تشخیص فعالیت انسانی در زمان حقیقی از داده های شتاب سنج
ترجمه نشده

تشخیص فعالیت انسانی در زمان حقیقی از داده های شتاب سنج

عنوان فارسی مقاله: تشخیص فعالیت انسانی در زمان حقیقی از داده های شتاب سنج با استفاده از شبکه های عصبی پیچشی
عنوان انگلیسی مقاله: Real-time human activity recognition from accelerometer data using Convolutional Neural Networks
مجله/کنفرانس: محاسبات نرم کاربردی - Applied Soft Computing
رشته های تحصیلی مرتبط: مهندسی کامپیوتر، مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: هوش مصنوعی، مهندسی الگوریتم ها و محاسبات، معماری سیستم های کامپیوتری
کلمات کلیدی فارسی: تشخیص فعالیت، یادگیری عمیق، شبکه های عصبی پیچشی، طبقه بندی سری های زمانی، استخراج ویژگی
کلمات کلیدی انگلیسی: Activity recognition، Deep learning، Convolutional Neural Networks، Time series classification، Feature extraction
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journal List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.asoc.2017.09.027
دانشگاه: Swiss Federal Institute of Technology in Zurich (ETHZ), Switzerland
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 6/031 در سال 2018
شاخص H_index: 110 در سال 2019
شاخص SJR: 1/216 در سال 2018
شناسه ISSN: 1568-4946
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 20
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E11313
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Related work


3- Algorithms


4- Experiments and evaluation


5- Computational performance


6- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


With a widespread of various sensors embedded in mobile devices, the analysis of human daily activities becomes more common and straightforward. This task now arises in a range of applications such as healthcare monitoring, fitness tracking or user-adaptive systems, where a general model capable of instantaneous activity recognition of an arbitrary user is needed. In this paper, we present a user-independent deep learning-based approach for online human activity classification. We propose using Convolutional Neural Networks for local feature extraction together with simple statistical features that preserve information about the global form of time series. Furthermore, we investigate the impact of time series length on the recognition accuracy and limit it up to 1 s that makes possible continuous real-time activity classification. The accuracy of the proposed approach is evaluated on two commonly used WISDM and UCI datasets that contain labeled accelerometer data from 36 and 30 users respectively, and in cross-dataset experiment. The results show that the proposed model demonstrates state-of-the-art performance while requiring low computational cost and no manual feature engineering.


Introduction


The current generation of portable mobile devices, such as smartphones, music players, smart watches or fitness trackers incorporates a wide variety of sensors that can be used for human activity and behavior analysis. This opens up new areas of intelligent applications that use this data for making inferences about different aspects of human life. Among the traditional examples here are healthcare monitoring, life logging, fitness tracking and security applications. Another emerged and rapidly evolving field is an unobtrusive user activity recognition in adaptive mobile applications that adjust their behavior and setup to the current mode of use. One common property of these applications is that they usually need to work out of box for an arbitrary user in an arbitrary environment, since in most cases there is no way of asking the user for training data. Another common challenge is a real-time activity recognition that is especially crucial for security and adaptive apps. The task of human activity recognition can be generally divided into two main steps. The first step is time series segmentation, and the basic approach to this problem is to use a sliding window of a fixed length and split each time series into equal segments. The question that can arise here is how the recognition accuracy depends on the window length, however it was not covered in previous works. Particularly, for WISDM dataset a sliding window of size 10 seconds was used in all studies [1, 2, 3, 4, 5] except for [6], where an adaptive time series segmentation technique was proposed.

  • اشتراک گذاری در

دیدگاه خود را بنویسید:

تاکنون دیدگاهی برای این نوشته ارسال نشده است

تشخیص فعالیت انسانی در زمان حقیقی از داده های شتاب سنج
نوشته های مرتبط
مقالات جدید
لوگوی رسانه های برخط

logo-samandehi

پیوندها