Sensors Technologies for Human Activity Analysis Based on SVM Optimized by PSO Algorithm

Batool, Mouazma; Jalal, Ahmad; Kim, Ki-Bum

doi:10.1109/icaem.2019.8853770

Cited by 85 publications

(26 citation statements)

References 32 publications

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“…After extracting features, Random forest is applied to classify interactions that result in good performance in human motion detection. In order to recognize the physical activities of humans, wearable sensors are used by M. Batool et al in [ 33 ]. They used both the gyroscopic and accelerometer sensor data.…”

Section: Related Workmentioning

confidence: 99%

Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors

Jalal

Khalid

Kim

2020

Entropy

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Automatic identification of human interaction is a challenging task especially in dynamic environments with cluttered backgrounds from video sequences. Advancements in computer vision sensor technologies provide powerful effects in human interaction recognition (HIR) during routine daily life. In this paper, we propose a novel features extraction method which incorporates robust entropy optimization and an efficient Maximum Entropy Markov Model (MEMM) for HIR via multiple vision sensors. The main objectives of proposed methodology are: (1) to propose a hybrid of four novel features—i.e., spatio-temporal features, energy-based features, shape based angular and geometric features—and a motion-orthogonal histogram of oriented gradient (MO-HOG); (2) to encode hybrid feature descriptors using a codebook, a Gaussian mixture model (GMM) and fisher encoding; (3) to optimize the encoded feature using a cross entropy optimization function; (4) to apply a MEMM classification algorithm to examine empirical expectations and highest entropy, which measure pattern variances to achieve outperformed HIR accuracy results. Our system is tested over three well-known datasets: SBU Kinect interaction; UoL 3D social activity; UT-interaction datasets. Through wide experimentations, the proposed features extraction algorithm, along with cross entropy optimization, has achieved the average accuracy rate of 91.25% with SBU, 90.4% with UoL and 87.4% with UT-Interaction datasets. The proposed HIR system will be applicable to a wide variety of man–machine interfaces, such as public-place surveillance, future medical applications, virtual reality, fitness exercises and 3D interactive gaming.

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Section: Related Workmentioning

confidence: 99%

Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors

Jalal

Khalid

Kim

2020

Entropy

Self Cite

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“…Statistical features include mean, variance, peak to peak and peak magnitude to RMS ratio which together achieve a reasonable accuracy rate. Batool et al [19] proposed Mel-frequency cepstral coefficients and statistical features to detect physical activity using accelerometer and gyroscopic sensors. Shahar et al [20] analyzed the accelerometer and gyroscope signal via sensors mounted at the chest, waist, and left and right wrists of the body.…”

Section: Feature Based Activity Recognition Using Wearable Sensorsmentioning

confidence: 99%

Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier

2020

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Human behavior modeling (HBM) is a challenging classification task for researchers seeking to develop sustainable systems that precisely monitor and record human life-logs. In recent years, several models have been proposed; however, HBM remains an inspiring problem that is only partly solved. This paper proposes a novel framework of human behavior modeling based on wearable inertial sensors; the system framework is composed of data acquisition, feature extraction, optimization and classification stages. First, inertial data is filtered via three different filters, i.e., Chebyshev, Elliptic and Bessel filters. Next, six different features from time and frequency domains are extracted to determine the maximum optimal values. Then, the Probability Based Incremental Learning (PBIL) optimizer and the K-Ary tree hashing classifier are applied to model different human activities. The proposed model is evaluated on two benchmark datasets, namely DALIAC and PAMPA2, and one self-annotated dataset, namely, IM-LifeLog, respectively. For evaluation, we used a leave-one-out cross validation scheme. The experimental results show that our model outperformed existing state-of-the-art methods with accuracy rates of 94.23%, 94.07% and 96.40% over DALIAC, PAMPA2 and IM-LifeLog datasets, respectively. The proposed system can be used in healthcare, physical activity detection, surveillance systems and medical fitness fields.

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“…By analyzing the hotspots and keywords of international research in a certain field, scholars can provide necessary reference and identify implications for the development direction, policy formulation, knowledge base, and frontier trends of the field [73][74][75][76]. In addition to scientometrics and knowledge mapping, many scholars and experts also use other algorithms and technologies to study AI, such as data extraction, features fusion, and classification and recognition technologies [77][78][79][80][81][82][83][84]. Based on these studies, from the perspective of evolution and cooperation, this study used the methods of scientometrics and knowledge map visualization to research, which is helpful for further enrichment of the field and gives this research a certain uniqueness and novelty.…”

Section: Knowledge Map Visualization Analysis Of International Researmentioning

confidence: 99%

Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018

2019

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In the last decade, artificial intelligence (AI) has undergone many important developments in China and has risen to the level of national strategy, which is closely related to the areas of research and policy promotion. The interactive relationship between the hotspots of China's international AI research and its national-level policy keywords is the basis for further clarification and reference in academics and political circles. There has been very little research on the interaction between academic research and policy making. Understanding the relationship between the content of academic research and the content emphasized by actual operational policy will help scholars to better apply research to practice, and help decision-makers to manage effectively. Based on 3577 English publications about AI published by Chinese scholars in 2009-2018, and 262 Chinese national-level policy documents published during this period, this study carried out scientometric analysis and quantitative analysis of policy documents through the knowledge maps of AI international research hotspots in China and the co-occurrence maps of Chinese policy keywords, and conducted a comparative analysis that divided China's AI development into three stages: the initial exploration stage, the steady rising stage, and the rapid development stage. The studies showed that in the initial exploration stage (2009)(2010)(2011)(2012), research hotspots and policy keywords had a certain alienation relationship; in the steady rising stage (2013)(2014)(2015), research hotspots focused more on cutting-edge technologies and policy keywords focused more on macro-guidance, and the relationship began to become close; and in the rapid development stage (2016)(2017)(2018), the research hotspots and policy keywords became closely integrated, and they were mutually infiltrated and complementary, thus realizing organic integration and close connection. Through comparative analysis between international research hotspots and national-level policy keywords on AI in China from 2009 to 2018, the development of AI in China was revealed to some extent, along with the interaction between academics and politics in the past ten years, which is of great significance for the sustainable development and effective governance of China's artificial intelligence.abstract symbols using logical rules) was the most productive way to pursue a computer that could "think"; however, as originally unimagined by Turing and others, the strategy of constructing an inference engine did not achieve the initial cognitive tasks, and it seemed that the theoretically possible concepts had not produced many feasible applications in practice [1]. Li and Wang gave five common definitions of artificial intelligences (AI) in their book Artificial Intelligence: (1) AI is a computer program which makes people feel inconceivable; (2) AI is a computer program similar to human thinking;(3) AI is a computer program similar to human behavior; (4) AI is a computer program that can learn; (5) AI is a computer ...

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Sensors Technologies for Human Activity Analysis Based on SVM Optimized by PSO Algorithm

Cited by 85 publications

References 32 publications

Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors

Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors

Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier

Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018

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