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. 2017 Apr 26;17(5):952.
doi: 10.3390/s17050952.

Mining Productive-Associated Periodic-Frequent Patterns in Body Sensor Data for Smart Home Care

Walaa N Ismail  1 , Mohammad Mehedi Hassan  2
Affiliations

Mining Productive-Associated Periodic-Frequent Patterns in Body Sensor Data for Smart Home Care

Walaa N Ismail et al. Sensors (Basel). .

Abstract

The understanding of various health-oriented vital sign data generated from body sensor networks (BSNs) and discovery of the associations between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where occupants' health status is continuously monitored remotely, it is essential to provide the required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach for mining the periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce productive-associated periodic-frequent patterns as the set of correlated periodic-frequent items. The combination of these measures has the advantage of empowering healthcare providers and patients to raise the quality of diagnosis as well as improve treatment and smart care, especially for elderly people in smart homes. We develop an efficient algorithm named PPFP-growth (Productive Periodic-Frequent Pattern-growth) to discover all productive-associated periodic frequent patterns using these measures. PPFP-growth is efficient and the productiveness measure removes uncorrelated periodic items. An experimental evaluation on synthetic and real datasets shows the efficiency of the proposed PPFP-growth algorithm, which can filter a huge number of periodic patterns to reveal only the correlated ones.

Keywords: body sensor network; frequent patterns; knowledge discovery in BSN data; periodic patterns; productive pattern; smart home.

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Conflict of interest statement

The authors declare no conflict of interest. The funding sponsors had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, and in the decision to publish the results.

Figures

Figure 1
Figure 1
The workflow of productive-associated periodic-frequent pattern mining.
Figure 2
Figure 2
PPFP-tree construction with MinSup = 3, Maxpe r = 3, MPRD = 2. (a) PPFP-tree after inserting TID = 1, (b) PPFP-tree after inserting all BSD epochs, (c) Final PPFP-tree.
Figure 3
Figure 3
Prefix-tree and conditional tree construction with the PPFP-tree. (a) Prefix-tree for ‘Bs4’ (b) Conditional tree for ‘Bs4’ and (c) PPFP-tree after removing item ‘Bs4’.
Figure 4
Figure 4
Execution times of PPFP-growth and CPFP. (a) Execution time on T10I4D100K with MinSup = 4%. (b) Execution time on T10I4D100K with MinSup = 3%. (c) Execution time on Kosarak25K with MinSup = 0.8%. (d) Execution time on Kosarak25K with MinSup = 0.7%. (e) Execution time on accident with MinSup = 80%. (f) Execution time on accident with MinSup = 75%.
Figure 5
Figure 5
Execution time of PPFP-growth and PPFP. (a) Execution time on T10I4D100K dataset. (b) Execution time on Kosarak25K dataset. (c) Execution time on accident dataset.
See this image and copyright information in PMC

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