مقاله انگلیسی رایگان در مورد رویکردهای یادگیری ماشین برای درک تاثیر محیط های شهری بر پاسخ فیزیولوژیکی انسان – الزویر 2019

 

مشخصات مقاله
ترجمه عنوان مقاله رویکردهای یادگیری ماشین برای درک تاثیر محیط های شهری بر پاسخ فیزیولوژیکی انسان
عنوان انگلیسی مقاله Machine learning approaches to understand the influence of urban environments on human’s physiological response
انتشار مقاله سال 2019
تعداد صفحات مقاله انگلیسی 16 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) Scopus – Master Journal List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
6.774 در سال 2018
شاخص H_index 154 در سال 2019
شاخص SJR 1.620 در سال 2018
شناسه ISSN 0020-0255
شاخص Quartile (چارک) Q1 در سال 2017
رشته های مرتبط مهندسی کامپیوتر
گرایش های مرتبط هوش مصنوعی، مهندسی نرم افزار، امنیت اطلاعات
نوع ارائه مقاله
ژورنال
مجله  علوم اطلاعات – Information Sciences
دانشگاه Chair of Information Architecture, ETH Zürich, Switzerland
کلمات کلیدی پردازش سیگنال، ترکیب داده ها، انتخاب ویژگی ها، دستگاه های پوشیدنی، داده فیزیولوژیکی
کلمات کلیدی انگلیسی Signal processing، Data fusion، Features selection، Wearable devices، Physiological data
شناسه دیجیتال – doi
https://doi.org/10.1016/j.ins.2018.09.061
کد محصول E11245
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

فهرست مطالب مقاله:
Abstract

1- Introduction

2- Human perception of the urban environment

3- Methodologies

4- Results

5- Discussion

6- Challenges and opportunities

7- Conclusions

References

بخشی از متن مقاله:

Abstract

This research proposes a framework for signal processing and information fusion of spatial-temporal multi-sensor data pertaining to understanding patterns of humans physiological changes in an urban environment. The framework includes signal frequency unification, signal pairing, signal filtering, signal quantification, and data labeling. Furthermore, this paper contributes to human-environment interaction research, where a field study to understand the influence of environmental features such as varying sound level, illuminance, field-of-view, or environmental conditions on humans’ perception was proposed. In the study, participants of various demographic backgrounds walked through an urban environment in Zürich, Switzerland while wearing physiological and environmental sensors. Apart from signal processing, four machine learning techniques, classification, fuzzy rule-based inference, feature selection, and clustering, were applied to discover relevant patterns and relationship between the participants’ physiological responses and environmental conditions. The predictive models with high accuracies indicate that the change in the field-of-view corresponds to increased participant arousal. Among all features, the participants’ physiological responses were primarily affected by the change in environmental conditions and field-of-view.

Introduction

Understanding influence of the environmental conditions on human perception is complex. Various environmental features, e.g., sound level, temperature, and illuminance affect our senses. Therefore, we adopted enhanced measurement and analysis techniques to define and measure what influences citizens in dynamic urban environments. The environmental features measured in this research include sound level, dust, temperature, humidity, illuminance and the field-of-view since they influence a person’s sense that, in this research, was represented by the physiological state of a person, which was measured through electro-dermal activity (EDA). With the advent of technology, researchers explore the utility of sensor-based physiological data in real-world scenarios. Thus, researchers now have the means to explore how environmental features can affect individuals’ physiological response-based perceptual quality and overall experience [23]. How to capture and define such a perceptual quality is an ongoing research topic in Cognitive Science and Behavioral Science [21,36]. This research presents a controlled study, conducted in Zürich, Switzerland, to acquire data on humans physiological responses and environmental conditions. In the study, 30 participants were asked to walk through an urban environment, while equipped with wearable sensor devices [15]. The study was designed to address the following research questions: (a) Can we predict the physiological responses of participants based on particular environmental conditions? (b) Can we infer the relationship between the physiological responses and the environmental conditions? (c) What are the most significant environmental features affecting the participants’ physiological responses? (d) What are the patterns in the environmental conditions, for which the participants exhibit aroused and normal physiological responses? The features of the data were recorded through devices and sensors at varying frequencies, which had both temporal and spatial properties. The features had a temporal property due to continuous recording, and the features had spatial characteristics because of the recording’s association with the change in locations–global positioning system (GPS). Hence, in this research, we proposed a framework that performs signal preprocessing, signal filtering, signal quantifications, data fusion, and data labeling to answer the defined research questions. Machine learning based techniques have been successfully applied for knowledge mining and pattern recognition in various real-world situations [32,39] since they are useful in identifying the underlying patterns within data [1,25]. Thus, we formulated the processed data such that four state-of-the-art machine learning techniques, classification, fuzzy rulebased inference, feature selection, and clustering, were applied for discovering patterns in the participants’ physiological responses related to the urban environmental conditions.

دیدگاهتان را بنویسید

نشانی ایمیل شما منتشر نخواهد شد. بخش‌های موردنیاز علامت‌گذاری شده‌اند *

دکمه بازگشت به بالا