{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T06:28:25Z","timestamp":1774592905813,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["2022R1A5A8019303"],"award-info":[{"award-number":["2022R1A5A8019303"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Various sensing modalities, including external and internal sensors, have been employed in research on human activity recognition (HAR). Among these, internal sensors, particularly wearable technologies, hold significant promise due to their lightweight nature and simplicity. Recently, HAR techniques leveraging wearable biometric signals, such as electrocardiography (ECG) and photoplethysmography (PPG), have been proposed using publicly available datasets. However, to facilitate broader practical applications, a more extensive analysis based on larger databases with cross-subject validation is required. In pursuit of this objective, we initially gathered PPG signals from 40 participants engaged in five common daily activities. Subsequently, we evaluated the feasibility of classifying these activities using deep learning architecture. The model\u2019s performance was assessed in terms of accuracy, precision, recall, and F-1 measure via cross-subject cross-validation (CV). The proposed method successfully distinguished the five activities considered, with an average test accuracy of 95.14%. Furthermore, we recommend an optimal window size based on a comprehensive evaluation of performance relative to the input signal length. These findings confirm the potential for practical HAR applications based on PPG and indicate its prospective extension to various domains, such as healthcare or fitness applications, by concurrently analyzing behavioral and health data through a single biometric signal.<\/jats:p>","DOI":"10.3390\/s24051610","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T03:31:23Z","timestamp":1709263883000},"page":"1610","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Exploring the Possibility of Photoplethysmography-Based Human Activity Recognition Using Convolutional Neural Networks"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4183-0014","authenticated-orcid":false,"given":"Semin","family":"Ryu","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence Convergence, Hallym University, Chuncheon 24252, Republic of Korea"},{"name":"Cerebrovascular Disease Research Center, Hallym University, Chuncheon 24252, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0589-3816","authenticated-orcid":false,"given":"Suyeon","family":"Yun","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence Convergence, Hallym University, Chuncheon 24252, Republic of Korea"},{"name":"Cerebrovascular Disease Research Center, Hallym University, Chuncheon 24252, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8694-7144","authenticated-orcid":false,"given":"Sunghan","family":"Lee","sequence":"additional","affiliation":[{"name":"Cerebrovascular Disease Research Center, Hallym University, Chuncheon 24252, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9314-5601","authenticated-orcid":false,"given":"In cheol","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence Convergence, Hallym University, Chuncheon 24252, Republic of Korea"},{"name":"Cerebrovascular Disease Research Center, Hallym University, Chuncheon 24252, Republic of Korea"},{"name":"Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e1254","DOI":"10.1002\/widm.1254","article-title":"Recent trends in machine learning for human activity recognition\u2014A survey","volume":"8","author":"Roy","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105440","DOI":"10.1109\/ACCESS.2023.3320069","article-title":"Efficient Wi-Fi-Based Human Activity Recognition Using Adaptive Antenna Elimination","volume":"11","author":"Jannat","year":"2023","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"88","DOI":"10.3390\/computers2020088","article-title":"A review on video-based human activity recognition","volume":"2","author":"Ke","year":"2013","journal-title":"Computers"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1109\/TVT.2018.2878754","article-title":"TW-See: Human activity recognition through the wall with commodity Wi-Fi devices","volume":"68","author":"Wu","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"210816","DOI":"10.1109\/ACCESS.2020.3037715","article-title":"Human activity recognition using inertial, physiological and environmental sensors: A comprehensive survey","volume":"8","author":"Demrozi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/JSEN.2017.2776127","article-title":"Detection of an intruder and prediction of his state of motion by using seismic sensor","volume":"18","author":"Mukhopadhyay","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Azar, S.M., Atigh, M.G., Nickabadi, A., and Alahi, A. (2019, January 15\u201320). Convolutional relational machine for group activity recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00808"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1007\/s00500-021-06238-7","article-title":"Skeleton-based human activity recognition using ConvLSTM and guided feature learning","volume":"26","author":"Yadav","year":"2022","journal-title":"Soft Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3454167","DOI":"10.1155\/2022\/3454167","article-title":"Vision transformer and deep sequence learning for human activity recognition in surveillance videos","volume":"2022","author":"Hussain","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_10","first-page":"1","article-title":"Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities","volume":"54","author":"Chen","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4095","DOI":"10.1007\/s00371-021-02283-3","article-title":"A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data","volume":"38","author":"Challa","year":"2022","journal-title":"Vis. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1108\/IR-09-2020-0187","article-title":"Wearable sensor-based pattern mining for human activity recognition: Deep learning approach","volume":"49","author":"Bijalwan","year":"2022","journal-title":"Ind. Robot. Int. J. Robot. Res. