Context-Aware Human Activity and Smartphone Position-Mining with Motion Sensors

Gao, Zhiqiang; Liu, Dawei; Huang, Kaizhu; Huang, Yi

doi:10.3390/rs11212531

Cited by 15 publications

(11 citation statements)

References 45 publications

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“…This separation was typically performed using a high-pass Butterworth filter of low order (e.g., order 3) with a cutoff frequency below 1 Hz. Other approaches transformed tri-axial into bi-axial measurement with horizontal and vertical axes 49 , or projected the data from the device coordinate system into a fixed coordinate system (e.g., the coordinate system of a smartphone that lies flat on the ground) using a rotation matrix (Euler angle-based 66 or quaternion 47,67 ).…”

Section: Data Preprocessingmentioning

confidence: 99%

A systematic review of smartphone-based human activity recognition methods for health research

2021

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Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited for health research. Researchers have proposed various human activity recognition (HAR) systems aimed at translating measurements from smartphones into various types of physical activity. In this review, we summarized the existing approaches to smartphone-based HAR. For this purpose, we systematically searched Scopus, PubMed, and Web of Science for peer-reviewed articles published up to December 2020 on the use of smartphones for HAR. We extracted information on smartphone body location, sensors, and physical activity types studied and the data transformation techniques and classification schemes used for activity recognition. Consequently, we identified 108 articles and described the various approaches used for data acquisition, data preprocessing, feature extraction, and activity classification, identifying the most common practices, and their alternatives. We conclude that smartphones are well-suited for HAR research in the health sciences. For population-level impact, future studies should focus on improving the quality of collected data, address missing data, incorporate more diverse participants and activities, relax requirements about phone placement, provide more complete documentation on study participants, and share the source code of the implemented methods and algorithms.

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Section: Data Preprocessingmentioning

confidence: 99%

A systematic review of smartphone-based human activity recognition methods for health research

2021

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“…As sensor techniques develop, activity recognition with artificial intelligence has become a hot topic in areas of remote sensing [2], smart homes, and smart cities [3,4]. Violence recognition with AI techniques [5,6] has also gained more and more attention.…”

Section: Related Workmentioning

confidence: 99%

“…A lot of work has been done to detect violent behaviors all over the world, but most of these studies were about social violence such as street fights, and few covered campus violence. Campus violence differs from social violence in the following aspects: (1) the victims in campus violence events usually do not dare to resist, (2) no weapons are used, and (3) campus violence is generally not as strong as social violence, so campus violence sometimes can be confused with playing or sports with physical confrontation. Therefore, in this paper, the authors build their campus violence databases and design a campus violence detecting method.…”

Section: Related Workmentioning

confidence: 99%

Campus Violence Detection Based on Artificial Intelligent Interpretation of Surveillance Video Sequences

et al. 2021

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Campus violence is a common social phenomenon all over the world, and is the most harmful type of school bullying events. As artificial intelligence and remote sensing techniques develop, there are several possible methods to detect campus violence, e.g., movement sensor-based methods and video sequence-based methods. Sensors and surveillance cameras are used to detect campus violence. In this paper, the authors use image features and acoustic features for campus violence detection. Campus violence data are gathered by role-playing, and 4096-dimension feature vectors are extracted from every 16 frames of video images. The C3D (Convolutional 3D) neural network is used for feature extraction and classification, and an average recognition accuracy of 92.00% is achieved. Mel-frequency cepstral coefficients (MFCCs) are extracted as acoustic features, and three speech emotion databases are involved. The C3D neural network is used for classification, and the average recognition accuracies are 88.33%, 95.00%, and 91.67%, respectively. To solve the problem of evidence conflict, the authors propose an improved Dempster–Shafer (D–S) algorithm. Compared with existing D–S theory, the improved algorithm increases the recognition accuracy by 10.79%, and the recognition accuracy can ultimately reach 97.00%.

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“…A wide range of fields, from the human posture classification field considering simple postures such as sitting, standing, and lying down [45,46] to the medical [47,48], industrial [49], and sports [50] fields, have focused on the definition and recognition of human postures. As hardware for collecting posture recognition data, three-dimensional depth cameras [51], smartphones [52,53], and inertial measurement unit sensors [17] are used. In addition, machine learning algorithms such as support vector machine (SVM) [54], CNN [19], and When the welding arc and the welder's body overlap, it is difficult to capture motion accurately because the depth hole obscures the body in the depth image captured by an RGB-D camera.…”

Section: Machine Learning Technique For Posture Recognitionmentioning

confidence: 99%

Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera

Kim

Nam

2021

Applied Sciences

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The proportion of welding work in total man-hours required for shipbuilding processes has been perceived to be significant, and welding man-hours are greatly affected by working posture. Continuous research has been conducted to identify the posture in welding by utilizing the relationship between man-hours and working posture. However, the results that reflect the effect of the welding posture on man-hours are not available. Although studies on posture recognition based on depth image analysis are being positively reviewed, welding operation has difficulties in image interpretation because an external obstacle caused by arcs exists. Therefore, any obstacle element must be removed in advance. This study proposes a method to acquire work postures using a low-cost RGB-D camera and recognize the welding position through image analysis. It removes obstacles that appear as depth holes in the depth image and restores the removed part to the desired state. The welder’s body joints are extracted, and a convolution neural network is used to determine the corresponding welding position. The restored image showed significantly improved recognition accuracy. The proposed method acquires, analyzes, and automates the recognition of welding positions in real-time. It can be applied to all areas where image interpretation is difficult due to obstacles.

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Context-Aware Human Activity and Smartphone Position-Mining with Motion Sensors

Cited by 15 publications

References 45 publications

A systematic review of smartphone-based human activity recognition methods for health research

A systematic review of smartphone-based human activity recognition methods for health research

Campus Violence Detection Based on Artificial Intelligent Interpretation of Surveillance Video Sequences

Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera

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