Activity Recognition Using Fusion of Low-Cost Sensors on a Smartphone for Mobile Navigation Application

Saeedi, Sara; El‐Sheimy, Naser

doi:10.3390/mi6081100

Cited by 40 publications

(40 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work, we only investigate the most common four device carrying positions [17][18][19][20], namely, held in hand (hand-held), against ear during phone call (phone-call), placed in trouser pocket (in-pocket), and swinging in hand (swinging-hand). For pedestrian activities, we only consider two situations, namely, normal walking and standing, which may be easily recognized by their different acceleration patterns [14,15]. For simplicity, we assume that the two activities are already accurately recognized.…”

Section: System Overviewmentioning

confidence: 99%

“…Finally, the estimation results are further smoothed by filtering outliers caused by hand movements undetected or hand movements misclassified as user turns. For simplicity, in this work, we only consider the two main pedestrian activities, namely, walking and standing, and assume that they have been already accurately recognized according to their different acceleration patterns [14,15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Deng

Liu

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Heading estimation using inertial sensors built-in smartphones has been considered as a central problem for indoor pedestrian navigation. For practical daily lives, it is necessary for heading estimation to allow an unconstrained use of smartphones, which means the varying device carrying positions and orientations. As a result, three special human body motion states, namely, random hand movements, carrying position transitions, and user turns, are introduced. However, most existing heading estimation approaches neglect the three motion states, which may render large estimation errors. We propose a robust heading estimation system adapting to the unconstrained use of smartphones. A novel detection and classification method is developed to detect the three motion states timely and discriminate them accurately. For normal working, the user heading is estimated by a PCA-based approach. If a user turn occurs, it is estimated by adding horizontal heading change to previous user heading directly. If one of the other two motion states occurs, it is obtained by averaging estimation results of the adjacent normal walking steps. Finally, an outlier filtering algorithm is developed to smooth the estimation results. Experimental results show that our approach is capable of handling the unconstrained situation of smartphones and outperforms previous approaches in terms of accuracy and applicability.

show abstract

Section: System Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Deng

Liu

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Johnson and Trivedi (2011) have used accelerometers to determine the driving style. In general, such low-cost sensors are suitable for a wide variety of activity recognition applications, as shown by Saedi and El-Sheimy (2015).…”

Section: Sensing Accelerationmentioning

confidence: 99%

A Low-cost Intelligent Car Break-in Alert System - Using Smartphone Accelerometers for Detecting Vehicle Break-ins

Behringer

Ramachandran

Chang

2016

Proceedings of the International Conference on Internet of Things and Big Data

View full text Add to dashboard Cite

Abstract:Smartphones provide sensors and online data connectivity which makes them suitable for individual alert systems. A prototype for a car break-in alert system has been developed which can detect activity through smartphone accelerometers. The main goal of this system is to provide a low-cost alternative to expensive embedded systems with similar functionality. It can be controlled remotely solely through text messages from another mobile phone, which provides the option to use SMS instead of internet data access, in case of high cost of internet data connection (international roaming charges). In case of a detected break-in the system will send a text message to the user's second phone. The user also can query information and request the location of the vehicle. The prototype has been tested in various situations, and data have been collected to distinguish different scenarios. The system has been programmed with MIT App Inventor and will be made available for free on the Google Play Store.

show abstract

“…However, there is still a gap between raw data collected from mobile sensors and suitable context information. The context-aware algorithm uses semantic modelling and machine learning techniques to automatically recognize the contexts from heterogeneous and noisy sensor data [3]. The current challenge in recommendation systems is to design a pervasive, real-time service which is adaptable to user's modes and context.…”

Section: Introductionmentioning

confidence: 99%

“…The appropriate set of sensors and features is carefully selected to perform real-time and accurate activity recognition. The details of a mobile application and performance analysis of different classification techniques is already stated in the previous publications [2][3][4]. The restaurant of interest can be chosen by the user from the recommended list, followed by an illustration of the optimal route and travel mode via the launching of the Google Maps app.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of a Context-aware Personalized Recommendation System: Mobile and Web Application

Saeedi¹,

Zou²,

Gonzales³

et al. 2017

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract:The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user's circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones' sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user's context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item's ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both webbased and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.

show abstract

Activity Recognition Using Fusion of Low-Cost Sensors on a Smartphone for Mobile Navigation Application

Cited by 40 publications

References 63 publications

Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

A Low-cost Intelligent Car Break-in Alert System - Using Smartphone Accelerometers for Detecting Vehicle Break-ins

Design and Development of a Context-aware Personalized Recommendation System: Mobile and Web Application

Contact Info

Product

Resources

About