Systematic Review on Wearable Lower Extremity Robotic Exoskeletons for Assisted Locomotion

Qiu, Shuang; Pei, Zhongcai; Wang, Chen; Tang, Zhiyong

doi:10.1007/s42235-022-00289-8

Cited by 37 publications

(12 citation statements)

References 179 publications

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“…Employing a wearable exoskeleton robot to assist patients with sit-to-stand transitions is an effective way for patients with weak lower limb muscle function, balance disorders, and poor coordination [4]. Furthermore, the utilization of a wearable exoskeleton robot not only reduces the risk of falls in patients, but also reduces the cost of care [5], [6]. In the control of wearable exoskeleton robots, accurately identifying the sit-to-stand transition phases is vital for determining the assisting moments.…”

Section: Introductionmentioning

confidence: 99%

A Novel CNN-BiLSTM Ensemble Model With Attention Mechanism for Sit-to-Stand Phase Identification Using Wearable Inertial Sensors

Chen,

Cai,

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Sit-to-stand transition phase identification is vital in the control of a wearable exoskeleton robot for assisting patients to stand stably. In this study, we aim to propose a method for segmenting and identifying the sit-tostand phase using two inertial sensors. First, we defined the sit-to-stand transition into five phases, namely, the initial sitting phase, the flexion momentum phase, the momentum transfer phase, the extension phase, and the stable standing phase based on the preprocessed acceleration and angular velocity data. We then employed a threshold method to recognize the initial sitting and the stable standing phases. Finally, we designed a novel CNN-BiLSTM-Attention algorithm to identify the three transition phases, namely, the flexion momentum phase, the momentum transfer phase, and the extension phase. Fifteen subjects were recruited to perform sit-to-stand transition experiments under a specific paradigm. A combination of the acceleration and angular velocity data features for the sit-to-stand transition phase identification were validated for the model performance improvements. The integration of the CNN, Bi-LSTM, and Attention modules demonstrated the reasonableness of the proposed algorithms. The experimental results showed that the proposed CNN-BiLSTM-Attention algorithm achieved the highest average classification accuracy of 99.5% for all five phases when compared to both traditional machine learning algorithms and deep learning algorithms on our customized dataset (STS-PD). The proposed sit-to-stand phase recognition algorithm could serve as a foundation for the control of wearable exoskeletons and is important for the further development of intelligent wearable exoskeleton rehabilitation robots.

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Section: Introductionmentioning

confidence: 99%

A Novel CNN-BiLSTM Ensemble Model With Attention Mechanism for Sit-to-Stand Phase Identification Using Wearable Inertial Sensors

Chen,

Cai,

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Several types of LLRRs have been designed for the patients with different movement abilities [6]. They can be divided into the following three categories: wearable, suspended, and sitting/lying LLRRs.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Movement Primitives Modulation-Based Compliance Control for a New Sitting/Lying Lower Limb Rehabilitation Robot

Zhou,

Sun,

Song

et al. 2024

IEEE Access

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Compliant physical human-robot interaction (pHRI), as well as the accuracy and robustness of trajectory tracking, are crucial for rehabilitation robots. In this paper, a new sitting/lying lower limb rehabilitation robot, SUT-SLLRR, has been designed for patients with lower extremity motor dysfunction. A dynamic movement primitives modulation-based compliance control strategy (DMPM-CCS) has been proposed for the SUT-SLLRR. The high-level trajectory planner consists of the trajectory generator based on dynamic movement primitives (DMPs), acceleration layer modulation generator, and velocity layer modulation generator, which can reshape the reference trajectory to generate desired trajectory within a constrained joint space through pHRI. Besides, the linear active disturbance rejection controller (LADRC) is adopted as the low-level position controller to ensure that each joint can accurately and robustness track the desired trajectory under internal and external disturbances. Simulation and experimental results indicate that the proposed strategy can provide the compliant pHRI within the constrained joint space and ensure the accuracy and robustness of trajectory tracking.

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“…T HE lower-limb exoskeleton for human performance augmentation (LEHPA) is a special class of wearable robotic system that runs in parallel to the human body, that transfers the payload weight to the ground, and that enhances human strength and endurance [1]- [3]. Combining the human intelligence with the robot power, the coupled human-exoskeleton system shows a natural superiority over the biped or quadruped robots in adapting to rough and unstructured terrains and presents a promising prospect in performing dangerous and difficult tasks such as battlefield missions, disaster relief, firefighting, manufacturing, etc.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Adaptation of Lower-Limb Exoskeleton for Human Performance Augmentation Based on Deep Reinforcement Learning

Zheng

Liu

et al. 2023

IEEE Access

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The lower-limb exoskeleton for human performance augmentation (LEHPA) in sensitivity amplification control (SAC) is vulnerable to model parameter uncertainties and unmodeled dynamics due to its large sensitivity to external disturbances. This paper proposes sensitivity adaptation based on deep reinforcement learning (SADRL) to reduce the dependence on the model accuracy and tackle the ever-changing human-exoskeleton interaction (HEI) dynamics by interpreting the sensitivity adjustment as a Markov Decision Process (MDP). The agent learns an appropriate sensitivity for each exoskeleton state. To train the control policy safely and efficiently, a multibody simulation environment is created to implement the training process, accompanied by a novel hybrid inverse-forward dynamics simulation method to carry out the simulation. For comparison purposes, the SAC controller is introduced as a benchmark. A novel performance evaluation method based on the HEI forces at the back, thighs, and shanks is proposed to evaluate the control effect of the trained SADRL controller quantitatively. The SADRL controller is compared with the SAC controller at five specified walking speeds, resulting in a lumped HEI force ratio of 0.54. The total decrease of HEI forces demonstrates the superior control effect of the SADRL strategy.

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Systematic Review on Wearable Lower Extremity Robotic Exoskeletons for Assisted Locomotion

Cited by 37 publications

References 179 publications

A Novel CNN-BiLSTM Ensemble Model With Attention Mechanism for Sit-to-Stand Phase Identification Using Wearable Inertial Sensors

A Novel CNN-BiLSTM Ensemble Model With Attention Mechanism for Sit-to-Stand Phase Identification Using Wearable Inertial Sensors

Dynamic Movement Primitives Modulation-Based Compliance Control for a New Sitting/Lying Lower Limb Rehabilitation Robot

Sensitivity Adaptation of Lower-Limb Exoskeleton for Human Performance Augmentation Based on Deep Reinforcement Learning

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