Suspicious human activity recognition: a review

Tripathi, Ramesh Kumar; Jalal, Anand Singh; Agrawal, Subhash Chand

doi:10.1007/s10462-017-9545-7

Cited by 116 publications

(46 citation statements)

References 113 publications

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“…The automated recognition of human activity using various types of sensors is an interesting research area that can have multiple practical applications [ 1 ], e.g., in healthcare [ 2 ], surveillance [ 3 ], entertainment [ 4 ], security [ 5 ], building management [ 6 ], and others. Daily or unexpected activities, such as walking, sitting, running, cycling, standing, falling, fighting, crowd assembling, etc., can be detected using non pervasive sensors that are either remotely positioned, e.g., a camera, or carried by humans, e.g., smart phones, smart watches, smart wristbands [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning

Althobaiti

Katsigiannis

Ramzan

2020

Sensors

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The detection of activities of daily living (ADL) and the detection of falls is of utmost importance for addressing the issue of serious injuries and death as a consequence of elderly people falling. Wearable sensors can provide a viable solution for monitoring people in danger of falls with minimal external involvement from health or care home workers. In this work, we recorded accelerometer data from 35 healthy individuals performing various ADLs, as well as falls. Spatial and frequency domain features were extracted and used for the training of machine learning models with the aim of distinguishing between fall and no fall events, as well as between falls and other ADLs. Supervised classification experiments demonstrated the efficiency of the proposed approach, achieving an F1-score of 98.41% for distinguishing between fall and no fall events, and an F1-score of 88.11% for distinguishing between various ADLs, including falls. Furthermore, the created dataset, named “ShimFall&ADL” will be publicly released to facilitate further research on the field.

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Section: Introductionmentioning

confidence: 99%

Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning

Althobaiti

Katsigiannis

Ramzan

2020

Sensors

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“…The problems of skin color-based methods reside mainly in choosing the relevant color space and with false detections because of the objects of the scene whose color is close to that of the skin. Very common solutions to these problems are the separation of brightness and chromaticity components, elimination of shadow effect and background subtraction [37,164,194]. Other problems may influence the results of these methods, like the inherent discrepancy of skin color variations according to ethnicity group, lighting conditions and the sensitivity of the employed acquisition devices.…”

Section: I) First Stage Methods (Detection)mentioning

confidence: 99%

Vision-based human activity recognition: a survey

Beddiar

Nini

Sabokrou

et al. 2020

Multimed Tools Appl

264

117

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Human activity recognition (HAR) systems attempt to automatically identify and analyze human activities using acquired information from various types of sensors. Although several extensive review papers have already been published in the general HAR topics, the growing technologies in the field as well as the multi-disciplinary nature of HAR prompt the need for constant updates in the field. In this respect, this paper attempts to review and summarize the progress of HAR systems from the computer vision perspective. Indeed, most computer vision applications such as human computer interaction, virtual reality, security, video surveillance and home monitoring are highly correlated to HAR tasks. This establishes new trend and milestone in the development cycle of HAR systems. Therefore, the current survey aims to provide the reader with an up to date analysis of vision-based HAR related literature and recent progress in the field. At the same time, it will highlight the main challenges and future directions.

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“…There are five main tasks performed by HAR, as shown in Figure 1a, namely recognition of basic activities [14], recognition of daily activities [15], recognition of unusual events [16], identification of biometric subjects [17], and prediction of energy expenditures [18]. As illustrated in Figure 1b, various sensors are employed for the performance of these tasks, such as video cameras, circumstantial sensors that measure temperature, relative humidity, light, pressure, and wearable sensors.…”

Section: Human Activity Recognition Via Machine Learning and Deep Leamentioning

confidence: 99%

Biometric User Identification Based on Human Activity Recognition Using Wearable Sensors: An Experiment Using Deep Learning Models

2021

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Currently, a significant amount of interest is focused on research in the field of Human Activity Recognition (HAR) as a result of the wide variety of its practical uses in real-world applications, such as biometric user identification, health monitoring of the elderly, and surveillance by authorities. The widespread use of wearable sensor devices and the Internet of Things (IoT) has led the topic of HAR to become a significant subject in areas of mobile and ubiquitous computing. In recent years, the most widely-used inference and problem-solving approach in the HAR system has been deep learning. Nevertheless, major challenges exist with regard to the application of HAR for problems in biometric user identification in which various human behaviors can be regarded as types of biometric qualities and used for identifying people. In this research study, a novel framework for multi-class wearable user identification, with a basis in the recognition of human behavior through the use of deep learning models, is presented. In order to obtain advanced information regarding users during the performance of various activities, sensory data from tri-axial gyroscopes and tri-axial accelerometers of the wearable devices are applied. Additionally, a set of experiments were shown to validate this work, and the proposed framework’s effectiveness was demonstrated. The results for the two basic models, namely, the Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM) deep learning, showed that the highest accuracy for all users was 91.77% and 92.43%, respectively. With regard to the biometric user identification, these are both acceptable levels.

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Suspicious human activity recognition: a review

Cited by 116 publications

References 113 publications

Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning

Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning

Vision-based human activity recognition: a survey

Biometric User Identification Based on Human Activity Recognition Using Wearable Sensors: An Experiment Using Deep Learning Models

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