{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T16:47:16Z","timestamp":1762015636788,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,11]],"date-time":"2019-08-11T00:00:00Z","timestamp":1565481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The IoT describes a development field where new approaches and trends are in constant change. In this scenario, new devices and sensors are offering higher precision in everyday life in an increasingly less invasive way. In this work, we propose the use of spatial-temporal features by means of fuzzy logic as a general descriptor for heterogeneous sensors. This fuzzy sensor representation is highly efficient and enables devices with low computing power to develop learning and evaluation tasks in activity recognition using light and efficient classifiers. To show the methodology\u2019s potential in real applications, we deploy an intelligent environment where new UWB location devices, inertial objects, wearable devices, and binary sensors are connected with each other and describe daily human activities. We then apply the proposed fuzzy logic-based methodology to obtain spatial-temporal features to fuse the data from the heterogeneous sensor devices. A case study developed in the UJAmISmart Lab of the University of Jaen (Jaen, Spain) shows the encouraging performance of the methodology when recognizing the activity of an inhabitant using efficient classifiers.<\/jats:p>","DOI":"10.3390\/s19163512","type":"journal-article","created":{"date-parts":[[2019,8,12]],"date-time":"2019-08-12T06:38:02Z","timestamp":1565591882000},"page":"3512","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Activity Recognition for IoT Devices Using Fuzzy Spatio-Temporal Features as Environmental Sensor Fusion"],"prefix":"10.3390","volume":"19","author":[{"given":"Miguel \u00c1ngel","family":"L\u00f3pez Medina","sequence":"first","affiliation":[{"name":"Council of Health for the Andalusian Health Service, Av. de la Constituci\u00f3n 18, 41071 Sevilla, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1118-7782","authenticated-orcid":false,"given":"Macarena","family":"Espinilla","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Campus Las Lagunillas, 23071 Ja\u00e9n, Spain"}]},{"given":"Cristiano","family":"Paggeti","sequence":"additional","affiliation":[{"name":"I + Srl, Piazza G.Puccini, 26, 50144 Firenze, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8577-8772","authenticated-orcid":false,"given":"Javier","family":"Medina Quero","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Campus Las Lagunillas, 23071 Ja\u00e9n, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s00779-010-0358-9","article-title":"Theme issue: Ubiquitous computing and ambient intelligence","volume":"15","author":"Bravo","year":"2011","journal-title":"Pers. 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