A mobile platform for real-time human activity recognition

Activity Classification Using Realistic Data From Wearable Sensors

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Activity Recognition from Accelerometer Data on a Mobile Phone

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COSAR: hybrid reasoning for context-aware activity recognition

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Centinela: A human activity recognition system based on acceleration and vital sign data

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Activity recognition from acceleration data using AR model representation and SVM

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ActiServ: Activity Recognition Service for mobile phones

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Online classifier construction algorithm for human activity detection using a tri-axial accelerometer

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A Survey on Human Activity Recognition using Wearable Sensors

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Real-time human activity recognition from accelerometer data using Convolutional Neural Networks

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A Survey of Online Activity Recognition Using Mobile Phones

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Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey

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Review of Wearable Devices and Data Collection Considerations for Connected Health

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A Survey on Activity Detection and Classification Using Wearable Sensors

Cornacchia, M., Ozcan, K., Zheng, Y., & Velipasalar, S. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2628346 · Cited by 427 · see citing works

A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer

Chen, Y., & Xue, Y. (2015) · 2015 IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.263 · Cited by 378 · see citing works

Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches

Vaizman, Y., Ellis, K., & Lanckriet, G. (2017) · IEEE Pervasive Computing · doi:10.1109/mprv.2017.3971131 · Cited by 324 · see citing works

Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview

Lima, W. S., Souto, E., El‐Khatib, K., Jalali, R., & da Gama, J. M. P. (2019) · Sensors · doi:10.3390/s19143213 · Cited by 260 · see citing works

Context-Awareness for Mobile Sensing: A Survey and Future Directions

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LSTM Networks for Mobile Human Activity Recognition

Chen, Y., Zhong, K., Zhang, J., Sun, Q., & Zhao, X. (2016) · International Conference on Artificial Intelligence Technologies and Applications (ICAITA) · doi:10.2991/icaita-16.2016.13 · Cited by 204 · see citing works

JUCS - Journal of Universal Computer Science

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Activity Recognition and Abnormal Behaviour Detection with Recurrent Neural Networks

Arifoğlu, D., & Bouchachia, A. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.06.121 · Cited by 182 · see citing works

Automated cognitive health assessment in smart homes using machine learning

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AHAR: Adaptive CNN for Energy-Efficient Human Activity Recognition in Low-Power Edge Devices

Rashid, N., Demirel, B. U., & Faruque, M. A. A. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3140465 · Cited by 175 · see citing works

Human activity recognition using multisensor data fusion based on Reservoir Computing

Palumbo, F., Gallicchio, C., Pucci, R., & Micheli, A. (2016) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-160372 · Cited by 157 · see citing works

Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs

Khan, A., Tufail, A., Khattak, A. M., & Laine, T. H. (2014) · International Journal of Distributed Sensor Networks · doi:10.1155/2014/503291 · Cited by 149 · see citing works

Body Sensor Networks: In the Era of Big Data and Beyond

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A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison

Ek, S., Portet, F., Lalanda, P., & Vega, G. (2021) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom50583.2021.9439129 · Cited by 127 · see citing works

Dynamic sensor event segmentation for real-time activity recognition in a smart home context

Wan, J., O’Grady, M. J., & O’Hare, G. M. P. (2014) · Personal and Ubiquitous Computing · doi:10.1007/s00779-014-0824-x · Cited by 121 · see citing works

Detection of abnormal behaviour for dementia sufferers using Convolutional Neural Networks

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Exploratory Data Analysis of Acceleration Signals to Select Light-Weight and Accurate Features for Real-Time Activity Recognition on Smartphones

Khan, A., Siddiqi, M., & Lee, S. W. (2013) · Sensors · doi:10.3390/s131013099 · Cited by 114 · see citing works

Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network

Zhou, B., Yang, J., & Li, Q. (2019) · Sensors · doi:10.3390/s19030621 · Cited by 88 · see citing works

Survey on Human Activity Recognition based on Acceleration Data

Slim, S. O., Atia, A., Marwa, M. A., & Mostafa, M.-S. M. (2019) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2019.0100311 · Cited by 87 · see citing works

Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk

Kańtoch, E. (2018) · Sensors · doi:10.3390/s18103219 · Cited by 77 · see citing works

Human Activity Recognition for Production and Logistics—A Systematic Literature Review

Reining, C., Niemann, F., Rueda, F. M., Fink, G. A., & ten Hompel, M. (2019) · Information · doi:10.3390/info10080245 · Cited by 70 · see citing works

A perspective on human activity recognition from inertial motion data

Gomaa, W. E., & Khamis, M. A. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08863-9 · Cited by 60 · see citing works

Research on Construction Workers’ Activity Recognition Based on Smartphone

Zhang, M., Chen, S., Zhao, X., & Yang, Z. C. (2018) · Sensors · doi:10.3390/s18082667 · Cited by 54 · see citing works

EFM: evolutionary fuzzy model for dynamic activities recognition using a smartphone accelerometer

Fahim, M., Fatima, I., Lee, S., & Park, Y.-T. (2013) · Applied Intelligence · doi:10.1007/s10489-013-0427-7 · Cited by 49 · see citing works

Human activity recognition: A comprehensive review

Kaur, H., Rani, V., & Kumar, M. (2024) · Expert Systems · doi:10.1111/exsy.13680 · Cited by 50 · see citing works

Time-Bounded Activity Recognition for Ambient Assisted Living

Wan, J., Li, M., O’Grady, M. J., Gu, X., Al-awlaqi, M. A. A. H., & O’Hare, G. M. P. (2018) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2018.2870047 · Cited by 45 · see citing works

A Comparative Analysis of the Impact of Features on Human Activity Recognition with Smartphone Sensors

Sousa, W., Souto, E., Rodrigres, J., Sadarc, P., Jalali, R., & El‐Khatib, K. (2017) · Brazilian Symposium on Multimedia and the Web · doi:10.1145/3126858.3126859 · Cited by 44 · see citing works

Managing Perceived Loneliness and Social-Isolation Levels for Older Adults: A Survey with Focus on Wearables-Based Solutions

Site, A., Lohan, E. S., Jolanki, O. H., Valkama, O., Hernández, R. R., Latikka, R., Alekseeva, D., Vasudevan, S., Afolaranmi, S. O., Ometov, A., et al. (2022) · Sensors · doi:10.3390/s22031108 · Cited by 48 · see citing works

Evaluation of federated learning aggregation algorithms

Ek, S., Portet, F., Lalanda, P., & Vega, G. (2020) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3410530.3414321 · Cited by 42 · see citing works

User-Independent Activity Recognition via Three-Stage GA-Based Feature Selection

Saputri, T. R. D., Khan, A., & Lee, S.-W. (2014) · International Journal of Distributed Sensor Networks · doi:10.1155/2014/706287 · Cited by 36 · see citing works

Real-Time Activity Recognition on Smartphones Using Deep Neural Networks

Zhang, L., Wu, X., & Luo, D. (2015) · 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom) · doi:10.1109/uic-atc-scalcom-cbdcom-iop.2015.224 · Cited by 35 · see citing works

kNN Prototyping Schemes for Embedded Human Activity Recognition with Online Learning

Ferreira, P., Cardoso, J. M. P., & Mendes‐Moreira, J. (2020) · Computers · doi:10.3390/computers9040096 · Cited by 34 · see citing works

A review of mobile sensing systems, applications, and opportunities

Laport, F., Serrano, E., Bajo, J., & Campbell, A. T. (2019) · Knowledge and Information Systems · doi:10.1007/s10115-019-01346-1 · Cited by 33 · see citing works

Novel Sampling Algorithm for Human Mobility-Based Mobile Phone Sensing

Thejaswini, M., Rajalakshmi, P., & Desai, U. B. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2388074 · Cited by 32 · see citing works

Adaptive and Energy Efficient Context Representation Framework in Mobile Sensing

Yurur, O., Labrador, M. A., & Moreno, W. A. (2013) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2013.47 · Cited by 32 · see citing works

European Journal for Biomedical Informatics

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BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces

