scholarly journals A Risk-Based IoT Decision-Making Framework Based on Literature Review with Human Activity Recognition Case Studies

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4504
Author(s):  
Tazar Hussain ◽  
Chris Nugent ◽  
Adrian Moore ◽  
Jun Liu ◽  
Alfie Beard
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Case Studies ◽  
Activity Recognition ◽  
Human Activity ◽  
Domain Knowledge ◽  
Human Activity Recognition ◽  
Sources Of Uncertainty ◽  
Decision Making Framework

The Internet of Things (IoT) is a key and growing technology for many critical real-life applications, where it can be used to improve decision making. The existence of several sources of uncertainty in the IoT infrastructure, however, can lead decision makers into taking inappropriate actions. The present work focuses on proposing a risk-based IoT decision-making framework in order to effectively manage uncertainties in addition to integrating domain knowledge in the decision-making process. A structured literature review of the risks and sources of uncertainty in IoT decision-making systems is the basis for the development of the framework and Human Activity Recognition (HAR) case studies. More specifically, as one of the main targeted challenges, the potential sources of uncertainties in an IoT framework, at different levels of abstraction, are firstly reviewed and then summarized. The modules included in the framework are detailed, with the main focus given to a novel risk-based analytics module, where an ensemble-based data analytic approach, called Calibrated Random Forest (CRF), is proposed to extract useful information while quantifying and managing the uncertainty associated with predictions, by using confidence scores. Its output is subsequently integrated with domain knowledge-based action rules to perform decision making in a cost-sensitive and rational manner. The proposed CRF method is firstly evaluated and demonstrated on a HAR scenario in a Smart Home environment in case study I and is further evaluated and illustrated with a remote health monitoring scenario for a diabetes use case in case study II. The experimental results indicate that using the framework’s raw sensor data can be converted into meaningful actions despite several sources of uncertainty. The comparison of the proposed framework to existing approaches highlights the key metrics that make decision making more rational and transparent.

Depth-based Human Activity Recognition: vINCI Case Study

2020 ◽  
Author(s):  
Lidia Bajenaru ◽  
Ciprian Dobre ◽  
Radu-Ioan Ciobanu ◽  
Georgiana Dedu ◽  
Silviu-George Pantelimon ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition

Improvised methods for tackling big data stream mining challenges: case study of human activity recognition

2016 ◽  
Vol 72 (10) ◽  
pp. 3927-3959 ◽  
Author(s):  
Simon Fong ◽  
Kexing Liu ◽  
Kyungeun Cho ◽  
Raymond Wong ◽  
Sabah Mohammed ◽  
...  
Keyword(s):  
Big Data ◽  
Activity Recognition ◽  
Human Activity ◽  
Data Stream ◽  
Human Activity Recognition ◽  
Data Stream Mining ◽  
Stream Mining

scholarly journals AttnSense: Multi-level Attention Mechanism For Multimodal Human Activity Recognition

2019 ◽  
Author(s):  
HaoJie Ma ◽  
Wenzhong Li ◽  
Xiao Zhang ◽  
Songcheng Gao ◽  
Sanglu Lu
Keyword(s):  
Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Domain Knowledge ◽  
Research Problem ◽  
Human Activity Recognition ◽  
Attention Mechanism ◽  
The Rich ◽  
Public Datasets ◽  
Gated Recurrent Units

Sensor-based human activity recognition is a fundamental research problem in ubiquitous computing, which uses the rich sensing data from multimodal embedded sensors such as accelerometer and gyroscope to infer human activities. The existing activity recognition approaches either rely on domain knowledge or fail to address the spatial-temporal dependencies of the sensing signals. In this paper, we propose a novel attention-based multimodal neural network model called AttnSense for multimodal human activity recognition. AttnSense introduce the framework of combining attention mechanism with a convolutional neural network (CNN) and a Gated Recurrent Units (GRU) network to capture the dependencies of sensing signals in both spatial and temporal domains, which shows advantages in prioritized sensor selection and improves the comprehensibility. Extensive experiments based on three public datasets show that AttnSense achieves a competitive performance in activity recognition compared with several state-of-the-art methods.

