A Review on Scaling Mobile Sensing Platforms for Human Activity Recognition: Challenges and Recommendations for Future Research

Carvalho, Liliana Inocêncio; Sofia, Rute C.

doi:10.3390/iot1020025

Cited by 10 publications

(6 citation statements)

References 127 publications

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“…Numerous reviews in HAR have been published, but our observations show that most of the studies are associated with either vision-based (Beddiar et al 2020 ; Dhiman Chhavi 2019 ; Ke et al 2013 ) or sensor-based (Carvalho and Sofia 2020 ; Lima et al 2019 ), while very few have considered RFID-based and device-free HAR. Further, there is no AI review article that covers the detailed analysis of all the four device types that includes all four types of devices such as sensor-based (Yao et al 2017 , 2019 ; Hx et al 2017 ; Hsu et al 2018 ; Xia et al 2020 ; Murad and Pyun 2017 ), vision-based (Feichtenhofer et al 2018 ; Simonyan and Zisserman 2014 ; Newell Alejandro 2016 ; Crasto et al 2019 ), RFID-based (Han et al 2014 ), and device-free (Zhang et al 2011 ).…”

Section: Introductionmentioning

confidence: 86%

“…We observed a total of 9 review articles arranged in chronological order (see Table 1 ). These reviews focused mainly on three sets of devices such as sensor-based (marked in light shade color) (Carvalho and Sofia 2020 ; Lima et al 2019 ; Wang et al 2016a , 2019b ; Lara and Labrador 2013 ; Hx et al 2017 ; Demrozi et al 2020 ; Crasto et al 2019 ; De-La-Hoz-Franco et al 2018 ) or vision-based (marked with dark shade color) (Beddiar et al 2020 ; Dhiman Chhavi 2019 ; Ke et al 2013 ; Obaida and Saraee 2017 ; Popoola and Wang 2012 ), device-free HAR (Hussain et al 2020 ). Table 1 summarizes the nine articles based on the focus area, keywords, number of keywords, research period, and #citations.…”

Section: Search Strategy and Literature Reviewmentioning

confidence: 99%

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Human activity recognition in artificial intelligence framework: a narrative review

et al. 2022

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Human activity recognition (HAR) has multifaceted applications due to its worldly usage of acquisition devices such as smartphones, video cameras, and its ability to capture human activity data. While electronic devices and their applications are steadily growing, the advances in Artificial intelligence (AI) have revolutionized the ability to extract deep hidden information for accurate detection and its interpretation. This yields a better understanding of rapidly growing acquisition devices, AI, and applications, the three pillars of HAR under one roof. There are many review articles published on the general characteristics of HAR, a few have compared all the HAR devices at the same time, and few have explored the impact of evolving AI architecture. In our proposed review, a detailed narration on the three pillars of HAR is presented covering the period from 2011 to 2021. Further, the review presents the recommendations for an improved HAR design, its reliability, and stability. Five major findings were: (1) HAR constitutes three major pillars such as devices, AI and applications; (2) HAR has dominated the healthcare industry; (3) Hybrid AI models are in their infancy stage and needs considerable work for providing the stable and reliable design. Further, these trained models need solid prediction, high accuracy, generalization, and finally, meeting the objectives of the applications without bias; (4) little work was observed in abnormality detection during actions; and (5) almost no work has been done in forecasting actions. We conclude that: (a) HAR industry will evolve in terms of the three pillars of electronic devices, applications and the type of AI. (b) AI will provide a powerful impetus to the HAR industry in future. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-021-10116-x.