Márquez-Sánchez, S., Campero-Jurado, I., Robles-Camarillo, D., Rodrı́guez, S., & Corchado, J. M. (2021) · Sensors · doi:10.3390/s21103372 · Cited by 31 · see citing works

In-depth analysis of design & development for sensor-based human activity recognition system

Choudhury, N. A., & Soni, B. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16423-5 · Cited by 29 · see citing works

Smartphone-based human activity recognition

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A Survey on Human Activity Recognition Based on Temporal Signals of Portable Inertial Sensors

Elbasiony, R. M., & Gomaa, W. E. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-14118-9_72 · Cited by 27 · see citing works

SARM: Salah Activities Recognition Model Based on Smartphone

Ahmad, N., Han, L., Iqbal, K., Ahmad, R., Abid, M. A., & Iqbal, N. (2019) · Electronics · doi:10.3390/electronics8080881 · Cited by 26 · see citing works

Human Activity Recognition Based on Acceleration Data From Smartphones Using HMMs

Iloga, S., Bordat, A., Kernec, J. L., & Romain, O. (2021) · IEEE Access · doi:10.1109/access.2021.3117336 · Cited by 24 · see citing works

ADL Classification Based on Autocorrelation Function of Inertial Signals

Gomaa, W. E., Elbasiony, R. M., & Ashry, S. (2017) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2017.00-53 · Cited by 20 · see citing works

Human Activity Recognition by Means of Online Semi-supervised Learning

Cardoso, H. L., & Mendes‐Moreira, J. (2016) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2016.93 · Cited by 19 · see citing works

Gait Activity Authentication Using LSTM Neural Networks with Smartphone Sensors

Zhang, M. (2019) · International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) · doi:10.1109/msn48538.2019.00092 · Cited by 17 · see citing works

PEMAR: A pervasive middleware for activity recognition with smart phones

Vaka, P., Shen, F., Chandrashekar, M., & Lee, Y. (2015) · 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) · doi:10.1109/percomw.2015.7134073 · Cited by 16 · see citing works

HarMI: Human Activity Recognition Via Multi-Modality Incremental Learning

Zhang, X., Yu, H., Yang, Y., Gu, J., Li, Y., Zhuang, F., Yu, D., & Ren, Z. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3085602 · Cited by 16 · see citing works

A Survey on Human Activity Recognition Using Accelerometer Sensor

Alrazzak, U., & Alhalabi, B. (2019) · International Conference on Informatics, Electronics & Vision (ICIEV) · doi:10.1109/iciev.2019.8858578 · Cited by 15 · see citing works

Unobtrusive Posture Recognition via Online Learning of Multi-dimensional RFID Received Signal Strength

Yao, L., Sheng, Q. Z., Ruan, W., Li, X., Wang, S., & Yang, Z. Q. (2015) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads.2015.23 · Cited by 14 · see citing works

A Lightweight Deep Learning Network for Sensor-based Human Activity Recognition using IMU sensors of a Low-Power Wearable Device

Jantawong, P., Hnoohom, N., Jitpattanakul, A., & Mekruksavanich, S. (2021) · International Computer Science and Engineering Conference · doi:10.1109/icsec53205.2021.9684631 · Cited by 13 · see citing works

ARService: A Smartphone based Crowd-Sourced Data Collection and Activity Recognition Framework

İncel, Ö. D., & Özgövde, A. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.04.142 · Cited by 13 · see citing works

A novel smartphone-based activity recognition modeling method for tracked equipment in forest operations

Becker, R. M., & Keefe, R. F. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266568 · Cited by 13 · see citing works

GPS-Based Daily Context Recognition for Lifelog Generation Using Smartphone

Tanaka, G., Okada, M., & Mineno, H. (2015) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2015.060216 · Cited by 11 · see citing works

ManySense: An Extensible and Accessible Middleware for Consumer-Oriented Heterogeneous Body Sensor Networks

Westlin, J., & Laine, T. H. (2014) · International Journal of Distributed Sensor Networks · doi:10.1155/2014/321534 · Cited by 11 · see citing works

Resource consumption analysis of online activity recognition on mobile phones and smartwatches

