gait in healthy adults and patients affected with gait disorders. This paper aims to build a low-cost, intelligent and lightweight wearable gait analysis platform based on the emerging body sensor networks, which can be used for rehabilitation assessment of patients with gait impairments. A calibration method for accelerometer and magnetometer was proposed to deal with ubiquitous orthoronal error and magnetic disturbance. Proportional integral controller based complementary filter and error correction of gait parameters have been defined with a multi-sensor data fusion algorithm. The purpose of the current work is to investigate the effectiveness of obtained gait data in differentiating healthy subjects and patients with gait impairments. Preliminary clinical gait experiments results showed that the proposed system can be effective in auxiliary diagnosis and rehabilitation plan formulation compared to existing methods, which indicated that the proposed method has great potential as an auxiliary for medical rehabilitation assessment.

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“…Since human gait has a certain regularity and periodicity, many researchers have started to use hidden Markov models for gait behavior recognition (e.g., gait used to distinguish actions under different behaviors, gait used to identify different people for identification, and determining pathological gait) [26][27][28][29][30][31].…”

Section: Related Workmentioning

confidence: 99%

Ambulatory Human Gait Phase Detection Using Wearable Inertial Sensors and Hidden Markov Model

Liu

et al. 2021

Sensors

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Gait analysis, as a common inspection method for human gait, can provide a series of kinematics, dynamics and other parameters through instrumental measurement. In recent years, gait analysis has been gradually applied to the diagnosis of diseases, the evaluation of orthopedic surgery and rehabilitation progress, especially, gait phase abnormality can be used as a clinical diagnostic indicator of Alzheimer Disease and Parkinson Disease, which usually show varying degrees of gait phase abnormality. This research proposed an inertial sensor based gait analysis method. Smoothed and filtered angular velocity signal was chosen as the input data of the 15-dimensional temporal characteristic feature. Hidden Markov Model and parameter adaptive model are used to segment gait phases. Experimental results show that the proposed model based on HMM and parameter adaptation achieves good recognition rate in gait phases segmentation compared to other classification models, and the recognition results of gait phase are consistent with ground truth. The proposed wearable device used for data collection can be embedded on the shoe, which can not only collect patients’ gait data stably and reliably, ensuring the integrity and objectivity of gait data, but also collect data in daily scene and ambulatory outdoor environment.

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Heterogeneous data fusion for three-dimensional gait analysis using wearable MARG sensors

Cited by 7 publications

References 15 publications

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion

Ambulatory Human Gait Phase Detection Using Wearable Inertial Sensors and Hidden Markov Model

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