Relevant bibliographies by topics / Smartphone sensing

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Smartphone sensing'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 June 2024

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Journal articles on the topic "Smartphone sensing"

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Katuk, Norliza, Nur Haryani Zakaria, and Ku-Ruhana Ku-Mahamud. "Mobile Phone Sensing using the Built-in Camera." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 02 (February 22, 2019): 102. http://dx.doi.org/10.3991/ijim.v13i02.10166.

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Modern mobile phones or smartphones have multipurpose functions apart from being used as a device for voice and text communications. They are also embedded with many useful sensors, including camera, barometer, accelerometer, and digital compass. Unlike other types of sensor, the smartphone camera has been underutilized. This paper aims to fill the gap by analyzing and reviewing the hardware and software components of smartphones and highlighting the potential uses of the smartphone camera to support human daily life activities. A simple search of research papers indexed in Google Scholar was carried out using guided terms. The papers were filtered to match the research questions for this study. Only relevant papers were analyzed and reviewed. The results of the analysis suggested that the rapid development in the smartphone hardware has extended the use of the smartphone cameras beyond personal and social photography. Consequently, applications (apps) based on smartphone cameras emerge, such as barcode readers, document digitizing tools, augmented reality, translation tools, and indoor positioning device. These apps benefit users and facilitate them in their daily life activities.
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Bui, The Huy, Balamurugan Thangavel, Mirkomil Sharipov, Kuangcai Chen, and Joong Ho Shin. "Smartphone-Based Portable Bio-Chemical Sensors: Exploring Recent Advancements." Chemosensors 11, no. 9 (August 22, 2023): 468. http://dx.doi.org/10.3390/chemosensors11090468.

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Traditionally, analytical chemistry and diagnosis relied on wet laboratories and skilled professionals utilizing sophisticated instruments for sample handling and analysis. However, with the development of novel materials and sensing techniques, there has been a significant shift towards the use of standalone sensors, allowing tests to be conducted on-site or even in real time, leading to cost- and time-efficiency. With their widespread adoption globally, smartphones have emerged as an ideal platform for such sensors, boasting extensive sensor capabilities, advanced processing power, and communication functionalities. Smartphone-based assays make use of optical and electrochemical sensors, utilizing built-in cameras, ambient light sensors, and other features for optical sensing, while the micro-USB port, Bluetooth, and wireless connection facilitate data transmission and analog voltage application for electrochemical sensing. Previous overview papers have explored smartphone-based sensing in specific domains; this review provides a comprehensive examination of recent advancements in smartphone-based sensors, encompassing both optical and electrochemical sensing methods. The review provides the fundamental principles of these sensors and their implementation using smartphones, showcases recent applications, and presents innovative designs that take advantage of the inherent functionalities and sensor capabilities of smartphones. The review concludes by offering an outlook on the prospects of smartphone-based sensing and includes a reflective section emphasizing the potential impact of sensors in chemical and biological analyses. This comprehensive resource aims to provide information to researchers and practitioners interested in using smartphones for cutting-edge analytical methodologies.
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Kulkarni, Pranav, Reuben Kirkham, and Roisin McNaney. "Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review." Sensors 22, no. 10 (May 20, 2022): 3893. http://dx.doi.org/10.3390/s22103893.

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Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years.
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Zhao, Bei, Siwen Zheng, and Jianhui Zhang. "Optimal policy for composite sensing with crowdsourcing." International Journal of Distributed Sensor Networks 16, no. 5 (May 2020): 155014772092733. http://dx.doi.org/10.1177/1550147720927331.

