Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion

Chen, Wei; Li, Jun; Zhu, Shanying; Zhang, Xiaodong; Men, Yutao; Wu, Haiyuan

doi:10.1155/2022/9933018

Cited by 14 publications

(3 citation statements)

References 138 publications

(134 reference statements)

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“…However, the proposed system only detected six types of movements, requiring more effort. In [41], multiple gait recognition systems have been reviewed using lower limb exoskeletons. Multiple levels of data fusion, different features, a variety of pre-processing methods, and diverse classification models have been adopted.…”

Section: B Imu Sensors For Outdoor-environmentsmentioning

confidence: 99%

Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools

et al. 2022

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Unhealthy lifestyle causes several chronic diseases in humans. Many products are introduced to avoid such illnesses and provide e-learning-based healthcare services. However, the main focus is still on providing comfortable and reliable solutions. Inertial measurement units (IMU) are considered as the most independent and non-intrusive way to monitor and analyze human health via motion patterns detection. Deep learning is also taken as an excellent tool to detect motion patterns from IMU data. In this paper, a deep-learning-based human motion detection approach for smart healthcare learning tool has been proposed. A novel hybrid descriptors-based pre-classification and multi-features analysis algorithm is proposed to classify the human motion for healthcare e-learning. For pre-processing, a quaternion-based filter is utilized to filter the IMU signals. An experiment is performed over the acceleration signals by using minimum and average gravity removal techniques. Next, signal segmentation of multiple time intervals has been applied to segment data and ultimately compare the results to decide which type provides better performance. Then, pre-classification is done using motion pattern identification in the form of active and passive patterns. During the features analysis phase, features have been extracted based on both active and passive motion patterns. Further, an orthogonal fuzzy neighborhood discriminant analysis technique has been used to reduce the dimensionality of the extracted feature vector. Finally, a deep learner known as long-short term memory has been applied to classify the actions of both active and passive motion features for healthcare e-learning systems. For this purpose, we utilized two datasets: REALDISP and wearable computing. The experimental results show that our proposed system for smart healthcare learning outperformed other state-of-the-art systems. The proposed implemented system provided 87.35% accuracy for REALDISP and 85.18% accuracy for wearable computing datasets. Furthermore, the classified motion patterns are provided to a smart healthcare advisor in order to provide live feedback about human health for immediate action.

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Section: B Imu Sensors For Outdoor-environmentsmentioning

confidence: 99%

Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools

et al. 2022

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“…Gait recognition is a technique that evaluates an individual's health status and rehabilitation level by analyzing their walking patterns [1][2][3] . In the medical and rehabilitation fields, gait recognition is of great significance for evaluating the rehabilitation process of patients with neurological diseases, musculoskeletal diseases, and neurological injuries [4][5][6] .…”

Section: Introductionmentioning

confidence: 99%

EEG signal motion recognition based on GSA-SVM

Liu,

Chang,

Zhou

et al. 2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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This paper reviews the research progress of gait recognition and rehabilitation training control based on EEG signals. Firstly, the importance and application scenarios of gait recognition research were introduced, with a focus on discussing gait recognition methods based on EEG signals. Then, the basic characteristics of EEG signals and the main EEG signal analysis methods used for gait recognition were summarized, including time-domain analysis, frequency-domain analysis, and time-frequency analysis. Secondly, this article collected some motion EEG signals and selected corresponding recognition algorithms through data processing, feature extraction, and classifier selection to perform gait recognition on the EEG signals. The results show that the algorithm proposed in this paper has a high recognition rate.

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“…The ULLE applies movement intention recognition, intelligent gait relearning, redundant sensor fusion technology, and bionic wearable exoskeleton design to improve the comfort and training experience of patients. Additionally, the intelligent relearning feature of the ULLE is superior as it promotes active use of the brain in controlling the unaffected limb to lead the interactive movement of both limbs, thereby achieving individualized bilateral combined functional training (BCFT) [24]. The ULLE ideally connects the CNS and PNS to provide a closed-loop control for complete neurorehabilitation in patients with hemiplegia (Fig.…”

Section: Introductionmentioning

confidence: 99%

Efficacy and safety of using a unilateral lower limb exoskeleton in neurorehabilitation: a randomized controlled trial

Jin,

Zhang,

Chen

et al. 2023

Preprint

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Background Stroke remains the leading cause of both mortality and disability globally. Recovery of limb function in patients with stroke is usually poor and requires an extended period. Consequently, rehabilitation technology in stroke has gained attention. A unilateral lower limb exoskeleton (ULLE), which has an intelligent relearning feature that promotes active engagement of the patient’s brain in controlling of encouraging a patient to actively use their brain to control the unaffected limb to lead the interactive movement of both limbs, thereby achieving individualized bilateral combined functional training, was recently developed to be used in patients with hemiplegia after stroke or traumatic brain injury. However, data on the efficacy and safety of ULLE in patients with stroke are scarce. We aimed to assess the effectiveness and safety of the LiteStepper® ULLE in gait training of patients with post-stroke hemiplegia. Methods This study was a multicenter, optimal, open, loaded, randomized controlled trial. Overall, 92 patients in their post-stroke phase from Hangzhou First People’s Hospital, The Second Affiliated Hospital Zhejiang University School of Medicine, The First Hospital of Jiaxing, and The Fifth Affiliated Hospital of Zhengzhou University were enrolled in this study. The experimental group (EG) adopted the LiteStepper® ULLE based on a once-daily 21-day routine rehabilitation. The conventional group (CG) only underwent the once-daily 21-day routine rehabilitation. Results The efficacy analysis outcomes (Berg balance scale, Functional Ambulation Category scale, 6-minute walking distance, and Barthel Index) between EG and CG had significant differences (P < 0.05) (analyzed using full analysis and per protocol sets). EG showed better improvements than CG (lower limit value [EG-CG] > 2). Safety analysis showed that only one adverse event related to the device occurred during the study, which verified the safety of using the ULLE for gait training in patients after stroke. Conclusions The LiteStepper®ULLE is effective and safe for gait training in patients after stroke. Trial registration ClinicalTrials.gov identifier: NCT05360017

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Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion

Cited by 14 publications

References 138 publications

Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools

Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools

EEG signal motion recognition based on GSA-SVM

Efficacy and safety of using a unilateral lower limb exoskeleton in neurorehabilitation: a randomized controlled trial

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