Mining Regularities in Body Sensor Network Data

Tanbeer, Syed Khairuzzaman; Hassan, Mohammad Mehedi; Alrubaian, Majed; Jeong, Byeongmoon

doi:10.1007/978-3-319-23237-9_9

Cited by 7 publications

(8 citation statements)

References 20 publications

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“…The periodic-frequent pattern is defined as an important class of interesting patterns that occur frequently and periodically in time series data. A great deal of attention has been paid to the problem of finding these patterns [62], [64], [65]. All of these studies have considered time series as a symbolic series, and the events' temporal occurrence information has been ignored.…”

Section: B Pattern-mining Approachesmentioning

confidence: 99%

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Context-Enriched Regular Human Behavioral Pattern Detection From Body Sensors Data

2019

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Extracting indicative characteristics from the sensor data provide diverse avenues for improving the well-being of the elderly people living alone in their homes through understanding and identifying their behavioral patterns while considering any environmental changes. In this paper, we present a new model to explore the challenges associated with mining patterns from the body sensor data and their potential use in discovering regular human routines through mining periodic patterns from a non-uniform temporal database. The non-uniform nature of the temporal database adds more challenges to the mining of periodic patterns as the items may have different periodicity and frequency occurrences. Another challenge is how to discover the correlation between the discovered patterns. In addition, we examine the contextenriched periodic patterns which provide more insights about residents' health. A new algorithm for the contextualized-correlated periodic pattern mining from a non-uniform temporal database is presented along with an extensive evaluation of its performance using a real-life dataset.

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Section: B Pattern-mining Approachesmentioning

confidence: 99%

“…All of these studies have considered time series as a symbolic series, and the events' temporal occurrence information has been ignored. Tanbeer et al [62], [65] have devised many algorithms for periodic pattern mining from a transactional database.…”

Section: B Pattern-mining Approachesmentioning

confidence: 99%

Context-Enriched Regular Human Behavioral Pattern Detection From Body Sensors Data

2019

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“…Thus, many researchers are extending Tanbeer's work to mine top−k [42,43,44] periodic patterns, but their approaches remain limited to k items. The work presented in [24,25] proposed an efficient and scalable regular mining algorithm with one database scan. The algorithm can be conducted in either single or multiple distributed BSN data for the purpose of following up the health conditions of users.…”

Section: Related Workmentioning

confidence: 99%

“…algorithms because the approaches in [24][25][26] try to discover those patterns that are frequent and have complete cyclic repetitions in the entire database. Most of these algorithms use a maximum periodicity threshold to discover periodic patterns, which measures pattern periodicity based on the largest amount of time difference or number of timeslots between two occurrences of a pattern.…”

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confidence: 99%

Mining Productive-associated Periodic Frequent Pattern in Body Sensor Data for Smart Home Care

Ismail¹,

Hassan²

2017

Preprint

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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 to mine the periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce productiveassociated 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 a smart home. 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; Smart home, knowledge discovery in BSN data; frequent patterns; periodic patterns and productive pattern.

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“…Furthermore, periodic-frequent or regular-frequent pattern mining, which aims to discover those frequent patterns that occur at regular intervals in a temporally ordered transactional database, was studied by Tanbeer et al [ 24 , 25 , 26 ] with the aim of identifying frequent periodic patterns since the shapes of a pattern's occurrence in databases cannot be determined by the interesting measures (such as support and closure) used in frequent pattern-mining approaches. Additionally, Rashid [ 27 ] proposed a different measure (regular-frequent pattern mining), measured as the variance among frequent pattern periods, in order to detect periodic patterns in transaction-like databases.…”

Section: Introductionmentioning

confidence: 99%

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

Ismail

Hassan

2017

Sensors

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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.

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Mining Regularities in Body Sensor Network Data

Cited by 7 publications

References 20 publications

Context-Enriched Regular Human Behavioral Pattern Detection From Body Sensors Data

Context-Enriched Regular Human Behavioral Pattern Detection From Body Sensors Data

Mining Productive-associated Periodic Frequent Pattern in Body Sensor Data for Smart Home Care

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

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