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The mobile crowdsourcing technology has been widely researched and applied with the wide popularity of smartphones in recent years. In the applications, the smartphone and its user act as a whole, which called as the composite node in this article. Since smartphone is usually under the operation of its user, the user’s participation cannot be excluded out the applications. But there are a few works noticed that humans and their smartphones depend on each other. In this article, we first present the relation between the smartphone and its user as the conditional decision and sensing. Under this relation, the composite node performs the sensing decision of the smartphone which based on its user’s decision. Then, this article studies the performance of the composite sensing process under the scenario which composes of an application server, some objects, and users. In the progress of the composite sensing, users report their sensing results to the server. Then, the server returns rewards to some users to maximize the overall reward. Under this scenario, this article maps the composite sensing process as the partially observable Markov decision process, and designs a composite sensing solution for the process to maximize the overall reward. The solution includes optimal and myopic policies. Besides, we provide necessary theoretical analysis, which ensures the optimality of the optimal algorithm. In the end, we conduct some experiments to evaluate the performance of our two policies in terms of the average quality, the sensing ratio, the success report ratio, and the approximate ratio. In addition, the delay and the progress proportion of optimal policy are analyzed. In all, the experiments show that both policies we provide are obviously superior to the random policy.
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Joshi, Ranjana, and Hong Nie. "A Joint Power Harvesting and Communication Technology for Smartphone Centric Ubiquitous Sensing Applications." International Journal of Handheld Computing Research 6, no. 2 (April 2015): 34–44. http://dx.doi.org/10.4018/ijhcr.2015040103.

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Smartphone centric ubiquitous sensing applications use a smartphone with external sensors. The 3.5mm audio interface provide a common data interface for communication in different smartphones. The 3.5mm audio interface cannot provide DC power to external sensors. Thus, power needs to be harvested from an earphone channel. The existing technology uses one earphone channel to harvest power. Consequently, for many smartphones the technology cannot harvest enough power to support external sensors. In this paper, based on frequency shift keying (FSK) modulation scheme, the authors have proposed a joint power harvesting and communication technology that can simultaneously harvest power and transfer data with the same earphone channels. Circuit measurements show that, the proposed technology can extract more than two times of power as from one earphone channel. Meanwhile, demodulation tests show that our newly-developed timer-based FSK demodulator can reliably recover the data transferred from a smartphone to external sensors without any error.
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Wei, Qingshan. "(Invited) Smartphone Diagnostics Meets CRISPR." ECS Meeting Abstracts MA2023-02, no. 63 (December 22, 2023): 2970. http://dx.doi.org/10.1149/ma2023-02632970mtgabs.

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Smartphone-based optical imaging and sensing devices are among the next-generation biosensors that have shown great potential to transform the field of point-of-care (POC) diagnostics. With the rapid improvement of hardware (e.g., lens, image sensor, and CPU), smartphone has become a transformative microscopy and sensing platform that can support various detection or biomedical measurement applications, especially for resource-limited settings. On the other side, smartphone diagnostics can maximize its potential for early disease detection by coupling with specific molecular assays, such as nucleic acid amplification tests and immunoassays. This talk will highlight our recent effort in creating advanced field-portable biosensor platforms based on smartphones and the integration of smartphone device with CRISPR assay to generate a new functional POC diagnostic platform. An unexpected trans-cleavage behavior of Cas12a against double-stranded DNA substrates is discovered, which greatly eases the design of CRISPR-Dx reporter molecules. Promising applications ranging from POC diagnostics of human diseases to rapid assessment of biomanufacturing products will also be illustrated. Figure 1
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Chandra Kishore, Somasundaram, Kanagesan Samikannu, Raji Atchudan, Suguna Perumal, Thomas Nesakumar Jebakumar Immanuel Edison, Muthulakshmi Alagan, Ashok K. Sundramoorthy, and Yong Rok Lee. "Smartphone-Operated Wireless Chemical Sensors: A Review." Chemosensors 10, no. 2 (January 30, 2022): 55. http://dx.doi.org/10.3390/chemosensors10020055.

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Wireless chemical sensors have been developed as a result of advances in chemical sensing and wireless communication technology. Because of their mobility and widespread availability, smartphones have been extensively combined with sensors such as hand-held detectors, sensor chips, and test strips for biochemical detection. Smartphones are frequently used as controllers, analyzers, and displayers for quick, authentic, and point-of-care monitoring, which may considerably streamline the design and lower the cost of sensing systems. This study looks at the most recent wireless and smartphone-supported chemical sensors. The review is divided into four different topics that emphasize the basic types of wireless smartphone-operated chemical sensors. According to a study of 114 original research publications published during recent years, market opportunities for wireless and smartphone-supported chemical sensor systems include environmental monitoring, healthcare and medicine, food quality, sport, and fitness. The issues and illustrations for each of the primary chemical sensors relevant to many application areas are covered. In terms of performance, the advancement of technologies related to chemical sensors will result in smaller and more lightweight, cost-effective, versatile, and durable devices. Given the limitations, we suggest that wireless and smartphone-supported chemical sensor systems play a significant role in the sensor Internet of Things.
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Tonti, Simone, Brunella Marzolini, and Maria Bulgheroni. "Smartphone-Based Passive Sensing for Behavioral and Physical Monitoring in Free-Life Conditions: Technical Usability Study." JMIR Biomedical Engineering 6, no. 2 (May 11, 2021): e15417. http://dx.doi.org/10.2196/15417.

