Adaptive Switching Control Based on Dynamic Zero-Moment Point for Versatile Hip Exoskeleton Under Hybrid Locomotion

Miao, Yinan; Wang, Xingjian; Wang, Shaoping; Li, Rufei

doi:10.1109/tie.2022.3229343

Cited by 25 publications

(5 citation statements)

References 37 publications

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“…Kurtosis ( Lu et al, 2023 ; Liu et al, 2023b ; Liu et al, 2023c ; Liu et al, 2023d ) on the other hand, measures the tailedness of the distribution. Higher kurtosis indicates a more extreme result, meaning that more of the variance is the result of infrequent extreme deviations, as opposed to frequent modestly sized deviations ( Miao et al, 2023 ; Di et al, 2023 ; Ahmad et al, 2020 ; Liu et al, 2023e ). The mathematical equation for kurtosis is

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Section: Methodsmentioning

confidence: 99%

Robust human locomotion and localization activity recognition over multisensory

Khan,

Alonazi,

Abdelhaq

et al. 2024

Front. Physiol.

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Human activity recognition (HAR) plays a pivotal role in various domains, including healthcare, sports, robotics, and security. With the growing popularity of wearable devices, particularly Inertial Measurement Units (IMUs) and Ambient sensors, researchers and engineers have sought to take advantage of these advances to accurately and efficiently detect and classify human activities. This research paper presents an advanced methodology for human activity and localization recognition, utilizing smartphone IMU, Ambient, GPS, and Audio sensor data from two public benchmark datasets: the Opportunity dataset and the Extrasensory dataset. The Opportunity dataset was collected from 12 subjects participating in a range of daily activities, and it captures data from various body-worn and object-associated sensors. The Extrasensory dataset features data from 60 participants, including thousands of data samples from smartphone and smartwatch sensors, labeled with a wide array of human activities. Our study incorporates novel feature extraction techniques for signal, GPS, and audio sensor data. Specifically, for localization, GPS, audio, and IMU sensors are utilized, while IMU and Ambient sensors are employed for locomotion activity recognition. To achieve accurate activity classification, state-of-the-art deep learning techniques, such as convolutional neural networks (CNN) and long short-term memory (LSTM), have been explored. For indoor/outdoor activities, CNNs are applied, while LSTMs are utilized for locomotion activity recognition. The proposed system has been evaluated using the k-fold cross-validation method, achieving accuracy rates of 97% and 89% for locomotion activity over the Opportunity and Extrasensory datasets, respectively, and 96% for indoor/outdoor activity over the Extrasensory dataset. These results highlight the efficiency of our methodology in accurately detecting various human activities, showing its potential for real-world applications. Moreover, the research paper introduces a hybrid system that combines machine learning and deep learning features, enhancing activity recognition performance by leveraging the strengths of both approaches.

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…”

Section: Methodsmentioning

confidence: 99%

Robust human locomotion and localization activity recognition over multisensory

Khan,

Alonazi,

Abdelhaq

et al. 2024

Front. Physiol.

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show abstract

“…During training, the best model weights ( Hou et al, 2017 ; Wu C. et al, 2019 ; Wu W. et al, 2019 ; Zhang X. et al, 2024 ; Zhang J. et al, 2024 ) are saved using a checkpoint callback based on validation accuracy, which monitors the performance on a validation set separated from the training data ( Lu et al, 2022 ). The network weights are optimized during the training phase to minimize the loss function ( Lu et al, 2024 ) and increase the accuracy of pixel-wise classification ( Miao et al, 2023 ; Mou et al, 2023 ; Xu et al, 2024 ). The model is trained using batches of photos with matching segmentation masks, as shown in Figures 4 , 5 .…”

Section: Methodsmentioning

confidence: 99%

Remote intelligent perception system for multi-object detection

Alazeb,

Chughtai,

Al Mudawi

et al. 2024

Front. Neurorobot.

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IntroductionDuring the last few years, a heightened interest has been shown in classifying scene images depicting diverse robotic environments. The surge in interest can be attributed to significant improvements in visual sensor technology, which has enhanced image analysis capabilities.MethodsAdvances in vision technology have a major impact on the areas of multiple object detection and scene understanding. These tasks are an integral part of a variety of technologies, including integrating scenes in augmented reality, facilitating robot navigation, enabling autonomous driving systems, and improving applications in tourist information. Despite significant strides in visual interpretation, numerous challenges persist, encompassing semantic understanding, occlusion, orientation, insufficient availability of labeled data, uneven illumination including shadows and lighting, variation in direction, and object size and changing background. To overcome these challenges, we proposed an innovative scene recognition framework, which proved to be highly effective and yielded remarkable results. First, we perform preprocessing using kernel convolution on scene data. Second, we perform semantic segmentation using UNet segmentation. Then, we extract features from these segmented data using discrete wavelet transform (DWT), Sobel and Laplacian, and textual (local binary pattern analysis). To recognize the object, we have used deep belief network and then find the object-to-object relation. Finally, AlexNet is used to assign the relevant labels to the scene based on recognized objects in the image.ResultsThe performance of the proposed system was validated using three standard datasets: PASCALVOC-12, Cityscapes, and Caltech 101. The accuracy attained on the PASCALVOC-12 dataset exceeds 96% while achieving a rate of 95.90% on the Cityscapes dataset.DiscussionFurthermore, the model demonstrates a commendable accuracy of 92.2% on the Caltech 101 dataset. This model showcases noteworthy advancements beyond the capabilities of current models.

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“…We trained the MLP using a backpropagation algorithm with a stochastic gradient descent optimizer [99,100]. A categorical cross-entropy [101][102][103] loss function was employed, suitable for the multi-class classification challenges presented by our datasets. The key elements of our training process included:…”

Section: Training Processmentioning

confidence: 99%

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Almujally,

Khan,

Al Mudawi

et al. 2024

Sensors

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The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo–Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities.

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Adaptive Switching Control Based on Dynamic Zero-Moment Point for Versatile Hip Exoskeleton Under Hybrid Locomotion

Cited by 25 publications

References 37 publications

Robust human locomotion and localization activity recognition over multisensory

Robust human locomotion and localization activity recognition over multisensory

Remote intelligent perception system for multi-object detection

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

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