Towards a Federated Repository of Mobile Sensing Datasets for Pervasive Healthcare

2016 ◽  
Author(s):  
Jesus Favela ◽  
Luis A. Castro ◽  
Layla Michan
Keyword(s):  
Mobile Sensing ◽  
Pervasive Healthcare

Data Quality in Mobile Sensing Datasets for Pervasive Healthcare

2017 ◽  
pp. 217-238 ◽  
Author(s):  
Netzahualcóyotl Hernández ◽  
Luis A. Castro ◽  
Jesús Favela ◽  
Layla Michán ◽  
Bert Arnrich
Keyword(s):  
Data Quality ◽  
Mobile Sensing ◽  
Pervasive Healthcare

Adapting to Unknown Conditions in Learning-based Mobile Sensing

2021 ◽  
pp. 1-1
Author(s):  
Taesik Gong ◽  
Yeonsu Kim ◽  
Ryuhaerang Choi ◽  
Jinwoo Shin ◽  
Sung-Ju Lee
Keyword(s):  
Mobile Sensing

Mobile Sensing of Alertness, Sleep and Circadian Rhythm

2020 ◽  
Vol 23 (3) ◽  
pp. 16-22
Author(s):  
Akane Sano ◽  
Tauhidur Rahman ◽  
Mi Zhang ◽  
Deepak Ganesan ◽  
Tanzeem Choudhury
Keyword(s):  
Circadian Rhythm ◽  
Mobile Sensing

Pervasive Healthcare IRBs and Ethics Reviews in Research: Going Beyond the Paperwork

2021 ◽  
Vol 20 (1) ◽  
pp. 40-44
Author(s):  
Jina Huh-Yoo ◽  
Reema Kadri ◽  
Lorraine R. Buis ◽  
Gabriela Marcu
Keyword(s):  
Pervasive Healthcare

scholarly journals AutoQual: task-oriented structural vibration sensing quality assessment leveraging co-located mobile sensing context

2021 ◽  
Author(s):  
Yue Zhang ◽  
Zhizhang Hu ◽  
Susu Xu ◽  
Shijia Pan
Keyword(s):  
Task Performance ◽  
Expert Knowledge ◽  
Vibration Signal ◽  
Absolute Error ◽  
Mobile Sensing ◽  
Learning Performance ◽  
Structural Vibration ◽  
Vibration Sensing ◽  
Assessment Approach ◽  
Task Oriented

AbstractIn this paper, we introduce AutoQual, a mobile-based assessment scheme for infrastructure sensing task performance prediction under new deployment environments. With the growth of the Internet-of-Things (IoT), many non-intrusive sensing systems have been explored for various indoor applications, such as structural vibration sensing. This indirect sensing approach’s learning performance is prone to deployment variance when signals propagate through the environment. As a result, current systems heavily rely on expert knowledge and manual assessment to achieve effective deployments and high sensing task performance. In order to mitigate this expert effort, we propose to systematically study factors that reflect deployment environment characteristics and methods to measure them autonomously. We present AutoQual that measures a series of assessment factors (AFs) reflecting how the deployment environment impacts the system performance. AutoQual outputs a task-oriented sensing quality (TSQ) score by integrating measured AFs trained from known deployments as a prediction of untested system’s performance. In addition, AutoQual achieves this assessment without manual effort by leveraging co-located mobile sensing context to extract structural vibration signal for processing automatically. We evaluate AutoQual by using it to predict untested systems’ performance over multiple sensing tasks. We conduct real-world experiments and investigate 48 deployments in 11 environments. AutoQual achieves less than 0.10 average absolute error when auto-assessing multiple tasks at untested deployments, which shows a $$\le 0.018$$ ≤ 0.018 absolute error difference compared to the manual assessment approach.

scholarly journals Privacy Leakage in Mobile Sensing: Your Unlock Passwords Can Be Leaked through Wireless Hotspot Functionality

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Jie Zhang ◽  
Xiaolong Zheng ◽  
Zhanyong Tang ◽  
Tianzhang Xing ◽  
Xiaojiang Chen ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Mobile Sensing ◽  
Experimental Results ◽  
Smart Devices ◽  
Detection Accuracy ◽  
Phase Information ◽  
Current Sensing ◽  
Privacy Leakage ◽  
Sensing Systems ◽  
Additional Hardware

Mobile sensing has become a new style of applications and most of the smart devices are equipped with varieties of sensors or functionalities to enhance sensing capabilities. Current sensing systems concentrate on how to enhance sensing capabilities; however, the sensors or functionalities may lead to the leakage of users’ privacy. In this paper, we present WiPass, a way to leverage the wireless hotspot functionality on the smart devices to snoop the unlock passwords/patterns without the support of additional hardware. The attacker can “see” your unlock passwords/patterns even one meter away. WiPass leverages the impacts of finger motions on the wireless signals during the unlocking period to analyze the passwords/patterns. To practically implement WiPass, we are facing the difficult feature extraction and complex unlock passwords matching, making the analysis of the finger motions challenging. To conquer the challenges, we use DCASW to extract feature and hierarchical DTW to do unlock passwords matching. Besides, the combination of amplitude and phase information is used to accurately recognize the passwords/patterns. We implement a prototype of WiPass and evaluate its performance under various environments. The experimental results show that WiPass achieves the detection accuracy of 85.6% and 74.7% for passwords/patterns detection in LOS and in NLOS scenarios, respectively.

Sign in / Sign up

Export Citation Format

Share Document