Human Activity Tracking by Mobile Phones Through Hebbian Learning

Morales, Jafet; Akopian, David

doi:10.5121/ijaia.2016.7601

Cited by 3 publications

(2 citation statements)

References 13 publications

(47 reference statements)

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“…Then, principal component analysis (PCA) that obtains orientation-invariant motion signals was used to classify activities with different device orientations. In addition, a method of Hebbian Learning (Morales and Akopian, 2016) renders the sensor signals independent to orientation of the smartphone and achieves high accuracy of 95.3 per cent.…”

Section: Activity Data Collection and Preprocessmentioning

confidence: 99%

An overview of human activity recognition based on smartphone

Yuan

Wang

Meng

et al. 2019

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Purpose Currently, ubiquitous smartphones embedded with various sensors provide a convenient way to collect raw sequence data. These data bridges the gap between human activity and multiple sensors. Human activity recognition has been widely used in quite a lot of aspects in our daily life, such as medical security, personal safety, living assistance and so on. Design/methodology/approach To provide an overview, the authors survey and summarize some important technologies and involved key issues of human activity recognition, including activity categorization, feature engineering as well as typical algorithms presented in recent years. In this paper, the authors first introduce the character of embedded sensors and dsiscuss their features, as well as survey some data labeling strategies to get ground truth label. Then, following the process of human activity recognition, the authors discuss the methods and techniques of raw data preprocessing and feature extraction, and summarize some popular algorithms used in model training and activity recognizing. Third, they introduce some interesting application scenarios of human activity recognition and provide some available data sets as ground truth data to validate proposed algorithms. Findings The authors summarize their viewpoints on human activity recognition, discuss the main challenges and point out some potential research directions. Originality/value It is hoped that this work will serve as the steppingstone for those interested in advancing human activity recognition.

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Section: Activity Data Collection and Preprocessmentioning

confidence: 99%

An overview of human activity recognition based on smartphone

Yuan

Wang

Meng

et al. 2019

View full text Add to dashboard Cite

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“…Accessing data-In current data collection platforms, both participant and researcher can access the participant's data. The data comes from data collection modules which gather data from internet-connected sensors and sensors (e.g., motion sensors) attached to personal computing devices such as tablets, smartphones, and computers [88,89]. However, in future platforms the data may also be extracted from educational, gaming, and messaging components.…”

Section: 27mentioning

confidence: 99%

Technology-based health promotion: Current state and perspectives in emerging gig economy

Morales

Inupakutika

Kaghyan

et al. 2019

Biocybernetics and Biomedical Engineering

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It has been a decade since smartphone application stores started allowing developers to post their own applications. This paper presents a narrative review on the state-of-the-art and the future of technology used by researchers in the field of mobile health promotion. Researchers build high cost, complex systems with the purpose of promoting health and collecting data. These systems promote health by using a feedback component that "educates" the subject. Other researchers instead use platforms which provide them with data collected by others, which allows for no communication with subjects, but may be cheaper than building a system to collect the data. This second type of systems cannot be used directly for health promotion. However, both types of systems are relevant to the field of health promotion, because they are precursors to a third type of systems that are emerging, the gig economy systems for mobile health data collection, which are low cost, globally available, and provide limited communication with subjects. If such systems evolve to include more channels for communication with the data-generating subjects, and also bring developers into the economy, they may eventually revolutionize the field of mobile health promotion and data collection by giving researchers new capabilities, such as the ability to replicate existing health promotion campaigns with the click of a button and the appropriate licenses. In this paper we present a review of state-of-the-art systems for mobile health promotion and data collection and a model for what these systems may look like in the future.

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An Application of Convolutional Neural Networks on Human Intention Prediction

Zhang¹,

Li²,

Xiong³

et al. 2019

IJAIA

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Due to the rapidly increasing need of human-robot interaction (HRI), more intelligent robots are in demand. However, the vast majority of robots can only follow strict instructions, which seriously restricts their flexibility and versatility. A critical fact that strongly negates the experience of HRI is that robots cannot understand human intentions. This study aims at improving the robotic intelligence by training it to understand human intentions. Different from previous studies that recognizing human intentions from distinctive actions, this paper introduces a method to predict human intentions before a single action is completed. The experiment of throwing a ball towards designated targets are conducted to verify the effectiveness of the method. The proposed deep learning based method proves the feasibility of applying convolutional neural networks (CNN) under a novel circumstance. Experiment results show that the proposed CNN-vote method out competes three traditional machine learning techniques. In current context, the CNN-vote predictor achieves the highest testing accuracy with relatively less data needed.

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Human Activity Tracking by Mobile Phones Through Hebbian Learning

Abstract: ABSTRACT

Cited by 3 publications

References 13 publications

An overview of human activity recognition based on smartphone

An overview of human activity recognition based on smartphone

Technology-based health promotion: Current state and perspectives in emerging gig economy

An Application of Convolutional Neural Networks on Human Intention Prediction

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