Dynamic sensor event segmentation for real-time activity recognition in a smart home context

Wan, Jie; O’Grady, Michael J.; O’Hare, Gregory M. P.

doi:10.1007/s00779-014-0824-x

Cited by 101 publications

(53 citation statements)

References 28 publications

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“…The reason for this preference is that, in general, we do not believe that sensor activations within the kitchen should necessarily influence the classifier's belief about activities being performed in the bedroom. Defining ground truth functional regions in the smart home has been used by a number of researchers [34,35] in the smart environments, but to our knowledge these have not been explicitly to determine sensor adjacency from data. We present our partitioning and tracking method in algorithm 1.…”

Section: Imposing Topological Constraints On Feature Functionsmentioning

confidence: 99%

“…Attribution of activities to residents is still a challenging task, in particular for bathroombased activities. Many segmentation algorithms have been investigated, with naïve segmentation incorporating sliding time/size windows over sensor events, and more sophisticated approaches utilising classifiers/rules [16,17,18,19,20], and practitioners have investigated a wide range of classification models applied, e.g. Logistic Regression (LR), Support Vector Machines (SVMs), decision trees [9,10,11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Imposing Topological Constraints On Feature Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…The monitoring technology can only detect a few simple activities. The fifth monitoring technology is based on ambient sensors [8][9][10][11][12][13][14] that are placed in various rooms. Generally, the ambient sensors include light sensors, temperature sensors, magnetic door sensors, etc.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach Based on Time Cluster for Activity Recognition of Daily Living in Smart Homes

et al. 2017

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Abstract:With the trend of the increasing ageing population, more elderly people often encounter some problems in their daily lives. To enable these people to have more carefree lives, smart homes are designed to assist elderly people by recognizing their daily activities. Although different models and algorithms that use temporal and spatial features for activity recognition have been proposed, the rigid representations of these features damage the accuracy of activity recognition. In this paper, a two-stage approach is proposed to recognize the activities of a single resident. Firstly, in terms of temporal features, the approximate duration, start and end time are extracted from the activity records. Secondly, a set of activity records is clustered according to the records' temporal features. Then, the classifiers are used to recognize the daily activities in each cluster according to the spatial features. Finally, two experiments are done to validate the recognition of daily activities in order to compare the proposed approach with a one-dimensional model. The results demonstrate that the proposed approach favorably outperforms the one-dimensional model. Two public datasets are used to evaluate the proposed approach. The experiment results show that the proposed approach achieves average accuracies of 80% and 89%, respectively.

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“…There are many studies on activity recognition in a smart home [5], [6]. These studies estimate user activities utilizing acoustic sound captured or the usage of electric appliances in the smart home.…”

Section: Introductionmentioning

confidence: 99%

Implementation and Evaluation of Analog-PIR-Sensor-Based Activity Recognition

Fujiwara

Kashimoto

Fujimoto

et al. 2017

SICE Journal of Control, Measurement, and System Integration

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: These days, smart home applications such as concierge service for residents, home appliance control, and so on are attracting attention. To realize these applications, we need a system which recognizes various human activities accurately with a low cost device. There are many studies on the activity recognition in a smart home. We also have proposed an activity recognition technique in a smart home by utilizing digital-output-PIR (passive infrared) sensors, door sensors, and power meters. However, the study has an unsolved issue: we cannot distinguish similar activities happening at the same place, for example, "eating" and "reading" while sitting on a sofa. In order to cope with this challenge, we introduce ALPAS: analog-output-PIR-sensor-based activity recognition technique which recognizes the different activities in the same place. Our technique recognizes user's activity by utilizing machine learning with frequency components of the sensor's output as features. However, because the number of features used in ALPAS is 1000 for each analog PIR-sensor, a large capacity memory is required. To reduce the number of features, we select a part of the sensing data. We call the starting point of the selected data as starting frequency (SF) and ending part as ending frequency (EF). We searched SF and EF using a grid search, and evaluated the recognition accuracy. We evaluated the proposed technique in a smarthome testbed. In the evaluation, five participants performed four different activities while sitting on a sofa. As a result, we achieved F-Measure: 63.9% when the EF is 1.4 Hz, and F-Measure: 50% or lower when the SF is 9.9 Hz or higher.

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Dynamic sensor event segmentation for real-time activity recognition in a smart home context

Cited by 101 publications

References 28 publications

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

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

A Novel Approach Based on Time Cluster for Activity Recognition of Daily Living in Smart Homes

Implementation and Evaluation of Analog-PIR-Sensor-Based Activity Recognition

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