Context Feature Learning through Deep Learning for Adaptive Context-Aware Decision Making in the Home

Brenon, Alexis; Portet, François; Vacher, Michel

doi:10.1109/ie.2018.00013

Cited by 7 publications

(5 citation statements)

References 35 publications

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“…The study case concluded that the control response time was decreased with 6.98 ms compared to other architectures, while the precision was kept at 95.14%. On the other hand, two works to enhance comfort and home automation based on voice control are presented in [91], [92]. They use different classification algorithms of context data.…”

Section: Urban Planningmentioning

confidence: 99%

“…The decisive context information is defined as activity, time and location after the processing of acquired data (speech, water consumption, lamp status, temperature and human presence sensor). In contrast, the application from [92] employs Deep Q Network (DQN) for context inference, any time the environment context changes. Thus, a graphical representation projects data on a two-dimensional map of the smart-home, integrating the context data from many heterogeneous sensors (e.g., temperature, contact-door and speech) into a unique image.…”

Section: Urban Planningmentioning

confidence: 99%

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Context Aware Control Systems: An Engineering Applications Perspective

et al. 2020

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Cyber-physical systems revolve around context awareness, empowering objective-oriented services, products and operations based on real data. Self-aware and self-control systems are core elements in the Industry 4.0 framework towards self-sustainable adaptive manufacturing and personalized services. This development is witnessed by the context-aware pervasive assistance to users and machines in decisions making process for optimizing product performance and economic yield. While integration of the virtual and the physical world entails smart sensors communication and complex data analytics, it relies on artificial intelligence tools to manage process operations. The objective of the article is to create awareness that systems & control community must address theoretical and practical aspects from a larger perspective. Context aware control is emerging as a natural solution to maximize the use of available sensing instrumentation and the relatively low cost data logging, i.e. an important source for extracting information, interpreting and using context information and adapt its functionality to the current context of use. This article presents a concise overview of applications where context aware systems and control methodologies are relevant in the seven societal challenges acknowledged by European policy-makers: Digital Society;

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Section: Urban Planningmentioning

confidence: 99%

Section: Urban Planningmentioning

confidence: 99%

Context Aware Control Systems: An Engineering Applications Perspective

et al. 2020

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“…The activity recognition with deep learning becomes a hot topic recently (e.g., Wang et al [14] and Brenon et al [15]). Although the deep learning is a powerful approach to recognize image data, a huge amount of data is required to build a high-quality model.…”

Section: Related Workmentioning

confidence: 99%

“…One may try to recognize home contexts via image recognition based on deep learning. However, constructing a custom recognition model dedicated for a single house requires a huge amount of labeled datasets and computing resources [14,15]. Thus, there is still a big gap between the research and real life.…”

Section: Introductionmentioning

confidence: 99%

Toward Affordable and Practical Home Context Recognition: –Framework and Implementation with Image-based Cognitive API–

Chen¹,

Saiki²,

Nakamura³

2019

IJNDC

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To provide affordable context recognition for general households, this paper presents a novel technique that integrate imagebased cognitive Application Program Interface (API) and lightweight machine learning. Our key idea is to regard every image as a document by exploiting "tags" derived by the API. We first present a framework that specifies a common workflow of the machine-learning-based home context recognition. We then propose a pragmatic method that implements the framework using the "image-as-a-document" approach.

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“…For this, one may try to recognize home contexts via image recognition based on naive deep learning. However, constructing a custom recognition model dedicated to a single house requires a huge amount of labeled datasets and computing resources [17,18]. It is not only hard to construct a universal recognition model from one house to another, but also the security and privacy issues that come with a large amount of data influence acceptability for the users.…”

Section: Introductionmentioning

confidence: 99%

Integrating Multiple Models Using Image-as-Documents Approach for Recognizing Fine-Grained Home Contexts

Chen

Saiki

Nakamura

2020

Sensors

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To implement fine-grained context recognition that is accurate and affordable for general households, we present a novel technique that integrates multiple image-based cognitive APIs and light-weight machine learning. Our key idea is to regard every image as a document by exploiting "tags" derived by multiple APIs. The aim of this paper is to compare API-based models' performance and improve the recognition accuracy by preserving the affordability for general households. We present a novel method for further improving the recognition accuracy based on multiple cognitive APIs and four modules, fork integration, majority voting, score voting, and range voting.Our interest is to use little image data, applying cognitive services to implement affordable context sensing that can adapt to custom contexts in every single house. The cognitive service is a cloud service with cognitive computing functions that provide the capability to understand multimedia data (i.e., vision (object recognition) [19], speech recognition [20], natural language processing [21], etc.), based on sophisticated machine-learning algorithms powered by the offered big data and large-scale computing. They are offered by cloud companies, but the data processing algorithms behind them are not public. They are also widely used in various fields of research, such as modern knowledge management solutions [22] and criminal detection and recognition [23]. The cognitive API is an application program interface via the HTTP/REST protocol [24], with which developers can easily integrate powerful recognition features in their own applications. An image-based cognitive API receives an image from an external application, i.e., extracts specific information from the image, and returns the information in JavaScript Object Notation (JSON) [25] format from the cloud server rather than the local. The information usually contains a set of words called "tags", representing objects and concepts that the API has recognized in the given image. Examples of tags from the API are: [Living, room, indoors, classroom, basement, supporting structure]. The information of interest and the way of recognizing the image vary among individual cognitive services. Related work uses image tagging technology with deep learning, as in [26][27][28], but the implementation is more complex. In our future realistic implementation, for security and privacy of the users the images are not saved after sending the APIs over.The main contribution of this paper is to present a novel method which is not only affordable but also has higher accuracy in recognizing fine-grained home contexts. For this purpose, we are currently investigating techniques that integrate inexpensive camera devices, multiple image-based cognitive APIs, and light-weight machine learning. We previously encoded the tags of a single API to document vectors, then applied them into machine learning for the model construction [29]. However, we found that the accuracy significantly decreased for contexts with multiple people (e.g., "...

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Context Feature Learning through Deep Learning for Adaptive Context-Aware Decision Making in the Home

Cited by 7 publications

References 35 publications

Context Aware Control Systems: An Engineering Applications Perspective

Context Aware Control Systems: An Engineering Applications Perspective

Toward Affordable and Practical Home Context Recognition: –Framework and Implementation with Image-based Cognitive API–

Integrating Multiple Models Using Image-as-Documents Approach for Recognizing Fine-Grained Home Contexts

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