Recognizing Continuous Multiple Degrees of Freedom Foot Movements With Inertial Sensors

Zhu, Chenyao; Luo, Lan; Mai, Jingeng; Wang, Qining

doi:10.1109/tnsre.2022.3149793

Cited by 9 publications

(3 citation statements)

References 39 publications

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“…The predefined gait trajectory is suitable for patients who lack the ability to walk independently in the early stage of rehabilitation. However, in the middle and late stages of rehabilitation, the predefined trajectory may conflict with the patient's active intention (Zhu et al, 2022;Na et al, 2023). The continuous estimation of human motion intention through gait prediction method exhibits potential for compliant human-robot interaction (Xiong et al, 2021).…”

Section: )mentioning

confidence: 99%

Synergetic gait prediction and compliant control of SEA-driven knee exoskeleton for gait rehabilitation

Liu,

Zhu,

Zhou

et al. 2024

Front. Bioeng. Biotechnol.

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In recent years, lower limb exoskeletons have achieved satisfactory clinical curative effects in rehabilitating stroke patients. Furthermore, generating individualized trajectories for each patient and avoiding secondary injury in rehabilitation training are important issues. This paper explores the utilization of series elastic actuator (SEA) to deliver compliant force and enhance impact resistance in human-robot interaction, and we present the design of novel knee exoskeleton driven by SEA. Subsequently, the novel gait trajectory prediction method and compliant control method are proposed. The attention-based CNN-LSTM model is established to generate personalized gait trajectories for affected limbs, in which the spatial-temporal attention mechanism is adopted to improve the prediction accuracy. The compliant control strategy is proposed to nonlinearly and adaptively tune impedance parameters based on artificial potential field (APF) method, and active rehabilitation training is carried out in the coordination space to guarantee patient safety. The experimental results based on four healthy subjects demonstrated that synergetic gait prediction model could satisfactorily characterize the coordination movement with higher accuracy. The compliant control could limit the patient’s movement in the safe coordination tunnel while considering personalization and flexibility.

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Section: )mentioning

confidence: 99%

Synergetic gait prediction and compliant control of SEA-driven knee exoskeleton for gait rehabilitation

Liu,

Zhu,

Zhou

et al. 2024

Front. Bioeng. Biotechnol.

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“…There is a specific type of interface that may be utilized to predict patients’ volitional movement from their residual muscle contractions or neuroactivities [ 10 , 11 ]. However, detecting a user’s motion intention fast enough to coordinate with devices is an important issue that requires further study [ 12 ]. A range of sensing modalities have been used to regulate human–machine interfaces.…”

Section: Introductionmentioning

confidence: 99%

Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review

Nazari

Zheng

2023

Sensors

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This paper presents a critical review and comparison of the results of recently published studies in the fields of human–machine interface and the use of sonomyography (SMG) for the control of upper limb prothesis. For this review paper, a combination of the keywords “Human Machine Interface”, “Sonomyography”, “Ultrasound”, “Upper Limb Prosthesis”, “Artificial Intelligence”, and “Non-Invasive Sensors” was used to search for articles on Google Scholar and PubMed. Sixty-one articles were found, of which fifty-nine were used in this review. For a comparison of the different ultrasound modes, feature extraction methods, and machine learning algorithms, 16 articles were used. Various modes of ultrasound devices for prosthetic control, various machine learning algorithms for classifying different hand gestures, and various feature extraction methods for increasing the accuracy of artificial intelligence used in their controlling systems are reviewed in this article. The results of the review article show that ultrasound sensing has the potential to be used as a viable human–machine interface in order to control bionic hands with multiple degrees of freedom. Moreover, different hand gestures can be classified by different machine learning algorithms trained with extracted features from collected data with an accuracy of around 95%.

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“…In summary, sensors and actuators, when judiciously integrated into the designs, can successfully accomplish diverse functionalities such as ensuring the user's gait stability, creating precise human models, and gathering motion data [46,82]. As discussed, various sensors are required for different measurements, for instance, pressure sensors are required for the detection of limb and plantar pressure and acceleration, inertial sensors for the measurement of human motion trajectory, and clear-vision sensors for the detection of road obstacles.…”

mentioning

confidence: 99%

Advancements in Sensor Technologies and Control Strategies for Lower-Limb Rehabilitation Exoskeletons: A Comprehensive Review

Yao,

Shao,

Tarabini

et al. 2024

Micromachines

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Lower-limb rehabilitation exoskeletons offer a transformative approach to enhancing recovery in patients with movement disorders affecting the lower extremities. This comprehensive systematic review delves into the literature on sensor technologies and the control strategies integrated into these exoskeletons, evaluating their capacity to address user needs and scrutinizing their structural designs regarding sensor distribution as well as control algorithms. The review examines various sensing modalities, including electromyography (EMG), force, displacement, and other innovative sensor types, employed in these devices to facilitate accurate and responsive motion control. Furthermore, the review explores the strengths and limitations of a diverse array of lower-limb rehabilitation-exoskeleton designs, highlighting areas of improvement and potential avenues for further development. In addition, the review investigates the latest control algorithms and analysis methods that have been utilized in conjunction with these sensor systems to optimize exoskeleton performance and ensure safe and effective user interactions. By building a deeper understanding of the diverse sensor technologies and monitoring systems, this review aims to contribute to the ongoing advancement of lower-limb rehabilitation exoskeletons, ultimately improving the quality of life for patients with mobility impairments.

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Recognizing Continuous Multiple Degrees of Freedom Foot Movements With Inertial Sensors

Cited by 9 publications

References 39 publications

Synergetic gait prediction and compliant control of SEA-driven knee exoskeleton for gait rehabilitation

Synergetic gait prediction and compliant control of SEA-driven knee exoskeleton for gait rehabilitation

Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review

Advancements in Sensor Technologies and Control Strategies for Lower-Limb Rehabilitation Exoskeletons: A Comprehensive Review

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