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Walking in the Wild – Using an Always-On Smartphone Application to Increase Physical Activity [chapter]

Tim Harries, Parisa Eslambolchilar, Chris Stride, Ruth Rettie, Simon Walton
2013 Lecture Notes in Computer Science  
Longitudinal data analysis showed that use of the app increased walking by an average of 64% but did not find any evidence to suggest that the inclusion of comparative social feedback improves the impact  ...  walking that is incidental to normal everyday activities.  ...  Thanks, too, to Mubaloo, Bristol, for their constructive technical comments on the bActive app.  ... 
doi:10.1007/978-3-642-40498-6_2 fatcat:kwwg37kxurfsxha44t6ih4nr5a

On-line Context Aware Physical Activity Recognition from the Accelerometer and Audio Sensors of Smartphones [chapter]

David Blachon, Doruk Coşkun, François Portet
2014 Lecture Notes in Computer Science  
is always available on all kinds of smartphones.  ...  Activity Recognition (AR) from smartphone sensors has become a hot topic in the mobile computing domain since it can provide services directly to the user (health monitoring, fitness, context-awareness  ...  That is to say, smartphones SP1-3 were not recording an activity always in the same location.  ... 
doi:10.1007/978-3-319-14112-1_17 fatcat:an3afbi24vei3kbpqvzksinyle

A Comparative Study on the Suitability of Smartphones and IMU for Mobile, Unsupervised Energy Expenditure Calculi

Angel Ruiz-Zafra, Eva Orantes-González, Manuel Noguera, Kawtar Benghazi, Jose Heredia-Jimenez
2015 Sensors  
The metabolic equivalent of task (MET) is currently the most used indicator for measuring the energy expenditure (EE) of a physical activity (PA) and has become an important measure for determining and  ...  This paper presents a study to discover whether the estimation of energy expenditure is dependent on the accelerometer of the device used in measurements and to discover the suitability of each device  ...  en Mayores (Ergoloc Conflicts of Interest The authors declare no conflict of interest.  ... 
doi:10.3390/s150818270 pmid:26225973 pmcid:PMC4570320 fatcat:yg5kb2e4ivagjoq4urfxf3jjua

LabelSens: Enabling Real-time Sensor Data Labelling at the point of Collection on Edge Computing [article]

Kieran Woodward, Eiman Kanjo, Andreas Oikonomou
2019 arXiv   pre-print
Currently, real-time sensor data labelling is an unwieldly process with limited tools available and poor performance characteristics that can lead to the performance of the machine learning models being  ...  In this paper, we introduce new techniques for labelling at the point of collection coupled with a systematic performance comparison of two popular types of Deep Neural Networks running on five custom  ...  However, it is not always possible to label using smartphone applications as they require explicit attention making them challenging to use when engaged in additional activities and not all users may own  ... 
arXiv:1910.01400v2 fatcat:3prl2uehdjdglglfuvb5gpuqny

ADLAuth: Passive Authentication Based on Activity of Daily Living Using Heterogeneous Sensing in Smart Cities

Naseer, Azam, Ul-Haq, Ejaz, Khalid
2019 Sensors  
With the evolution of the Internet of Things (IoTs), user dependence on smart systems and services, such as smart appliances, smartphone, security, and healthcare applications, has been increased.  ...  This paper proposes a heterogeneous framework "ADLAuth" for passive and implicit authentication of the user using either a smartphone's built-in sensor or wearable sensors by analyzing the physical activity  ...  Conflicts of Interest: The authors declare no conflicts of interest.  ... 
doi:10.3390/s19112466 fatcat:nn2hjrhezrgwnjas2pkcfhxsfu

Towards accurate models for predicting smartphone applications' QoE with data from a living lab study

Alexandre De Masi, Katarzyna Wac
2020 Quality and User Experience  
Previous works on smartphone applications' QoE prediction only focus on qualitative or quantitative data. We collected both qualitative and quantitative data "in the wild" through our living lab.  ...  activity is the most important feature (e.g., if walking).  ...  We showed that collecting in-situ QoE rating and collecting smartphone background data enables us to use common machine learning techniques to build an accurate predictive model for "High" and "Low" QoE  ... 
doi:10.1007/s41233-020-00039-w pmid:33088903 pmcid:PMC7548107 fatcat:76towmxu3nhfhdd5bvtzcqoq34

C2FHAR: Coarse-to-Fine Human Activity Recognition with Behavioral Context Modeling using Smart Inertial Sensors

Muhammad Ehatisham-ul-Haq, Muhammad Awais Azam, Yasar Amin, Usman Naeem
2020 IEEE Access  
Moreover, it is essential to infer a user's behavioral context along with the physical activity to enable context-aware and knowledge-driven applications in real-time.  ...  However, the accurate recognition of in-the-wild human activities in real-time is still a fundamental challenge to be addressed as human physical activity patterns are adversely affected by their behavioral  ...  FINE HAR IN-THE-WILD Human activity patterns are not always consistent and vary with the change in behavioral context.  ... 
doi:10.1109/access.2020.2964237 fatcat:zxmbpn3elrbelowwfldib3vhyi

Application of Machine Learning-Based Pattern Recognition in IoT Devices: Review [chapter]

Zachary Menter, Wei Zhong Tee, Rushit Dave
2021 Proceedings of International Joint Conference on Computational Intelligence  
Pattern recognition is extremely prevalent in IoT devices because of the many applications and benefits that can come from it.  ...  algorithms to be used with IoT devices are support vector machine, k-nearest neighbor, and random forest.  ...  This proposed method uses machine learning classifiers to detect and recognize physical activity patterns in smartphone users to provide continuous authentication.  ... 
doi:10.1007/978-981-16-3246-4_52 fatcat:ipb2x4ayhberbdctmfsgp475le

