Deep Learning Approach of Raw Human Activity Data

Amroun, Hamdi; Temkit, MʼHamed; Ammi, Mehdi

doi:10.1002/9781119549765.ch2

Cited by 1 publication

(2 citation statements)

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“…[11] reported in 2018, the treatment concerning mental stress detection has been concerned mainly with feature extraction; others have used various approaches to stress detection, e.g., a survey approach [12], saliva and cortisol levels [5]. Others have used controlled studies to evaluate the impact of fast food consumption [6] and lifestyle in terms of cardiovascular disease risk. Some have involved data capture in an IoT-enabled, controlled smart home context [13].…”

Section: State Of the Artmentioning

confidence: 99%

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Bulletin of the Polish Academy of Sciences: Technical Sciences

Tlija

Węgrzyn-Wolska²,

Istrate³

2020

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The objective of this work is to set up a methodology that considers missing data from a connected heartbeat sensor in order to propose a good replacement methodology in the context of heart rate variability (HRV) computation. The framework is a research project, which aims to build a system that can measure stress and other factors influencing the onset and development of heart disease. The research encompasses studying existing methods, and improving them by use of experimental data from case study that describe the participant's everyday life. We conduct a study to modelize stress from the HRV signal, which is extracted from a heart rate monitor belt connected to a smart watch. This paper describes data recording procedure and data imputation methodology. Missing data is a topic that has been discussed by several authors. The manuscript explains why we choose spline interpolation for data values imputation. We implement a random suppression data procedure and simulate removed data. After that, we implement several algorithms and choose the best one for our case study based on the mean square error.

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Section: State Of the Artmentioning

confidence: 99%

“…Patient data retrieved from the healthcare system can be used and analyzed as long as the patient's envi-the protocol and all the procedures were approved by the Ethical Committee in INSEAD. The data collected were treated anonymously [5,6].…”

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Tlija

Węgrzyn-Wolska²,

Istrate³

2020

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Deep Learning Approach of Raw Human Activity Data

Cited by 1 publication

References 61 publications

Bulletin of the Polish Academy of Sciences: Technical Sciences

Bulletin of the Polish Academy of Sciences: Technical Sciences

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