Analysis of Multi-Sensor Fusion for Mobile and Wearable Sensor Based Human Activity Recognition

The WEKA data mining software: an update

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750 · see citing works

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

Ordóñez, F., & Roggen, D. (2016) · Sensors · doi:10.3390/s16010115 · Cited by 2,761 · cited 2× · see citing works

A tutorial on human activity recognition using body-worn inertial sensors

Bulling, A., Blanke, U., & Schiele, B. (2014) · ACM Computing Surveys · doi:10.1145/2499621 · Cited by 1,668 · see citing works

2019 IEEE International Conference «Quality Management, Transport and Information Security, Information Technologies»

(2019) · International Conference "Quality Management, Transport and Information Security, Information Technologies" (IT&QM&IS) · doi:10.1109/itqmis.2019.8928339 · Cited by 954 · see citing works

Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.056 · Cited by 930 · see citing works

Window Size Impact in Human Activity Recognition

Baños, O., Gálvez, J. M., Damas, M., Pomares, H., & Rojas, I. (2014) · Sensors · doi:10.3390/s140406474 · Cited by 656 · see citing works

Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors

Shoaib, M., Bosch, S., Incel, O., Scholten, H., & Havinga, P. (2016) · Sensors · doi:10.3390/s16040426 · Cited by 423 · see citing works

Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions

Nweke, H. F., Wah, T. Y., Mujtaba, G., & Al-Garadi, M. A. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.06.002 · Cited by 400 · see citing works

Human Activity Recognition Using Inertial/Magnetic Sensor Units

Altun, K., & Barshan, B. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14715-9_5 · Cited by 283 · see citing works

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

Janidarmian, M., Roshan Fekr, A., Radecka, K., & Zilic, Z. (2017) · Sensors · doi:10.3390/s17030529 · Cited by 217 · see citing works

Hierarchical, Multi-Sensor Based Classification of Daily Life Activities: Comparison with State-of-the-Art Algorithms Using a Benchmark Dataset

Leutheuser, H., Schuldhaus, D., & Eskofier, B. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0075196 · Cited by 189 · see citing works

Improving Activity Recognition Accuracy in Ambient-Assisted Living Systems by Automated Feature Engineering

Zdravevski, E., Lameski, P., Trajkovik, V., Kulakov, A., Chorbev, I., Goleva, R., Pombo, N. G. C. C., & García, N. M. (2017) · IEEE Access · doi:10.1109/access.2017.2684913 · Cited by 172 · see citing works

GCHAR: An efficient Group-based Context—aware human activity recognition on smartphone

Cao, L., Wang, Y., Zhang, B., Jin, Q., & Vasilakos, A. V. (2017) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2017.05.007 · Cited by 154 · see citing works

Human daily activity recognition in robot-assisted living using multi-sensor fusion

Zhu, C., & Sheng, W. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152756 · Cited by 135 · see citing works

Towards detection of bad habits by fusing smartphone and smartwatch sensors

Shoaib, M. A., Bosch, S., Scholten, H., Havinga, P., & İncel, Ö. D. (2015) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2015.7134104 · Cited by 116 · see citing works

Multi-view stacking for activity recognition with sound and accelerometer data

Garcia-Ceja, E., Galvan-Tejada, C. E., & Brena, R. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.06.004 · Cited by 115 · see citing works

Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry

Chowdhury, A. K., Tjondronegoro, D., Chandran, V., & Trost, S. G. (2017) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001291 · Cited by 91 · see citing works

Multi-sensor fusion for human daily activity recognition in robot-assisted living

Zhu, C., & Sheng, W. (2009) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/1514095.1514187 · Cited by 51 · see citing works

Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study

Mujtaba, G., Shuib, L. M., Raj, R. G., Rajandram, R., & Shaikh, K. (2017) · Journal of Forensic and Legal Medicine · doi:10.1016/j.jflm.2017.07.001 · Cited by 33 · see citing works

A Mobile Application for Easy Design and Testing of Algorithms to Monitor Physical Activity in the Workplace

Spinsante, S., Angelici, A., Lundström, J., Espinilla, M., Cleland, I., & Nugent, C. (2016) · Mobile Information Systems · doi:10.1155/2016/5126816 · Cited by 28 · see citing works

