Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions

Data Mining: Practical Machine Learning Tools and Techniques

Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works

Activity Recognition from User-Annotated Acceleration Data

Bao, L., & Intille, S. S. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24646-6_1 · Cited by 3,147 · see citing works

Supervised and Unsupervised Discretization of Continuous Features

Dougherty, J., Kohavi, R., & Sahami, M. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50032-3 · Cited by 1,946 · see citing works

Unsupervised, dynamic identification of physiological and activity context in wearable computing

Krause, A., Siewiorek, D. P., Smailagic, A., & Farringdon, J. (2004) · Proceedings · doi:10.1109/iswc.2003.1241398 · Cited by 153 · see citing works

eWatch: a wearable sensor and notification platform

Maurer, U., Rowe, A., Smailagic, A., & Siewiorek, D. P. (2006) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2006.24 · Cited by 146 · see citing works

Empirical Study of Design Choices in Multi-Sensor Context Recognition Systems

Bharatula, N. B., & Stäger, M. (2005) · Cited by 37 · see citing works

Power and Size Optimized Multi-Sensor Context Recognition Platform

Bharatula, N. B., Stäger, M., Lukowicz, P., & Tröster, G. (2005) · Proceedings · doi:10.1109/iswc.2005.42 · Cited by 19 · see citing works

Ambient intelligence: Technologies, applications, and opportunities

Cook, D. J., Augusto, J. C., & Jakkula, V. R. (2009) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2009.04.001 · Cited by 968 · see citing works

Human Action Recognition From Various Data Modalities: A Review

Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., & Liu, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183112 · Cited by 691 · 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

Transfer learning for activity recognition: a survey

Cook, D., Feuz, K. D., & Krishnan, N. C. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-013-0665-3 · Cited by 447 · see citing works

Review of Wearable Devices and Data Collection Considerations for Connected Health

Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021) · Sensors · doi:10.3390/s21165589 · Cited by 435 · see citing works

Situation identification techniques in pervasive computing: A review

Ye, J., Dobson, S., & McKeever, S. (2011) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2011.01.004 · Cited by 434 · 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

Smartphone and Smartwatch-Based Biometrics Using Activities of Daily Living

Weiss, G. M., Yoneda, K., & Hayajneh, T. S. (2019) · IEEE Access · doi:10.1109/access.2019.2940729 · Cited by 361 · see citing works

A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time

Wang, Z., Yang, Z., & Dong, T. (2017) · Sensors · doi:10.3390/s17020341 · Cited by 357 · see citing works

Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers

Yang, J.-Y., Wang, J.-S., & Chen, Y.-P. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.08.002 · Cited by 335 · see citing works

A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks

Muzammal, M., Talat, R., Sodhro, A. H., & Pirbhulal, S. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.06.021 · Cited by 311 · see citing works

The Elderly’s Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development

Ni, Q., García, A. B., & Pau, I. (2015) · Sensors · doi:10.3390/s150511312 · Cited by 264 · see citing works

Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network

Steven Eyobu, O., & Han, D. S. (2018) · Sensors · doi:10.3390/s18092892 · Cited by 259 · see citing works

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

Lara, Ó. D., Pérez, A. J., Labrador, M. A., & Posada, J. (2011) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2011.06.004 · Cited by 241 · see citing works

Recognition of human activities using SVM multi-class classifier

Qian, H., Mao, Y., Xiang, W., & Wang, Z. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.09.019 · Cited by 210 · see citing works

A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living

Chaaraoui, A. A., Climent-Pérez, P., & Flórez‐Revuelta, F. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.03.005 · Cited by 211 · see citing works

Real-Time Human Ambulation, Activity, and Physiological Monitoring: Taxonomy of Issues, Techniques, Applications, Challenges and Limitations

Khusainov, R., Azzi, D., Achumba, I. E., & Bersch, S. D. (2013) · Sensors · doi:10.3390/s131012852 · Cited by 207 · see citing works

A Feature Selection-Based Framework for Human Activity Recognition Using Wearable Multimodal Sensors