scholarly journals Addressing funding issues for Danish mental health NGOs

2018 ◽  
Vol 17 (2) ◽  
pp. 79-89
Author(s):  
Nawal Farhat Aguilar ◽  
Zaza Nadja Lee Hansen
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Literature Review ◽  
Key Factors ◽  
Content Type ◽  
Factors Affecting ◽  
Non Governmental Organizations ◽  
Decision Making Framework ◽  
Optimal Approach

Purpose Research has shown that non-governmental organizations (NGOs) often fail to appreciate that in their market, donors represent clients. Moreover, the unstable income characteristics of NGOs emphasize the importance of conducting market analysis specific to such organizations. The purpose of this paper is to identify key factors that influence fundraising success for mental health NGOs and determine the most advantageous fundraising approach based on a mixed-methods study that encompass a literature review, two surveys and a case study. Design/methodology/approach Based on a structured literature review, the most important factors affecting NGO fundraising are unified into a decision-making framework. This framework is tested using a triangulation approach by combining quantitative and qualitative methods. The former based on a general survey and the latter based on a case study. Findings The results highlight 15 key factors determining the optimal approach for mental health NGOs when fundraising in Denmark. Practical implications The decision-making framework can be used to assess the most advantageous fundraising approach based on a variety of internal and external circumstances. Originality/value While private firms develop exhaustive market analyses, NGOs often lack analyses to cope with fluctuating environments and changing customer needs. This paper addresses this gap by identifying key factors that determine an optimal fundraising approach and proposes a novel decision-making framework for practitioners.

scholarly journals Human Activity Recognition: literature Review

2021 ◽  
Vol 30 (5) ◽  
pp. 12-29
Author(s):  
mais irreem atheed ◽  
Dena Ahmed ◽  
Rashad Kamal
Keyword(s):  
Literature Review ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition

scholarly journals Analysis of Deep Neural Networks For Human Activity Recognition in Videos – A Systematic Literature Review

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hadiqa Aman Ullah ◽  
Sukumar Letchmunan ◽  
M. Sultan Zia ◽  
Umair Muneer Butt ◽  
Fadratul Hafinaz Hassan
Keyword(s):  
Neural Networks ◽  
Literature Review ◽  
Activity Recognition ◽  
Human Activity ◽  
Systematic Literature Review ◽  
Deep Neural Networks ◽  
Human Activity Recognition

scholarly journals Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1208 ◽  
Author(s):  
Sebastian Scheurer ◽  
Salvatore Tedesco ◽  
Kenneth N. Brown ◽  
Brendan O’Flynn
Keyword(s):  
Machine Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Domain Knowledge ◽  
Binary Classification ◽  
Large Body ◽  
Decomposition Methods ◽  
Human Activity Recognition ◽  
Machine Learning Algorithms ◽  
Data Set

Human activity recognition (HAR) has become an increasingly popular application of machine learning across a range of domains. Typically the HAR task that a machine learning algorithm is trained for requires separating multiple activities such as walking, running, sitting, and falling from each other. Despite a large body of work on multi-class HAR, and the well-known fact that the performance on a multi-class problem can be significantly affected by how it is decomposed into a set of binary problems, there has been little research into how the choice of multi-class decomposition method affects the performance of HAR systems. This paper presents the first empirical comparison of multi-class decomposition methods in a HAR context by estimating the performance of five machine learning algorithms when used in their multi-class formulation, with four popular multi-class decomposition methods, five expert hierarchies—nested dichotomies constructed from domain knowledge—or an ensemble of expert hierarchies on a 17-class HAR data-set which consists of features extracted from tri-axial accelerometer and gyroscope signals. We further compare performance on two binary classification problems, each based on the topmost dichotomy of an expert hierarchy. The results show that expert hierarchies can indeed compete with one-vs-all, both on the original multi-class problem and on a more general binary classification problem, such as that induced by an expert hierarchy’s topmost dichotomy. Finally, we show that an ensemble of expert hierarchies performs better than one-vs-all and comparably to one-vs-one, despite being of lower time and space complexity, on the multi-class problem, and outperforms all other multi-class decomposition methods on the two dichotomous problems.

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