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"116764","DOI":"10.1016\/j.eswa.2022.116764","article-title":"Human activity recognition using wearable sensors by heterogeneous convolutional neural networks","volume":"198","author":"Han","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_14","first-page":"1242","article-title":"Wearable devices for precision medicine and health state monitoring","volume":"66","author":"Bychkov","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xefteris, V.R., Dominguez, M., Grivolla, J., Tsanousa, A., Zaffanela, F., Monego, M., Symeonidis, S., Diplaris, S., Wanner, L., and Vrochidis, S. (2023). A Multimodal Late Fusion Framework for Physiological Sensor and Audio-Signal-Based Stress Detection: An Experimental Study and Public Dataset. Electronics, 12.","DOI":"10.21203\/rs.3.rs-2877621\/v1"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2737","DOI":"10.1007\/s00521-022-07741-0","article-title":"Adaptive 1-dimensional time invariant learning for inertial sensor-based gait authentication","volume":"35","author":"Permatasari","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mekruksavanich, S., and Jitpattanakul, A. (2020, January 11\u201314). Exercise activity recognition with surface electromyography sensor using machine learning approach. Proceedings of the 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), Pattaya, Thailand.","DOI":"10.1109\/ECTIDAMTNCON48261.2020.9090711"},{"key":"ref_18","first-page":"1","article-title":"Classification of Human Activities Using Statistical Features from Electrodermal Activity and Heart Rate Variability","volume":"27","author":"Ahmed","year":"2022","journal-title":"Int. J. Biomed. Soft Comput. Hum. Sci. Off. J. Biomed. Fuzzy Syst. Assoc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"759136","DOI":"10.3389\/fcomp.2021.759136","article-title":"CSL-SHARE: A multimodal wearable sensor-based human activity dataset","volume":"3","author":"Liu","year":"2021","journal-title":"Front. Comput. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mekruksavanich, S., Jantawong, P., Hnoohom, N., and Jitpattanakul, A. (2022, January 4\u20135). Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network. Proceedings of the 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C), Bangkok, Thailand.","DOI":"10.1109\/RI2C56397.2022.9910287"},{"key":"ref_21","unstructured":"Alian, A.A., and Shelley, K.H. (2013). Monitoring Technologies in Acute Care Environments: A Comprehensive Guide to Patient Monitoring Technology, Springer."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/j.icte.2022.03.006","article-title":"Human activity recognition based on wrist PPG via the ensemble method","volume":"8","author":"Almanifi","year":"2022","journal-title":"ICT Express"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hnoohom, N., Mekruksavanich, S., and Jitpattanakul, A. (2023). Physical Activity Recognition Based on Deep Learning Using Photoplethysmography and Wearable Inertial Sensors. Electronics, 12.","DOI":"10.3390\/electronics12030693"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shi, W., Zhou, C., Zhang, Y., Li, K., Ren, X., Liu, H., and Ye, X. (2023). Hybrid modeling on reconstitution of continuous arterial blood pressure using finger photoplethysmography. Biomed. Signal Process. Control, 85.","DOI":"10.1016\/j.bspc.2023.104972"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"345","DOI":"10.3349\/ymj.2010.51.3.345","article-title":"Non-invasive estimation of systolic blood pressure and diastolic blood pressure using photoplethysmograph components","volume":"51","author":"Jeong","year":"2010","journal-title":"Yonsei Med. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1038\/s41598-023-28195-x","article-title":"IMU-based human activity recognition and payload classification for low-back exoskeletons","volume":"13","author":"Pesenti","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, Y., and Wang, L. (2022). Human activity recognition based on residual network and BiLSTM. Sensors, 22.","DOI":"10.3390\/s22020635"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kim, Y.W., Cho, W.H., Kim, K.S., and Lee, S. (2022). Inertial-Measurement-Unit-Based Novel Human Activity Recognition Algorithm Using Conformer. Sensors, 22.","DOI":"10.3390\/s22103932"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jaramillo, I.E., Chola, C., Jeong, J.G., Oh, J.H., Jung, H., Lee, J.H., Lee, W.H., and Kim, T.S. (2023). Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks. Sensors, 23.","DOI":"10.3390\/s23146491"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e13457","DOI":"10.1111\/exsy.13457","article-title":"An optimized deep learning model for human activity recognition using inertial measurement units","volume":"40","author":"Challa","year":"2023","journal-title":"Expert Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Brophy, E., Veiga, J.J.D., Wang, Z., and Ward, T.E. (2018, January 21\u201322). A machine vision approach to human activity recognition using photoplethysmograph sensor data. Proceedings of the 2018 29th Irish Signals and Systems Conference (ISSC), Belfast, UK.","DOI":"10.1109\/ISSC.2018.8585372"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Jarchi, D., and Casson, A.J. (2016). Description of a database containing wrist PPG signals recorded during physical exercise with both accelerometer and gyroscope measures of motion. Data, 2.","DOI":"10.3390\/data2010001"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mahmud, T., Akash, S.S., Fattah, S.A., Zhu, W.P., and Ahmad, M.O. (2020, January 9\u201312). Human activity recognition from multi-modal wearable sensor data using deep multi-stage LSTM architecture based on temporal feature aggregation. Proceedings of the 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS), Springfield, MA, USA.","DOI":"10.1109\/MWSCAS48704.2020.9184666"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"16633","DOI":"10.1038\/s41598-023-43542-8","article-title":"Risk of data leakage in estimating the diagnostic performance of a deep-learning-based computer-aided system for