Zhang, M., & Sawchuk, A. A. (2011) · International Conference on Body Area Networks · doi:10.4108/icst.bodynets.2011.247018 · Cited by 182 · see citing works

Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition

Banos, O., Toth, M., Damas, M., Pomares, H., & Rojas, I. (2014) · Sensors · doi:10.3390/s140609995 · Cited by 159 · see citing works

Sensing Technology for Human Activity Recognition: A Comprehensive Survey

Fu, B., Damer, N., Kirchbuchner, F., & Kuijper, A. (2020) · IEEE Access · doi:10.1109/access.2020.2991891 · Cited by 162 · 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

A Secure IoT-Based Modern Healthcare System With Fault-Tolerant Decision Making Process

Gope, P., Gheraibia, Y., Kabir, S., & Sikdar, B. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3007488 · Cited by 137 · see citing works

HARTH: A Human Activity Recognition Dataset for Machine Learning

Logacjov, A., Bach, K., Kongsvold, A., Bårdstu, H. B., & Mork, P. J. (2021) · Sensors · doi:10.3390/s21237853 · Cited by 133 · see citing works

Using Mobile Phones for Activity Recognition in Parkinson’s Patients

Albert, M. V., Toledo, S. D., Shapiro, M. B., & Kording, K. P. (2012) · Frontiers in Neurology · doi:10.3389/fneur.2012.00158 · Cited by 125 · see citing works

Daily living activity recognition based on statistical feature quality group selection

Baños, O., Damas, M., Pomares, H., Prieto, A., & Rojas, I. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.164 · Cited by 127 · see citing works

Sensor Data Acquisition and Processing Parameters for Human Activity Classification

Bersch, S. D., Azzi, D., Khusainov, R., Achumba, I. E., & Ries, J. (2014) · Sensors · doi:10.3390/s140304239 · Cited by 114 · see citing works

Cloud-based Activity-aaService cyber–physical framework for human activity monitoring in mobility

Gravina, R., Ma, C., Pace, P., Aloi, G., Russo, W., Li, W., & Fortino, G. (2016) · Future Generation Computer Systems · doi:10.1016/j.future.2016.09.006 · Cited by 112 · see citing works

Motion- and location-based online human daily activity recognition

Zhu, C., & Sheng, W. (2010) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2010.11.004 · Cited by 104 · see citing works

A Java-Based Agent Platform for Programming Wireless Sensor Networks

Aiello, F., Fortino, G., Gravina, R., & Guerrieri, A. (2010) · The Computer Journal · doi:10.1093/comjnl/bxq019 · Cited by 101 · see citing works

Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

Hayat, A., Morgado‐Dias, F., Bhuyan, B. P., & Tomar, R. (2022) · Information · doi:10.3390/info13060275 · Cited by 102 · see citing works

Human Action Recognition from Various Data Modalities: A ReviewPreprint

Rahmani, H., Bennamoun, M., & Ke, Q. (2021) · doi:10.36227/techrxiv.13708270 · Cited by 99 · see citing works

A Comparative Review of Footwear-Based Wearable Systems

Hegde, N., Bries, M., & Sazonov, E. E. (2016) · Electronics · doi:10.3390/electronics5030048 · Cited by 99 · see citing works

Better Physical Activity Classification using Smartphone Acceleration Sensor

Arif, M., Bilal, M., Kattan, A. J., & Ahamed, S. I. (2014) · Journal of Medical Systems · doi:10.1007/s10916-014-0095-0 · Cited by 97 · see citing works

A perspective on intelligent devices and environments in medical rehabilitation

Cooper, R. A., Dicianno, B. E., Brewer, B. R., LoPresti, E., Ding, D., Simpson, R. C., Grindle, G. G., & Wang, H. (2008) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2008.09.003 · Cited by 94 · see citing works

Multi-Sensor Fusion for Enhanced Contextual Awareness of Everyday Activities with Ubiquitous Devices

Guiry, J. J., van de Ven, P. W. J., & Nelson, J. (2014) · Sensors · doi:10.3390/s140305687 · Cited by 91 · see citing works

Multi-agent smart environments

Cook, D. J. (2009) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-2009-0007 · Cited by 85 · see citing works

