Design and Voluntary Motion Intention Estimation of a Novel Wearable Full-Body Flexible Exoskeleton Robot

The wearable full-body exoskeleton robot developed in this study is one application of mobile cyberphysical system (CPS), which is a complex mobile system integrating mechanics, electronics, computer science, and artificial intelligence. Steel wire was used as the flexible transmission medium and a group of special wire-locking structures was designed. Additionally, we designed passive joints for partial joints of the exoskeleton. Finally, we proposed a novel gait phase recognition method for full-body exoskeletons using only joint angular sensors, plantar pressure sensors, and inclination sensors. The method consists of four procedures. Firstly, we classified the three types of main motion patterns: normal walking on the ground, stair-climbing and stair-descending, and sit-to-stand movement. Secondly, we segregated the experimental data into one gait cycle. Thirdly, we divided one gait cycle into eight gait phases. Finally, we built a gait phase recognition model based on k-Nearest Neighbor perception and trained it with the phase-labeled gait data. The experimental result shows that the model has a 98.52% average correct rate of classification of the main motion patterns on the testing set and a 95.32% average correct rate of phase recognition on the testing set. So the exoskeleton robot can achieve human motion intention in real time and coordinate its movement with the wearer.

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A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons

Frontiers in Neurorobotics ◽

10.3389/fnbot.2021.704226 ◽

2021 ◽

Vol 15 ◽

Author(s):

Baichun Wei ◽

Zhen Ding ◽

Chunzhi Yi ◽

Hao Guo ◽

Zhipeng Wang ◽

...

Keyword(s):

Human Motion ◽

Human Machine Interaction ◽

Motion Patterns ◽

Decoding Algorithms ◽

Gait Phase ◽

Limb Kinematics ◽

Phase Recognition ◽

Machine Interaction ◽

Lower Limb Kinematics ◽

Discrete Motion

The interaction between human and exoskeletons increasingly relies on the precise decoding of human motion. One main issue of the current motion decoding algorithms is that seldom algorithms provide both discrete motion patterns (e.g., gait phases) and continuous motion parameters (e.g., kinematics). In this paper, we propose a novel algorithm that uses the surface electromyography (sEMG) signals that are generated prior to their corresponding motions to perform both gait phase recognition and lower-limb kinematics prediction. Particularly, we first propose an end-to-end architecture that uses the gait phase and EMG signals as the priori of the kinematics predictor. In so doing, the prediction of kinematics can be enhanced by the ahead-of-motion property of sEMG and quasi-periodicity of gait phases. Second, we propose to select the optimal muscle set and reduce the number of sensors according to the muscle effects in a gait cycle. Finally, we experimentally investigate how the assistance of exoskeletons can affect the motion intent predictor, and we propose a novel paradigm to make the predictor adapt to the change of data distribution caused by the exoskeleton assistance. The experiments on 10 subjects demonstrate the effectiveness of our algorithm and reveal the interaction between assistance and the kinematics predictor. This study would aid the design of exoskeleton-oriented motion-decoding and human–machine interaction methods.

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IMU-Based Gait Phase Recognition for Stroke Survivors

Robotica ◽

10.1017/s0263574719000328 ◽

2019 ◽

Vol 37 (12) ◽

pp. 2195-2208 ◽

Cited By ~ 2

Author(s):

Yu Lou ◽

Rongli Wang ◽

Jingeng Mai ◽

Ninghua Wang ◽

Qining Wang

Keyword(s):

Inertial Measurement Unit ◽

Detection System ◽

Effective Means ◽

Human Motion ◽

Measurement Unit ◽

Stroke Survivors ◽

Phase Detection ◽

Wearable Robots ◽

Gait Phase ◽

Phase Recognition

SummaryUsing wearable robots is an effective means of rehabilitation for stroke survivors, and effective recognition of human motion intentions is a key premise in controlling wearable robots. In this paper, we propose an inertial measurement unit (IMU)-based gait phase detection system. The system consists of two IMUs that are tied on the thigh and on the shank, respectively, for collecting acceleration and angular velocity. Features were extracted using a sliding window of 150 ms in length, which was then fed into a quadratic discriminant analysis (QDA) classifier for classification. We recruited five stroke survivors to test our system. They walked at their own preferred speed on the level ground. Experimental results show that our proposed system has the ability of recognizing the gait phase of stroke survivors. All recognition accuracy results are above 96.5%, and detections are about 5–15 ms in advance of time. In addition, using only one IMU can also give reliable recognition results. This paper proposes an idea about the further research on human–computer interaction for the control of wearable robots.