Shoaib, M. A., İncel, Ö. D., Scolten, H., & Havinga, P. (2017) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/pccc.2017.8280500 · Cited by 11 · see citing works

A Localized Learning Approach Applied to Human Activity Recognition

Amer, A. Y. A., Aerts, J., Vanrumste, B., & Luca, S. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2964738 · Cited by 12 · see citing works

Exploring Human Activities Using eSense Earable Device

Islam, M. S., Hossain, T., Ahad, M. A. R., & Inoue, S. (2020) · Smart innovation, systems and technologies · doi:10.1007/978-981-15-8944-7_11 · Cited by 11 · see citing works

A Survey Investigating the Combination and Number of IMUs on the Human Body Used for Detecting Activities and Human Tracking

Reich, O., Hübner, E., Ghita, B., Wagner, M., & Schäfer, J. (2020) · World Conference on Computing and Communication Technologies (WCCCT) · doi:10.1109/wccct49810.2020.9170009 · Cited by 10 · see citing works

Sensor - Based Human Activity Recognition Using Smartphones

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The effectiveness of simple heuristic features in sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer

Barua, A., Jiang, X., & Fuller, D. (2024) · BioMedical Engineering OnLine · doi:10.1186/s12938-024-01213-3 · Cited by 11 · see citing works

Human Activity Recognition: Challenges and Process Stages

Alzahrani, M. S., & Kammoun, S. (2016) · International Journal of Innovative Research in Computer and Communication Engineering · Cited by 9 · see citing works

An Enhanced Deep Learning Approach for Smartphone-Based Human Activity Recognition in IoHT

Soni, V., Jaiswal, S., Semwal, V. B., Roy, B., Choubey, D. K., & Mallick, D. K. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-5868-7_37 · Cited by 10 · see citing works

A Review on the Artificial Intelligence Algorithms for the Recognition of Activities of Daily Living Using Sensors in Mobile Devices

Pires, I. M., Marques, G., García, N. M., Pombo, N. G. C. C., Flórez‐Revuelta, F., Zdravevski, E., & Spinsante, S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-40305-8_33 · Cited by 8 · see citing works

The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm

Anbananthen, K. S. M., Subbiah, S., Baskar, S. G., Selvaraj, R., Krishnan, J., Kannan, S., & Chelliah, D. (2023) · Emerging Science Journal · doi:10.28991/esj-2023-07-05-011 · Cited by 7 · see citing works

HARE: Unifying the Human Activity Recognition Engineering Workflow

Konak, O., van de Water, R. P., Döring, V., Fiedler, T., Liebe, L., Masopust, L., Postnov, K. V., Sauerwald, F., Treykorn, F., Wischmann, A., et al. (2023) · Sensors · doi:10.3390/s23239571 · Cited by 7 · see citing works

Effect of Reduced Dimensionality on Deep learning for Human Activity Recognition

Kumari, G., Chakraborty, J., & Nandy, A. (2020) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt49239.2020.9225419 · Cited by 7 · see citing works

MM-HAR: Multi-Modal Human Activity Recognition Using Consumer Smartwatch and Earbuds

Rashid, N., Nemati, E., Ahmed, M. Y., Kuang, J., & Gao, J. A. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340984 · Cited by 7 · see citing works

Uncertainty Visualization for Mobile and Wearable Devices Based Activity Recognition Systems

Dong, M., Chen, L., Wang, L., Jiang, X., & Chen, G. (2016) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2016.1224527 · Cited by 7 · see citing works

Hybrid outlier detection (HOD) method in sensor data for human activity classification

Nivetha, G., & Venkatalakshmi, K. (2018) · Intelligent Data Analysis · doi:10.3233/ida-163329 · Cited by 7 · see citing works

Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Theilig, M.-M., Korbel, J. J., Mayer, G., Hoffmann, C., & Zarnekow, R. (2019) · IEEE Access · doi:10.1109/access.2019.2958474 · Cited by 7 · see citing works

Artifact: A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison

Ek, S., Portet, F., Lalanda, P., & Vega, G. (2021) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops51409.2021.9431080 · Cited by 6 · see citing works

