{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T17:49:36Z","timestamp":1774720176417,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T00:00:00Z","timestamp":1685232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MISE-Italian Ministry for Economic Development","award":["F\/180016\/01-05\/X43"],"award-info":[{"award-number":["F\/180016\/01-05\/X43"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Air quality monitoring is a very important aspect of providing safe indoor conditions, and carbon dioxide (CO2) is one of the pollutants that most affects people\u2019s health. An automatic system able to accurately forecast CO2 concentration can prevent a sudden rise in CO2 levels through appropriate control of heating, ventilation and air-conditioning (HVAC) systems, avoiding energy waste and ensuring people\u2019s comfort. There are several works in the literature dedicated to air quality assessment and control of HVAC systems; the performance maximisation of such systems is typically achieved using a significant amount of data collected over a long period of time (even months) to train the algorithm. This can be costly and may not respond to a real scenario where the habits of the house occupants or the environment conditions may change over time. To address this problem, an adaptive hardware\u2013software platform was developed, following the IoT paradigm, with a high level of accuracy in forecasting CO2 trends by analysing only a limited window of recent data. The system was tested considering a real case study in a residential room used for smart working and physical exercise; the parameters analysed were the occupants\u2019 physical activity, temperature, humidity and CO2 in the room. Three deep-learning algorithms were evaluated, and the best result was obtained with the Long Short-Term Memory network, which features a Root Mean Square Error of about 10 ppm with a training period of 10 days.<\/jats:p>","DOI":"10.3390\/s23115139","type":"journal-article","created":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T15:29:52Z","timestamp":1685287792000},"page":"5139","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Multi-Sensor Platform for Predictive Air Quality Monitoring"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3374-2433","authenticated-orcid":false,"given":"Gabriele","family":"Rescio","sequence":"first","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5716-5824","authenticated-orcid":false,"given":"Andrea","family":"Manni","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0318-8347","authenticated-orcid":false,"given":"Andrea","family":"Caroppo","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1431-4653","authenticated-orcid":false,"given":"Anna Maria","family":"Carluccio","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1312-4593","authenticated-orcid":false,"given":"Pietro","family":"Siciliano","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8970-3313","authenticated-orcid":false,"given":"Alessandro","family":"Leone","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1093\/occmed\/kqi137","article-title":"Polluted air\u2013outdoors and indoors","volume":"55","author":"Myers","year":"2005","journal-title":"Occup. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.aca.2014.10.018","article-title":"Current air quality analytics and monitoring: A review","volume":"853","author":"Tobiszewski","year":"2015","journal-title":"Anal. Chim. Acta"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hattori, S., Iwamatsu, T., Miura, T., Tsutsumi, F., and Tanaka, N. (2022). Investigation of Indoor Air Quality in Residential Buildings by Measuring CO2 Concentration and a Questionnaire Survey. Sensors, 22.","DOI":"10.3390\/s22197331"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1034\/j.1600-0668.2000.010004246.x","article-title":"Association between indoor CO2 concentrations and Sick Building Syndrome in U.S. office buildings: An analysis of the 1994\u20131996 base study data","volume":"10","author":"Apte","year":"2000","journal-title":"Indoor Air"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.envint.2018.08.059","article-title":"Effects of low-level inhalation exposure to carbon dioxide in indoor environments: A short review on human health and psychomotor performance","volume":"121","author":"Azuma","year":"2018","journal-title":"Environ. Int."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1111\/ina.12383","article-title":"Carbon dioxide generation rates for building occupants","volume":"27","author":"Persily","year":"2017","journal-title":"Indoor Air"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Folk, A.L., Wagner, B.E., Hahn, S.L., Larson, N., Barr-Anderson, D.J., and Neumark-Sztainer, D. (2021). Changes to Physical Activity during a Global Pandemic: A Mixed Methods Analysis among a Diverse Population-Based Sample of Emerging Adults. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18073674"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1111\/ina.12580","article-title":"Machine learning and statistical models for predicting indoor air quality","volume":"29","author":"Wei","year":"2019","journal-title":"Indoor Air"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.enbuild.2015.11.071","article-title":"Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models","volume":"112","author":"Candanedo","year":"2016","journal-title":"Energy Build"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1007\/s13762-018-1642-x","article-title":"Modeling indoor air carbon dioxide concentration using artificial neural network","volume":"16","author":"Khazaei","year":"2019","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ahn, J., Shin, D., Kim, K., and Yang, J. (2017). Indoor air quality analysis using deep learning with sensor data. Sensors, 17.","DOI":"10.3390\/s17112476"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Putra, J.C.P., and Ihsan, M. (2017, January 13\u201314). The prediction of indoor air quality in office room using artificial neural network. Proceedings of the 4th International Conference on Engineering, Technology, and Industrial Application (ICETIA), Surakarta, Indonesia.","DOI":"10.1063\/1.5042896"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"140978","DOI":"10.1155\/2015\/140978","article-title":"An intelligent wireless sensing and control system to improve indoor air quality: Monitoring, prediction, and preaction","volume":"11","author":"Yu","year":"2015","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107409","DOI":"10.1016\/j.buildenv.2020.107409","article-title":"Forecasting office indoor CO2 concentration using machine learning with a one-year dataset","volume":"187","author":"Kallio","year":"2021","journal-title":"Build. