Design of a Situation-Aware System for Abnormal Activity Detection of Elderly People

Wang, Junbo; Cheng, Zixue; Zhang, Mengqiao; Zhou, Yinghui; Lei, Jing

doi:10.1007/978-3-642-35236-2_57

Cited by 6 publications

(7 citation statements)

References 13 publications

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“…Jiang et al [2010] automatically learn high-frequency events and declare them normal-events deviating from these rules are anomalies. Wang et al [2012] first analyze and design features from the data and then detect abnormal activities using the designed features. Weiss and Hirsh [1998] propose a genetic algorithm to predict rare events from sequences of events with categorical features.…”

Section: Related Workmentioning

confidence: 99%

“…In all of these applications, experts have identified "known" patterns to look for. These known patterns include both harmless and harmful behavior-much work has focused on learning patterns of behavior [Zhang et al 2005Kim and Grauman 2009;Yin et al 2008;Jiang et al 2009;Mahajan et al 2004;Mecocci and Pannozzo 2005;Jiang et al 2010;Wang et al 2012] so that statistically-significant variations of these known patterns can be flagged. Moreover, in the era of big data, the volumes of such data are constantly increasing, leading to a significantly increased load on servers required to monitor these patterns.…”

Section: Introductionmentioning

confidence: 99%

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Padua

Molinaro

Moscato

Picariello

et al. 2014

ACM Trans. Internet Technol.

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There are numerous applications (e.g., video surveillance, fraud detection, cybersecurity) in which we wish to identify unexplained sets of events. Most related past work has been domain-dependent (e.g., video surveillance, cybersecurity) and has focused on the valuable class of statistical anomalies in which statistically unusual events are considered. In contrast, suppose there is a set A of known activity models (both harmless and harmful) and a log L of time-stamped observations. We define a part L ⊆ L of the log to represent an unexplained situation when none of the known activity models can explain L with a score exceeding a userspecified threshold. We represent activities via probabilistic penalty graphs (PPGs) and show how a set of PPGs can be combined into one Super-PPG for which we define an index structure. Given a compute cluster of (K + 1) nodes (one of which is a master node), we show how to split a Super-PPG into K subgraphs, each of which can be independently processed by a compute node. We provide algorithms for the individual compute nodes to ensure seamless handoffs that maximally leverage parallelism. PADUA is domain-independent and can be applied to many domains (perhaps with some specialization). We conducted detailed experiments with PADUA on two real-world datasets-the ITEA CANDELA video surveillance dataset and a network traffic dataset appropriate for cybersecurity applications. PADUA scales extremely well with the number of processors and significantly outperforms past work both in accuracy and time. Thus, PADUA represents the first parallel architecture and algorithm for identifying unexplained situations in observation data, offering both scalability and accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Padua

Molinaro

Moscato

Picariello

et al. 2014

ACM Trans. Internet Technol.

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“…Chatterjee et al build a monitoring system to track the activities of elderly people suffering from diabetes [7]. Wang et al designed a model for elderly people to be aware of the current situation [8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Diabetes mellitus (DM) is the most common and serious chronic disease in the United States [7]. Many adults use drugs to manage chronic conditions such as heart disease, lung disease, arthritis, pain, and depression [8]. Human activity detection within smart homes is one of the bases of unobtrusive wellness monitoring of a rapidly aging population in developed countries [9].…”

Section: Introductionmentioning

confidence: 99%

Activity Detection of Elderly People Using Smartphone Accelerometer and Machine Learning Methods

Khan

Imran

Butt

et al. 2021

IJIST

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Elderly activity detection is one of the significant applications in machine learning. A supportive lifestyle can help older people with their daily activities to live their lives easier. But the current system is ineffective, expensive, and impossible to implement. Efficient and cost-effective modern systems are needed to address the problems of aged people and enable them to adopt effective strategies. Though smartphones are easily accessible nowadays, thus a portable and energy-efficient system can be developed using the available resources. This paper is supposed to establish elderly people's activity detection based on available resources in terms of robustness, privacy, and cost-effectiveness. We formulated a private dataset by capturing seven activities, including working, standing, walking, and talking, etc. Furthermore, we performed various preprocessing techniques such as activity labeling, class balancing, and concerning the number of instances. The proposed system describes how to identify and classify the daily activities of older people using a smartphone accelerometer to predict future activities. Experimental results indicate that the highest accuracy rate of 93.16% has been achieved by using the J48 Decision Tree algorithm. Apart from the proposed method, we analyzed the results by using various classifiers such as Naïve Bays (NB), Random Forest (RF), and Multilayer Perceptron (MLP). In the future, various other human activities like opening and closing the door, watching TV, and sleeping can also be considered for the evaluation of the proposed model.

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“…,Wang et al (2012),Araújo et al (2018),Chatzaki et al (2017),Kozina et al (2013),Mellone et al (2012),Popescu et al (2012),Fontecha et al (2013),, Mitas et al (2014), Caroux et al (2014), Liu, Hsieh and Chan (2018), Ouchi and Doi (2013), Tirkel et al (2018), Scheurer et al (2017), Chen et al (2018), Mahitha et al (2017), Soraya et al (2017), Sebestyen, Stoica and Hangan (2016), Alam and Roy (2014), Chernbumroong, Cang and Yu (2015), Capela, Lemaire and Baddour (2015), Ge and Xu (2014), Chernbumroong et al (2013), Cherian et al (2017), Zeng, Chiu and Chang (2015), Hong et al (2008), Ranasinghe, Torres and Wickramasinghe (2013), Mannini and Sabatini (2015), Rosero-Montalvo et al (2017), Morales and Akopian (2017), Benmansour, Bouchachia and Feham (2015), Ruan (2016), Medjahed et al (2009), Sansrimahachai and Toahchoodee (2016), Song, Jang and Park (2008), Lim et al (2008), Fareed (2015), Janjua, Riboni and Bettini (2016), Rafael-Palou et al (2015) Elderly in-care Zhao et al (2014), Osmani, Zhang and Balasubramaniam (2009), Ugulino et al (2012), Taati et al (2010), Bojanovsky et al (2017), Haescher et al (2018), Chigateri et al (2018), Bravo et al (2011), Tahavori et al (2017), Capela, Lemaire and Baddour…”

mentioning

confidence: 99%

Elderly activity recognition using smartphones and wearable devices

Zimmermann¹

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Design of a Situation-Aware System for Abnormal Activity Detection of Elderly People

Cited by 6 publications

References 13 publications

Padua

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Activity Detection of Elderly People Using Smartphone Accelerometer and Machine Learning Methods

Elderly activity recognition using smartphones and wearable devices

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