Human Activity Recognition Using Smartphone and Smartwatch

Ali, Hamid; Muslim, Ali M.

doi:10.22362/ijcert/2016/v3/i10/48906

Cited by 8 publications

(7 citation statements)

References 1 publication

(1 reference statement)

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“…Ali M. Muslim et al [44] presented a new approach that incorporated a smart watch, fastened to the human's ankle, and another smartphone freely carried by the user. They used threshold-based mechanism to classify 20 different human activities.…”

Section: Wu Et Al[32b]mentioning

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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Section: Wu Et Al[32b]mentioning

confidence: 99%

Survey on Human Activity Recognition using Smartphone

Kh¹,

Ibrahim²

2021

AL-Rafidain Journal of Computer Sciences and Mathematics

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“…Current smartphones are equipped with integrated GPS, making it exceptionally advantageous for context-aware applications such as the recognition of the individual’s transportation mode [37]. Accelerometers can be categorized into two types: either as body wearable sensors or incorporated with new models of mobile devices such as smartphones and smart watches which record the body movement [38,39]. The majority of research work in HAR applications is performed using wearable sensors.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors

Rahim

Elamvazuthi

Izhar

et al. 2018

Sensors

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Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier.

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“…This technique involves the readings controlled by inertial sensors on the device, which could then display incorrect results. Smartwatches that are worn in a consistent location [8], tackle this restriction.…”

Section: Introductionmentioning

confidence: 99%

Smartphone and Smartwatch for Human Activity Recognition

Amer¹

2021

AABSc

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Human activity recognition (HAR) systems are developed as aspect of a model to allow continual assessment of human behaviors in IoT environments in the areas of ambient assisted living, sports injury detection, elderly care, rehabilitation, and entertainment and close monitoring. Smartphones are already used to recognize activity. Most of the research done in this field placed a restriction on fixing the smartphone securely in a certain location on the human body, along with the machine learning system, to promote the process of classifying raw data from smartphone sensors to human activities. Smartwatches solve this limitation by placing them in a consistent position, which becomes steady and precisely sensitive to body movements. For this experiment, we evaluate both the accelerometer and the gyroscope sensor on the smartphone and the smartwatch, and decide which sensors hybrid does superiorly. Five daily physical human activities are evaluated using five classifiers from WEKA, in addition to Artificial Neural Network (ANN), K- Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms builtin MATLAB 2018a. We used confusion matrix and random simulation to compare the accuracy and efficiency of those models. The results showed that the accelerometer sensors combination has the highest accuracy among other combinations and achieved an overall accuracy of 97.7% using SVM that gives the best performance among all other classifiers.

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Human Activity Recognition Using Smartphone and Smartwatch

Cited by 8 publications

References 1 publication

Survey on Human Activity Recognition using Smartphone

Survey on Human Activity Recognition using Smartphone

Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors

Smartphone and Smartwatch for Human Activity Recognition

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