{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:43:37Z","timestamp":1760035417196,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T00:00:00Z","timestamp":1752105600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Japan Science and Technology (JST) Agency MOONSHOT R&amp;D","award":["JPMJMS2021"],"award-info":[{"award-number":["JPMJMS2021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, the use of photoplethysmography (PPG)-based heart rate detection has gained considerable attention as a cost-effective alternative to conventional electrocardiography (ECG) for applications in healthcare and fitness tracking. Although deep learning methods have shown promise in heart rate estimation and motion artifact removal from PPG signals recorded during physical activity, their computational requirements and need for extensive training data make them less practical for real-world conditions when ground truth data is unavailable for calibration. This study presents a one-size-fits-all approach for heart rate estimation during physical activity that employs aggregation-based techniques to track heart rate and minimize the effects of motion artifacts, without relying on complex machine learning or deep learning techniques. We evaluate our method on four publicly available datasets\u2014PPG-DaLiA, WESAD, IEEE_Training, and IEEE_Test, all recorded using wrist-worn devices\u2014along with a new dataset, UTOKYO, which includes PPG and accelerometer data collected from a smart ring. The proposed method outperforms the CNN ensemble model for the PPG-DaLiA dataset and the IEEE_Test dataset and reduces the mean absolute error (MAE) by 1.45 bpm and 5.71 bpm, respectively, demonstrating that effective signal processing techniques can match the performance of more complex deep learning models without requiring extensive computational resources or dataset-specific tuning.<\/jats:p>","DOI":"10.3390\/s25144315","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T10:26:53Z","timestamp":1752229613000},"page":"4315","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Stability- and Aggregation-Based Method for Heart Rate Estimation Using Photoplethysmographic Signals During Physical Activity"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3428-010X","authenticated-orcid":false,"given":"Sabrina C.","family":"Crepaldi","sequence":"first","affiliation":[{"name":"SOXAI Inc., Kanagawa 231-0032, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiabin","family":"Wang","sequence":"additional","affiliation":[{"name":"SOXAI Inc., Kanagawa 231-0032, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fumiya","family":"Matsumoto","sequence":"additional","affiliation":[{"name":"Educational Physiology Laboratory, Graduate School of Education, The University of Tokyo, Tokyo 113-0033, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1407-1558","authenticated-orcid":false,"given":"Hiroki","family":"Takeuchi","sequence":"additional","affiliation":[{"name":"Educational Physiology Laboratory, Graduate School of Education, The University of Tokyo, Tokyo 113-0033, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tatsuhiko","family":"Watanabe","sequence":"additional","affiliation":[{"name":"SOXAI Inc., Kanagawa 231-0032, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9667-0572","authenticated-orcid":false,"given":"Yoshiharu","family":"Yamamoto","sequence":"additional","affiliation":[{"name":"Educational Physiology Laboratory, Graduate School of Education, The University of Tokyo, Tokyo 113-0033, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,10]]},"reference":[{"key":"ref_1","first-page":"195","article-title":"\u2019A review on wearable photoplethysmography sensors and their potential future applications in health care","volume":"4","author":"Castaneda","year":"2018","journal-title":"Int. J. Biosens. Bioelectron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1145\/2629633","article-title":"Wearables: Has the age of smartwatches finally arrived?","volume":"58","author":"Rawassizadeh","year":"2014","journal-title":"Commun. ACM"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kim, K.B., and Baek, H.J. (2023). Photoplethysmography in wearable devices: A comprehensive review of technological advances, current challenges, and future directions. Electronics, 12.","DOI":"10.3390\/electronics12132923"},{"key":"ref_4","first-page":"1221","article-title":"Wearable heart rate monitor technology accuracy in research: A comparative study between PPG and ECG technology","volume":"61","author":"Weiler","year":"2017","journal-title":"Proc. Hum. Factors Ergonom. Soc. Annu. Meet."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Park, J., Seok, H.S., Kim, S.S., and Shin, H. (2022). Photoplethysmogram analysis and applications: An integrative review. Front. Physiol., 12.","DOI":"10.3389\/fphys.2021.808451"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1007\/s11036-019-01323-6","article-title":"Detection and removal of motion artifacts in PPG signals","volume":"27","author":"Pollreisz","year":"2022","journal-title":"Mobile Netw. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1007\/s10916-010-9505-0","article-title":"Relationship between measurement site and motion artifacts in wearable reflected photoplethysmography","volume":"35","author":"Maeda","year":"2010","journal-title":"J. Med. Syst."