Assessment of Foot Trajectory for Human Gait Phase Detection Using Wireless Ultrasonic Sensor Network

Qi, Yongbin; Soh, Christina; Gunawan, E.; Low, Kian Hsiang; Thomas, Rijil

doi:10.1109/tnsre.2015.2409123

Cited by 63 publications

(36 citation statements)

References 23 publications

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“…The remaining 40% is occupied by the swing phase which happens when one foot is in total contact with the ground while the other is swinging. To measure the periods of both gait phases, wearable sensors have been used such as the ultrasonic sensors in [5]. Other solutions are based on motion capture systems to get accurate information of the motion trajectories [6].…”

Section: Introductionmentioning

confidence: 99%

Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers

Abdulatif

Aziz

Armanious

et al. 2019

2019 IEEE Radar Conference (RadarConf)

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Obtaining a smart surveillance requires a sensing system that can capture accurate and detailed information for the human walking style. The radar micro-Doppler (µ-D) analysis is proved to be a reliable metric for studying human locomotions. Thus, µ-D signatures can be used to identify humans based on their walking styles. Additionally, the signatures contain information about the radar cross section (RCS) of the moving subject. This paper investigates the effect of human body characteristics on human identification based on their µ-D signatures. In our proposed experimental setup, a treadmill is used to collect µ-D signatures of 22 subjects with different genders and body characteristics. Convolutional autoencoders (CAE) are then used to extract the latent space representation from the µ-D signatures. It is then interpreted in two dimensions using t-distributed stochastic neighbor embedding (t-SNE). Our study shows that the body mass index (BMI) has a correlation with the µ-D signature of the walking subject. A 50-layer deep residual network is then trained to identify the walking subject based on the µ-D signature. We achieve an accuracy of 98% on the test set with high signal-to-noise-ratio (SNR) and 84% in case of different SNR levels.

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Section: Introductionmentioning

confidence: 99%

Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers

Abdulatif

Aziz

Armanious

et al. 2019

2019 IEEE Radar Conference (RadarConf)

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“…T. T. Huu [23] quantified the walking parameters during each gait phase to evaluate the fuzzy-based control strategy on a powered lower-limb exoskeleton. To assess the foot trajectory, Y. Qi [24] adopted the wireless ultrasonic sensor network to detect the human gait phase and to obtain the accurate estimates for the gait cycle, stance phase, swing phase and other gait events.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, this work took two staff member two months’ time. In [24], three force plates were used to acquire ground reaction forces while walking. The robot data and the ground reaction forces are combined to classify the gait phases.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the existing research results, we can get that the gait phase recognition generally used single-type sensors or a combination of multiple types of sensors [18], such as angular velocity, attitude, force, electromyography (EMG), IMU, camera, and so on [24,25,26,27,28,30,36,37,38]. However, for a lower-limb exoskeleton system, as few as possible sensors should be used to achieve the control goal, which can simplify the sensory system and enhance the stability of the exoskeleton.…”

Section: Introductionmentioning

confidence: 99%

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Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

Liu

Wang

et al. 2016

Sensors

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Gait phase is widely used for gait trajectory generation, gait control and gait evaluation on lower-limb exoskeletons. So far, a variety of methods have been developed to identify the gait phase for lower-limb exoskeletons. Angular sensors on lower-limb exoskeletons are essential for joint closed-loop controlling; however, other types of sensors, such as plantar pressure, attitude or inertial measurement unit, are not indispensable.Therefore, to make full use of existing sensors, we propose a novel gait phase recognition method for lower-limb exoskeletons using only joint angular sensors. The method consists of two procedures. Firstly, the gait deviation distances during walking are calculated and classified by Fisher’s linear discriminant method, and one gait cycle is divided into eight gait phases. The validity of the classification results is also verified based on large gait samples. Secondly, we build a gait phase recognition model based on multilayer perceptron and train it with the phase-labeled gait data. The experimental result of cross-validation shows that the model has a 94.45% average correct rate of set (CRS) and an 87.22% average correct rate of phase (CRP) on the testing set, and it can predict the gait phase accurately. The novel method avoids installing additional sensors on the exoskeleton or human body and simplifies the sensory system of the lower-limb exoskeleton.

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“…To this aim, particularly relevant seems to be the problem of recognizing at least the two main gait phases, namely stance and swing. Single-type sensors or a combination of multiple types of sensors, such as angular velocity, attitude, force, electromyography, and cameras are typically used for gait phase quantification [1][2][3][4][5]. Recent availability of technological advancements is allowing to limit the experimental complexity of gait-analysis set-up, providing a less expensive, less intrusive, and more comfortable estimation of gait data.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Gait Phases with a Single Knee Electrogoniometer: A Deep Learning Approach

et al. 2020

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Artificial neural networks were satisfactorily implemented for assessing gait events from different walking data. This study aims to propose a novel approach for recognizing gait phases and events, based on deep-learning analysis of only sagittal knee-joint angle measured by a single electrogoniometer per leg. Promising classification/prediction performances have been previously achieved by surface-EMG studies; thus, a further aim is to test if adding electrogoniometer data could improve classification performances of state-of-the-art methods. Gait data are measured in about 10,000 strides from 23 healthy adults, during ground walking. A multi-layer perceptron model is implemented, composed of three hidden layers and a one-dimensional output. Classification/prediction accuracy is tested vs. ground truth represented by foot–floor-contact signals, through samples acquired from subjects not seen during training phase. Average classification-accuracy of 90.6 ± 2.9% and mean absolute value (MAE) of 29.4 ± 13.7 and 99.5 ± 28.9 ms in assessing heel-strike and toe-off timing are achieved in unseen subjects. Improvement of classification-accuracy (four points) and reduction of MAE (at least 35%) are achieved when knee-angle data are used to enhance sEMG-data prediction. Comparison of the two approaches shows as the reduction of set-up complexity implies a worsening of mainly toe-off prediction. Thus, the present electrogoniometer approach is particularly suitable for the classification tasks where only heel-strike event is involved, such as stride recognition, stride-time computation, and identification of toe walking.

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Assessment of Foot Trajectory for Human Gait Phase Detection Using Wireless Ultrasonic Sensor Network

Cited by 63 publications

References 23 publications

Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers

Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

Recognition of Gait Phases with a Single Knee Electrogoniometer: A Deep Learning Approach

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