Context recognition and ubiquitous computing in smart cities: a systematic mapping

Nascimento, Leonardo Vianna do; Machado, Guilherme Medeiros; Maran, Vinícius; Oliveira, José Palazzo M. de

doi:10.1007/s00607-020-00878-7

Cited by 7 publications

(1 citation statement)

References 109 publications

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“…Gait analysis with wearable sensors is now a highly popular area of study in ubiquitous computing [1] and HCI [2] due to the variety of disciplines in which it may be used, including survey systems, expert systems, sports support, and security surveillance. Real-time bodily activity tracking is currently feasible because to the quick improvements in sensor technology.…”

Section: Introductionmentioning

confidence: 99%

Wearable Sensors for Evaluation Over Smart Home Using Sequential Minimization Optimization-based Random Forest

Gupta

Janani

et al. 2022

Int. j. commun. netw. inf. secur.

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In our everyday life records, human activity identification utilizing MotionNode sensors is becoming more and more prominent. A difficult issue in ubiquitous computing and HCI is providing reliable data on human actions and behaviors. In this study, we put forward a practical methodology for incorporating statistical data into Sequential Minimization Optimization-based random forests. In order to extract useful features, we first prepared a 1-Dimensional Hadamard transform wavelet and a 1-Dimensional Local Binary Pattern-dependent extraction technique. Over two benchmark datasets, the University of Southern California-Human Activities Dataset, and the IM-Sporting Behaviors datasets, we employed sequential minimum optimization together with Random Forest to classify activities. Experimental findings demonstrate that our suggested model may successfully be utilized to identify strong human actions for matters related to efficiency and accuracy, and may challenge with existing cutting-edge approaches.

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Section: Introductionmentioning

confidence: 99%