Real-Time Recognition of Calling Pattern and Behaviour of Mobile Phone Users through Anomaly Detection and Dynamically-Evolving Clustering

Iglesias, José Antonio; Ledezma, Agapito; Sanchís, Araceli; Angelov, Plamen

doi:10.3390/app7080798

Cited by 11 publications

(4 citation statements)

References 49 publications

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“…Finally, discriminative approaches model the boundaries between data events [7][8][9]. Although these machine learning algorithms operate with little prior information, they nevertheless provide good classification results [10][11][12][13][14][15]. However, their feature engineering requires deep expertise that can significantly reduce discriminant errors and improve the performance of a recognition system.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Recognition of Physical Activity and Healthcare Using Tri-Axial Inertial Wearable Sensors

2020

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The classiﬁcation of human activity is becoming one of the most important areas of human health monitoring and physical ﬁtness. With the use of physical activity recognition applications, people suffering from various diseases can be efﬁciently monitored and medical treatment can be administered in a timely fashion. These applications could improve remote services for health care monitoring and delivery. However, the ﬁxed health monitoring devices provided in hospitals limits the subjects’ movement. In particular, our work reports on wearable sensors that provide remote monitoring that periodically checks human health through different postures and activities to give people timely and effective treatment. In this paper, we propose a novel human activity recognition (HAR) system with multiple combined features to monitor human physical movements from continuous sequences via tri-axial inertial sensors. The proposed HAR system ﬁlters 1D signals using a notch ﬁlter that examines the lower/upper cutoff frequencies to calculate the optimal wearable sensor data. Then, it calculates multiple combined features, i.e., statistical features, Mel Frequency Cepstral Coefﬁcients, and Gaussian Mixture Model features. For the classiﬁcation and recognition engine, a Decision Tree classiﬁer optimized by the Binary Grey Wolf Optimization algorithm is proposed. The proposed system is applied and tested on three challenging benchmark datasets to assess the feasibility of the model. The experimental results show that our proposed system attained an exceptional level of performance compared to conventional solutions. We achieved accuracy rates of 88.25%, 93.95%, and 96.83% over MOTIONSENSE, MHEALTH, and the proposed self-annotated IM-AccGyro human-machine dataset, respectively.

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Section: Introductionmentioning

confidence: 99%

Stochastic Recognition of Physical Activity and Healthcare Using Tri-Axial Inertial Wearable Sensors

2020

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“…One of the main features of such systems is their ability to adapt and evolve autonomously according to the natural changes on the data over time. Evolving intelligent systems are object of study of many authors [7,8] and solutions based on this concept were recently introduced to many different problems [9], such as systems modeling, process controls, data prediction and classification [10,11,12,13,14,15,16].…”

Section: Introductionmentioning

confidence: 99%

An evolving approach to data streams clustering based on typicality and eccentricity data analytics

Bezerra

Costa

Guedes

et al. 2020

Information Sciences

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In this paper we propose an algorithm for online clustering of data stream. This algorithm is called AutoCloud and it is based on the recently introduced concept of Typicality and Eccentricity Data Analytics, mainly used for anomaly detection tasks. AutoCloud is an evolving, online and recursive technique that does not need training or prior knowledge about the data set to be processed. Thus, AutoCloud is fully online, requiring no offline processing. It allows creation and merging of clusters in an autonomous manner as new data observations become available. The clusters created by AutoCloud are called data clouds, which are structures without pre-defined shape or boundaries. Auto-Cloud allows each data sample to belong to multiple data clouds simultaneously using fuzzy concepts. AutoCloud is also able to handle concept drift and concept evolution, which are problems that are inherent to data streams in general. Since the algorithm is recursive and online, its suitable for applications that requires real-time

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“…Contrary to the supervised approaches, there are works that apply completely unsupervised approaches to monitor changes in the severity of the depressive and manic symptoms [24] or to analyze behavioural data about smartphone usage [15,16]. However, unsupervised learning approaches insufficiently benefit of the a-priori knowledge given by labeled data of the psychiatric assessments [27].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Incremental Semi-supervised Fuzzy Clustering for Bipolar Disorder Episode Prediction

et al. 2020

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Bipolar Disorder (BD) is a chronic mental illness characterized by changing episodes from euthymia (healthy state) through depression and mania to the mixed states. In this context, data collected through the interaction of patients with smartphones enable the creation of predictive models to support the early prediction of a starting episode. Previous research on predicting a new BD episode use mostly supervised learning methods that require labeled data and hence force a filtering of the available data to retain only those data that have valid labels (from the psychiatric assessment). To avoid limitations of supervised learning, in this paper we investigate the use of a semi-supervised learning approach that combines both labeled and unlabeled data to derive a model for BD episode prediction. Specifically we apply the DISSFCM (Dynamic Incremental Semi-Supervised Fuzzy C-Means) algorithm which offers the possibility to process in an incremental fashion the data stream of the voice signal captured by the smartphone, thus exploiting the evolving time structure of data which is ignored by static learning methods. DISS-FCM processes data in form of chunks and creates a dynamic collection of clusters thanks to a splitting mechanism that generates new clusters to better capture the hidden geometrical structure of data. This gives DISSFCM the ability to detect changes in data and dynamically adapt the model to them, thus improving the prediction accuracy. Preliminary results on real-world data collected at the Department of Affective Disorders, Institute of Psychiatry and Neurology in Warsaw (Poland) show that DISSFCM is able to predict some of healthy episodes (euthymia) and disease episodes even when only 25% of labeled data are available. Moreover DISSFM performs better than its previous version without split (ISSFCM) and it also overcomes the batch algorithm (SSFCM) that uses the whole dataset to create the model.

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Real-Time Recognition of Calling Pattern and Behaviour of Mobile Phone Users through Anomaly Detection and Dynamically-Evolving Clustering

Cited by 11 publications

References 49 publications

Stochastic Recognition of Physical Activity and Healthcare Using Tri-Axial Inertial Wearable Sensors

Stochastic Recognition of Physical Activity and Healthcare Using Tri-Axial Inertial Wearable Sensors

An evolving approach to data streams clustering based on typicality and eccentricity data analytics

Dynamic Incremental Semi-supervised Fuzzy Clustering for Bipolar Disorder Episode Prediction

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