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Background Smartphone use is widely spreading in society. Their embedded functions and sensors may play an important role in therapy monitoring and planning. However, the use of smartphones for intrapersonal behavioral and physical monitoring is not yet fully supported by adequate studies addressing technical reliability and acceptance. Objective The objective of this paper is to identify and discuss technical issues that may impact on the wide use of smartphones as clinical monitoring tools. The focus is on the quality of the data and transparency of the acquisition process. Methods QuantifyMyPerson is a platform for continuous monitoring of smartphone use and embedded sensors data. The platform consists of an app for data acquisition, a backend cloud server for data storage and processing, and a web-based dashboard for data management and visualization. The data processing aims to extract meaningful features for the description of daily life such as phone status, calls, app use, GPS, and accelerometer data. A total of health subjects installed the app on their smartphones, running it for 7 months. The acquired data were analyzed to assess impact on smartphone performance (ie, battery consumption and anomalies in functioning) and data integrity. Relevance of the selected features in describing changes in daily life was assessed through the computation of a k-nearest neighbors global anomaly score to detect days that differ from others. Results The effectiveness of smartphone-based monitoring depends on the acceptability and interoperability of the system as user retention and data integrity are key aspects. Acceptability was confirmed by the full transparency of the app and the absence of any conflicts with daily smartphone use. The only perceived issue was the battery consumption even though the trend of battery drain with and without the app running was comparable. Regarding interoperability, the app was successfully installed and run on several Android brands. The study shows that some smartphone manufacturers implement power-saving policies not allowing continuous sensor data acquisition and impacting integrity. Data integrity was 96% on smartphones whose power-saving policies do not impact the embedded sensor management and 84% overall. Conclusions The main technological barriers to continuous behavioral and physical monitoring (ie, battery consumption and power-saving policies of manufacturers) may be overcome. Battery consumption increase is mainly due to GPS triangulation and may be limited, while data missing because of power-saving policies are related only to periods of nonuse of the phone since the embedded sensors are reactivated by any smartphone event. Overall, smartphone-based passive sensing is fully feasible and scalable despite the Android market fragmentation.
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Astukar, Dr Gajendra. "Intelligent Road Condition Assessment and Pothole Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (March 31, 2024): 2941–49. http://dx.doi.org/10.22214/ijraset.2024.59542.

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Abstract: Pavement surface monitoring is crucial in order to provide a secure and seamless road infrastructure. Road surface anomalies like potholes, cracks, speed bumps, manmade joints on the road surface that hamper the driving comfort must be accurately located in order to effectively monitor the state of roadways. Lately, sensing via smartphones has acquired more popularity since inbuilt sensors in smartphones are easily obtainable. Smartphone based sensing is starting to become more and more common. The area of research in locating surface irregularities in the pavement is quite massive. If device or smartphone have the capacity to spot abnormalities in the pavement surface. The comparatively low-frequency of today’s mobile device sensors and under sampled data collected from the sensors lead to unsatisfactory accuracy in detection. The most recent techniques for employing cellular devices to detect imperfections on the roadways are contrasted and investigated in this study. Further directions for pavement anomaly detection utilizing smartphones are also highlighted
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Lane, Nicholas D. "Community-Aware Smartphone Sensing Systems." IEEE Internet Computing 16, no. 3 (May 2012): 60–64. http://dx.doi.org/10.1109/mic.2012.48.

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Dissertations / Theses on the topic "Smartphone sensing"

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Yang, Zhenyu. "Smartphone-based Optical Sensing." University of Dayton / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1461863029.

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Vecchiotti, Andrea. "Sensing della presenza di scale tramite smartphone." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8232/.

