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

Liu, Du-Xin; Wang, Xinyu; Du, Wei; Wang, Can; Xu, Tiantian

doi:10.3390/s16101579

Cited by 67 publications

(49 citation statements)

References 40 publications

(53 reference statements)

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“…In the left and right traversing structures, there is a certain order of transition between the states and each state cannot be easily converted. (28) The two structures of the HMM are shown in Fig. 10.…”

Section: Conventional Hidden Markov Modelmentioning

confidence: 99%

A New Hidden Markov Model Algorithm to Detect Human Gait Phase Based on Information Fusion Combining Inertial with Plantar Pressure

Wang¹,

Yan²,

Jiang³

2019

Sensors and Materials

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Keywords: gait phase detection, inertial sensor, information fusion, new hidden Markov modelGait phase detection is important in the field of motion analysis and exoskeleton-assisted walking so that the accurate control of exoskeleton robots can be achieved. Therefore, to obtain accurate motion gait information and ensure good detection accuracy of the gait phase, in this study, a new hidden Markov model (N-HMM) algorithm is proposed to improve the accuracy of gait phase detection. A multisensor gait data acquisition system was developed to determine the acceleration and plantar pressure of the human body. Data were collected from 10 healthy subjects and sensors were attached to the subjects' legs and feet to detect the motion gait phase. A comparison of results with hidden Markov model (HMM) algorithms shows that the proposed algorithm improves the recall and precision rates by 3 and 3.5%, respectively. The N-HMM was used for gait phase detection and the detection accuracy of the N-HMM was compared with that of the HMM, support vector machine (SVM), decision tree, and back propagation (BP) network algorithms. The average accuracy of the N-HMM was 96.2%, outperforming all other algorithms. The results show that the N-HMM is capable of detecting the human gait phase with high accuracy. The results of this study provide a theoretical basis for the design and control of exoskeleton robots.

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Section: Conventional Hidden Markov Modelmentioning

confidence: 99%

A New Hidden Markov Model Algorithm to Detect Human Gait Phase Based on Information Fusion Combining Inertial with Plantar Pressure

Wang¹,

Yan²,

Jiang³

2019

Sensors and Materials

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“…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. Robust artificial intelligence techniques for managing a lot of biological data and signals coming from smart sensors such as inertial measurements units (IMU) are undoubtedly among the most used approaches to this aim [6][7][8][9][10][11][12][13][14]. Specifically, the problem of estimating temporal parameters of gait could take great advantage by the development of these new approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Frequently, the use of IMUs appears to be suitable for a smart assessment of walking parameters, such as gait-phase duration and timing of heel strike (time when the foot touches the ground) and toe off (time when the foot-toes clear the ground) [11]. Attempts based on artificial intelligence were also applied in a satisfactory way for the assessment of gait parameters during walking [6,7,9,10,[12][13][14][15].…”

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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“…Xin et al used discriminant analysis algorithms and a pressure sensor to classify gait; however, because only one pressure sensor was used, it could not accurately reflect the characteristics of the gait phase. (17,18) To obtain more accurate gait information and improve the recognition accuracy of the gait phase, we propose a DM-CNN gait phase recognition algorithm in this paper. We collected motion data from 10 healthy test subjects, including plantar pressure and leg acceleration data, and fused these data to recognize the motion phase.…”

Section: Introductionmentioning

confidence: 99%

Recognition of the Gait Phase Based on New Deep Learning Algorithm Using Multisensor Information Fusion

Wang¹,

Yan²,

Jiang³

2019

Sensors and Materials

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Gait phase recognition is an effective method of analyzing human motion and behavior that can be very meaningful in people's daily life, especially when struggling with assisted rehabilitation. In this paper, a new algorithm that can recognize a human gait phase more accurately is proposed. The new gait phase recognition algorithm is based on a deep memory convolutional neural network (DM-CNN) using multiple sensor fusion. We used the plantar pressure sensor array and acceleration sensor array gait data, and then extracted the gait features using the DM-CNN. The measured data of the continuous gait cycle were divided into unit steps, and the data were analyzed and preprocessed. Then, a feature map of each sensor array was extracted by constructing a separate DM-CNN. Finally, each feature map was combined into a fully connected network, and a memory function was introduced to simulate historical behavior. We then tested the algorithm on the phases of a gait cycle and compared the evaluation indicators of each phase. In the experiment, we compared single-mode and multimode recognition results, and compared those with the new hidden Markov model (N-HMM), K-nearest neighbor (KNN), and hidden Markov model (HMM) algorithms. The experimental results show that when the multisensor data are fused, the average recognition accuracy can reach 97.1%, which is higher than those of the other algorithms and improves the recognition of a human gait phase. The accurate recognition of human gait can provide a better theoretical basis for the design of exoskeleton robot control strategies.

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

Cited by 67 publications

References 40 publications

A New Hidden Markov Model Algorithm to Detect Human Gait Phase Based on Information Fusion Combining Inertial with Plantar Pressure

A New Hidden Markov Model Algorithm to Detect Human Gait Phase Based on Information Fusion Combining Inertial with Plantar Pressure

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

Recognition of the Gait Phase Based on New Deep Learning Algorithm Using Multisensor Information Fusion

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