scholarly journals Architecture and Protocol of a Semantic System Designed for Video Tagging with Sensor Data in Mobile Devices

Sensors ◽  
2012 ◽  
Vol 12 (2) ◽  
pp. 2062-2087 ◽  
Author(s):  
Elsa Macias ◽  
Jaime Lloret ◽  
Alvaro Suarez ◽  
Miguel Garcia
Keyword(s):  
Mobile Devices ◽  
Sensor Data ◽  
Semantic System

Deep Compressive Offloading

2021 ◽  
Vol 25 (1) ◽  
pp. 39-42
Author(s):  
Shuochao Yao ◽  
Jinyang Li ◽  
Dongxin Liu ◽  
Tianshi Wang ◽  
Shengzhong Liu ◽  
...  
Keyword(s):  
Embedded Systems ◽  
Mobile Devices ◽  
Physical Environment ◽  
Sensor Data ◽  
Important Research ◽  
Resource Limitations ◽  
Visual Inputs ◽  
Research Questions ◽  
User Friendly ◽  
Tracking Objects

Future mobile and embedded systems will be smarter and more user-friendly. They will perceive the physical environment, understand human context, and interact with end-users in a human-like fashion. Daily objects will be capable of leveraging sensor data to perform complex estimation and recognition tasks, such as recognizing visual inputs, understanding voice commands, tracking objects, and interpreting human actions. This raises important research questions on how to endow low-end embedded and mobile devices with the appearance of intelligence despite their resource limitations.

scholarly journals From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

Sensors ◽  
2016 ◽  
Vol 16 (2) ◽  
pp. 184 ◽  
Author(s):  
Ivan Pires ◽  
Nuno Garcia ◽  
Nuno Pombo ◽  
Francisco Flórez-Revuelta
Keyword(s):  
Data Fusion ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
State Of The Art ◽  
The State ◽  
Sensor Data ◽  
Battery Life ◽  
Sensor Data Fusion ◽  
Processing Power

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).

scholarly journals Analysis of the use of sensors in mobile devices with modified operating systems

2017 ◽  
Vol 5 ◽  
pp. 193-199
Author(s):  
Mateusz Dobrowolski ◽  
Michał Dobrowolski ◽  
Piotr Kopniak
Keyword(s):  
Operating System ◽  
Mobile Devices ◽  
Operating Systems ◽  
Sensor Data ◽  
Light Sensor ◽  
Orientation Sensor

This publication concentrate on the posibility of the use of sensors in mobile devices with modified operating systems. Presented research focuses on Android devices. The gyroscope, the accelerometer, the orientation sensor and the light sensor data was acquired with use of Physics Toolbox Sensor software. The research has been conducted on two mobile devices of Xiaomi under control of six different kinds of operating system. Measured values were compared to values recorded by very accurate, reference sensors

scholarly journals Towards MANET-based Recommender Systems for Open Facilities

2021 ◽  
Author(s):  
Jürgen Dunkel ◽  
Ramón Hermoso
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Mobile Devices ◽  
Recommender System ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Performance Metrics ◽  
Sensor Data ◽  
Mobile Ad Hoc

AbstractNowadays, most recommender systems are based on a centralized architecture, which can cause crucial issues in terms of trust, privacy, dependability, and costs. In this paper, we propose a decentralized and distributed MANET-based (Mobile Ad-hoc NETwork) recommender system for open facilities. The system is based on mobile devices that collect sensor data about users locations to derive implicit ratings that are used for collaborative filtering recommendations. The mechanisms of deriving ratings and propagating them in a MANET network are discussed in detail. Finally, extensive experiments demonstrate the suitability of the approach in terms of different performance metrics.

scholarly journals INDOOR POSITIONING USING WLAN FINGERPRINT MATCHING AND PATH ASSESSMENT WITH RETROACTIVE ADJUSTMENT ON MOBILE DEVICES

2018 ◽  
Vol XLII-4 ◽  
pp. 253-260
Author(s):  
E. Gulo ◽  
G. Sohn ◽  
A. Afnan
Keyword(s):  
Mobile Devices ◽  
Inertial Sensor ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Sensor Data ◽  
Fingerprint Matching ◽  
Positioning Accuracy ◽  
Viable Approach ◽  
Similar Accuracy ◽  
Pedestrian Dead Reckoning

