Demultiplexing activities of daily living in IoT enabled smarthomes

Kodeswaran, Palanivel; Kokku, Ravi; Mallick, Madhumita; Sen, Souvik

doi:10.1109/infocom.2016.7524359

Cited by 29 publications

(13 citation statements)

References 11 publications

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“…Deploying autonomous systems for enabling or enhancing tasks -especially in support of human activities has become a reality [21], [22]. In such situations, autonomous systems execute specific tasks in a collaborative manner, reducing the cognitive burden on human users.…”

Section: A Autonomous Systemsmentioning

confidence: 99%

Generative Policies for Coalition Systems - A Symbolic Learning Framework

Bertino

White²,

Lobo

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

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Policy systems are critical for managing missions and collaborative activities carried out by coalitions involving different organizations. Conventional policy-based management approaches are not suitable for next-generation coalitions that will involve not only humans, but also autonomous computing devices and systems. It is critical that those parties be able to generate and customize policies based on contexts and activities. This paper introduces a novel approach for the autonomic generation of policies by autonomous parties. The framework combines context free grammars, answer set programs, and inductionbased learning. It allows a party to generate its own policies, based on a grammar and some semantic constraints, by learning from examples. The paper also outlines initial experiments in the use of such a symbolic approach and outlines relevant research challenges, ranging from explainability to quality assessment of policies.

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Section: A Autonomous Systemsmentioning

confidence: 99%

Generative Policies for Coalition Systems - A Symbolic Learning Framework

Bertino

White²,

Lobo

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

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“…Over the last decade, the field of activity recognition has received significant attention [12], [16], [18], [20], [32], [34]. For further studies, the activity prediction field has attracted serious consideration and has been approached with various technologies, including sensors, wearable devices, mobile devices, images and video [5], [19], [21], [28].…”

Section: Related Workmentioning

confidence: 99%

Next-Activity Set Prediction Based on Sequence Partitioning to Reduce Activity Pattern Complexity in the Multi-User Smart Space

Kim

Lee

2017

IEICE Trans. Inf. & Syst.

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SUMMARYHuman activity prediction has become a prerequisite for service recommendation and anomaly detection systems in a smart space including ambient assisted living (AAL) and activities of daily living (ADL). In this paper, we present a novel approach to predict the nextactivity set in a multi-user smart space. Differing from the majority of the previous studies considering single-user activity patterns, our study considers multi-user activities that occur with a large variety of patterns. Its complexity increases exponentially according to the number of users. In the multi-user smart space, there can be inevitably multiple next-activity candidates after multi-user activities occur. To solve the next-activity problem in a multi-user situation, we propose activity set prediction rather than one activity prediction. We also propose activity sequence partitioning to reduce the complexity of the multi-user activity pattern. This divides an activity sequence into start, ongoing, and finish zones based on the features in the tendency of activity occurrences. The majority of the activities in a multi-user environment occur at the beginning or end, rather than the middle, of an activity sequence. Furthermore, the types of activities typically occurring in each zone can be sufficiently distinguishable. Exploiting these characteristics, we suggest a two-step procedure to predict the nextactivity set utilizing a long short-term memory (LSTM) model. The first step identifies the zones to which current activities belong. In the next step, we construct three different LSTM models to predict the next-activity set in each zone. To evaluate the proposed approach, we experimented using a real dataset generated from our campus testbed. Our experiments confirmed the complexity reduction and high accuracy in the next-activity set prediction. Thus, it can be effectively utilized for various applications with context-awareness in a multi-user smart space. key words: activity prediction, sequence partitioning, multi-user smart space, LSTM

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“…It is, however, necessary to classify daily activities such as cooking, eating, and working to realize various applications. Daily human activities were identified in an IoT-enabled smart home equipped with a variety of sensors in a previous study [12]. An activity is characterized by a combination of sensing data obtained from multiple sensors.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

Kwon

Choi

2018

Wireless Communications and Mobile Computing

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Human activity recognition using wearable devices has been actively investigated in a wide range of applications. Most of them, however, either focus on simple activities wherein whole body movement is involved or require a variety of sensors to identify daily activities. In this study, we propose a human activity recognition system that collects data from an off-the-shelf smartwatch and uses an artificial neural network for classification. The proposed system is further enhanced using location information. We consider 11 activities, including both simple and daily activities. Experimental results show that various activities can be classified with an accuracy of 95%.

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Demultiplexing activities of daily living in IoT enabled smarthomes

Cited by 29 publications

References 11 publications

Generative Policies for Coalition Systems - A Symbolic Learning Framework

Generative Policies for Coalition Systems - A Symbolic Learning Framework

Next-Activity Set Prediction Based on Sequence Partitioning to Reduce Activity Pattern Complexity in the Multi-User Smart Space

Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

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