Wandertroper

Beatrice Monastero, David McGookin, Giuseppe Torre
2016 Proceedings of the 15th International Conference on Mobile and Ubiquitous Multimedia - MUM '16  
Through an iterative participatory design process using semi-structured group discussions and 'in-the-wild' evaluations, we outline how design aspects, such as the degree of output abstraction and aesthetic  ...  In this paper we present the design and evaluation of Wandertroper, a mobile system designed to support reengagement with everyday surroundings during daily walks.  ...  Participants were given both applications to use over one week during their everyday walks.  ... 
doi:10.1145/3012709.3012725 dblp:conf/mum/MonasteroMT16 fatcat:6dd6vbvdoba63d6pw3j3tt5a5q

LabelSens: enabling real-time sensor data labelling at the point of collection using an artificial intelligence-based approach

Kieran Woodward, Eiman Kanjo, Andreas Oikonomou, Alan Chamberlain
2020 Personal and Ubiquitous Computing  
In recent years, machine learning has developed rapidly, enabling the development of applications with high levels of recognition accuracy relating to the use of speech and images.  ...  Currently, real-time sensor data labelling is an unwieldy process, with a limited range of tools available and poor performance characteristics, which can lead to the performance of the machine learning  ...  always possible to label real-time data using a smartphone application.  ... 
doi:10.1007/s00779-020-01427-x fatcat:gvad57uxibcpholbua4wup35fq

Attention and engagement-awareness in the wild: A large-scale study with adaptive notifications

Tadashi Okoshi, Kota Tsubouchi, Masaya Taji, Takanori Ichikawa, Hideyuki Tokuda
2017 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom)  
To the best of our knowledge, this study is the first to investigate user interruptibility and engagement using a real-world large-scale mobile application and real-world notifications consisting of actual  ...  In today's advancing ubiquitous computing age, with its ever-increasing amount of information from various applications and services available for consumption, the management of people's attention has  ...  She stands up, walks to the kitchen, pours a coffee, walks back, sits down on the couch, and enjoys her coffee while watching a video on her smartphone.  ... 
doi:10.1109/percom.2017.7917856 dblp:conf/percom/OkoshiTTIT17 fatcat:hvhawvjgujgf5gvqruljkwliby

Commentaries on Viewpoint: Expending our physical activity (measurement) budget wisely

2011 Journal of applied physiology  
TO THE EDITOR: The authors of "Expending our physical activity (measurement) budget wisely" (2) discussed and provided data on potential pitfalls with using self-reported physical activity (PA) to understand  ...  The error is thought to originate from a disproportionate focus on volitional type exercise (biking, jogging, and walking), while not capturing low to moderate intensity movements that accumulate a significant  ...  Activity Level Estimator (ALE) is an Android-based smartphone application, running unobtrusively in the phone's background allowing estimating physical activity related energy expenditure (PAEE) (3).  ... 
doi:10.1152/japplphysiol.00650.2011 pmid:21828253 fatcat:ew5ha5hdx5grzidhfjcgojpmfm

Actitracker: A Smartphone-Based Activity Recognition System for Improving Health and Well-Being

Gary M. Weiss, Jeffrey W. Lockhart, Tony T. Pulickal, Paul T. McHugh, Isaac H. Ronan, Jessica L. Timko
2016 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)  
Detailed activity reports and statistics are maintained on the Actitracker server and are available to the user via a secure web interface.  ...  This free service allows people to set personal activity goals and monitor their progress toward these goals. Actitracker uses data mining to generate its activity recognition models.  ...  ACKNOWLEDGMENTS This material is based upon work supported by the National Science Foundation "Smart Health and Wellbeing" program under Grant No. 1116124, a Google Faculty Award, and a variety of Fordham  ... 
doi:10.1109/dsaa.2016.89 dblp:conf/dsaa/WeissLPMRT16 fatcat:ymufmghsxjazxdxuzqrg2zwxkm

On-Device Deep Learning Inference for Efficient Activity Data Collection

Nattaya Mairittha, Tittaya Mairittha, Sozo Inoue
2019 Sensors  
method to alleviate the labeling effort and ground truth data collection in activity recognition systems using smartphone sensors.  ...  We compare the proposed method showing estimated activities using on-device deep learning inference with the traditional method showing sentences without estimated activities through smartphone notifications  ...  Smartphone-based activity recognition systems aimed at physical activities recognition such as walking or running, are based on mobile sensor data.  ... 
doi:10.3390/s19153434 pmid:31387314 pmcid:PMC6696120 fatcat:nakqwserz5co5lksnjba4uhzye

Wearable Physical Activity Tracking Systems for Older Adults—A Systematic Review

Dimitri Vargemidis, Kathrin Gerling, Katta Spiel, Vero Vanden Abeele, Luc Geurts
2020 ACM Transactions on Computing for Healthcare  
Physical activity (PA) positively impacts the quality of life of older adults, with technology as a promising factor in maintaining motivation.  ...  Moreover, systems are often narrowly limited to walking, although older adults may enjoy a broader range of activities.  ...  Some applications on smartphones track activities, like walking, by collecting and processing data while attached to people's bodies either by being carried in hand or in pockets (e.g., [88, 174] ).  ... 
doi:10.1145/3402523 fatcat:zkfqzw2pqngehkf5u4vzeud2t4
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