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(searched for: doi:10.22362/ijcert/2016/v3/i10/48906)
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Published: 26 September 2021
by MDPI
Journal: Sensors
Sensors, Volume 21; https://doi.org/10.3390/s21196434

Abstract:
The study of human activity recognition (HAR) plays an important role in many areas such as healthcare, entertainment, sports, and smart homes. With the development of wearable electronics and wireless communication technologies, activity recognition using inertial sensors from ubiquitous smart mobile devices has drawn wide attention and become a research hotspot. Before recognition, the sensor signals are typically preprocessed and segmented, and then representative features are extracted and selected based on them. Considering the issues of limited resources of wearable devices and the curse of dimensionality, it is vital to generate the best feature combination which maximizes the performance and efficiency of the following mapping from feature subsets to activities. In this paper, we propose to integrate bee swarm optimization (BSO) with a deep Q-network to perform feature selection and present a hybrid feature selection methodology, BAROQUE, on basis of these two schemes. Following the wrapper approach, BAROQUE leverages the appealing properties from BSO and the multi-agent deep Q-network (DQN) to determine feature subsets and adopts a classifier to evaluate these solutions. In BAROQUE, the BSO is employed to strike a balance between exploitation and exploration for the search of feature space, while the DQN takes advantage of the merits of reinforcement learning to make the local search process more adaptive and more efficient. Extensive experiments were conducted on some benchmark datasets collected by smartphones or smartwatches, and the metrics were compared with those of BSO, DQN, and some other previously published methods. The results show that BAROQUE achieves an accuracy of 98.41% for the UCI-HAR dataset and takes less time to converge to a good solution than other methods, such as CFS, SFFS, and Relief-F, yielding quite promising results in terms of accuracy and efficiency.
Rosa Andrie Asmara, Indrazno Siradjuddin, Nofrian Hendrawan
Abstract:
Development of Internet of Things (IoT) devices become popular to make it easier for people to recognize activity from wireless devices. Activity recognition has been widely used at various levels of computing. Smartwatch is one of IoT wearable devices used by researchers since its advantage for open source Human Activity Recognition (HAR) programming usage. Smartwatch in many published articles uses two sensors to accomplish HAR, which are accelerometer and gyroscope. However, the data obtained from the two sensors still too many restrictions in detecting sports activities such as basketball, football, and many more activities having an extreme movement. Moreover, previous experiments evaluate the impact caused by combining another sensor to get more precise of the activity recognition accuracy. Samsung Smartwatch Gear S3 has an audio sensor data that can be obtained from devices and have a promising result to improve recognition accuracy. This research proposed recognition accuracy by combining Accelerometer, Gyroscope, and Audio sensor to achieve improving accuracy from 69% become around 90% extreme movement recognition accuracy. The experiments show that Human Activity Recognition proposed is capable to detect Basketball activities on the Samsung Gear S3 smartwatch.
Published: 26 November 2018
by MDPI
Journal: Sensors
Sensors, Volume 18; https://doi.org/10.3390/s18124132

Abstract:
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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