Strategies for Inference Mechanism of Conditional Random Fields for Multiple-Resident Activity Recognition in a Smart Home

Hsu, Kuo-Chung; Chiang, Yi-Ting; Lin, Gu-Yang; Lu, Ching-Hu; Hsu, Jane Yung-jen; Fu, Li‐Chen

doi:10.1007/978-3-642-13022-9_42

Cited by 37 publications

(26 citation statements)

References 12 publications

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“…Nevertheless, this paper will focus on single type of sensor specifically the environmental sensor for tracking and predicting the activity interaction of multi resident in smart home environment. [48] Enhance model tracking and recognize the interaction of cooperative activity using data association recognizer. Recent works on multi resident complex activity recognition approach focus on high accurate performance model and some of it discuss on interaction of multi resident in the same environment.…”

Section: Multi Resident Current Methodologies and Approachmentioning

confidence: 99%

Multi Resident Complex Activity Recognition in Smart Home: A Literature Review

Mohamed¹,

Perumal²,

Sulaiman³

et al. 2017

IJSH

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Section: Multi Resident Current Methodologies and Approachmentioning

confidence: 99%

Multi Resident Complex Activity Recognition in Smart Home: A Literature Review

Mohamed¹,

Perumal²,

Sulaiman³

et al. 2017

IJSH

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“…Therefore, rather than evaluating a number of possible classification methods, we focus on the utility gained by incorporating these methods within the CRF model as previous work [26,18,27,28,29] has shown the utility of CRF for classification of activities of daily living.…”

Section: Activity Modelmentioning

confidence: 99%

Unsupervised learning of sensor topologies for improving activity recognition in smart environments

et al. 2017

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. AbstractThere has been significant recent interest in sensing systems and 'smart environments', with a number of longitudinal studies in this area. Typically the goal of these studies is to develop methods to predict, at any one moment of time, the activity or activities that the resident(s) of the home are engaged in, which may in turn be used for determining normal or abnormal patterns of behaviour (e.g. in a health-care setting). Classification algorithms, such as Conditional Random Fields (CRFs), typically consider sensor activations as features but these are often treated as if they were independent, which in general they are not. Our hypothesis is that learning patterns based on combinations of sensors will be more powerful than single sensors alone. The exhaustive approach -to take all possible combinations of sensors and learn classifier weights for each combination -is clearly computationally prohibitive. We show that through the application of signal processing and informationtheoretic techniques we can learn about the sensor topology in the home (i.e. learn an adjacency matrix) which enables us to determine the combinations of sensors that will be useful for classification ahead of time. As a result we can achieve classification performance better than that of the exhaustive approach, whilst only incurring a small cost in terms of computational resources. We demonstrate our results on several datasets, showing that our method is robust in terms of variations in the layout and the number of residents in the house. Furthermore, we have incorporated the adjacency matrix into the CRF learning framework and have shown that it can improve performance over multiple baselines.

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“…Their work explores seven types of individual activities: filling medication dispenser, hanging up clothes, reading magazine, sweeping floor, setting the table, watering plants, preparing dinner, and 4 types of cooperative activities: moving furniture, playing checkers, paying bills, gathering and packing picnic food. Validated against the same data set, Hsu et al [25] employed Conditional Random Fields (CRFs) with strategies of iterative and decomposition inference. They found that data association of non-obstructive sensor data is important to improve the performance of activity recognition in a multi-resident environment.…”

Section: Related Workmentioning

confidence: 99%

“…They found that data association of non-obstructive sensor data is important to improve the performance of activity recognition in a multi-resident environment. Chiang et al [38] further improved the work in [25] with DBNs that extend coupled HMMs by adding vertices to model both individual and cooperative activities.…”

Section: Related Workmentioning

confidence: 99%

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Using Sensors to Study Home Activities

Jiang

Pozza

Gunnarsdóttir

et al. 2017

JSAN

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Abstract:Understanding home activities is important in social research to study aspects of home life, e.g., energy-related practices and assisted living arrangements. Common approaches to identifying which activities are being carried out in the home rely on self-reporting, either retrospectively (e.g., interviews, questionnaires, and surveys) or at the time of the activity (e.g., time use diaries). The use of digital sensors may provide an alternative means of observing activities in the home. For example, temperature, humidity and light sensors can report on the physical environment where activities occur, while energy monitors can report information on the electrical devices that are used to assist the activities. One may then be able to infer from the sensor data which activities are taking place. However, it is first necessary to calibrate the sensor data by matching it to activities identified from self-reports. The calibration involves identifying the features in the sensor data that correlate best with the self-reported activities. This in turn requires a good measure of the agreement between the activities detected from sensor-generated data and those recorded in self-reported data. To illustrate how this can be done, we conducted a trial in three single-occupancy households from which we collected data from a suite of sensors and from time use diaries completed by the occupants. For sensor-based activity recognition, we demonstrate the application of Hidden Markov Models with features extracted from mean-shift clustering and change points analysis. A correlation-based feature selection is also applied to reduce the computational cost. A method based on Levenshtein distance for measuring the agreement between the activities detected in the sensor data and that reported by the participants is demonstrated. We then discuss how the features derived from sensor data can be used in activity recognition and how they relate to activities recorded in time use diaries.

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Strategies for Inference Mechanism of Conditional Random Fields for Multiple-Resident Activity Recognition in a Smart Home

Cited by 37 publications

References 12 publications

Multi Resident Complex Activity Recognition in Smart Home: A Literature Review

Multi Resident Complex Activity Recognition in Smart Home: A Literature Review

Unsupervised learning of sensor topologies for improving activity recognition in smart environments

Using Sensors to Study Home Activities

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