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314523\/2009-0"]}]},{"name":"CNPq","award":["422728\/2021-7"],"award-info":[{"award-number":["422728\/2021-7"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Adaptive AI for context and activity recognition remains a relatively unexplored field due to difficulty in collecting sufficient information to develop supervised models. Additionally, building a dataset for human context activities \u201cin the wild\u201d demands time and human resources, which explains the lack of public datasets available. Some of the available datasets for activity recognition were collected using wearable sensors, since they are less invasive than images and precisely capture a user\u2019s movements in time series. However, frequency series contain more information about sensors\u2019 signals. In this paper, we investigate the use of feature engineering to improve the performance of a Deep Learning model. Thus, we propose using Fast Fourier Transform algorithms to extract features from frequency series instead of time series. We evaluated our approach on the ExtraSensory and WISDM datasets. The results show that using Fast Fourier Transform algorithms to extract features performed better than using statistics measures to extract features from temporal series. Additionally, we examined the impact of individual sensors on identifying specific labels and proved that incorporating more sensors enhances the model\u2019s effectiveness. On the ExtraSensory dataset, the use of frequency features outperformed that of time-domain features by 8.9 p.p., 0.2 p.p., 39.5 p.p., and 0.4 p.p. in Standing, Sitting, Lying Down, and Walking activities, respectively, and on the WISDM dataset, the model performance improved by 1.7 p.p., just by using feature engineering.<\/jats:p>","DOI":"10.3390\/s23125756","type":"journal-article","created":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T02:30:51Z","timestamp":1687314651000},"page":"5756","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Study on the Influence of Sensors in Frequency and Time Domains on Context Recognition"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1562-9349","authenticated-orcid":false,"given":"Pedro","family":"de Souza","sequence":"first","affiliation":[{"name":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife 50740-560, PE, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Di\u00f3genes","family":"Silva","sequence":"additional","affiliation":[{"name":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife 50740-560, PE, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4432-8449","authenticated-orcid":false,"given":"Isabella","family":"de Andrade","sequence":"additional","affiliation":[{"name":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife 50740-560, PE, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00falia","family":"Dias","sequence":"additional","affiliation":[{"name":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife 50740-560, PE, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1834-5221","authenticated-orcid":false,"given":"Jo\u00e3o Paulo","family":"Lima","sequence":"additional","affiliation":[{"name":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife 50740-560, PE, Brazil"},{"name":"Visual Computing Lab, Departamento de Computa\u00e7\u00e3o, Universidade Federal Rural de Pernambuco, Recife 52171-900, PE, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Veronica","family":"Teichrieb","sequence":"additional","affiliation":[{"name":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife 50740-560, PE, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4667-2243","authenticated-orcid":false,"given":"Jonysberg P.","family":"Quintino","sequence":"additional","affiliation":[{"name":"Projeto CIn-UFPE Samsung, Centro de Inform\u00e1tica, Av. 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