{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:34:04Z","timestamp":1760232844343,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1925371","2054343","R01HD098154-03","T420H008414","TL1TR002540"],"award-info":[{"award-number":["1925371","2054343","R01HD098154-03","T420H008414","TL1TR002540"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1925371","2054343","R01HD098154-03","T420H008414","TL1TR002540"],"award-info":[{"award-number":["1925371","2054343","R01HD098154-03","T420H008414","TL1TR002540"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Rocky Mountain Center for Occupational and Environmental Health from the National Institute of Occupational Safety and Health Education and Research Center","award":["1925371","2054343","R01HD098154-03","T420H008414","TL1TR002540"],"award-info":[{"award-number":["1925371","2054343","R01HD098154-03","T420H008414","TL1TR002540"]}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["1925371","2054343","R01HD098154-03","T420H008414","TL1TR002540"],"award-info":[{"award-number":["1925371","2054343","R01HD098154-03","T420H008414","TL1TR002540"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Many people struggle with mobility impairments due to lower limb amputations. To participate in society, they need to be able to walk on a wide variety of terrains, such as stairs, ramps, and level ground. Current lower limb powered prostheses require different control strategies for varying ambulation modes, and use data from mechanical sensors within the prosthesis to determine which ambulation mode the user is in. However, it can be challenging to distinguish between ambulation modes. Efforts have been made to improve classification accuracy by adding electromyography information, but this requires a large number of sensors, has a low signal-to-noise ratio, and cannot distinguish between superficial and deep muscle activations. An alternative sensing modality, A-mode ultrasound, can detect and distinguish between changes in superficial and deep muscles. It has also shown promising results in upper limb gesture classification. Despite these advantages, A-mode ultrasound has yet to be employed for lower limb activity classification. Here we show that A- mode ultrasound can classify ambulation mode with comparable, and in some cases, superior accuracy to mechanical sensing. In this study, seven transfemoral amputee subjects walked on an ambulation circuit while wearing A-mode ultrasound transducers, IMU sensors, and their passive prosthesis. The circuit consisted of sitting, standing, level-ground walking, ramp ascent, ramp descent, stair ascent, and stair descent, and a spatial\u2013temporal convolutional network was trained to continuously classify these seven activities. Offline continuous classification with A-mode ultrasound alone was able to achieve an accuracy of 91.8\u00b13.4%, compared with 93.8\u00b13.0%, when using kinematic data alone. Combined kinematic and ultrasound produced 95.8\u00b12.3% accuracy. This suggests that A-mode ultrasound provides additional useful information about the user\u2019s gait beyond what is provided by mechanical sensors, and that it may be able to improve ambulation mode classification. By incorporating these sensors into powered prostheses, users may enjoy higher reliability for their prostheses, and more seamless transitions between ambulation modes.<\/jats:p>","DOI":"10.3390\/s22239350","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T03:32:25Z","timestamp":1669865545000},"page":"9350","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Ambulation Mode Classification of Individuals with Transfemoral Amputation through A-Mode Sonomyography and Convolutional Neural Networks"],"prefix":"10.3390","volume":"22","author":[{"given":"Rosemarie","family":"Murray","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, and Robotics Center, The University of Utah, Salt Lake City, UT 84112, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3844-2325","authenticated-orcid":false,"given":"Joel","family":"Mendez","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, and Robotics Center, The University of Utah, Salt Lake City, UT 84112, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5569-9099","authenticated-orcid":false,"given":"Lukas","family":"Gabert","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, and Robotics Center, The University of Utah, Salt Lake City, UT 84112, USA"},{"name":"Rocky Mountain Center for Occupational and Environmental Health, Salt Lake City, UT 84111, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas P.","family":"Fey","sequence":"additional","affiliation":[{"name":"Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Honghai","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Shenzhen 518055, China"},{"name":"School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0957-6412","authenticated-orcid":false,"given":"Tommaso","family":"Lenzi","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, and Robotics Center, The University of Utah, Salt Lake City, UT 84112, USA"},{"name":"Rocky Mountain Center for Occupational and Environmental Health, Salt Lake City, UT 84111, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/j.apmr.2007.11.005","article-title":"Estimating the Prevalence of Limb Loss in the United States: 2005 to 2050","volume":"89","author":"MacKenzie","year":"2008","journal-title":"Arch. 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