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Lo scopo di questa tesi è di usare i sensori dello smartphone per cercare di rilevare alcune tra le attività umane più frequenti, come il salire una scala. Si presenta quindi lo smartphone in termini di hardware elencando i possibili sensori contenuti in esso e descrivendone le qualità. Vengono presentati i principali sistemi operativi mobili adottati per questi tipi di dispositivi mostrando un confronto tra di essi in termini di pregi e difetti e viene narrata un minimo di storia relativa ai dispositivi mobili in generale spiegando come lo smartphone abbia sostituito il classico telefono cellulare. Nel documento di tesi verranno poi presentati i principali strumenti con cui verrà sviluppata l'applicazione di rilevamento e verranno presi in considerazione due programmi di sviluppo per due linguaggi e tipologie di smartphone differenti: Windows Phone e Android. Tuttavia per lo sviluppo del progetto di tesi verrà impiegata solo la strumentazione relativa a Windows Phone. In seguito verrà presentata l'applicazione d'ausilio alla progettazione dei metodi, Scilab. Per il rilevamento dell'attività verranno esposti due diversi metodi e ne verranno mostrati il codice per l'implementazione e i relativi risultati. Questi verranno poi discussi e confrontati per analizzare l'affidabilità di entrambi i metodi.
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Ghorpade, Ajinkya (Ajinkya Ranjeet). "Inferring travel activity pattern from smartphone sensing data using deep learning." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120642.

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Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 77-85).
Understanding the travel routine of the individuals is important in many domains. In transport research understanding daily travel routine is crucial for modeling the travel behavior of the individuals. Such models help predict the travel demand and develop strategies for managing that demand. Understanding travel patterns of the individuals is also important to develop effective incentive mechanisms. Location-based services like personal digital assistants and journey planners use historical travel routine to build preferences of the user and make useful recommendations. In health sciences logging the routine travel behavior is important to monitor health of the patients and make recommendations wherever necessary. Several fitness tracking applications available on smartphones utilize the travel activity diary to evaluate the fitness of the individuals and make recommendations. The proliferation of sensing-enabled smartphone devices engendered the development of tools for logging travel routine of individuals. The research in this thesis uses the sensor data collected from smartphone devices to develop a travel activity inference algorithm. Presently, the research into travel activity inference has been focused on developing supervised learning algorithms. These algorithms require a large amount of labeled data for training algorithms that generalize well. Generalization in personalized travel activity inference is a challenging problem due to the concept drift. The problem of concept drift is magnified as the more personalized information is introduced in the input variables. Once the users start using the applications they are constantly generating new data. Expecting the users to label all the data generated by them is impractical. Instead, it would be useful to identify only those examples which would help most improve the algorithm and have the user label such instance. This reduces the burden on the user and does not discourage them from participating in the data collection process. In other words, we need a model that is identifies concept drift in data and adapts accordingly. There has been advances in the deep learning research in last few years. The deep learning algorithms provide a framework for learning feature representation from raw data. The convolutional neural networks have been particularly effective in learning feature representations on many datasets. These models have achieved significant improvement on many complex problems over other machine learning approaches. For the sequential classification problems like the travel activity inference, the recurrent neural network like long short term memory networks are particularly suitable. This thesis proposes to use the deep learning algorithms for travel activity inference. To develop an end-to-end deep learning algorithm that learns feature representations from raw sensor data and incorporates different sensors with differing frequencies. The research proposes using a combination of convolutional neural network for feature representation learning in both time and frequency domain and long short term memory network for sequential classification. In practical situations, the users of the smartphones cannot be asked to carry their smartphones in a fixed position every time. The proposed algorithm for travel activity inference need to be robust to changes in orientation of the smartphones. We compared the performance of the proposed deep learning algorithm against a baseline model based on the current supervised machine learning approaches. The deep learning algorithm achieved an overall average accuracy of 95.98% compared to the baseline method which achieved an overall average accuracy of 89%. We also show that the proposed deep learning algorithm is robust to changes in the orientation of the smartphone.
by Ajinkya Ghorpade.
S.M. in Transportation
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Li, Dong. "Enabling Smart Driving through Sensing and Communication in Vehicular Networks." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1397760624.

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Hossain, Md Arafat. "Lab-in-a-Phone for Smart Sensing." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/16951.