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Section: Introductionmentioning

confidence: 86%

Section: Search Strategy and Literature Reviewmentioning

confidence: 99%

Human activity recognition in artificial intelligence framework: a narrative review

et al. 2022

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“…MCS Smart City apps leverage IoT infrastructures and personal CPS to enhance people-centric services. MCS is used in various Smart City services, including infrastructure monitoring (e.g., energy consumption), social behavior awareness [19,20], traffic pattern improvement [21], and detecting points of interest based on user behavior and preferences [22]. The focus is on identifying sites of interest (POIs) based on user movement behavior in a city, rather than making suggestions based on pre-established criteria.…”

Section: A Framework For Continuous Poi Detection In Smart Cities 31 ...mentioning

confidence: 99%

An Examination of Machine Learning-Based Outlier Identification from Mobile Phone Tracks

2024

IJFMR

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In this paper, two machine learning algorithms—local outlier factor (LOF) and density-based spatial clustering of applications with noise (DBSCAN)—that are used to identify outliers in the context of a continuous framework for point of interest (PoI) detection are analyzed. The mobile trajectories of users are continuously and almost instantaneously loaded into this system. These frameworks are still in their infancy, but they are already essential for large-scale sensing deployments, such as Smart City planning deployments, where the anonymous individual mobile user trajectories can be valuable to improve urban planning. There are two contributions made by this paper. First, the functional design of the entire PoI detection architecture is provided by the study. Second, the study evaluates the effectiveness.

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“…MCS applications, therefore, rely on available IoT infrastructures and on personal CPS to improve people-centric services provided by Smart Cities. MCS is today applied to a wide range of services that enrich the notion of a Smart City, for instance, monitoring of infrastructures (e.g., energy consumption); increased awareness on social behaviour [19,20]; improvement of traffic patterns [21]; detection of PoIs based on user behaviour and user preferences [22]. The use of MCS based on pervasive, opportunistic sensing [20] needs to be seen as a key component of Smart Cities, where collected data can be used to better plan the city, by incorporating personal preferences of users to planned PoIs.…”

Section: Smart Cities and Urban Sensing Backgroundmentioning

confidence: 99%

“…MCS is today applied to a wide range of services that enrich the notion of a Smart City, for instance, monitoring of infrastructures (e.g., energy consumption); increased awareness on social behaviour [19,20]; improvement of traffic patterns [21]; detection of PoIs based on user behaviour and user preferences [22]. The use of MCS based on pervasive, opportunistic sensing [20] needs to be seen as a key component of Smart Cities, where collected data can be used to better plan the city, by incorporating personal preferences of users to planned PoIs. In the related literature, most work focuses on recommendations for PoIs, where PoIs are defined in a static way by municipalities, and PoI detection is considered in recommendation engines, e.g., to provide recommendations about potential itineraries around a city [23].…”

Section: Smart Cities and Urban Sensing Backgroundmentioning

confidence: 99%

An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories

Pereira

Sofia

2022

Future Internet

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This paper provides an analysis of two machine learning algorithms, density-based spatial clustering of applications with noise (DBSCAN) and the local outlier factor (LOF), applied in the detection of outliers in the context of a continuous framework for the detection of points of interest (PoI). This framework has as input mobile trajectories of users that are continuously fed to the framework in close to real time. Such frameworks are today still in their infancy and highly required in large-scale sensing deployments, e.g., Smart City planning deployments, where individual anonymous trajectories of mobile users can be useful to better develop urban planning. The paper’s contributions are twofold. Firstly, the paper provides the functional design for the overall PoI detection framework. Secondly, the paper analyses the performance of DBSCAN and LOF for outlier detection considering two different datasets, a dense and large dataset with over 170 mobile phone-based trajectories and a smaller and sparser dataset, involving 3 users and 36 trajectories. Results achieved show that LOF exhibits the best performance across the different datasets, thus showing better suitability for outlier detection in the context of frameworks that perform PoI detection in close to real time.

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A Review on Scaling Mobile Sensing Platforms for Human Activity Recognition: Challenges and Recommendations for Future Research

Cited by 10 publications

References 127 publications

Human activity recognition in artificial intelligence framework: a narrative review

Human activity recognition in artificial intelligence framework: a narrative review

An Examination of Machine Learning-Based Outlier Identification from Mobile Phone Tracks

An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories

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