Validation Techniques for Sensor Data in Mobile Health Applications

Pires, Ivan Miguel; García, Nuno M.; Flórez-Revuelta, Francisco; Rodrı́guez, Natalia

doi:10.1155/2016/2839372

Cited by 33 publications

(23 citation statements)

References 46 publications

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“…There are challenges in using data not originally collected for research; hence, there is a large body of literature focused on assessing the validity of routinely collected health data . This is also now extending to validation of some innovative data sources, such as sensor data . However, there is a rapidly growing number of new data sources for which the limitations and potential biases have not been identified .…”

Section: Current Barriers To Achieving Our Visionmentioning

confidence: 99%

“…27,28 This is also now extending to validation of some innovative data sources, such as sensor data. 29 However, there is a rapidly growing number of new data sources for which the limitations and potential biases have not been identified. 30,31 A particular challenge for users of many nontraditional data sources lies in creating replicable case or concept definitions that are critical to understanding the extent to which the data source offers unbiased and complete information on the topic under study.…”

Section: Data Quality Barriersmentioning

confidence: 99%

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Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond

Ford

Boyd

Bowles

et al. 2019

Learning Health Systems

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The last 6 years have seen sustained investment in health data science in the United Kingdom and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and well‐being. However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper, we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency. We believe a step change can be achieved through meaningful stakeholder involvement at every stage of research planning, design, and execution and team‐based data science, as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social licence for health data research and ensure transparent and secure data usage for public benefit.

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Section: Current Barriers To Achieving Our Visionmentioning

confidence: 99%

Section: Data Quality Barriersmentioning

confidence: 99%

Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond

Ford

Boyd

Bowles

et al. 2019

Learning Health Systems

View full text Add to dashboard Cite

show abstract

“…Analisis potensi serangan hama terhadap kondisi iklim dan perubahan iklim masa depan diharapkan dapat menjadi sumber informasi dalam early warning system (peringatan dini) serta dapat mendukung penyusunan manajemen pengendalian hama yang lebih efektif. (Mamenun et al, 2014;Pires et al, 2016;Staffell and Pfenninger, 2016). Data stasiun observasi Citeko digunakan untuk koreksi data iklim ECMWF.…”

Section: Pendahuluanunclassified

Potential of Green Leafhopper Attack (Empoasca sp.) in Tea Plantation Based on Climate Change Scenarios

Adelianingsih¹,

Hidayati²,

Sugiarto³

2019

J.Agromet

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Pest growth is closely related to the climate conditions. This study aimed to analyze the impact of climate variability and climate change on the potential attack of green leafhopper (Empoasca sp.) on tea plantations at PTPN VIII Gunung Mas. The analysis was carried out to calculate the value of Ecoclimatic Index (EI) based on the functions of the compare years and the compare location in CLIMEX model. Pest suitability in the future was projected using RCP 4.5 and 8.5 climate scenarios, which were derived from MIROC 5 and CCSM 4 climate model outputs. The result indicated that Gunung Mas Tea Plantation was suitable for Empoasca sp. growth. The EI value (58) in the baseline year (2012-2017) confirmed the suitability. Climate variability influences the suitability for Empoasca sp. growth. During El-Niño, the EI value decrease substantially (~26%). On the other hand, the EI value is projected to slightly increase in the future for both climate scenarios.

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“…The framework implemented in this research is composed of several stages, these being: data acquisition, data processing, data fusion and classification methods. The data acquisition and processing depends on the types of sensors, where for the accelerometer, gyroscope and magnetometer data a low-pass filter is applied; for the acoustic data the Fast Fourier Transform (FFT) for the extraction of the relevant frequencies of the audio signal is applied; and for the location data no filters are applied as the raw data acquired is used for the measurement of the distance travelled [69]. Following the application of the low-pass filter and the FFT to clean the data acquired, some features related to the sensors' signals were extracted and fused.…”

Section: Purpose Of This Studymentioning

confidence: 99%

Android Library for Recognition of Activities of Daily Living: Implementation Considerations, Challenges, and Solutions

Pires¹,

Teixeira²,

Pombo³

et al. 2018

TOBIOIJ

Self Cite

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Background:Off-the-shelf-mobile devices have several sensors available onboard that may be used for the recognition of Activities of Daily Living (ADL) and the environments where they are performed. This research is focused on the development of Ambient Assisted Living (AAL) systems, using mobile devices for the acquisition of the different types of data related to the physical and physiological conditions of the subjects and the environments. Mobile devices with the Android Operating Systems are the least expensive and exhibit the biggest market while providing a variety of models and onboard sensors.Objective:This paper describes the implementation considerations, challenges and solutions about a framework for the recognition of ADL and the environments, provided as an Android library. The framework is a function of the number of sensors available in different mobile devices and utilizes a variety of activity recognition algorithms to provide a rapid feedback to the user.Methods:The Android library includes data fusion, data processing, features engineering and classification methods. The sensors that may be used are the accelerometer, the gyroscope, the magnetometer, the Global Positioning System (GPS) receiver and the microphone. The data processing includes the application of data cleaning methods and the extraction of features, which are used with Deep Neural Networks (DNN) for the classification of ADL and environment. Throughout this work, the limitations of the mobile devices were explored and their effects have been minimized.Results:The implementation of the Android library reported an overall accuracy between 58.02% and 89.15%, depending on the number of sensors used and the number of ADL and environments recognized. Compared with the results available in the literature, the performance of the library reported a mean improvement of 2.93%, and they do not differ at the maximum found in prior work, that based on the Student’s t-test.Conclusion:This study proves that ADL like walking, going upstairs and downstairs, running, watching TV, driving, sleeping and standing activities, and the bedroom, cooking/kitchen, gym, classroom, hall, living room, bar, library and street environments may be recognized with the sensors available in off-the-shelf mobile devices. Finally, these results may act as a preliminary research for the development of a personal digital life coach with a multi-sensor mobile device commonly used daily.

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Validation Techniques for Sensor Data in Mobile Health Applications

Cited by 33 publications

References 46 publications

Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond

Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond

Potential of Green Leafhopper Attack (Empoasca sp.) in Tea Plantation Based on Climate Change Scenarios

Android Library for Recognition of Activities of Daily Living: Implementation Considerations, Challenges, and Solutions

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