Impact of Sliding Window Length in Indoor Human Motion Modes and Pose Pattern Recognition Based on Smartphone Sensors

Wang, G., Li, Q., Wang, L., Wang, W., Wu, M., & Liu, T. (2018) · Sensors · doi:10.3390/s18061965 · Cited by 82 · see citing works

SPHERE: A Sensor Platform for Healthcare in a Residential Environment

Woznowski, P., Burrows, A., Diethe, T., Fafoutis, X., Hall, J., Hannuna, S., Camplani, M., Twomey, N., Kozłowski, M., Tan, B., et al. (2016) · Springer eBooks · doi:10.1007/978-3-319-44924-1_14 · Cited by 77 · see citing works

Travel Mode Detection with Varying Smartphone Data Collection Frequencies

Shafique, M. A., & Hato, E. (2016) · Sensors · doi:10.3390/s16050716 · Cited by 77 · see citing works

Smartphone-Based Patients’ Activity Recognition by Using a Self-Learning Scheme for Medical Monitoring

Guo, J., Zhou, X., Sun, Y., Gong, P., Zhao, G., & Li, Z. (2016) · Journal of Medical Systems · doi:10.1007/s10916-016-0497-2 · Cited by 74 · see citing works

Measuring functional arm movement after stroke using a single wrist-worn sensor and machine learning

Bochniewicz, E. M., Emmer, G., McLeod, A., Barth, J., Dromerick, A. W., & Lum, P. (2017) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2017.07.004 · Cited by 73 · see citing works

Positioning Methods and the Use of Location and Activity Data in Forests

Keefe, R. F., Wempe, A. M., Becker, R. M., Zimbelman, E. G., Nagler, E. S., Gilbert, S. L., & Caudill, C. C. (2019) · Forests · doi:10.3390/f10050458 · Cited by 79 · 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

An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence

Liu, R., Ramli, A. A., Zhang, H., Henricson, E., & Liu, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-96068-1_1 · Cited by 72 · see citing works

Feature extraction for robust physical activity recognition

Zhu, J., San-Segundo, R., & Pardo, J. M. (2017) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-017-0097-2 · Cited by 68 · see citing works

Activity knowledge transfer in smart environments

Rashidi, P., & Cook, D. J. (2011) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2011.02.007 · Cited by 61 · see citing works

The Benefits of Personalized Smartphone-Based Activity Recognition Models

Lockhart, J. W., & Weiss, G. M. (2014) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611973440.71 · Cited by 60 · see citing works

Comparison of offline and real-time human activity recognition results using machine learning techniques

Sütő, J., Oniga, S., Lung, C., & Orha, I. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3437-x · Cited by 59 · 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

On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition

Baños, O., Damas, M., Pomares, H., & Rojas, I. (2012) · Sensors · doi:10.3390/s120608039 · Cited by 57 · see citing works

A Review of Smart House Analysis Methods for Assisting Older People Living Alone

Sánchez, V. G., Pfeiffer, C., & Skeie, N. (2017) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan6030011 · Cited by 55 · see citing works

Human activity recognition using wearable accelerometer sensors

Zubair, M., Song, K., & Yoon, C. (2016) · IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) · doi:10.1109/icce-asia.2016.7804737 · Cited by 54 · see citing works

Earthquake Detection in a Static and Dynamic Environment Using Supervised Machine Learning and a Novel Feature Extraction Method

Khan, I., Choi, S., & Kwon, Y. (2020) · Sensors · doi:10.3390/s20030800 · Cited by 55 · see citing works

Activity Learning as a Foundation for Security Monitoring in Smart Homes

Dahmen, J., Thomas, B., Cook, D. J., & Wang, X. (2017) · Sensors · doi:10.3390/s17040737 · Cited by 54 · see citing works

Implementation of a Virtual Training Simulator Based on 360° Multi-View Human Action Recognition

Kwon, B., Kim, J., Lee, K., Lee, Y. K., Park, S., & Lee, S. (2017) · IEEE Access · doi:10.1109/access.2017.2723039 · Cited by 52 · see citing works

Sensor selection to support practical use of health‐monitoring smart environments

