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PPG signals can easily be collected continuously and remotely using portable wearable devices. However, these measuring devices are vulnerable to motion artifacts caused by daily life activities. The most common ways to eliminate motion artifacts use extra accelerometer sensors, which suffer from two limitations: (i) high power consumption, and (ii) the need to integrate an accelerometer sensor in a wearable device (which is not required in certain wearables). This paper proposes a low-power non-accelerometer-based PPG motion artifacts removal method outperforming the accuracy of the existing methods. We use Cycle Generative Adversarial Network to reconstruct clean PPG signals from noisy PPG signals. Our novel machine-learning-based technique achieves 9.5 times improvement in motion artifact removal compared to the state-of-the-art without using extra sensors such as an accelerometer, which leads to 45% improvement in energy efficiency.\n          <\/jats:p>","DOI":"10.1145\/3563949","type":"journal-article","created":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T11:22:15Z","timestamp":1664277735000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":33,"title":["An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5797-3215","authenticated-orcid":false,"given":"Amir Hosein","family":"Afandizadeh Zargari","sequence":"first","affiliation":[{"name":"University of California, Irvine, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1747-6980","authenticated-orcid":false,"given":"Seyed Amir Hossein","family":"Aqajari","sequence":"additional","affiliation":[{"name":"University of California, Irvine, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3850-6739","authenticated-orcid":false,"given":"Hadi","family":"Khodabandeh","sequence":"additional","affiliation":[{"name":"University of California, Irvine, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0725-1155","authenticated-orcid":false,"given":"Amir","family":"Rahmani","sequence":"additional","affiliation":[{"name":"University of California, Irvine, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6982-365X","authenticated-orcid":false,"given":"Fadi","family":"Kurdahi","sequence":"additional","affiliation":[{"name":"University of California, Irvine, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,2,27]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"https:\/\/www.analog.com\/media\/en\/technical-documentation\/data-sheets\/adxl343.pdf Analog Devices | ADXL343"},{"key":"e_1_3_1_3_2","unstructured":"https:\/\/www.empatica.com\/ Empatica | Medical devices AI and algorithms for remote patient monitoring"},{"key":"e_1_3_1_4_2","unstructured":"https:\/\/ameridroid.com\/products\/smartpower2-5vdc-power-supply SmartPower2 5VDC Power Supply"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1088\/0967-3334\/28\/3\/R01"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.2196\/25258"},{"key":"e_1_3_1_7_2","article-title":"An end-to-end and accurate PPG-based respiratory rate estimation approach using cycle generative adversarial networks","author":"Aqajari Seyed Amir Hossein","year":"2021","unstructured":"Seyed Amir Hossein Aqajari, Rui Cao, Amir Hosein Afandizadeh Zargari, and Amir M. 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