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Field-portable instruments for ubiquitous detection and analysis of biological, environmental and agricultural items are becoming ever more popular over their traditional non-portable benchtop counterparts as a means to ensure hazard minimization in many fields. One significantly more convenient approach that has emerged in recent years is the harnessing of smartphone capabilities to create field-deployable scientific instruments, allowing measurements to be made onsite and in real-time. In this thesis, a number of self-contained lab-in-a-phone devices have been presented with the particular focus on spectroscopic measurements. In the first work to be presented, a smartphone-based intensity fluorimeter has been developed by using the in-built white flash LED as an optical source and the CMOS camera as a detector. For spectroscopic measurements, a low-cost dispersive element fabricated by nano-imprinted technology has been added into the system within a 3D-printed enclosure to smartphone camera. By adding an optical fibre bundle, the functionality of the device was extended further to make possible spectral acquisitions from solid surfaces in hard-to-reach places. In advance to the steady-state measurements, time-resolved fluorescence intensity measurements at various temperatures is demonstrated. Finally, the utilisation of smartphone-based devices has also gone beyond chemical analysis by demonstrating a simple, low-cost, portable smartphone-based laser beam profiler. Performances of all these instruments have been analysed by some proof-of-principle demonstrations. With the IoT compatibility, the instrumentation on a smartphone can be integrated into a large network of other instruments. As such, data can be monitored, shared, and processed to build real-time regional and global maps for a range of phenomena.
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Mehl, M. R. "The Electronically Activated Recorder or EAR: A Method for the Naturalistic Observation of Daily Social Behavior." SAGE PUBLICATIONS INC, 2017. http://hdl.handle.net/10150/623432.

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This article reviews the Electronically Activated Recorder (EAR) as an ambulatory ecological momentary assessment tool for the real-world observation of daily behavior. Technically, the EAR is an audio recorder that intermittently records snippets of ambient sounds while participants go about their lives. Conceptually, it is a naturalistic observation method that yields an acoustic log of a person’s day as it unfolds. The power of the EAR lies in unobtrusively collecting authentic real-life observational data. In preserving a high degree of naturalism at the level of the raw recordings, it resembles ethnographic methods; through its sampling and coding, it enables larger empirical studies. This article provides an overview of the EAR method; reviews its validity, utility, and limitations; and discusses it in the context of current developments in ambulatory assessment, specifically the emerging field of mobile sensing.
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Choi, Daeyoung. "Participatory Air Quality Monitoring System." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1276047032.

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Nieznanska, Marta. "Experimental evaluation of the smartphone as a remote game controller for PC racing games." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4000.

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Context. Both smartphones and PC games are increasingly commonplace nowadays. There are more and more people who own smartphones and – at the same time – like playing video games. Since the smartphones are becoming widely affordable and offer more and more advanced features (such as multi-touch screens, a variety of sensors, vibration feedback, and others), it is justifiable to study their potential in new application areas. The aim of this thesis is to adapt the smartphone for the use as a game controller in PC racing games and evaluate this solution taking into account such aspects as race results and user experience. Objectives. To this end, two applications were developed - a game controller application for Android‑based smartphones (i.e. the client application) and a PC server application. The applications support a selected open-source PC racing game called SuperTuxKart. The evaluation of the smartphone encompasses both race results (whether the smartphone enables players to achieve comparable results to the keyboard – a standard game controller) and user experience (whether this game controller may be satisfying to players). Methods. An experiment was conducted where 20 participants controlled the SuperTuxKart game using first the smartphone, and then the keyboard or vice versa. The experiment was followed by a questionnaire-based survey of the user experience. Results. The experiment results indicate that the smartphone may achieve results which are comparable to those achieved by the keyboard. The race times corresponding to the smartphone were somewhat longer than those obtained with the keyboard, but the average relative difference was below 18%. The questionnaire results show that more than half of the participants enjoyed the smartphone more than the keyboard, despite the fact that the smartphone turned out to be a more challenging game controller for many players and did not provide so good control of the racing game as the keyboard. Conclusions. Overall, this study shows that the smartphone has a potential to be a suitable, satisfying and enjoyable controller for PC racing games.
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Nguyen, Van Khang. "Détection et agrégation d'anomalies dans les données issues des capteurs placés dans des smartphones." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL021/document.

