{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:44:56Z","timestamp":1782841496697,"version":"3.54.5"},"reference-count":0,"publisher":"ECMS","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,23]]},"abstract":"<jats:p>Cheap IMUs struggle with indoor heading estimation because magnetic interference ruins magnetometer readings, causing classical fusion methods to fail. We explored whether deep learning could solve this by testing four CNN-RNN architectures (LSTM, BiLSTM, GRU, BiGRU) across all seven possible sensor combinations of accelerometer, gyroscope, and magnetometer data. Evaluated on the DoorINet dataset (Zakharchenko et al., 2024) using five error metrics, our seven-seed trial setup confirmed that gyroscopes are essential for low error. Yet, our key takeaway is that you shouldn\u2019t give up on the magnetometer entirely. When we combined gyroscope and magnetometer data in a CNN-BiLSTM, it significantly outperformed the gyroscope-only baseline (p=0.024), hitting a mean RMSE of just 0.2275 and an R2 of 0.9703.<\/jats:p>","DOI":"10.7148\/2026-0397","type":"proceedings-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:55:14Z","timestamp":1782838514000},"page":"397-403","source":"Crossref","is-referenced-by-count":0,"title":["Inertial heading estimation for door-mounted imus via cnn\u2013rnn models"],"prefix":"10.7148","author":[{"given":"Ankit","family":"Kumar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yadangi","family":"Abhishek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Filippo","family":"Sanfilippo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4144","published-online":{"date-parts":[[2026,6,23]]},"event":{"name":"40th ECMS International Conference on Modelling and Simulation"},"container-title":["ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina"],"original-title":[],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:55:16Z","timestamp":1782838516000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.scs-europe.net\/dlib\/2026\/ecms2026acceptedpapers\/0397_simai_ecms2026_0068.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7148\/2026-0397","relation":{},"subject":[],"published":{"date-parts":[[2026,6,23]]}}}