Depth Sensors-Based Action Recognition Using a Modified K-Ary Entropy Classifier

Batool, Mouazma; Alotaibi, Saud S.; Alatiyyah, Mohammed Hamad; Alnowaiser, Khaled; Aljuaid, Hanan; Jalal, Ahmad; Park, Jeongmin

doi:10.1109/access.2023.3260403

Cited by 26 publications

(5 citation statements)

References 78 publications

(94 reference statements)

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“…Shannon entropy is first extracted, as seen in Figure 4. Shannon entropy [62,63] measures the unpredictability [64][65][66] or randomness of a signal. Mathematically, it can be calculated as:…”

Section: Shannon Entropymentioning

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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Section: Shannon Entropymentioning

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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“…D P Acharya et al concluded that a defensive possibilistic fuzzy c-mean clustering technique was best for the image detection of abnormal images in their FCM technique [16]. Mouazma Batool et al provided a method to spike the gap in the normal intra-substructure nodes of a tree through entropy clustering accumulation [17]. A cross-modality dual attention fusion module (CMDA) was proposed by Dafeng Wei et al to specifically communicate spatialtemporal information between any of the two routes in a two-stream SlowFast network [18].…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Fine-Tuning I3D and SlowFast Networks for Action Recognition in Surveillance Videos

Gopalakrishnan,

Wason,

Krishna

et al. 2024

RAiSE-2023

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“…It provides information about the system's sensitivity to small perturbations and can be used to characterize the chaotic behavior of the system. Mathematically, MLE is defined as the limit of the logarithm of the ratio of the distance between two nearby trajectories to their initial separation, as the separation goes to zero, averaged over all pairs of nearby trajectories [53,54]. More formally, given a dynamical system described by the set of differential equations:…”

Section: Maximum Lyapunov Exponentmentioning

confidence: 99%

Intelligent Localization and Deep Human Activity Recognition through IoT Devices

Alazeb,

Azmat,

Al Mudawi

et al. 2023

Sensors

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Ubiquitous computing has been a green research area that has managed to attract and sustain the attention of researchers for some time now. As ubiquitous computing applications, human activity recognition and localization have also been popularly worked on. These applications are used in healthcare monitoring, behavior analysis, personal safety, and entertainment. A robust model has been proposed in this article that works over IoT data extracted from smartphone and smartwatch sensors to recognize the activities performed by the user and, in the meantime, classify the location at which the human performed that particular activity. The system starts by denoising the input signal using a second-order Butterworth filter and then uses a hamming window to divide the signal into small data chunks. Multiple stacked windows are generated using three windows per stack, which, in turn, prove helpful in producing more reliable features. The stacked data are then transferred to two parallel feature extraction blocks, i.e., human activity recognition and human localization. The respective features are extracted for both modules that reinforce the system’s accuracy. A recursive feature elimination is applied to the features of both categories independently to select the most informative ones among them. After the feature selection, a genetic algorithm is used to generate ten different generations of each feature vector for data augmentation purposes, which directly impacts the system’s performance. Finally, a deep neural decision forest is trained for classifying the activity and the subject’s location while working on both of these attributes in parallel. For the evaluation and testing of the proposed system, two openly accessible benchmark datasets, the ExtraSensory dataset and the Sussex-Huawei Locomotion dataset, were used. The system outperformed the available state-of-the-art systems by recognizing human activities with an accuracy of 88.25% and classifying the location with an accuracy of 90.63% over the ExtraSensory dataset, while, for the Sussex-Huawei Locomotion dataset, the respective results were 96.00% and 90.50% accurate.

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Depth Sensors-Based Action Recognition Using a Modified K-Ary Entropy Classifier

Cited by 26 publications

References 78 publications

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

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

Comparative Analysis of Fine-Tuning I3D and SlowFast Networks for Action Recognition in Surveillance Videos

Intelligent Localization and Deep Human Activity Recognition through IoT Devices

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