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Segala, G., Doriguzzi-Corin, R., Peroni, C., Gazzini, T., and Siracusa, D. (2021). A Practical and Adaptive Approach to Predicting Indoor CO2. Appl. Sci., 11.","DOI":"10.3390\/app112210771"},{"key":"ref_16","unstructured":"(2023, March 09). Available online: https:\/\/www.upsens.com\/."},{"key":"ref_17","unstructured":"(2023, March 20). Available online: https:\/\/www.upsens.com\/images\/pdf\/QuAirLite_datasheetRevC_EN.pdf."},{"key":"ref_18","unstructured":"(2023, March 09). Available online: https:\/\/www.smartex.it\/."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Leone, A., Rescio, G., Diraco, G., Manni, A., Siciliano, P., and Caroppo, A. (2022). Ambient and Wearable Sensor Technologies for Energy Expenditure Quantification of Ageing Adults. Sensors, 22.","DOI":"10.3390\/s22134893"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Leone, A., Rescio, G., Caroppo, A., Siciliano, P., and Manni, A. (2023). Human Postures Recognition by Accelerometer Sensor and ML Architecture Integrated in Embedded Platforms: Benchmarking and Performance Evaluation. Sensors, 23.","DOI":"10.3390\/s23021039"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yan, W., and Oates, T. (2017, January 14\u201319). Time series classification from scratch with deep neural networks: A strong baseline. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A search space odyssey","volume":"28","author":"Greff","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1109\/72.548162","article-title":"Learning long-term dependencies in NARX recurrent neural networks","volume":"7","author":"Lin","year":"1996","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"162","DOI":"10.21629\/JSEE.2017.01.18","article-title":"Convolutional neural networks for time series classification","volume":"28","author":"Zhao","year":"2017","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_25","unstructured":"Tzoumpas, K., Estrada, A., Miraglio, P., and Zambelli, P. (2022). A data filling methodology for time series based on CNN and (Bi) LSTM neural networks. arXiv."},{"key":"ref_26","first-page":"228","article-title":"Time series classification via topological data analysis","volume":"12","author":"Umeda","year":"2017","journal-title":"Inf. Media Technol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shu, P., Sun, Y., Zhao, Y., and Xu, G. (2020, January 20\u201321). Spatial-temporal taxi demand prediction using LSTM-CNN. Proceedings of the 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), Hong Kong, China.","DOI":"10.1109\/CASE48305.2020.9217007"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2130001","DOI":"10.1142\/S0129065721300011","article-title":"An experimental review on deep learning architectures for time series forecasting","volume":"31","author":"Riquelme","year":"2021","journal-title":"Int. J. Neural Syst."},{"key":"ref_29","unstructured":"Lipton, Z.C., Berkowitz, J., and Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv."},{"key":"ref_30","unstructured":"Bengio, Y., Frasconi, P., and Simard, P. (April, January 28). The problem of learning long-term dependencies in recurrent networks. Proceedings of the 1993 IEEE International Conference on Neural Networks (ICNN \u201993), San Francisco, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1016\/j.procs.2020.03.049","article-title":"Stock market prediction using LSTM recurrent neural network","volume":"170","author":"Moghar","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Graves, A., Jaitly, N., and Mohamed, A.R. (2013, January 8\u201313). Hybrid speech recognition with deep bidirectional LSTM. Proceedings of the 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, Olomouc, Czech Republic.","DOI":"10.1109\/ASRU.2013.6707742"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"100085","DOI":"10.1016\/j.array.2021.100085","article-title":"Prediction of the number of COVID-19 confirmed cases based on K-means-LSTM","volume":"11","author":"Vadyala","year":"2021","journal-title":"Array"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105342","DOI":"10.1016\/j.compbiomed.2022.105342","article-title":"Forecasting COVID-19 new cases using deep learning methods","volume":"144","author":"Xu","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_35","unstructured":"(2023, March 13). Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.preprocessing.MinMaxScaler.html."},{"key":"ref_36","first-page":"281","article-title":"Random Search for Hyper-Parameter Optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","unstructured":"Kingma, D., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5139\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:43:47Z","timestamp":1760125427000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5139"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,28]]},"references-count":37,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23115139"],"URL":"https:\/\/doi.org\/10.3390\/s23115139","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,28]]}}}