Indoor corner recognition from crowdsourced trajectories using smartphone sensors

Sun, Y., & Wang, B. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.04.024 · Cited by 18 · see citing works

Activity Recognition Based on a Multi-sensor Meta-classifier

Baños, O., Damas, M., Pomares, H., & Rojas, I. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38682-4_24 · Cited by 14 · see citing works

A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion

Ali, F., El–Sappagh, S., Islam, S. M. R., Kwak, D., Ali, A., Imran, M. A., & Kwak, K.-S. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.008 · Cited by 840 · see citing works

Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges

Qiu, S., Zhao, H., Jiang, N., Wang, Z., Liu, L., An, Y., Zhao, H., Miao, X., Liu, R., & Fortino, G. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.006 · Cited by 671 · see citing works

Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

Demrozi, F., Pravadelli, G., Bihorac, A., & Rashidi, P. (2020) · IEEE Access · doi:10.1109/access.2020.3037715 · Cited by 368 · see citing works

An intelligent healthcare monitoring framework using wearable sensors and social networking data

Ali, F., El-Sappagh, S., Islam, S. M. R., Ali, A., Attique, M., Imran, M., & Kwak, K.-S. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.07.047 · Cited by 355 · see citing works

Deep learning-based breast cancer classification through medical imaging modalities: State of the art and research challenges

Murtaza, G., Shuib, L., Abdul Wahab, A. W., Mujtaba, G., Mujtaba, G., Nweke, H. F., Al-garadi, M. A., Zulfiqar, F., Raza, G., & Azmi, N. A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09716-5 · Cited by 342 · see citing works

A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep Ensemble Learning

Ihnaini, B., Khan, M. A., Khan, T. A., Abbas, S., Daoud, M. S., Ahmad, M., & Khan, M. A. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/4243700 · Cited by 148 · see citing works

Multi-sensor fusion based on multiple classifier systems for human activity identification

Nweke, H. F., Wah, T. Y., Mujtaba, G., Alo, U. R., & Al-Garadi, M. A. (2019) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-019-0194-5 · Cited by 67 · see citing works

New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition

Alemayoh, T. T., Lee, J. H., & Okamoto, S. (2021) · Sensors · doi:10.3390/s21082814 · Cited by 52 · see citing works

On the Challenges and Potential of Using Barometric Sensors to Track Human Activity

Manivannan, A., Chin, W. C. B., Barrat, A., & Bouffanais, R. (2020) · Sensors · doi:10.3390/s20236786 · Cited by 49 · see citing works

Human Activity Recognition Based on Deep Learning Regardless of Sensor Orientation

He, Z., Sun, Y., & Zhang, Z. (2024) · Applied Sciences · doi:10.3390/app14093637 · Cited by 11 · see citing works

CIM: A Novel Clustering-based Energy-Efficient Data Imputation Method for Human Activity Recognition

Hussein, D., & Bhat, G. (2023) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3609111 · Cited by 10 · see citing works

Smartphone-sensor-based human activities classification for forensics: a machine learning approach

Moubarak, N. N. I., Omar, N. M. M., & Youssef, V. N. (2024) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-024-00157-x · Cited by 10 · 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

A Study on the Influence of Sensors in Frequency and Time Domains on Context Recognition

de Souza, P., Silva, D., de Andrade, I., Dias, J., Lima, J. P., Teichrieb, V., Quintino, J. P., da Silva, F. Q. B., & Santos, A. L. M. (2023) · Sensors · doi:10.3390/s23125756 · Cited by 4 · see citing works

Class imbalance data handling with deep learning–based ubiquitous healthcare monitoring system using wearable devices

Sowmyalakshmi, R., Venkatesh, P. M., Jayasankar, T., & Shankar, K. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-85854-0.00010-1 · Cited by 4 · see citing works

The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing

Hernandez-Cruz, N., Nugent, C., Zhang, S., & McChesney, I. (2021) · Applied Sciences · doi:10.3390/app11167660 · Cited by 3 · see citing works