Cook, D. J., & Holder, L. B. (2011) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.20 · Cited by 51 · see citing works

Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors

Rahim, K. N. K. A., Elamvazuthi, I., Izhar, L. I., & Capi, G. (2018) · Sensors · doi:10.3390/s18124132 · Cited by 53 · 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

Indoor Human Activity Recognition Based on Ambient Radar with Signal Processing and Machine Learning

Zhu, S., Xu, J., Guo, H., Liu, Q., Wu, S., & Wang, H. (2018) · International Conference on Communications · doi:10.1109/icc.2018.8422107 · Cited by 49 · see citing works

A Comprehensive Study of Activity Recognition Using Accelerometers

Twomey, N., Diethe, T., Fafoutis, X., Elsts, A., McConville, R., Flach, P., & Craddock, I. (2018) · doi:10.20944/preprints201803.0147.v1 · Cited by 143 · see citing works

Designing Wearable Sensing Platforms for Healthcare in a Residential Environment

Fafoutis, X., Vafeas, A., Janko, B., Sherratt, R. S., Pope, J., Elsts, A., Mellios, E., Hilton, G., Oikonomou, G., Piechocki, R. J., et al. (2017) · EAI Endorsed Transactions on Pervasive Health and Technology · doi:10.4108/eai.7-9-2017.153063 · Cited by 46 · see citing works

Learning Deep and Shallow Features for Human Activity Recognition

Sani, S., Massie, S., Wiratunga, N., & Cooper, K. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-63558-3_40 · Cited by 45 · see citing works

Exploration of human activity recognition using a single sensor for stroke survivors and able-bodied people

Meng, L., Zhang, A., Chen, C., Wang, X., Jiang, X., Tao, L., Fan, J., Wu, X., Dai, C., Zhang, Y., et al. (2021) · Sensors · doi:10.3390/s21030799 · Cited by 45 · see citing works

Analysis of Optimal Sensor Positions for Activity Classification and Application on a Different Data Collection Scenario

Pannurat, N., Thiemjarus, S., Nantajeewarawat, E., & Anantavrasilp, I. (2017) · Sensors · doi:10.3390/s17040774 · Cited by 45 · see citing works

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

Saeedi, S., & El‐Sheimy, N. (2015) · Micromachines · doi:10.3390/mi6081100 · 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

SensCare: Semi-automatic Activity Summarization System for Elderly Care

Wu, P., Peng, H.-K., Zhu, J., & Zhang, Y. (2012) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-32320-1_1 · Cited by 41 · see citing works

Online Recognition of Driver-Activity Based on Visual Scanpath Classification

Braunagel, C., Geisler, D., Rosenstiel, W., & Kasneci, E. (2017) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2017.2743171 · Cited by 41 · see citing works

A Deep Machine Learning Method for Concurrent and Interleaved Human Activity Recognition

Thapa, K., Al, Z. M. A., Lamichhane, B., & Yang, S. (2020) · Sensors · doi:10.3390/s20205770 · Cited by 39 · see citing works

Bimodal HAR-An efficient approach to human activity analysis and recognition using bimodal hybrid classifiers

Venkatachalam, K., Yang, Z., Trojovský, P., Bačanin, N., Deveci, M., & Ding, W. (2023) · Information Sciences · doi:10.1016/j.ins.2023.01.121 · Cited by 37 · see citing works

A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions

Altulyan, M. S., Yao, L., Wang, X., Huang, C., Kanhere, S. S., & Sheng, Q. Z. (2021) · The Computer Journal · doi:10.1093/comjnl/bxab049 · Cited by 50 · see citing works

PUCK: an automated prompting system for smart environments: toward achieving automated prompting—challenges involved

Das, B., Cook, D. J., Schmitter‐Edgecombe, M., & Seelye, A. M. (2011) · Personal and Ubiquitous Computing · doi:10.1007/s00779-011-0445-6 · Cited by 34 · see citing works

Long-Term Impact of Intelligent Monitoring Technology on People with Cognitive Impairment: An Observational Study