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Les réseaux sans fils et mobiles se sont énormément développés au cours de ces dernières années. Loin d'être réservés aux pays industrialisés, ces réseaux nécessitant une infrastructure fixe limitée se sont aussi imposés dans les pays émergents et les pays en voie de développement. En effet, avec un investissement structurel relativement très faible en comparaison de celui nécessaire à l'implantation d'un réseau filaire, ces réseaux permettent aux opérateurs d'offrir une couverture du territoire très large, avec un coût d'accès au réseau (prix du téléphone et des communications) tout à fait acceptable pour les utilisateurs. Aussi, il n'est pas surprenant qu'aujourd'hui, dans la majorité des pays, le nombre de téléphones sans fil soit largement supérieur à celui des téléphones fixes. Ce grand nombre de terminaux disséminé sur l'ensemble de la planète est un réservoir inestimable d'information dont une infime partie seulement est aujourd'hui exploitée. En effet, en combinant la position d'un mobile et sa vitesse de déplacement, il devient possible d'en déduire la qualité des routes ou du trafic routier. Dans un autre registre, en intégrant un thermomètre et/ou un hygromètre dans chaque terminal, ce qui à grande échelle impliquerait un coût unitaire dérisoire, ces terminaux pourraient servir de relai pour une météo locale plus fiable. Dans ce contexte, l'objectif de cette thèse consiste à étudier et analyser les opportunités offertes par l'utilisation des données issues des terminaux mobiles, de proposer des solutions originales pour le traitement de ces grands masses de données, en insistant sur les optimisations (fusion, agrégation, etc.) pouvant être réalisées de manière intermédiaire dans le cadre de leur transport vers les(s) centre(s) de stockage et de traitement, et éventuellement d'identifier les données non disponibles aujourd'hui sur ces terminaux mais qui pourraient avoir un impact fort dans les années à venir. Un prototype présentant un exemple typique d'utilisation permettra de valider les différentes approches
Mobile and wireless networks have developed enormously over the recent years. Far from being restricted to industrialized countries, these networks which require a limited fixed infrastructure, have also imposed in emerging countries and developing countries. Indeed, with a relatively low structural investment as compared to that required for the implementation of a wired network, these networks enable operators to offer a wide coverage of the territory with a network access cost (price of devices and communications) quite acceptable to users. Also, it is not surprising that today, in most countries, the number of wireless phones is much higher than landlines. This large number of terminals scattered across the planet is an invaluable reservoir of information that only a tiny fraction is exploited today. Indeed, by combining the mobile position and movement speed, it becomes possible to infer the quality of roads or road traffic. On another level, incorporating a thermometer and / or hygrometer in each terminal, which would involve a ridiculous large-scale unit cost, these terminals could serve as a relay for more reliable local weather. In this context, the objective of this thesis is to study and analyze the opportunities offered by the use of data from mobile devices to offer original solutions for the treatment of these big data, emphasizing on optimizations (fusion, aggregation, etc.) that can be performed as an intermediate when transferred to center(s) for storage and processing, and possibly identify data which are not available now on these terminals but could have a strong impact in the coming years. A prototype including a typical sample application will validate the different approaches
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Rachuri, Kiran Kumar. "Smartphones based social sensing : adaptive sampling, sensing and computation offloading." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648104.

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Books on the topic "Smartphone sensing"

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Zhixian, Yan. Semantics in mobile sensing. San Rafael, California]: Morgan & Claypool Publishers, 2014.

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Book chapters on the topic "Smartphone sensing"

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Gustafsson, Fredrik, and Gustaf Hendeby. "Exploring New Localization Applications Using a Smartphone." In Sensing and Control for Autonomous Vehicles, 161–79. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55372-6_8.

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Kalogirou, I. P., A. Kallipolitis, and Ilias Maglogiannis. "Passive Emotion Recognition Using Smartphone Sensing Data." In Advanced Computational Intelligence in Healthcare-7, 17–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61114-2_2.

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Liu, Tong, and Yanmin Zhu. "Social Welfare Maximization in Participatory Smartphone Sensing." In Wireless Algorithms, Systems, and Applications, 351–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39701-1_29.

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Liu, Kaikai, Xinxin Liu, and Xiaolin Li. "Guoguo: Enabling Fine-Grained Smartphone Localization." In Mobile SmartLife via Sensing, Localization, and Cloud Ecosystems, 71–101. Boca Raton : CRC Press, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315369907-5.

