Comparison of offline and real-time human activity recognition results using machine learning techniques

Sütő, József; Oniga, Stefan; Lung, Claudiu; Orha, Ioan

doi:10.1007/s00521-018-3437-x

Cited by 43 publications

(21 citation statements)

References 35 publications

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“…MLP is made up of the input layer, one (or more) hidden layer and the output layer [88]. Used in HAR as baseline for deep learning techniques, it has been often compared with machine learning, such as SVM [48,89], RF [48], k-NN [89], DT [89], and deep learning techniques, LSTM [90], CNN [89,90].…”

Section: Traditional Deep Learningmentioning

confidence: 99%

Trends in human activity recognition using smartphones

Ferrari

Micucci

Mobilio

et al. 2021

J Reliable Intell Environ

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Recognizing human activities and monitoring population behavior are fundamental needs of our society. Population security, crowd surveillance, healthcare support and living assistance, and lifestyle and behavior tracking are some of the main applications that require the recognition of human activities. Over the past few decades, researchers have investigated techniques that can automatically recognize human activities. This line of research is commonly known as Human Activity Recognition (HAR). HAR involves many tasks: from signals acquisition to activity classification. The tasks involved are not simple and often require dedicated hardware, sophisticated engineering, and computational and statistical techniques for data preprocessing and analysis. Over the years, different techniques have been tested and different solutions have been proposed to achieve a classification process that provides reliable results. This survey presents the most recent solutions proposed for each task in the human activity classification process, that is, acquisition, preprocessing, data segmentation, feature extraction, and classification. Solutions are analyzed by emphasizing their strengths and weaknesses. For completeness, the survey also presents the metrics commonly used to evaluate the goodness of a classifier and the datasets of inertial signals from smartphones that are mostly used in the evaluation phase.

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Section: Traditional Deep Learningmentioning

confidence: 99%

Trends in human activity recognition using smartphones

Ferrari

Micucci

Mobilio

et al. 2021

J Reliable Intell Environ

View full text Add to dashboard Cite

show abstract

“…At the same time as smartphones are becoming an incorporated part of the human's daily life for its ability to do intricate computation, internet connection and include vast quantities of hardware sensors. Therefore, they can provide new opportunities in the HAR research [8][9][10]. Putting in plain terms, it includes the use of diverse sensing technologies to collect and categorize user activities in different domains, starting from medical applications, home monitoring & assisted living, to sports and leisure applications [11][12][13][14][15].…”

Section: Recognition Of Human Activity Using Smartphone Sensorsmentioning

confidence: 99%

Survey on Human Activity Recognition using Smartphone

Kh¹,

Ibrahim²

2021

AL-Rafidain Journal of Computer Sciences and Mathematics

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The field of Human Activity Recognition (HAR) is an active research field in which methods are being developed to understand human behavior by interpreting features obtained from various sources, these activities can be recognized using interactive sensors that are affected by human movement. Sensor can embed elements within Smartphones or Personal Digital Assistants (PDAs). The great increase in smart phone users and the increase in the sensor ability of these smart phones, and users usually carry their smartphones with them. This fact makes HAR more important and accepted.In this survey, A number of previous studies were studied and analyzed, where we prepared a comparison of the research works conducted over the period 2010-2020 in human activity recognition using Smartphone sensors. Comparison charts highlight their most important aspects such as a type of sensor used, activities, sensor placement, HAR-system type (offline, online), computing device, classifier (type of algorithms) and system accuracy levels.

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“…In that study, KTH was used and the highest accuracy rate was obtained as 93.4%. In addition to these studies, there are many different studies [42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61] related to the HAR, because, although the HAR task is quite common, it still needs to be further developed.…”

Section: Related Workmentioning

confidence: 99%

Human action recognition with bag of visual words using different machine learning methods and hyperparameter optimization

Aslan

Durdu

Sabancı

2019

Neural Comput & Applic

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Human activity recognition (HAR) has quite a wide range of applications. Due to its widespread use, new studies have been developed to improve the HAR performance. In this study, HAR is carried out using the commonly preferred KTH and Weizmann dataset, as well as a dataset which we created. Speeded up robust features (SURF) are used to extract features from these datasets. These features are reinforced with bag of visual words (BoVW). Different from the studies in the literature that use similar methods, SURF descriptors are extracted from binary images as well as grayscale images. Moreover, four different machine learning (ML) methods such as k-nearest neighbors, decision tree, support vector machine and naive Bayes are used for classification of BoVW features. Hyperparameter optimization is used to set the hyperparameters of these ML methods. As a result, ML methods are compared with each other through a comparison with the activity recognition performances of binary and grayscale image features. The results show that if the contrast of the environment decreases when a human enters the frame, the SURF of the binary image are more effective than the SURF of the gray image for HAR. Keywords Human activity recognition Á Image processing Á Speeded up robust features Á Bag of visual words Á Machine learning Á k-Nearest neighbors Á Decision tree Á Support vector machine Á Naive Bayes Á Hyperparameter optimization

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Comparison of offline and real-time human activity recognition results using machine learning techniques

Cited by 43 publications

References 35 publications

Trends in human activity recognition using smartphones

Trends in human activity recognition using smartphones

Survey on Human Activity Recognition using Smartphone

Human action recognition with bag of visual words using different machine learning methods and hyperparameter optimization

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