A Frequent Pattern Mining Approach for ADLs Recognition in Smart Environments

Chikhaoui, Belkacem; Wang, Shengrui; Pigot, Hélène

doi:10.1109/aina.2011.13

Cited by 36 publications

(27 citation statements)

References 22 publications

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“…The use of data mining, pattern recognition and machine learning techniques allows the classification of the sensors' data. Several studies have been carried out to identify ADLs [28][29][30][31][32][33][34][35].…”

Section: Data Mining Pattern Recognition and Machine Learning Technimentioning

confidence: 99%

Identification of Activities of Daily Living Using Sensors Available in off-the-shelf Mobile Devices: Research and Hypothesis

Pires

García

Flórez-Revuelta³

2016

Advances in Intelligent Systems and Computing

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This paper presents a PhD project related to the identification of a set of Activities of Daily Living (ADLs) using different techniques applied to the sensors available in off-the-shelf mobile devices. This project consists on the creation of new methodologies, to identify ADLs, and to present some concepts, such as definition of the set of ADLs relevant to be identified, the mobile device as a multi-sensor system, review of the best techniques for data acquisition, data processing, data validation, data imputation, and data fusion processes, and creation of the methods for the identification of ADLs with data mining, pattern recognition and/or machine learning techniques. However, mobile devices present several limitations, therefore techniques at each stage have to be adapted. As result I.M. Pires() · N.M. Garcia · N. Pombo

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Section: Data Mining Pattern Recognition and Machine Learning Technimentioning

confidence: 99%

Identification of Activities of Daily Living Using Sensors Available in off-the-shelf Mobile Devices: Research and Hypothesis

Pires

García

Flórez-Revuelta³

2016

Advances in Intelligent Systems and Computing

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“…Farrahi and Gatica-Perez defined topic model as a probabilistic model for identifying latent structure of contextual information with a set of features [4]. Also, several researches have focused on adopting association rules and frequent events mining from trajectories [2,10].…”

Section: Related Workmentioning

confidence: 99%

GPS Trace Mining for Discovering Behaviour Patterns

Qiu

Bandara

2015

2015 International Conference on Intelligent Environments

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There are diverse sensor applications built into different personal devices, which have the ability to record data related to various aspects of the user. With the ever increasing popularity and lowering costs of such personal devices such as Smart Phones, collecting data from the mobile sensors available in these devices becomes feasible. A wealth of information can be gleaned from such data collected from these sensors which reveals various aspects of the individual's behaviour and activity. Existing approaches for analyzing such data mainly focuses on inferring semantic context and detecting associations from such data. For example, GPS enabled devices allow users to record their movements in the form of spatio-temporal stream points, and meaningful information can be extracted based on different research objectives.In this paper, we have investigated a computation framework in order to identify users' activity categories and their event's associations from GPS trajectory data. This framework has several progressive stages and is designed based on different approaches in each stage, which will facilitate to analyse people's everyday lifestyles that are related to outdoor behaviours. Moreover, we have proposed an approach to improve the performance of the semantic annotation process of this framework, by combining different sources of mobile sensor data (i.e. GPS and audio data). The proposed framework and approaches have been validated on actual data sets which include the Microsoft's Geolife data set and a data set collected by ourselves.

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“…Since we are interested in simple paths, k will be less than n and thus the worst-case complexity of the heuristic algorithm for a given k will be O(n 4 ). Since we gradually compute M k by calling the heuristic algorithm k times, the overall worstcase complexity of our solution will be O(kn 4 ). However, in practice, since the sequence graphs are sparse and the matrix M k storing the weights of the heaviest k-hop paths gets more and more zero elements as the k increases, we would not need to find maximum or search for loops in paths for every element in the matrix.…”

Section: (K)mentioning

confidence: 99%

“…[1][2][3][4]39] One form of the sequential pattern mining is to find Frequent Patterns (FPM). It was first introduced by Agrawal et al [1,5] and was to analyze the item sets that appear frequently in the customers' shopping baskets.…”

Section: Introductionmentioning

confidence: 99%

Heavy path based super-sequence frequent pattern mining on web log dataset

Korkmaz

2015

AIR

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Mining web log datasets has been extensively studied using Frequent Pattern Mining (FPM) and its various other forms. Identifying frequent patterns in different sequences can help in analyzing the most common sub-sequences (e.g., the pages visited together). However, this approach would not be able to identify general structures spanning over multiple sequences. In response to understanding general structures, we introduce a new form of sequential pattern mining called super-sequence frequent pattern mining (SS-FPM). In contrast to sub-sequences determined by FPM, SS-FPM determines the super-sequences that can contain the common parts from different sequences. This can be useful in understanding the general behavior/flow of users in web usage mining, classifying web pages and users, making predictions etc. In essence, finding frequent super-sequence patterns turns out to be related to the well-known heaviest (longest) path problem in graphs, which is known to be NP-hard. Accordingly, we transform a given sequential dataset into a sequence graph and formulate the problem as k-hop heaviest path problem. We then propose an efficient heuristic called sequence matrix method using dynamic programming techniques. We compared our method to the existing Heavypath method. The results show that our method is more efficient especially on large datasets.

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A Frequent Pattern Mining Approach for ADLs Recognition in Smart Environments

Cited by 36 publications

References 22 publications

Identification of Activities of Daily Living Using Sensors Available in off-the-shelf Mobile Devices: Research and Hypothesis

Identification of Activities of Daily Living Using Sensors Available in off-the-shelf Mobile Devices: Research and Hypothesis

GPS Trace Mining for Discovering Behaviour Patterns

Heavy path based super-sequence frequent pattern mining on web log dataset

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