Activity recognition from smartphone data using weighted learning methods

Abidine, M. B., & Fergani, B. (2021) · Intelligenza Artificiale · doi:10.3233/ia-200059 · Cited by 6 · see citing works

Defining a Roadmap Towards Comparative Research in Online Activity Recognition on Mobile Phones

Shoaib, M. A., Bosch, S., İncel, Ö. D., Scholten, H., & Havinga, P. (2015) · International Conference on Pervasive and Embedded Computing and Communication Systems · doi:10.5220/0005324401540159 · Cited by 6 · see citing works

CORREDOR, A mobile Human-Centric Sensing System for Activity Recognition.

Jaimes, L. G., & Vergara-Laurens, I. J. (2015) · Cited by 6 · see citing works

Wearable Devices for Human Activity Recognition and User Detection

Capobianco, G., Giacomo, U. D., Martinelli, F., Mercaldo, F., & Santone, A. (2019) · Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) · doi:10.1109/empdp.2019.8671636 · Cited by 6 · see citing works

Collaborative Learning Analysis Using Business Card-Type Sensors

Yamaguchi, S., Ohtawa, S., Oshima, R., Oshima, J., Fujihashi, T., Saruwatari, S., & Watanabe, T. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-67788-6_22 · Cited by 6 · see citing works

Detecting Events of Daily Living Using Multimodal DataPreprint

Oh, H., & Jain, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.09402 · Cited by 6 · see citing works

Abnormal Activity Detection for the Elderly People using ConvLSTM Autoencoder

Nazerfard, E., Atashgahy, Z., & Nadali, A. (2021) · doi:10.21203/rs.3.rs-693084/v1 · Cited by 5 · see citing works

Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain

Usmanova, A., Portet, F., Lalanda, P., & Vega, G. (2022) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops53856.2022.9767246 · Cited by 5 · see citing works

Toward robust activity recognition: Hierarchical classifier based on Gaussian Process

Wang, X., Zhang, B., Zhang, F., Teng, G., Sun, Z., & Wei, J. (2016) · Intelligent Data Analysis · doi:10.3233/ida-160827 · Cited by 5 · see citing works

The Review of Human Activity Recognition SurveyPreprint

Chatterjee, S. (2023) · Preprints.org · doi:10.20944/preprints202309.1939.v1 · Cited by 5 · see citing works

Balancing Timing and Accuracy Requirements in Human Activity Recognition Mobile Applications

Jabla, R., Buendía, F., Khemaja, M., & Faïz, S. (2019) · International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI) · doi:10.3390/proceedings2019031015 · Cited by 4 · see citing works

Energy Saving in Forward Fall Detection using Mobile Accelerometer

Viet, V. Q., Negara, A. F. P., Thang, H. M., & Choi, D. (2013) · International Journal of Distributed Systems and Technologies · doi:10.4018/jdst.2013010106 · Cited by 4 · see citing works

Long Short Term Memory Networks for Stroke Activity Recognition base on Smartphone

Siagian, P., & Fernando, E. (2021) · International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) · doi:10.1109/icitisee53823.2021.9655817 · Cited by 3 · see citing works

A method of estimating outdoor situation for lifelog generation

Tanaka, G., & Mineno, H. (2013) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce.2013.6664855 · Cited by 3 · see citing works

A Framework for Activity Recognition Through Deep Learning and Abnormality Detection in Daily Activities

Mocanu, I. G., Cramariuc, B., Balan, O. C., & Moldoveanu, A. D. B. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68548-9_66 · Cited by 3 · see citing works

Battery Optimization of Android Phones by Sensing the Phone Using Hidden Markov Model

Sivakumar, R., Sathyanarayanan, R., & Harikrishnan, T. R. (2015) · Asian Journal of Computer Science And Information Technology · doi:10.15520/jccst.2015.vol5.iss05.34 · Cited by 2 · see citing works

A Computing Framework to Check Real-Time Requirements in Ambient Intelligent Systems

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Estimation of Record Contents for Automatic Generation of Care Records

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