A Hybrid Reasoning Approach for Activity Recognition Based on Answer Set Programming and Dempster–Shafer Theory

Machot, Fadi Al; Mayr, Heinrich C.; Ranasinghe, Suneth

doi:10.1007/978-3-319-58996-1_14

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…Pattern-based methods are classified as supervised and unsupervised learning techniques. The most known supervised learning techniques include k-Nearest Neighbors (kNN) [4,18,71,91], Decision Tree (DT) [26,87,92,93,94], Decision Table [11], Random Forests (RF) [7,83], Naive Bayes (NB) [15,79,83,87,95,96] and Support Vector Machine (SVM) [2,12,33,34,58,60,76,79,81,83,88,97,98,99,100]. Classification techniques have been summarized in Figure 2.…”

Section: Impact Of Human Activity Recognition (Har) Stages On Enermentioning

confidence: 99%

“…The main drawback of Gaussian Mixture Model (GMM) is a request for too many empirical parameters, which decreases the possibility of its implementation in practice [109]. However, in some cases, such as with the recognition of static postures and non-temporal event patterns, it appears to have good classification performance [88].…”

Section: Impact Of Human Activity Recognition (Har) Stages On Enermentioning

confidence: 99%

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Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Dinarevic

Husić

Baraković

2019

Sensors

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Human activity recognition (HAR) is a classification process that is used for recognizing human motions. A comprehensive review of currently considered approaches in each stage of HAR, as well as the influence of each HAR stage on energy consumption and latency is presented in this paper. It highlights various methods for the optimization of energy consumption and latency in each stage of HAR that has been used in literature and was analyzed in order to provide direction for the implementation of HAR in health and wellbeing applications. This paper analyses if and how each stage of the HAR process affects energy consumption and latency. It shows that data collection and filtering and data segmentation and classification stand out as key stages in achieving a balance between energy consumption and latency. Since latency is only critical for real-time HAR applications, the energy consumption of sensors and devices stands out as a key challenge for HAR implementation in health and wellbeing applications. Most of the approaches in overcoming challenges related to HAR implementation take place in the data collection, filtering and classification stages, while the data segmentation stage needs further exploration. Finally, this paper recommends a balance between energy consumption and latency for HAR in health and wellbeing applications, which takes into account the context and health of the target population.

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Section: Impact Of Human Activity Recognition (Har) Stages On Enermentioning

confidence: 99%

Section: Impact Of Human Activity Recognition (Har) Stages On Enermentioning

confidence: 99%

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Dinarevic

Husić

Baraković

2019

Sensors

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“…Although before Shafer's (1976) work there were no practical applications of RST, since 1990's there has been a geometrical growth on the number DST applications (Beynon, Curry, & Morgan, 2000). Some examples of the most recent publications on the topic include measuring uncertainty in big data (Dutta, 2018), multisensor-based activity recognition in smart homes (Al Machot, Mayr, & Ranasinghe, 2018), skin diseases (Khairina, Hatta, Rustam, & Maharani, 2018), cancer detection (Kim et al, 2018), fault in power transformers (Kari et al, 2018), heritage evaluation (Liu, Zhao, & Yang, 2018), thermal plants monitoring (Moradi, Chaibakhsh, & Ramezani, 2018) and chemical risk assessment (Rathman, Yang, & Zhou, 2018). These are just a few examples to illustrate how relevant is this approach to handle decision making under uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Updating the probability of failure of rock wedges

Hernández-Carrillo

Beltrán

2019

Earth sci. res. j.

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In hard rock masses, discontinuities control the slope stability, rather than block matrix breakage. The relative position of joints and slope face defines the most likely mechanisms of failure. Among these mechanisms, the wedge failure is one of the most common ways of failure in which joint sets dip and dip direction, slope geometry and direction, external forces (including water pressure and earthquake) and rock and joints mechanical properties control the stability. The determination of these input parameters is not straightforward, mainly due to their variability and the limited amount of information available. Besides, in most projects, input parameters come from different sources (e.g., expert opinion, back-calculation, laboratory tests, field test or different project stages). Therefore, this limited information from different sources should be appropriately incorporated into the stability analysis to assist the design and decision-making process. In this context, random sets arise as a powerful tool to combine different sources of information and to perform a reliability assessment under limited information. This feature makes it possible to update the probability of failure as new evidence is available. With this framework, this paper presents a reliability assessment of wedge stability in a rock slope of a sandstone quarry, located in Une Cundinamarca, where information on mechanical and geometrical parameters has been collected for 20 years.

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A roadmap towards energy‐efficient data fusion methods in the Internet of Things

Pourghebleh

Hekmati

Davoudnia

et al. 2022

Concurrency and Computation

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Nowadays, with the rapid progress of Internet-based and distributed systems such as cloud computing, peer-to-peer networking, and Internet of Things (IoT), significant improvements in almost every engineering and commercial field have been made. On the basis of IoT, smart cities are formed utilizing intelligent information processing, universal connectivity, ubiquitous sensing, and real-time monitoring. Energy conservation is one of the significant issues in current IoT development due to the poor battery endurance of IoT objects. Over the last years, with smart cities' explosive growth, a huge range of studies regarding energy efficiency have been done. The diversity of sparse data and multi-sourcing is utilized in developing IoT scenarios. In order to use efficiently of these data to improve the IoT services, data fusion plays an important role. It saves network resources, improves data transmission efficiency, and extracts useful information from raw data. To the best of our knowledge, there is still a lack of a comprehensive and systematic study about surveying and analyzing the available energy-efficient data fusion techniques in the IoT. Thus, this article aims to address this gap using a systematic method.

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A Hybrid Reasoning Approach for Activity Recognition Based on Answer Set Programming and Dempster–Shafer Theory

Cited by 6 publications

References 33 publications

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Updating the probability of failure of rock wedges

A roadmap towards energy‐efficient data fusion methods in the Internet of Things

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