Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments

Kamoen, Naomi; Nugent, Chris; Zhang, Shuai; Wang, Hui; Ng, Wing W. Y.

doi:10.3390/s20010216

Cited by 59 publications

(33 citation statements)

References 74 publications

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“…Of course, the improved accuracy using the four first molars indicates that CNNs learn partially independent information from the four different first molars, even for only one person. The improved performance upon majority voting may indicate that each individual prediction provides partially independent information 44 , 45 . Even within an individual, teeth in different locations and of different sizes and shapes can provide independent information.…”

Section: Discussionmentioning

confidence: 99%

“…First, for a wide range of age determination (20 y intervals, three groups in total), the accuracy of the AI model was analyzed. The young adults' group, which had the largest number of samples, was further divided into three subgroups (ages 20-29, ages 30-39, and ages [40][41][42][43][44][45][46][47][48][49]. Therefore, we compared the results obtained by dividing the participants into three age groups, and the results obtained by subdividing the young adults into further groups, thereby generating a total of five age groups.…”

Section: Methodsmentioning

confidence: 99%

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Age-group determination of living individuals using first molar images based on artificial intelligence

Kim

Lee

Noh

et al. 2021

Sci Rep

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Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs. The accuracy of the tooth-wise estimation was 89.05 to 90.27%. Performance accuracy was evaluated mainly using a majority voting system and area under curve (AUC) scores. The AUC scores ranged from 0.94 to 0.98 for all age groups, which indicates outstanding capacity. The learned features of CNNs were visualized as a heatmap, and revealed that CNNs focus on differentiated anatomical parameters, including tooth pulp, alveolar bone level, or interdental space, depending on the age and location of the tooth. With this, we provided a deeper understanding of the most informative regions distinguished by age groups. The prediction accuracy and heat map analyses support that this AI-based age-group determination model is plausible and useful.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Age-group determination of living individuals using first molar images based on artificial intelligence

Kim

Lee

Noh

et al. 2021

Sci Rep

View full text Add to dashboard Cite

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“…Abedin et al [72] 57. 19 Fang et al [73] 79.24 Maitre et al [74] 84.89 Rasnayaka et al [75] 85 O'Halloran et al [76] 90. 55 Sun et al [77] 88 Tahir et al [23] 90.91 Badawi et al [25] 88 Masum et al [78] 91.…”

Section: Methods Accuracy Using Mhealth (%) Methods Accuracy Using Hugamentioning

confidence: 99%

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

2021

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The daily life-log routines of elderly individuals are susceptible to numerous complications in their physical healthcare patterns. Some of these complications can cause injuries, followed by extensive and expensive recovery stages. It is important to identify physical healthcare patterns that can describe and convey the exact state of an individual’s physical health while they perform their daily life activities. In this paper, we propose a novel Sustainable Physical Healthcare Pattern Recognition (SPHR) approach using a hybrid features model that is capable of distinguishing multiple physical activities based on a multiple wearable sensors system. Initially, we acquired raw data from well-known datasets, i.e., mobile health and human gait databases comprised of multiple human activities. The proposed strategy includes data pre-processing, hybrid feature detection, and feature-to-feature fusion and reduction, followed by codebook generation and classification, which can recognize sustainable physical healthcare patterns. Feature-to-feature fusion unites the cues from all of the sensors, and Gaussian mixture models are used for the codebook generation. For the classification, we recommend deep belief networks with restricted Boltzmann machines for five hidden layers. Finally, the results are compared with state-of-the-art techniques in order to demonstrate significant improvements in accuracy for physical healthcare pattern recognition. The experiments show that the proposed architecture attained improved accuracy rates for both datasets, and that it represents a significant sustainable physical healthcare pattern recognition (SPHR) approach. The anticipated system has potential for use in human–machine interaction domains such as continuous movement recognition, pattern-based surveillance, mobility assistance, and robot control systems.

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“…Besides probabilistic models, the ANN, which has the ability to implicitly detect complex non-linear relationships between data and their classifications, is also widely used in home-based activity recognition [32][33][34]. Gochoo et al proposed a deep convolutional neural network (DCNN) classification approach to detect four basic activity classes [35].…”

Section: Related Workmentioning

confidence: 99%

Radial Basis Function Neural Network with Localized Stochastic-Sensitive Autoencoder for Home-Based Activity Recognition

Wang

et al. 2020

Sensors

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Over the past few years, the Internet of Things (IoT) has been greatly developed with one instance being smart home devices gradually entering into people's lives. To maximize the impact of such deployments, home-based activity recognition is required to initially recognize behaviors within smart home environments and to use this information to provide better health and social care services. Activity recognition has the ability to recognize people's activities from the information about their interaction with the environment collected by sensors embedded within the home. In this paper, binary data collected by anonymous binary sensors such as pressure sensors, contact sensors, passive infrared sensors etc. are used to recognize activities. A radial basis function neural network (RBFNN) with localized stochastic-sensitive autoencoder (LiSSA) method is proposed for the purposes of home-based activity recognition. An autoencoder (AE) is introduced to extract useful features from the binary sensor data by converting binary inputs into continuous inputs to extract increased levels of hidden information. The generalization capability of the proposed method is enhanced by minimizing both the training error and the stochastic sensitivity measure in an attempt to improve the ability of the classifier to tolerate uncertainties in the sensor data. Four binary home-based activity recognition datasets including OrdonezA, OrdonezB, Ulster, and activities of daily living data from van Kasteren (vanKasterenADL) are used to evaluate the effectiveness of the proposed method. Compared with well-known benchmarking approaches including support vector machine (SVM), multilayer perceptron neural network (MLPNN), random forest and an RBFNN-based method, the proposed method yielded the best performance with 98.35%, 86.26%, 96.31%, 92.31% accuracy on four datasets, respectively. which can be used to underpin a multitude of services such as personal health management [2], elderly care services [3], smart home services [4,5], to name but a few. However, it is a complicated process with many unsolved challenges such as multiple occupancy, interleaved activities, incomplete sensor data and differences in inter-and intra-inhabitant behaviors. As a result, due to the diversity of its data sources (smart home, smart wearable devices, mobile phones sensors and cameras), various solutions have been proposed to solve this problem [6].Some research studies in the area of activity recognition have been based on image data collected by cameras [7][8][9][10]. Limited by computational complexity and privacy issues, this type of activity recognition is mainly used in the field of public safety and human-computer interaction. As an alternative to image-based data, some research has been based on data collected from wearable devices and smartphones [11][12][13]. In this paper, we focus on home-based activity recognition, which is mainly used in elderly care service or smart home service provision. Binary data were collected through binary sensors embedded ...

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Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments

Cited by 59 publications

References 74 publications

Age-group determination of living individuals using first molar images based on artificial intelligence

Age-group determination of living individuals using first molar images based on artificial intelligence

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

Radial Basis Function Neural Network with Localized Stochastic-Sensitive Autoencoder for Home-Based Activity Recognition

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