Lazarou, I., Stavropoulos, T. G., Μεδίτσκος, Γ., Andreadis, S. T., Kompatsiaris, I. Y., & Tsolaki, M. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190423 · Cited by 35 · see citing works

Video based exercise recognition and correct pose detection

Rangari, T., Kumar, S., Roy, P. P., Dogra, D. P., & Kim, B. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12299-z · Cited by 33 · see citing works

Artificial Intelligence Based Approach for Classification of Human Activities Using MEMS Sensors Data

Khan, Y. A., Imaduddin, S., Singh, Y. P., Wajid, M., Usman, M. J., & Abbas, M. A. E. (2023) · Sensors · doi:10.3390/s23031275 · Cited by 33 · see citing works

HHAR-net: Hierarchical Human Activity Recognition using Neural Networks

Fazli, M., Kowsari, K., Gharavi, E., Barnes, L., & Doryab, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68449-5_6 · Cited by 32 · see citing works

Assessing Impact of Sensors and Feature Selection in Smart-Insole-Based Human Activity Recognition

D’Arco, L., Wang, H., & Zheng, H. (2022) · Methods and Protocols · doi:10.3390/mps5030045 · Cited by 32 · see citing works

Activity Recognition Using Complex Network Analysis

Jalloul, N., Porée, F., Viardot, G., Hostis, P. L., & Carrault, G. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2762404 · Cited by 32 · see citing works

Unobtrusive Stress Assessment Using Smartphones

Maxhuni, A., Hernandez-Leal, P., Morales, E., Sucar, L. E., Osmani, V., & Mayora, O. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.2974834 · Cited by 25 · see citing works

Human intention recognition in Smart Assisted Living Systems using a Hierarchical Hidden Markov Model

Zhu, C., Cheng, Q., & Sheng, W. (2008) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2008.4626513 · Cited by 31 · see citing works

Energy Efficient Activity Recognition Based on Low Resolution Accelerometer in Smart Phones

Liang, Y., Zhou, X., Yu, Z., Guo, B., & Yang, Y. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30767-6_11 · Cited by 31 · see citing works

Pre-Processing Effect on the Accuracy of Event-Based Activity Segmentation and Classification through Inertial Sensors

Fida, B., Bernabucci, I., Bibbo, D., Conforto, S., & Schmid, M. (2015) · Sensors · doi:10.3390/s150923095 · Cited by 29 · see citing works

A Novel Detection Model and Its Optimal Features to Classify Falls from Low- and High-Acceleration Activities of Daily Life Using an Insole Sensor System

Cates, B., Sim, T., Heo, H. M., Kim, B., Kim, H., & Mun, J. H. (2018) · Sensors · doi:10.3390/s18041227 · Cited by 29 · see citing works

Smartphone-based human activity recognition

Ortiz, J. (2014) · doi:10.5821/dissertation-2117-95551 · Cited by 29 · see citing works

A Novel Hybrid Deep Learning Model for Human Activity Recognition Based on Transitional Activities

Irfan, S., Anjum, N., Masood, N., Khattak, A. S., & Ramzan, N. (2021) · Sensors · doi:10.3390/s21248227 · Cited by 28 · see citing works

Activity Recognition in Wearable ECG Monitoring Aided by Accelerometer Data

Liu, J., Chen, J., Jiang, H., Jia, W. W., Lin, Q., & Wang, Z. (2018) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2018.8351076 · Cited by 27 · see citing works

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

Online Motion Mode Recognition for Portable Navigation Using Low-Cost Sensors

Elhoushi, M., Georgy, J., Noureldin, A. M., & Korenberg, M. J. (2015) · NAVIGATION Journal of the Institute of Navigation · doi:10.1002/navi.120 · Cited by 27 · see citing works

A Human Activity Recognition Framework for Healthcare Applications: Ontology, Labelling Strategies, and Best Practice

Woznowski, P., King, R., Harwin, W., & Craddock, I. J. (2016) · International Conference on Internet of Things, Big Data and Security (IoTBDS) · doi:10.5220/0005932503690377 · Cited by 27 · see citing works

Handling Imbalanced and Overlapping Classes in Smart Environments Prompting Dataset

