{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T15:47:56Z","timestamp":1783007276311,"version":"3.54.5"},"reference-count":38,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T00:00:00Z","timestamp":1640736000000},"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>Estimating applied force using force myography (FMG) technique can be effective in human-robot interactions (HRI) using data-driven models. A model predicts well when adequate training and evaluation are observed in same session, which is sometimes time consuming and impractical. In real scenarios, a pretrained transfer learning model predicting forces quickly once fine-tuned to target distribution would be a favorable choice and hence needs to be examined. Therefore, in this study a unified supervised FMG-based deep transfer learner (SFMG-DTL) model using CNN architecture was pretrained with multiple sessions FMG source data (Ds, Ts) and evaluated in estimating forces in separate target domains (Dt, Tt) via supervised domain adaptation (SDA) and supervised domain generalization (SDG). For SDA, case (i) intra-subject evaluation (Ds \u2260 Dt-SDA, Ts \u2248 Tt-SDA) was examined, while for SDG, case (ii) cross-subject evaluation (Ds \u2260 Dt-SDG, Ts \u2260 Tt-SDG) was examined. Fine tuning with few \u201ctarget training data\u201d calibrated the model effectively towards target adaptation. The proposed SFMG-DTL model performed better with higher estimation accuracies and lower errors (R2 \u2265 88%, NRMSE \u2264 0.6) in both cases. These results reveal that interactive force estimations via transfer learning will improve daily HRI experiences where \u201ctarget training data\u201d is limited, or faster adaptation is required.<\/jats:p>","DOI":"10.3390\/s22010211","type":"journal-article","created":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T08:12:15Z","timestamp":1640765535000},"page":"211","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Force Myography-Based Human Robot Interactions via Deep Domain Adaptation and Generalization"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7313-3969","authenticated-orcid":false,"given":"Umme","family":"Zakia","sequence":"first","affiliation":[{"name":"Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2309-9977","authenticated-orcid":false,"given":"Carlo","family":"Menon","sequence":"additional","affiliation":[{"name":"Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada"},{"name":"Biomedical and Mobile Health Technology Laboratory, ETH Zurich, Lengghalde 5, 8008 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/1743-0003-11-2","article-title":"Towards the development of a wearable feedback system for monitoring the activities of the upper-extremities","volume":"11","author":"Xiao","year":"2014","journal-title":"J. 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