An automatic data mining method to detect abnormal human behaviour using physical activity measurements

Candás, Juan Luis Carús; Peláez, Víctor; López, Gloria; Fernández, Miguel Ángel García; Álvarez, Eduardo Álvarez; Díaz, Gabriel

doi:10.1016/j.pmcj.2014.09.007

Cited by 35 publications

(5 citation statements)

References 52 publications

(75 reference statements)

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“…Human Activity Recognition (HAR) has a wide variety of applications in different domains such as healthcare (Tentori et al, 2008;Carús-Candás et al, 2014), smart homes (Soulas et al, 2015), ubiquitous computing, ambientassisted living (Stančić et al, 2017), surveillance and security (Choudhury et al, 2008;Turaga et al, 2008;Poppe et al, 2010). In healthcare, an important application is the supervision of disabled or elderly patients and their daily activities at home (Aiello et al, 2011), especially in cases of chronic illnesses such as Parkinson's disease (PD), Alzheimer (Corchado et al, 2008) or visual impairments.…”

Section: Introductionmentioning

confidence: 99%

Robust Human Activity Recognition using smartwatches and smartphones

San-Segundo¹,

Blunck

Moreno-Pimentel³

et al. 2018

Engineering Applications of Artificial Intelligence

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Section: Introductionmentioning

confidence: 99%

Robust Human Activity Recognition using smartwatches and smartphones

San-Segundo¹,

Blunck

Moreno-Pimentel³

et al. 2018

Engineering Applications of Artificial Intelligence

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“…This could be facilitated by automatic pattern recognition. For instance, prior work has generated alerts based on changes in physical activity as captured by wearable sensors [16], and changes in health status as captured through passive sensing in a housing facility [99]. Pattern recognition and interpretation can also be facilitated through automated data annotations, such as natural language captions for correlations between different self-tracked measures (e.g., sleep and stress) [10].…”

Section: Design Considerations For Spgd In Mental Healthmentioning

confidence: 99%

Provider Perspectives on Integrating Sensor-Captured Patient-Generated Data in Mental Health Care

Kornfield

Schueller

et al. 2019

Proc. ACM Hum.-Comput. Interact.

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The increasing ubiquity of health sensing technology holds promise to enable patients and health care providers to make more informed decisions based on continuously-captured data. The use of sensor-captured patient-generated data (sPGD) has been gaining greater prominence in the assessment of physical health, but we have little understanding of the role that sPGD can play in mental health. To better understand the use of sPGD in mental health, we interviewed care providers in an intensive treatment program (ITP) for veterans with post-traumatic stress disorder. In this program, patients were given Fitbits for their own voluntary use. Providers identified a number of potential benefits from patients' Fitbit use, such as patient empowerment and opportunities to reinforce therapeutic progress through collaborative data review and interpretation. However, despite the promise of sensor data as offering an "objective" view into patients' health behavior and symptoms, the relationships between sPGD and therapeutic progress are often ambiguous. Given substantial subjectivity involved in interpreting data from commercial wearables in the context of mental health treatment, providers emphasized potential risks to their patients and were uncertain how to adjust their practice to effectively guide collaborative use of the FitBit and its sPGD. We discuss the implications of these findings for designing systems to leverage sPGD in mental health care.?

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“…According to the models (e.g., active learning, semi-supervised learning, transfer learning, and multitask learning) and algorithms (e.g., Apriori, C4.5, and AdaBoost) of big data, users instantly predict the psychological condition of individual, group and even the industry when combining data with a visual information system, providing practical evidence for safety decision-making and management. For example, Candás et al (2014) used the data mining technology to predict people with mental disorders according to personal data gathered from wearables.…”

Section: The Type and Characteristic Of Bdspmentioning

confidence: 99%

Principles, Approaches and Challenges of Applying Big Data in Safety Psychology Research

Kang¹,

Wu²,

Wang³

2019

Front. Psychol.

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Big data is now widely used in many fields and is also widely applied to the integration of disciplines. Traditional methods of safety psychology are not well suited for analyzing psychological states, especially in the management of human factors in industrial production. Also, big data now becomes a new way to excavate related insight by analyzing a large amount of psychological data. So, this paper is to propose the concept of big data of safety psychology (BDSP) and to illustrate the challenges of applying big data in safety psychology. First, this paper puts forward the concept of BDSP and analyzes the difference between BDSP and traditional sample data. Subsequently, this paper summarizes the classification standard and basic characteristic of BDSP, explores the framework of BDSP and then constructs a three-dimensional structure of BDSP. Lastly, this paper discusses the challenges of using BDSP. This study is of great help to safety practitioners to solve psychological issues in the safety domain, and points out one of the research trends of human factor in industrial safety.

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An automatic data mining method to detect abnormal human behaviour using physical activity measurements

Cited by 35 publications

References 52 publications

Robust Human Activity Recognition using smartwatches and smartphones

Robust Human Activity Recognition using smartwatches and smartphones

Provider Perspectives on Integrating Sensor-Captured Patient-Generated Data in Mental Health Care

Principles, Approaches and Challenges of Applying Big Data in Safety Psychology Research

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