Aroma

Peng, Liangying; Chen, Ling; Ye, Zhenan; Zhang, Yi

doi:10.1145/3214277

Cited by 103 publications

(24 citation statements)

References 24 publications

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“…Secondly, the Opportunity dataset contains manipulative gestures of short duration such as opening and closing, of Doors, Dishwasher, and Drawers. These were collected for four subjects who were equipped with five different body attached sensors for the tracking of static and dynamic activities [38]. Due to the involvement of several sensors, data transmission problems among wireless sensors lead to segments of data being missed represented by Null.…”

Section: Data Descriptionmentioning

confidence: 99%

SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition

Razzaq

Cleland

Nugent

et al. 2020

Sensors

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The recognition of activities of daily living (ADL) in smart environments is a well-known and an important research area, which presents the real-time state of humans in pervasive computing. The process of recognizing human activities generally involves deploying a set of obtrusive and unobtrusive sensors, pre-processing the raw data, and building classification models using machine learning (ML) algorithms. Integrating data from multiple sensors is a challenging task due to dynamic nature of data sources. This is further complicated due to semantic and syntactic differences in these data sources. These differences become even more complex if the data generated is imperfect, which ultimately has a direct impact on its usefulness in yielding an accurate classifier. In this study, we propose a semantic imputation framework to improve the quality of sensor data using ontology-based semantic similarity learning. This is achieved by identifying semantic correlations among sensor events through SPARQL queries, and by performing a time-series longitudinal imputation. Furthermore, we applied deep learning (DL) based artificial neural network (ANN) on public datasets to demonstrate the applicability and validity of the proposed approach. The results showed a higher accuracy with semantically imputed datasets using ANN. We also presented a detailed comparative analysis, comparing the results with the state-of-the-art from the literature. We found that our semantic imputed datasets improved the classification accuracy with 95.78% as a higher one thus proving the effectiveness and robustness of learned models.

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Section: Data Descriptionmentioning

confidence: 99%

SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition

Razzaq

Cleland

Nugent

et al. 2020

Sensors

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“…There has been a rich array of recent work on modeling human activities using sensors and deep learning [13,14,49,50]. These efforts range in applications from health to activities of daily living and employ a wide variety of deep learning approaches, including deep neural networks (DNN), convolutional neural networks (CNN), autoencoders, restricted Boltzmann Machines, restricted neural networks (RNN), and long-short term memory (LSTM).…”

Section: Using Phone Logs and Machine Learning To Understand Individuals And Relationshipsmentioning

confidence: 99%

“…To make this inference of trusted ties, we use the recently emerging direction of using deep learning approaches for sensor and ubiquitous data [12][13][14][15]. However, while current deep learning architectures are typically well-designed to handle low-level neighborhood notions within an entity of interest (e.g., neighboring pixels within an image or the next Bluetooth reading within a stream), they typically do not consider the inter-entity notions of a neighborhood.…”

Section: Introductionmentioning

confidence: 99%

NADAL: A Neighbor-Aware Deep Learning Approach for Inferring Interpersonal Trust Using Smartphone Data

Bati

Singh

2020

Computers

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Interpersonal trust mediates multiple socio-technical systems and has implications for personal and societal well-being. Consequently, it is crucial to devise novel machine learning methods to infer interpersonal trust automatically using mobile sensor-based behavioral data. Considering that social relationships are often affected by neighboring relationships within the same network, this work proposes using a novel neighbor-aware deep learning architecture (NADAL) to enhance the inference of interpersonal trust scores. Based on analysis of call, SMS, and Bluetooth interaction data from a one-year field study involving 130 participants, we report that: (1) adding information about neighboring relationships improves trust score prediction in both shallow and deep learning approaches; and (2) a custom-designed neighbor-aware deep learning architecture outperforms a baseline feature concatenation based deep learning approach. The results obtained at interpersonal trust prediction are promising and have multiple implications for trust-aware applications in the emerging social internet of things.

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“…In their models, each task corresponds to a specific person. Peng et al [41] developed a model to recognize complex human activity based on MTL, which leverages the classical HAR as a related task to complex HAR. By using the representation learned from classical HAR as a low-level shared feature, state-of-the-art results of complex HAR are achieved.…”

Section: Multi-task Learning In Human Activity Recognitionmentioning

confidence: 99%

Context-Aware Human Activity and Smartphone Position-Mining with Motion Sensors

et al. 2019

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Today's smartphones are equipped with embedded sensors, such as accelerometers and gyroscopes, which have enabled a variety of measurements and recognition tasks. In this paper, we jointly investigate two types of recognition problems in a joint manner, e.g., human activity recognition and smartphone on-body position recognition, in order to enable more robust context-aware applications. So far, these two problems have been studied separately without considering the interactions between each other. In this study, by first applying a novel data preprocessing technique, we propose a joint recognition framework based on the multi-task learning strategy, which can reduce computational demand, better exploit complementary information between the two recognition tasks, and lead to higher recognition performance. We also extend the joint recognition framework so that additional information, such as user identification with biometric motion analysis, can be offered. We evaluate our work systematically and comprehensively on two datasets with real-world settings. Our joint recognition model achieves the promising performance of 0.9174 in terms of F1-score for user identification on the benchmark RealWorld Human Activity Recognition (HAR) dataset. On the other hand, in comparison with the conventional approach, the proposed joint model is shown to be able to improve human activity recognition and position recognition by 5.1% and 9.6% respectively.

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Aroma

Cited by 103 publications

References 24 publications

SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition

SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition

NADAL: A Neighbor-Aware Deep Learning Approach for Inferring Interpersonal Trust Using Smartphone Data

Context-Aware Human Activity and Smartphone Position-Mining with Motion Sensors

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