SPEED: An Inhabitant Activity Prediction Algorithm for Smart Homes

Alam, Muhammad Raisul; Reaz, Mamun Bin Ibne; Ali, Mohd

doi:10.1109/tsmca.2011.2173568

Cited by 85 publications

(59 citation statements)

References 11 publications

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“…The majority have focused on the single-user environment, particularly AAL and ADL routines. The research in [27] addressed a Markov model-based method derived from prediction by partial matching (PPM) that selects the most probable future activity. Because of the limitations based on simple sensor data, this method was modified to improve the accuracy by adding a time component [22]; however, the resulting study was limited because it considered only one specific situation: one person in one room.…”

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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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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“…Some recent works involve using variable-order Markov models for predicting the behavior of drivers in vehicular networks, such as in the work from Xue et al [26]; predicting the future location of mobile users, as in the work from Katsaros & Manolopoulos [15] or applying Markov-based models to the prediction of the inhabitants' activity in smart ubiquitous homes, as proposed by Kang et al [14] or Alam et al [1]. Also, an attempt to build a domainindependent predictor of user intention based on Markov model and considering fixed attributes from users is described by Antwarg et al [2].…”

Section: State Of the Artmentioning

confidence: 99%

Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study

Gómez¹,

Albacete²,

Merrero³

et al. 2016

IJIMAI

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-This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM) to build a probabilistic model that is able to use the historic behavior of gamers and to infer what will be their next actions. Being able to predict with accuracy the next user's actions can be of special interest to learn from the behavior of gamers, to make them more engaged and to reduce churn rate. In order to support a big volume and velocity of data, the system is built on top of the Hadoop ecosystem, using HBase for real-time processing; and the prediction tool is provided as a service (SaaS) and accessible through a RESTful API. The prediction system is evaluated using a case of study with two commercial videogames, attaining promising results with high prediction accuracies.

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“…Many works have also addressed the prediction of what an inhabitant will do in the near future, in order to enable planning and scheduling of services ahead of time [4,15], for instance, to implement energy-efficient control of appliances [16,17]. Typically, activity recognition solutions rely on static sensors placed throughout an environment [15,[18][19][20][21], but more recently frameworks have been produced to recognize human activities with limited guarantees about placement, nature and run-time availability of sensors, including static and wearable ones [22,23]. However, the strict computational and energy constraints imposed by WSN-based environments have constituted a major obstacle to translating the full potential benefits of these results in robotic ecologies.…”

Section: Learningmentioning

confidence: 99%

“…We have extended the test-bed with one Turtlebot 14 mobile robot and a IEEE802.15.46 compliant wireless sensor network. We employ motes based on the original open-source TelosB platform 15 . In addition, the user wears a bracelet carrying a mote providing radio signal strength (RSS) data.…”

Section: The Homelab Test-bedmentioning

confidence: 99%

Robotic Ubiquitous Cognitive Ecology for Smart Homes

Amato

Bacciu

Broxvall

et al. 2015

J Intell Robot Syst

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Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agentbased control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.

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SPEED: An Inhabitant Activity Prediction Algorithm for Smart Homes

Cited by 85 publications

References 11 publications

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

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

Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study

Robotic Ubiquitous Cognitive Ecology for Smart Homes

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