Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database

Khandelwal, Siddhartha; Wickström, Nicholas

doi:10.1016/j.gaitpost.2016.09.023

Cited by 112 publications

(81 citation statements)

References 24 publications

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“…For that purpose, we adopt the following procedure, used in literature to evaluate gait events prediction, e.g., in [32,33]. We first chose a temporal tolerance T, which we set to 600 milliseconds.…”

Section: Evaluation Measuresmentioning

confidence: 99%

A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking

et al. 2019

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Correctly identifying gait phases is a prerequisite to achieve a spatial/temporal characterization of muscular recruitment during walking. Recent approaches have addressed this issue by applying machine learning techniques to treadmill-walking data. We propose a deep learning approach for surface electromyographic (sEMG)-based classification of stance/swing phases and prediction of the foot–floor-contact signal in more natural walking conditions (similar to everyday walking ones), overcoming constraints of a controlled environment, such as treadmill walking. To this aim, sEMG signals were acquired from eight lower-limb muscles in about 10.000 strides from 23 healthy adults during level ground walking, following an eight-shaped path including natural deceleration, reversing, curve, and acceleration. By means of an extensive evaluation, we show that using a multi layer perceptron to learn hidden features provides state of the art performances while avoiding features engineering. Results, indeed, showed an average classification accuracy of 94.9 for learned subjects and 93.4 for unlearned ones, while mean absolute difference ( ± S D ) between phase transitions timing predictions and footswitch data was 21.6 ms and 38.1 ms for heel-strike and toe off, respectively. The suitable performance achieved by the proposed method suggests that it could be successfully used to automatically classify gait phases and predict foot–floor-contact signal from sEMG signals during level ground walking.

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Section: Evaluation Measuresmentioning

confidence: 99%

A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking

et al. 2019

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“…In [75] six state of the art methods are compared in terms of accuracy of the gait events versus the reference data using MAREA database. Supervised machine learning has been used for gait event detection during the application of functional electrical stimulation (FES) using a single shank mounted accelerometer [74].…”

Section: A Platforms For Validationmentioning

confidence: 99%

A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications

Jarchi

Pope

Lee

et al. 2018

IEEE Rev. Biomed. Eng.

170

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Gait analysis continues to be an important technique for many clinical applications to diagnose and monitor certain diseases. Many mental and physical abnormalities cause measurable differences in a person's gait. Gait analysis has applications in sport, computer games, physical rehabilitation, clinical assessment, surveillance, human recognition, modeling, and many other fields. There are established methods using various sensors for gait analysis, of which accelerometers are one of the most often employed. Accelerometer sensors are generally more user friendly and less invasive. In this paper, we review research regarding accelerometer sensors used for gait analysis with particular focus on clinical applications. We provide a brief introduction to accelerometer theory followed by other popular sensing technologies. Commonly used gait phases and parameters are enumerated. The details of selecting the papers for review are provided. We also review several gait analysis software. Then we provide an extensive report of accelerometry-based gait analysis systems and applications, with additional emphasis on trunk accelerometry. We conclude this review with future research directions.

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“…Equations (7) and (8) are the PSO with a finite search space. Detailed derivation and principle of PSO can be found in [42].…”

Section: Identified Valuementioning

confidence: 99%

“…The acceleration signal is affected by the vibration of the pedestal [42], resulting in an increase in the identification error. The vibration caused by the simultaneous swing of the hip and knee joint is larger than the vibration of the single knee joint.…”

Section: Data Acquisitionmentioning

confidence: 99%

Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO

et al. 2019

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The lower extremity exoskeleton is a device for auxiliary assistance of human movement. The interaction performance between the exoskeleton and the human is determined by the lower extremity exoskeleton's controller. The performance of the controller is affected by the accuracy of the dynamic equation. Therefore, it is necessary to study the dynamic parameter identification of lower extremity exoskeleton. The existing dynamic parameter identification algorithms for lower extremity exoskeletons are generally based on Least Square (LS). There are some internal drawbacks, such as complicated experimental processes and low identification accuracy. A dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by Recursive Least Square (RLS) is developed in this investigation. The developed algorithm is named RLS-PSO. By defining the search space of PSO, RLS-PSO not only avoids the convergence of identified parameters to the local minima, but also improves the identification accuracy of exoskeleton dynamic parameters. Under the same experimental conditions, the identification accuracy of RLS-PSO, PSO and LS was quantitatively compared and analyzed. The results demonstrated that the identification accuracy of RLS-PSO is higher than that of LS and PSO. Appl. Sci. 2019, 9, 324 2 of 17 dynamics of the exoskeleton. Third, obtaining the dynamic parameters through dynamic parameter identification algorithms. This method has high accuracy and requires a series of experiments for data acquisition [20][21][22]. The exoskeleton is made up of many non-standard parts, such as pipes and actuators [6][7][8][9][10], which cause the dynamic parameters of the exoskeleton to fluctuate during working. Obviously, it is difficult to obtain the dynamic parameters from the manufacturers or 3D design software. Therefore, parameter identification algorithms are expected to be an effective way to obtain more accurate dynamic parameters [23,24].In order to obtain more accurate dynamic parameters, researchers are increasingly paying attention to the application of parameter identification algorithms for the exoskeleton. Researchers have carried out some preliminary research works. Targeting the swing phase [25,26], Justin et al. identified the dynamic parameters of Berkeley Exoskeleton (BLEEX) [21] by means of Least Square (LS) [27,28]. The joint friction coefficients of BLEEX were identified by static experiments, and the inertial parameters [29] were identified by dynamic experiments. The method did not make full use of the non-correlation between the parameters and the linear relationship between the parameters and the parameterized dynamic equations [30,31]. Therefore, the method only identified one parameter in each experiment. Designing a corresponding identification experiment for each parameter was necessary. The process of parameter identification experiment was very complicated, and the friction parameters on the hip joint lost identifiability because of the limited range of motion. ...

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Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database

Cited by 112 publications

References 24 publications

A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking

A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking

A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications

Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO

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