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Singh, Harpinder, and Dheeraj Gambhir. "Use of a Smartphone to Map Noise Pollution." In Re-envisioning Advances in Remote Sensing, 41–47. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003224624-4.

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Dutta, Sibasish, and Pabitra Nath. "Smartphone Based Platform for Colorimetric Sensing of Dyes." In Springer Proceedings in Physics, 541–46. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2367-2_66.

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Calabretta, Maria Maddalena, Ruslan Alvarez-Diduk, Elisa Michelini, and Arben Merkoçi. "ATP Sensing Paper with Smartphone Bioluminescence-Based Detection." In Bioluminescence, 297–307. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2473-9_23.

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Akhund, Tajim Md Niamat Ullah, Nishat Tasnim Newaz, Md Rakib Hossain, and M. Shamim Kaiser. "Low-Cost Smartphone-Controlled Remote Sensing IoT Robot." In Information and Communication Technology for Competitive Strategies (ICTCS 2020), 569–76. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0882-7_49.

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Rafique, Sehrish, Muhammad Ehatisham-ul-Haq, Kainat Ibrar, Amanullah Yasin, Fiza Murtaza, and Muhammad Awais Azam. "Assessment of Human Personality Traits Using Smartphone Sensing." In Lecture Notes in Networks and Systems, 613–22. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37717-4_39.

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Chau, Dang Viet, Masao Kubo, Hiroshi Sato, and Akira Namatame. "Design of Safety Map with Collectives of Smartphone Sensors." In Human Behavior Understanding in Networked Sensing, 431–52. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10807-0_20.

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Conference papers on the topic "Smartphone sensing"

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Alam, Shahnawaz, Avik Ghose, Arijit Sinharay, Anirban Dutta Choudhury, and Arpan Pal. "Smartphone Sensing Framework." In The 8th EAI International Conference on Mobile Computing, Applications and Services. ACM, 2016. http://dx.doi.org/10.4108/eai.30-11-2016.2267130.

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Aram, S., A. Troiano, and E. Pasero. "Environment sensing using smartphone." In 2012 IEEE Sensors Applications Symposium (SAS). IEEE, 2012. http://dx.doi.org/10.1109/sas.2012.6166275.

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Yan, Zhixian, Jun Yang, and Emmanuel Munguia Tapia. "Smartphone bluetooth based social sensing." In UbiComp '13: The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2494091.2494118.

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Faggiani, Adriano, Enrico Gregori, Luciano Lenzini, Valerio Luconi, and Alessio Vecchio. "Network sensing through smartphone-based crowdsourcing." In the 11th ACM Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2517351.2517397.

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Zhang, Xiao, Fuzhen Zhuang, Wenzhong Li, Haochao Ying, Hui Xiong, and Sanglu Lu. "Inferring Mood Instability via Smartphone Sensing." In MM '19: The 27th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3343031.3350957.

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Ang, King-Seng, and Chen-Khong Tham. "Smartphone-based vehicular and activity sensing." In 2012 18th IEEE International Conference on Networks (ICON). IEEE, 2012. http://dx.doi.org/10.1109/icon.2012.6506524.

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Szu, Harold, Charles Hsu, Gyu Moon, Joseph Landa, Hiroshi Nakajima, and Yutaka Hata. "Smartphone home monitoring of ECG." In SPIE Defense, Security, and Sensing, edited by Harold Szu and Liyi Dai. SPIE, 2012. http://dx.doi.org/10.1117/12.923579.

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Liu, Kaikai, Di Wu, and Xiaolin Li. "Enhancing smartphone indoor localization via opportunistic sensing." In 2016 13th Annual IEEE International Conference on Sensing, Communication and Networking (SECON). IEEE, 2016. http://dx.doi.org/10.1109/sahcn.2016.7732988.

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Sen, Sougata, Karan Grover, Vigneshwaran Subbaraju, and Archan Misra. "Inferring smartphone keypress via smartwatch inertial sensing." In 2017 IEEE International Conference on Pervasive Computing and Communications: Workshops (PerCom Workshops). IEEE, 2017. http://dx.doi.org/10.1109/percomw.2017.7917646.

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Zou, Yongpan, Guanhua Wang, Kaishun Wu, and Lionel M. Ni. "SmartSensing: Sensing Through Walls with Your Smartphone!" In 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 2014. http://dx.doi.org/10.1109/mass.2014.46.

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