psychiatric disorders","volume":"13","author":"Lee","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1093\/ageing\/26.1.15","article-title":"Comfortable and maximum walking speed of adults aged 20\u201379 years: Reference values and determinants","volume":"26","author":"Bohannon","year":"1997","journal-title":"Age Ageing"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Reiss, A., Indlekofer, I., Schmidt, P., and Van Laerhoven, K. (2019). Deep PPG: Large-scale heart rate estimation with convolutional neural networks. Sensors, 19.","DOI":"10.3390\/s19143079"},{"key":"ref_37","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_38","first-page":"3995","article-title":"Deep convolutional neural networks on multichannel time series for human activity recognition","volume":"Volume 15","author":"Yang","year":"2015","journal-title":"Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI)"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zeng, M., Nguyen, L.T., Yu, B., Mengshoel, O.J., Zhu, J., Wu, P., and Zhang, J. (2014, January 6\u20137). Convolutional neural networks for human activity recognition using mobile sensors. Proceedings of the 6th International Conference on Mobile Computing, Applications and Services, Austin, TX, USA.","DOI":"10.4108\/icst.mobicase.2014.257786"},{"key":"ref_40","unstructured":"Lee, S.M., Yoon, S.M., and Cho, H. (2017, January 13\u201316). Human activity recognition from accelerometer data using Convolutional Neural Network. Proceedings of the 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), Jeju, Republic of Korea."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"M\u00fcnzner, S., Schmidt, P., Reiss, A., Hanselmann, M., Stiefelhagen, R., and D\u00fcrichen, R. (2017, January 11\u201315). CNN-based sensor fusion techniques for multimodal human activity recognition. Proceedings of the 2017 ACM International Symposium on Wearable Computers, Maui, HI, USA.","DOI":"10.1145\/3123021.3123046"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"51","DOI":"10.37965\/jait.2020.0051","article-title":"Human activity recognition and embedded application based on convolutional neural network","volume":"1","author":"Xu","year":"2021","journal-title":"J. Artif. Intell. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lee, K.S., Chae, S., and Park, H.S. (2019, January 24\u201328). Optimal time-window derivation for human-activity recognition based on convolutional neural networks of repeated rehabilitation motions. Proceedings of the 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), Toronto, ON, Canada.","DOI":"10.1109\/ICORR.2019.8779475"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Afzali Arani, M.S., Costa, D.E., and Shihab, E. (2021). Human activity recognition: A comparative study to assess the contribution level of accelerometer, ECG, and PPG signals. Sensors, 21.","DOI":"10.3390\/s21216997"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"51142","DOI":"10.1109\/ACCESS.2022.3174124","article-title":"Resnet-se: Channel attention-based deep residual network for complex activity recognition using wrist-worn wearable sensors","volume":"10","author":"Mekruksavanich","year":"2022","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1016\/j.medengphy.2015.04.005","article-title":"Varying behavior of different window sizes on the classification of static and dynamic physical activities from a single accelerometer","volume":"37","author":"Fida","year":"2015","journal-title":"Med. Eng. Phys."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/1964897.1964918","article-title":"Activity recognition using cell phone accelerometers","volume":"12","author":"Kwapisz","year":"2011","journal-title":"ACM SigKDD Explor. Newsl."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Reiss, A., and Stricker, D. (2012, January 18\u201322). Introducing a new benchmarked dataset for activity monitoring. Proceedings of the 2012 16th International Symposium on Wearable Computers, Newcastle, UK.","DOI":"10.1109\/ISWC.2012.13"},{"key":"ref_49","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2013, January 24\u201326). A public domain dataset for human activity recognition using smartphones. Proceedings of the ESANN 2013 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Banos, O., Villalonga, C., Garcia, R., Saez, A., Damas, M., Holgado-Terriza, J.A., Lee, S., Pomares, H., and Rojas, I. (2015). Design, implementation and validation of a novel open framework for agile development of mobile health applications. Biomed. Eng. Online, 14.","DOI":"10.1186\/1475-925X-14-S2-S6"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"105044","DOI":"10.1016\/j.dib.2019.105044","article-title":"Dataset from PPG wireless sensor for activity monitoring","volume":"29","author":"Biagetti","year":"2020","journal-title":"Data Brief"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Liu, H., Hartmann, Y., and Schultz, T. (2022, January 9\u201311). A Practical Wearable Sensor-based Human Activity Recognition Research Pipeline. Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022), Virtual Event.","DOI":"10.5220\/0010937000003123"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Hartmann, Y., Liu, H., and Schultz, T. (2021, January 11\u201313). Feature Space Reduction for Human Activity Recognition based on Multi-channel Biosignals. Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021), Virtual Event.","DOI":"10.5220\/0010260800002865"},{"key":"ref_54","unstructured":"Hartmann, Y., Liu, H., and Schultz, T. (, January 9\u201311). High-level features for human activity recognition and modeling. Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022), Virtual Event."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/5\/1610\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:07:43Z","timestamp":1760105263000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/5\/1610"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,1]]},"references-count":54,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["s24051610"],"URL":"https:\/\/doi.org\/10.3390\/s24051610","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,1]]}}}