14,085 research outputs found
Investigating Deep Neural Network Architecture and Feature Extraction Designs for Sensor-based Human Activity Recognition
The extensive ubiquitous availability of sensors in smart devices and the
Internet of Things (IoT) has opened up the possibilities for implementing
sensor-based activity recognition. As opposed to traditional sensor time-series
processing and hand-engineered feature extraction, in light of deep learning's
proven effectiveness across various domains, numerous deep methods have been
explored to tackle the challenges in activity recognition, outperforming the
traditional signal processing and traditional machine learning approaches. In
this work, by performing extensive experimental studies on two human activity
recognition datasets, we investigate the performance of common deep learning
and machine learning approaches as well as different training mechanisms (such
as contrastive learning), and various feature representations extracted from
the sensor time-series data and measure their effectiveness for the human
activity recognition task.Comment: Seventh International Conference on Internet of Things and
Applications (IoT 2023
Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers
Human activity recognition (HAR) by wearable sensor devices embedded in the
Internet of things (IOT) can play a significant role in remote health
monitoring and emergency notification, to provide healthcare of higher
standards. The purpose of this study is to investigate a human activity
recognition method of accrued decision accuracy and speed of execution to be
applicable in healthcare. This method classifies wearable sensor acceleration
time series data of human movement using efficient classifier combination of
feature engineering-based and feature learning-based data representation.
Leave-one-subject-out cross-validation of the method with data acquired from 44
subjects wearing a single waist-worn accelerometer on a smart textile, and
engaged in a variety of 10 activities, yields an average recognition rate of
90%, performing significantly better than individual classifiers. The method
easily accommodates functional and computational parallelization to bring
execution time significantly down
Privacy Mining from IoT-based Smart Homes
Recently, a wide range of smart devices are deployed in a variety of
environments to improve the quality of human life. One of the important
IoT-based applications is smart homes for healthcare, especially for elders.
IoT-based smart homes enable elders' health to be properly monitored and taken
care of. However, elders' privacy might be disclosed from smart homes due to
non-fully protected network communication or other reasons. To demonstrate how
serious this issue is, we introduce in this paper a Privacy Mining Approach
(PMA) to mine privacy from smart homes by conducting a series of deductions and
analyses on sensor datasets generated by smart homes. The experimental results
demonstrate that PMA is able to deduce a global sensor topology for a smart
home and disclose elders' privacy in terms of their house layouts.Comment: This paper, which has 11 pages and 7 figures, has been accepted BWCCA
2018 on 13th August 201
A CSI-Based Human Activity Recognition Using Deep Learning
The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users’ inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics ofWiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities
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