},{"key":"ref_8","first-page":"e9","article-title":"Practical usefulness of heart rate monitoring in physical exercise","volume":"25","author":"Povea","year":"2018","journal-title":"Rev. Colomb. Cardiol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Schneider, C., Hanakam, F., Wiewelhove, T., D\u00f6weling, A., Kellmann, M., Meyer, T., and Ferrauti, A. (2018). Heart rate monitoring in team sports\u2014A conceptual framework for contextualizing heart rate measures for training and recovery prescription. Front. Physiol., 9.","DOI":"10.3389\/fphys.2018.00639"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.pcad.2013.07.003","article-title":"Heart rate variability in risk stratification of cardiac patients","volume":"56","author":"Huikuri","year":"2013","journal-title":"Prog. Cardiovasc. Dis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"19556","DOI":"10.1097\/MD.0000000000019556","article-title":"Benefits and effectiveness of using a wrist heart rate monitor as a telerehabilitation device in cardiac patients: A randomized controlled trial","volume":"99","author":"Batalik","year":"2020","journal-title":"Medicine"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"11889","DOI":"10.2196\/11889","article-title":"Accuracy of Apple Watch measurements for heart rate and energy expenditure in patients with cardiovascular disease: Cross-sectional study","volume":"7","author":"Falter","year":"2019","journal-title":"JMIR mHealth uHealth"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13634-020-00714-2","article-title":"Heart rate tracking in photoplethysmography signals affected by motion artifacts: A review","volume":"2021","author":"Ismail","year":"2021","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Salehizadeh, S.M., Dao, D., Bolkhovsky, J., Cho, C., Mendelson, Y., and Chon, K.H. (2015). A novel time-varying spectral filtering algorithm for reconstruction of motion artifact corrupted heart rate signals during intense physical activities using a wearable photoplethysmogram sensor. Sensors, 16.","DOI":"10.3390\/s16010010"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Reiss, A., Schmidt, P., Indlekofer, I., and Laerhoven, K.V. (2018, January 8\u201312). PPG-based heart rate estimation with time-frequency spectra: A deep learning approach. Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Singapore.","DOI":"10.1145\/3267305.3274176"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1109\/TBME.2014.2359372","article-title":"TROIKA: A general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise","volume":"62","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1902","DOI":"10.1109\/TBME.2015.2406332","article-title":"Photoplethysmography-based heart rate monitoring in physical activities via joint sparse spectrum reconstruction","volume":"62","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Reiss, A., Indlekofer, I., Schmidt, P., and Laerhoven, K.V. (2019). Deep PPG: Large-scale heart rate estimation with convolutional neural networks. Sensors, 19.","DOI":"10.3390\/s19143079"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3441626","article-title":"DeepHeart: A deep learning approach for accurate heart rate estimation from PPG signals","volume":"17","author":"Chang","year":"2021","journal-title":"ACM Trans. Sensor Networks TOSN"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1109\/TBCAS.2019.2892297","article-title":"CorNET: Deep learning framework for PPG-based heart rate estimation and biometric identification in ambulant environment","volume":"13","author":"Biswas","year":"2019","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3616019","article-title":"A deep learning\u2013based PPG quality assessment approach for heart rate and heart rate variability","volume":"4","author":"Naeini","year":"2023","journal-title":"ACM Trans. Comput. Healthcare"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chowdhury, S.S., Hasan, M.S., and Sharmin, R. (2019, January 17\u201320). Robust heart rate estimation from PPG signals with intense motion artifacts using cascade of adaptive filter and recurrent neural network. Proceedings of the TENCON 2019\u20132019 IEEE Region 10 Conference (TENCON), Kochi, India.","DOI":"10.1109\/TENCON.2019.8929692"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shen, Y., Voisin, M., Aliamiri, A., Avati, A., Hannun, A., and Ng, A. (2019, January 4\u20138). Ambulatory atrial fibrillation monitoring using wearable photoplethysmography with deep learning. Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330657"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shashikumar, S.P., Shah, A.J., Li, Q., Clifford, G.D., and Nemati, S. (2017, January 16\u201319). A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology. Proceedings of the 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Orlando, FL, USA.","DOI":"10.1109\/BHI.2017.7897225"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5501","DOI":"10.1109\/JSEN.2023.3240854","article-title":"On-device deep learning for mobile and wearable sensing applications: A review","volume":"23","author":"Incel","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lane, N.D., Bhattacharya, S., Georgiev, P., Forlivesi, C., and Kawsar, F. (2015, January 1). An early resource characterization of deep learning on wearables, smartphones and internet-of-things devices. Proceedings of the 2015 International Workshop on Internet of Things towards Applications, Seoul, Republic of Korea.","DOI":"10.1145\/2820975.2820980"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., and Laerhoven, K.V. (2018, January 16\u201320). Introducing WESAD, a multimodal dataset for wearable stress and affect detection. Proceedings of the 20th ACM International Conference on Multimodal Interaction, Boulder, CO, USA.","DOI":"10.1145\/3242969.3242985"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.bspc.2018.05.020","article-title":"PQR signal quality indexes: A method for real-time photoplethysmogram signal quality estimation based on noise interferences","volume":"47","author":"Song","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sch\u00e4ck, T., Muma, M., and Zoubir, A.M. (September, January 28). Computationally efficient heart rate estimation during physical exercise using photoplethysmographic signals. Proceedings of the 2017 25th European Signal Processing Conference (EUSIPCO), Kos, Greece.","DOI":"10.23919\/EUSIPCO.2017.8081656"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/14\/4315\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:07:55Z","timestamp":1760033275000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/14\/4315"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,10]]},"references-count":29,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["s25144315"],"URL":"https:\/\/doi.org\/10.3390\/s25144315","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2025,7,10]]}}}