Generalized activity recognition using accelerometer in wearable devices for IoT applications

Safadi, Ebrahim Al; Mohammad, Fahim; Iyer, Darshan; Smiley, Benjamin J.; Jain, Nilesh

doi:10.1109/avss.2016.7738020

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Machine learning is the most popular research technique for data processing with 100 publications. This technique is used for data prediction [218,219], activity recognition [220,221], and data classification [222,223]. Next, data mining appears with 89 documents, with distributed data mining [224], and applications such as event detection [225,226] as sub-techniques.…”

Section: Software Processing Techniquesmentioning

confidence: 99%

Internet of Things: A Scientometric Review

et al. 2017

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Abstract:Internet of Things (IoT) is connecting billions of devices to the Internet. These IoT devices chain sensing, computation, and communication techniques, which facilitates remote data collection and analysis. wireless sensor networks (WSN) connect sensing devices together on a local network, thereby eliminating wires, which generate a large number of samples, creating a big data challenge. This IoT paradigm has gained traction in recent years, yielding extensive research from an increasing variety of perspectives, including scientific reviews. These reviews cover surveys related to IoT vision, enabling technologies, applications, key features, co-word and cluster analysis, and future directions. Nevertheless, we lack an IoT scientometrics review that uses scientific databases to perform a quantitative analysis. This paper develops a scientometric review about IoT over a data set of 19,035 documents published over a period of 15 years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) in two main scientific databases (Clarivate Web of Science and Scopus). A Python script called ScientoPy was developed to perform quantitative analysis of this data set. This provides insight into research trends by investigating a lead author's country affiliation, most published authors, top research applications, communication protocols, software processing, hardware, operating systems, and trending topics. Furthermore, we evaluate the top trending IoT topics and the popular hardware and software platforms that are used to research these trends.

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Section: Software Processing Techniquesmentioning

confidence: 99%

Internet of Things: A Scientometric Review

et al. 2017

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Application of Machine Learning-Based Pattern Recognition in IoT Devices: Review

Menter

Tee

Dave

2021

Algorithms for Intelligent Systems

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The Internet of Things (IoT) is a rapidly advancing area of technology that has quickly become more widespread in recent years. With greater numbers of everyday objects being connected to the internet, many different innovations have been presented to make our everyday lives more straightforward. Pattern recognition is extremely prevalent in IoT devices because of the many applications and benefits that can come from it. A multitude of studies have been conducted with the intention of improving speed and accuracy, decreasing complexity, and reducing the overall required processing power of pattern recognition algorithms in IoT devices. After reviewing the applications of different machine learning algorithms, results vary from case to case but a general conclusion can be drawn that the optimal machine learning based pattern recognition algorithms to be used with IoT devices are K-Nearest Neighbor, Random Forest, and Support Vector Machine.

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Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

et al. 2020

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Personal mobile devices such as smartwatches, smart jewelry, and smart clothes have launched a new trend in the Internet of Things (IoT) era, namely the Internet of Wearable Things (IoWT). These wearables are small IoT devices capable of sensing, storing, processing, and exchanging data to assist users by improving their everyday life tasks through various applications. However, the IoWT has also brought new challenges for the research community to address such as increasing demand for enhanced computational power, better communication capabilities, improved security and privacy features, reduced form factor, minimal weight, and better comfort. Most wearables are battery-powered devices that need to be recharged-therefore, the limited battery life remains the bottleneck leading to the need to enhance the energy efficiency of wearables, thus, becoming an active research area. This paper presents a survey of energy-efficient solutions proposed for diverse IoWT applications by following the systematic literature review method. The available techniques published from 2010 to 2020 are scrutinized, and the taxonomy of the available solutions is presented based on the targeted application area. Moreover, a comprehensive qualitative analysis compares the proposed studies in each application area in terms of their advantages, disadvantages, and main contributions. Furthermore, a list of the most significant performance parameters is provided. A more in-depth discussion of the main techniques to enhance wearables' energy efficiency is presented by highlighting the trade-offs involved. Finally, some potential future research directions are highlighted.

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Generalized activity recognition using accelerometer in wearable devices for IoT applications

Cited by 4 publications

References 13 publications

Internet of Things: A Scientometric Review

Internet of Things: A Scientometric Review

Application of Machine Learning-Based Pattern Recognition in IoT Devices: Review

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

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