Das, B., Krishnan, N. C., & Cook, D. J. (2014) · Studies in big data · doi:10.1007/978-3-642-45252-9_12 · Cited by 26 · see citing works

Recognizing Full-Body Exercise Execution Errors Using the Teslasuit

Caserman, P., Krug, C. K., & Göbel, S. P. (2021) · Sensors · doi:10.3390/s21248389 · Cited by 25 · see citing works

Classification of static postures with wearable sensors mounted on loose clothing

Jayasinghe, U., Janko, B., Hwang, F., & Harwin, W. (2023) · Scientific Reports · doi:10.1038/s41598-022-27306-4 · Cited by 25 · see citing works

Smartphone Location-Independent Physical Activity Recognition Based on Transportation Natural Vibration Analysis

Hur, T., Bang, J., Kim, D., Baños, O., & Lee, S. (2017) · Sensors · doi:10.3390/s17040931 · Cited by 23 · see citing works

SPAARC: A Fast Decision Tree Algorithm

Yates, D., Islam, M. Z., & Gao, J. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-6661-1_4 · Cited by 23 · see citing works

Detecting Abnormal Patterns of Daily Activities for the Elderly Living Alone

Zhao, T., Ni, H., Zhou, X., Lin, Q., Zhang, D., & Yu, Z. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-06269-3_11 · Cited by 22 · see citing works

AcTrak - Unobtrusive Activity Detection and Step Counting Using Smartphones

Chandel, V., Choudhury, A. D., Ghose, A., & Bhaumik, C. (2014) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-11569-6_35 · Cited by 21 · see citing works

Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms

Liu, Q., Zhou, Z., Shakya, S. R., Uduthalapally, P., Qiao, M., & Sung, A. H. (2018) · International Journal of Machine Learning and Computing · doi:10.18178/ijmlc.2018.8.2.674 · Cited by 21 · see citing works

Multiwindow Fusion for Wearable Activity Recognition

Baños, O., Gálvez, J. M., Damas, M., Guillén, A., Herrera, L. J., Pomares, H., Rojas, I., Villalonga, C., Hong, C. S., & Lee, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19222-2_24 · Cited by 21 · see citing works

Wearable Sensor-Based Human Activity Recognition via Two-Layer Diversity-Enhanced Multiclassifier Recognition Method

Tian, Y., Wang, X., Chen, L., & Liu, Z. (2019) · Sensors · doi:10.3390/s19092039 · Cited by 21 · see citing works

A deep learning approach for lower back-pain risk prediction during manual lifting

Snyder, K., Thomas, B., Lu, M., Jha, R., Barim, M. S., Hayden, M. A., & Werren, D. M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0247162 · Cited by 20 · see citing works

Location and Activity Recognition Using eWatch: A Wearable Sensor Platform

Maurer, U., Rowe, A., Smailagic, A., & Siewiorek, D. P. (2006) · Lecture notes in computer science · doi:10.1007/11825890_4 · Cited by 120 · see citing works

On the Development of a Real-Time Multi-sensor Activity Recognition System

Baños, O., Damas, M., Guillén, A., Herrera, L. J., Pomares, H., Rojas, I., Villalonga, C., & Lee, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-26410-3_17 · Cited by 3 · see citing works

A User-Independent and Sensor-Tolerant Wearable Activity Classifier

Blake, M. T., Younes, R., Dennis, J., Martin, T. L., & Jones, M. T. (2015) · Computer · doi:10.1109/mc.2015.296 · Cited by 7 · see citing works

The Mobile Sensing Platform: An Embedded Activity Recognition System

Choudhury, T., Borriello, G., Consolvo, S., Haehnel, D., Harrison, B. L., Hemingway, B., Hightower, J. R., Klasnja, P., Koscher, K., LaMarca, A., et al. (2008) · IEEE Pervasive Computing · doi:10.1109/mprv.2008.39 · Cited by 598 · see citing works

Human Activity Recognition - Using Sensor Data of Smartphones and Smartwatches

Sefen, B., Baumbach, S., Dengel, A. R., & Abdennadher, S. (2016) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0005816004880493 · Cited by 40 · see citing works