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The first shortcoming is that the neural network model of gait prediction only predicts dozens of future time frames of gait trajectory. The second shortcoming is that the gait prediction neural network model is uninterpretable. We propose the Interpretable-Concatenation former (IC-former) model, which can predict long-term gait trajectories and explain the prediction results by quantifying the importance of data at different positions in the input sequence. Experiments prove that the IC-former model we proposed not only makes a breakthrough in prediction accuracy but also successfully explains the data basis of the prediction.<\/jats:p>","DOI":"10.1017\/s0263574723001492","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T08:08:13Z","timestamp":1700467693000},"page":"367-388","source":"Crossref","is-referenced-by-count":2,"title":["Interpretable long-term gait trajectory prediction based on Interpretable-Concatenation former"],"prefix":"10.1017","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3150-8638","authenticated-orcid":false,"given":"Jie","family":"Yin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chongfeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7499-6160","authenticated-orcid":false,"given":"Tao","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2023,11,20]]},"reference":[{"key":"S0263574723001492_ref15","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"S0263574723001492_ref11","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3062562"},{"key":"S0263574723001492_ref21","article-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","author":"Li","year":"2019","journal-title":"Mach. 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