Exploration of Gait Parameters Affecting the Accuracy of Force Myography-Based Gait Phase Detection

Jiang, Xianta; Tory, L.; Khoshnam, Mahta; Chu, Kelvin Ht; Menon, Carlo

doi:10.1109/biorob.2018.8487790

Cited by 11 publications

(12 citation statements)

References 24 publications

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“…Such a step only changes the numeric representation of the signals and does not alter the signal pattern. Using these signals alone without additional signal conditioning or feature extraction steps, researchers were able to predict 48 static gestures with an averaged 90% cross-trial validations accuracy [51]. However, depending on the application, the prediction accuracy may have benefited from additional signal processing steps.…”

Section: Fmg Processing Methodsmentioning

confidence: 99%

“…The value of a window-based feature depends on the selected window size; therefore, it is important to optimize the feature window for the targeted application [29]. Currently, only a limited number of publications extracted features from the FMG signals for targeted applications [14,29,45,51,58,61,62]. The optimal feature set is highly dependent on the application and it is difficult to identify a universal feature set for FMG signals.…”

Section: Fmg Processing Methodsmentioning

confidence: 99%

“…We also investigated the factors influencing prediction accuracy. Among the many factors, the variation in stride length had the most influence on accuracy [51].…”

Section: Fmg Signal Acquisitionmentioning

confidence: 99%

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A Review of Force Myography Research and Development

Xiao

Menon

2019

Sensors

109

107

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Information about limb movements can be used for monitoring physical activities or for human-machine-interface applications. In recent years, a technique called Force Myography (FMG) has gained ever-increasing traction among researchers to extract such information. FMG uses force sensors to register the variation of muscle stiffness patterns around a limb during different movements. Using machine learning algorithms, researchers are able to predict many different limb activities. This review paper presents state-of-art research and development on FMG technology in the past 20 years. It summarizes the research progress in both the hardware design and the signal processing techniques. It also discusses the challenges that need to be solved before FMG can be used in an everyday scenario. This paper aims to provide new insight into FMG technology and contribute to its advancement.

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Section: Fmg Processing Methodsmentioning

confidence: 99%

Section: Fmg Processing Methodsmentioning

confidence: 99%

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A Review of Force Myography Research and Development

Xiao

Menon

2019

Sensors

109

107

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“…Information about the mode of locomotion can be used for the ankle's angle correction as the user walks over ramps, flat surfaces, or stairs [7,12]. Moreover, studies have shown promising results using FMG for gait phase detection [13] which can potentially be used for lower limb prostheses control [14].…”

Section: Introductionmentioning

confidence: 99%

“…Despite availability of FSR-based prosthetic pressure measurement systems in the market and their wide use in research communities, when these systems are evaluated in practical situations, higher errors are reported compared to their performance in constrained environments of the labs. In prostheses control using FMG, higher errors are reported for inter-subject and inter-trial cases [13,33]. In prosthetic fitting systems, higher errors are reported when FSR sensors are placed on moulds of the limbs [4,27,28].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Regression Methods for Reduction of Errors Caused by Bending of FSR-Based Pressure Sensing Systems Used for Prosthetic Applications

Ahmadizadeh

Menon

2019

Sensors

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The pressure map at the interface of a prosthetic socket and a residual limb contains information that can be used in various prosthetic applications including prosthetic control and prosthetic fitting. The interface pressure is often obtained using force sensitive resistors (FSRs). However, as reported by multiple studies, accuracies of the FSR-based pressure sensing systems decrease when sensors are bent to be positioned on a limb. This study proposes the use of regression-based methods for sensor calibration to address this problem. A sensor matrix was placed in a pressure chamber as the pressure was increased and decreased in a cyclic manner. Sensors' responses were assessed when the matrix was placed on a flat surface or on one of five curved surfaces with various curvatures. Three regression algorithms, namely linear regression (LR), general regression neural network (GRNN), and random forest (RF), were assessed. GRNN was selected due to its performance. Various error compensation methods using GRNN were investigated and compared to improve instability of sensors' responses. All methods showed improvements in results compared to the baseline. Developing a different model for each of the curvatures yielded the best results. This study proved the feasibility of using regression-based error compensation methods to improve the accuracy of mapping sensor readings to pressure values. This can improve the overall accuracy of FSR-based sensory systems used in prosthetic applications.

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Comparison of algorithms and classifiers for stride detection using wearables

Steinmetzer

Bönninger

Reckhardt

et al. 2019

Neural Comput & Applic

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Sensor-based systems for diagnosis or therapy support of motor dysfunctions need methodologies of automatically stride detection from movement sequences. In this proposal, we developed a stride detection system for daily life use. We compared mostly used algorithms min-max patterns, dynamic time warping, convolutional neural networks (CNN), and automatic framing using two data sets of 32 healthy and 28 Parkinson's disease (PD) persons. We developed an insole with force and IMU sensors to record the gait data. The PD patients carried out the standardized time up and go test, and the healthy persons a daily life activities test (walking, sitting, standing, ascending and descending stairs). As an automatically stride detection process for daily life use, we propose a first stride detection using automatic framing, and after normalization and resampling data a CNN is used. A F1-score of 0.938 (recall 0.968, precision 0.910) for time up and go test and of 0.944 (recall 0.992, precision 0.901) for daily life activities test were obtained for CNN. Compared to the other detection methods, up to 6% F-measure improvement was shown. Keywords Stride detection Á Gait analysis Á Inertial sensors Á Parkinson's disease Á Validation Á Dynamic time warping Á Time up and go test Á Convolutional neural networks & Tobias Steinmetzer

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Exploration of Gait Parameters Affecting the Accuracy of Force Myography-Based Gait Phase Detection

Cited by 11 publications

References 24 publications

A Review of Force Myography Research and Development

A Review of Force Myography Research and Development

Investigation of Regression Methods for Reduction of Errors Caused by Bending of FSR-Based Pressure Sensing Systems Used for Prosthetic Applications

Comparison of algorithms and classifiers for stride detection using wearables

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