<p><strong>Abstract.</strong> With the increasing number and usage of mobile devices in people’s daily life, indoor positioning has attracted a lot attention from both academia and industry for the purpose of providing location-aware services. This work proposes an indoor positioning system, primarily based on WLAN fingerprint matching, that includes various minor improvements to improve the positioning accuracy of the algorithm, as well as improve the quality and reduce the collection time of the reference fingerprints. In addition, a novel Path Evaluation and Retroactive Adjustment module is employed; it intends to improve the positioning accuracy of the system in a similar fashion to a Pedestrian Dead Reckoning implemented along with WLAN Fingerprint Matching in a Sensor Fusion system. The benefit of this approach being that it avoids the requirement of inertial sensor data, as well as its intensive computation and power use, while providing a similar accuracy improvement to Pedestrian Dead Reckoning. Our experimental results demonstrate that this may be a viable approach for positioning using mobile devices in an indoor environment.</p>

scholarly journals Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 170
Author(s):  
Robin Kraft ◽  
Manfred Reichert ◽  
Rüdiger Pryss
Keyword(s):  
Mobile Devices ◽  
Sound Level ◽  
Sensor Data ◽  
Environmental Sound ◽  
Mobile Crowdsensing ◽  
Sound Levels ◽  
Level Data ◽  
Combined Use ◽  
Smartphone Sensors ◽  
Ecological Momentary

The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users’ individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.

scholarly journals Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban Areas

Technologies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 58
Author(s):  
Ramona Ruiz Blázquez ◽  
Mario Muñoz-Organero
Keyword(s):  
Mobile Devices ◽  
Urban Areas ◽  
Value Added ◽  
Urban Setting ◽  
Sensor Data ◽  
Classification Techniques ◽  
Related Data ◽  
Multivariate Outliers ◽  
Physical Barriers ◽  
Computing Platforms

Nowadays, our mobile devices have become smart computing platforms, incorporating a wide number of embedded sensors such as accelerometers, gyroscopes, barometers, GPS receivers, and magnetometers. Smartphones are valuable devices for gathering user-related data and transforming it into value-added information for the user. In this study, a novel mechanism to process sensor data from mobile devices in order to detect the type of area the user is crossing while walking in an urban setting is presented. The method is based on combining outlier data analysis and classification techniques from data collected by several pedestrians while traversing an urban environment. A theoretical framework, composed of methods for detecting multivariate outliers combined with supervised classification techniques, has been proposed in order to identify different situations and physical barriers while walking. Each type of element to be detected is characterized by using a feature vector computed based on the outliers detected. Finally, a radial SVM is used for the classification task. The classifier is trained in a supervised way with data from 20 different segments containing several physical barriers and used later to assign a class to new un-labelled data. The results obtained with this approach are very promising with an average accuracy around 95% when detecting different types of physical barriers.

KATN

2021 ◽  
Vol 5 (4) ◽  
pp. 1-26
Author(s):  
Xuanke You ◽  
Lan Zhang ◽  
Haikuo Yu ◽  
Mu Yuan ◽  
Xiang-Yang Li
Keyword(s):  
Mobile Devices ◽  
Delivery System ◽  
Intelligent Systems ◽  
Large Scale ◽  
Estimation Method ◽  
Time Estimation ◽  
Food Delivery ◽  
Sensor Data ◽  
Adaptation Mechanism ◽  
Wide Range

Leveraging sensor data of mobile devices and wearables, activity detection is a critical task in various intelligent systems. Most recent work train deep models to improve the accuracy of recognizing specific human activities, which, however, rely on specially collected and accurately labeled sensor data. It is labor-intensive and time-consuming to collect and label large-scale sensor data that cover various people, mobile devices, and environments. In production scenarios, on the one hand, the lack of accurately labeled sensor data poses significant challenges to the detection of key activities; on the other hand, massive continuously generated sensor data attached with inexact information is severely underutilized. For example, in an on-demand food delivery system, detecting the key activity that the rider gets off his/her motorcycle to hand food over to the customer is essential for predicting the exact delivery time. Nevertheless, the system has only the raw sensor data and the clicking "finish delivery" events, which are highly relevant to the key activity but very inexact, since different riders may click "finish delivery" at any time in the last-mile delivery. Without exact labels of key activities, in this work, we propose a system, named KATN, to detect the exact regions of key activities based on inexact supervised learning. We design a novel siamese key activity attention network (SAN) to learn both discriminative and detailed sequential features of the key activity under the supervision of inexact labels. By interpreting the behaviors of SAN, an exact time estimation method is devised. We also provide a personal adaptation mechanism to cope with diverse habits of users. Extensive experiments on both public datasets and data from a real-world food delivery system testify the significant advantages of our design. Furthermore, based on KATN, we propose a novel user-friendly annotation mechanism to facilitate the annotation of large-scale sensor data for a wide range of applications.

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