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Scaffolding on-line segmentation of full body human motion patterns

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems ◽

10.1109/iros.2008.4650619 ◽

2008 ◽

Cited By ~ 28

Author(s):

D. Kulic ◽

Y. Nakamura

Keyword(s):

Human Motion ◽

Motion Patterns ◽

On Line ◽

Line Segmentation ◽

Full Body

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Quantifying Complexity and Variability of Gait Phase Portraits

ASME 2009 Summer Bioengineering Conference, Parts A and B ◽

10.1115/sbc2009-206847 ◽

2009 ◽

Author(s):

Louis A. DiBerardino ◽

John D. Polk ◽

Karl S. Rosengren ◽

Elizabeth T. Hsiao-Wecksler

Keyword(s):

Lower Extremity ◽

Joint Pain ◽

Gait Cycle ◽

Movement Patterns ◽

Current Work ◽

Phase Portraits ◽

Joint Motion ◽

Motion Patterns ◽

Gait Phase

Injuries to the lower extremity can limit joint motion and alter movement patterns of the limb segments during gait. Researchers have also found changes in variability and complexity as a function of injury. Injury or joint pain appears to decrease gait cycle variability [1]; however, the results on complexity are less clear. While some researchers have found a decrease in complexity due to disease and aging, e.g., [2], others have found an increase in complexity [3]. One of the goals of the current work was to quantify changes in the variability and complexity of motion patterns as assessed using phase portraits.

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Gait Phase Detection of Exoskeleton Robot Based on the Joints Angle of Lower Limb

Proceedings of the 4th International Conference on Computer Science and Application Engineering ◽

10.1145/3424978.3425067 ◽

2020 ◽

Author(s):

Wang Jiang ◽

Jianbin Zheng ◽

Liping Huang

Keyword(s):

Lower Limb ◽

Phase Detection ◽

Exoskeleton Robot ◽

Gait Phase

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Real-Time Gait Phase Recognition Based on Time Domain Features of Multi-MEMS Inertial Sensors

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2021.3108174 ◽

2021 ◽

Vol 70 ◽

pp. 1-12

Author(s):

Meiyan Zhang ◽

Qisong Wang ◽

Dan Liu ◽

Boqi Zhao ◽

Jiaze Tang ◽

...

Keyword(s):

Real Time ◽

Time Domain ◽

Inertial Sensors ◽

Gait Phase ◽

Phase Recognition

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Liquid State Machine to Generate the Movement Profiles for the Gait Cycle of a 6 DOF Bipedal Robot in a Sagittal Plane

Volume 5B: 42nd Mechanisms and Robotics Conference ◽

10.1115/detc2018-86206 ◽

2018 ◽

Author(s):

Jesús Franco-Robles ◽

Alejandro De Lucio-Rangel ◽

Karla A. Camarillo-Gómez ◽

Gerardo I. Pérez-Soto ◽

Jesús Rivera-Guillén

Keyword(s):

Time Series ◽

Multilayer Perceptron ◽

Liquid State ◽

Gait Cycle ◽

Sagittal Plane ◽

Experimental Result ◽

Forward Kinematics ◽

Neuronal System ◽

Bipedal Robot ◽

Input Time

In this paper, a neuronal system with the ability to generate motion profiles and profiles of the ZMP in a 6DoF bipedal robot in the sagittal plane, is presented. The input time series for LSM training are movement profiles of the oscillating foot trajectory obtained by forward kinematics performed by a previously trained ANN multilayer perceptron. The profiles of objective movement for training are acquired from the analysis of the human walk. Based on a previous simulation of the bipedal robot, a profile of the objective ZMP will be generated for the y–axis and another for the z–axis to know its behavior during the training walk. As an experimental result, the LSM generates new motion profiles and ZMP, given a different trajectory with which it was trained. With the LSM it will be possible to propose new trajectories of the oscillating foot, where it will be known if this trajectory will be stable, by the ZMP, and what movement profile for each articulation will be required to reach this trajectory.

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