Learning a taxonomy of predefined and discovered activity patterns

Krishnan, Narayanan C.; Cook, Diane J.; Wemlinger, Zachary

doi:10.3233/ais-130230

Cited by 18 publications

(8 citation statements)

References 51 publications

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“…Given the flexibility of the app in terms of logging and editing entries, as well as evidence in the literature of diverse interpretations of labels [ 57 ], we were interested in the language used for labelling activities. A common approach for exploration of terminology in use is to consider its distribution.…”

Section: Discussionmentioning

confidence: 99%

“…The use of taxonomies (i.e., lists of terms) in user interface design may ‘mask’ ambiguities of this nature—that is, valid terms are used at each point, but inconsistencies may nonetheless exist in participant interpretations of these terms and emerge in later data analysis. In fact, researchers working with sensor data sets from various projects noted that, although they all contained activities with similar connotations, each used slightly different labels [ 57 ]. Those researchers also suggested that differences would also occur across the projects in the data sequences corresponding to similar labels, owing to subjective interpretation of the activities by the annotators.…”

Section: Related Workmentioning

confidence: 99%

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Talk, Text, Tag? Understanding Self-Annotation of Smart Home Data from a User’s Perspective

Tonkin

Burrows

Woznowski

et al. 2018

Sensors

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Delivering effortless interactions and appropriate interventions through pervasive systems requires making sense of multiple streams of sensor data. This is particularly challenging when these concern people’s natural behaviours in the real world. This paper takes a multidisciplinary perspective of annotation and draws on an exploratory study of 12 people, who were encouraged to use a multi-modal annotation app while living in a prototype smart home. Analysis of the app usage data and of semi-structured interviews with the participants revealed strengths and limitations regarding self-annotation in a naturalistic context. Handing control of the annotation process to research participants enabled them to reason about their own data, while generating accounts that were appropriate and acceptable to them. Self-annotation provided participants an opportunity to reflect on themselves and their routines, but it was also a means to express themselves freely and sometimes even a backchannel to communicate playfully with the researchers. However, self-annotation may not be an effective way to capture accurate start and finish times for activities, or location associated with activity information. This paper offers new insights and recommendations for the design of self-annotation tools for deployment in the real world.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Talk, Text, Tag? Understanding Self-Annotation of Smart Home Data from a User’s Perspective

Tonkin

Burrows

Woznowski

et al. 2018

Sensors

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“…Due to their characteristics, SVMs are better in generating other kind of models with a machine learning approach than modeling directly the smart environment. For instance in [35] authors uses them combined with Naive Bayes Classifiers to learn the activity model built on hierarchical taxonomy formalism shown in Figure 3. Artificial Neural Networks (ANNs) are a sub-symbolic technique, originally inspired by biological neuron networks.…”

Section: Model Typesmentioning

confidence: 99%

Surveying Human Habit Modeling and Mining Techniques in Smart Spaces

et al. 2019

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A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field.

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“…The proposed technique develops profiles for each activity in the dataset, which is considered similar to our work where profiles are built for each sensor. Similar supervised technique has been proposed in Krishnan et al (2013), researchers proposed a data-driven technique that is able to create activity taxonomy (ontology). The proposed taxonomy was able to facilitate the analysis process of activity patterns and extract the relationships among labels and annotations automatically.…”

Section: Classification Of Segmented Datamentioning

confidence: 99%

A profile based data segmentation for in-home activity recognition

Nadi

Zamil

2019

IJSNET

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A major problem in smart-home activity recognition is the ambiguity of interpreting the actions that formulate activities. It resulted from the redundancy of irrelevant actions and the concurrent interleaving among activities themselves. In this paper, we present a framework to minimise the effect of such ambiguity using profile based data segmentation and actions refinement. The proposed methodology relies on defining a profile for each sensor in the environment for enriching existing features with semantic ones. Furthermore, according to these profiles, irrelevant actions within data segments are removed. Moreover, the proposed methodology addresses the interleaving among different activities. Experiments have been conducted to measure the performance of the proposed framework on different datasets. We evaluated our methodology using three different classifiers. The results indicated that the proposed framework achieved statistically significant improvements. Such enhancements resulted from minimising the effect of irrelevant actions and resolving the concurrent interleaving among activities within datasets.

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Learning a taxonomy of predefined and discovered activity patterns

Cited by 18 publications

References 51 publications

Talk, Text, Tag? Understanding Self-Annotation of Smart Home Data from a User’s Perspective

Talk, Text, Tag? Understanding Self-Annotation of Smart Home Data from a User’s Perspective

Surveying Human Habit Modeling and Mining Techniques in Smart Spaces

A profile based data segmentation for in-home activity recognition

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