CFSCare: ML-Based Activity Monitoring System for Chronic Fatigue Syndrome Patients Using Smartphone and Wrist Sensor

Mahmood, A., Sridevi, P., Rabbani, M., Meikandan, P. V., Syam, M., Shefa, S. T., Chu, W. C., & Ahamed, S. I. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00115 · Cited by 1 · see citing works

Comparative Assessment of Forest Optimization with Deep Ensembling Technique for Human Activity Recognition based on Data Collected from Wearable Devices

Pallavi, G. (2021) · Revista Gestão Inovação e Tecnologias · doi:10.47059/revistageintec.v11i4.2181 · Cited by 1 · see citing works

Advancements in Healthcare: Multi-Agent Based Intelligent Sensor Approach

Bisen, K. S. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-3675-2_12 · Cited by 1 · see citing works

Human motion data expansion from arbitrary sparse sensors with shallow recurrent decodersPreprint

Ebers, M. R., Pitts, M. N., Kutz, J. N., & Steele, K. M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.01.596487 · Cited by 1 · see citing works

Adoption of the activation function fusion approach to identify human activity recognition in a semi-supervised neural network

Hernandez-Cruz, N., Nugent, C., McChesney, I., Zhang, S., & Favela, J. (2019) · EAI Endorsed Transactions on Pervasive Health and Technology · doi:10.4108/eai.30-10-2018.161669

Sensor Fusion-based Cooperative Human Detection Scheme using MAVs in Indoor Environment

Kim, M.-E., Bang, H. C., Lee, S., & Jeon, J. H. (2020) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc49870.2020.9289611

An analysis of road geometry and characteristics that affects on accident rates in Tamilnadu roadways using novel decision tree algorithm compared with support vector machine algorithm

Gopi, B., & Rama, A. (2023) · AIP conference proceedings · doi:10.1063/5.0179812

Hybrid and Effective Model for the Prediction of DiabetesPreprint

Tripathy, N., Pande, S. K., & Das, S. (2024) · Research Square · doi:10.21203/rs.3.rs-3952667/v1

Survey on Objective Measurement and Sensor-Based Detection of Physical and Social Activities

Mahmood, A., Sridevi, P., Meikandan, P. V., & Ahamed, S. I. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00083

Quantitative Estimation of Human Height and Weight Using Motion Data From Multiple Smart Devices

Dong, J., & Cai, Z. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3488694

Intelligent Action-Based Fraud Detection System for Monitoring Emissions Testing in Motor Vehicle Inspection

Hernandez, P. V. C., Solis, F. D. B., Alonzo, J. A. S., & Asaba, H. (2025) · International Conference on Mechatronics and Machine Vision in Practice (M2VIP) · doi:10.1109/m2vip67511.2025.11165695

Sensor Fusion-Based and Machine Learning-Based Human Activity Recognition

Rajagoli, V. R., Naik, R., Devaraddi, R., Kulkarni, A., & Mavinkattimath, S. G. (2026) · doi:10.1109/incect72370.2026.11742838

Multi-sensor fusion based on multiple classifier systems for human activity identification

Nweke, H. F., Wah, T. Y., Mujtaba, G., Alo, U. R., & Al-Garadi, M. A. (2019) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-019-0194-5 · Cited by 67 · see citing works

Basic Human Activity Recognition based on sensor fusion in smartphones

Buenaventura, C. V. S., & Tiglao, N. M. C. (2017) · IFIP/IEEE International Symposium on Integrated Network Management (IM) · doi:10.23919/inm.2017.7987459 · Cited by 25 · see citing works

Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions

Nweke, H. F., Wah, T. Y., Mujtaba, G., & Al-Garadi, M. A. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.06.002 · Cited by 400 · see citing works

Multi-sensor fusion with triple reliability evaluation for human activity recognition

Zuo, K., Li, X., Li, X., Wang, K., Gao, T., & Shen, T. (2025) · Measurement · doi:10.1016/j.measurement.2025.118580 · Cited by 2 · see citing works

A Weighted Late Fusion Framework for Recognizing Human Activity from Wearable Sensors

Tsanousa, A., Μεδίτσκος, Γ., Vrochidis, S., & Kompatsiaris, I. (2019) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3507003