{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:50:11Z","timestamp":1760147411013,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T00:00:00Z","timestamp":1675382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["5R21AR076686-02"],"award-info":[{"award-number":["5R21AR076686-02"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the field of wearable robotics, assistance needs to be individualized for the user to maximize benefit. Information from muscle fascicles automatically recorded from brightness mode (B-mode) ultrasound has been used to design assistance profiles that are proportional to the estimated muscle force of young individuals. There is also a desire to develop similar strategies for older adults who may have age-altered physiology. This study introduces and validates a ResNet + 2x-LSTM model for extracting fascicle lengths in young and older adults. The labeling was generated in a semimanual manner for young (40,696 frames) and older adults (34,262 frames) depicting B-mode imaging of the medial gastrocnemius. First, the model was trained on young and tested on both young (R2 = 0.85, RMSE = 2.36 \u00b1 1.51 mm, MAPE = 3.6%, aaDF = 0.48 \u00b1 1.1 mm) and older adults (R2 = 0.53, RMSE = 4.7 \u00b1 2.51 mm, MAPE = 5.19%, aaDF = 1.9 \u00b1 1.39 mm). Then, the performances were trained across all ages (R2 = 0.79, RMSE = 3.95 \u00b1 2.51 mm, MAPE = 4.5%, aaDF = 0.67 \u00b1 1.8 mm). Although age-related muscle loss affects the error of the tracking methodology compared to the young population, the absolute percentage error for individual fascicles leads to a small variation of 3\u20135%, suggesting that the error may be acceptable in the generation of assistive force profiles.<\/jats:p>","DOI":"10.3390\/s23031670","type":"journal-article","created":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T01:40:25Z","timestamp":1675388425000},"page":"1670","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Age-Related Reliability of B-Mode Analysis for Tailored Exosuit Assistance"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0992-6526","authenticated-orcid":false,"given":"Letizia","family":"Gionfrida","sequence":"first","affiliation":[{"name":"Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Science and Engineering Complex, 150 Western Ave, Boston, MA 02134, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6543-2424","authenticated-orcid":false,"given":"Richard W.","family":"Nuckols","sequence":"additional","affiliation":[{"name":"Department of Systems Design Engineering, University of Waterloo, University Ave W, Waterloo, ON N2L 3G1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Conor J.","family":"Walsh","sequence":"additional","affiliation":[{"name":"Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Science and Engineering Complex, 150 Western Ave, Boston, MA 02134, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1392-227X","authenticated-orcid":false,"given":"Robert D.","family":"Howe","sequence":"additional","affiliation":[{"name":"Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Science and Engineering Complex, 150 Western Ave, Boston, MA 02134, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/TNSRE.2016.2521160","article-title":"State of the art and future directions for lower limb robotic exoskeletons","volume":"25","author":"Young","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"eaar5438","DOI":"10.1126\/scirobotics.aar5438","article-title":"Human-in-the-loop optimization of hip assistance with a soft exosuit during walking","volume":"3","author":"Ding","year":"2018","journal-title":"Sci. Robot."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1159\/000516910","article-title":"Reduced Achilles Tendon Stiffness Disrupts Calf Muscle Neuromechanics in Elderly Gait","volume":"68","author":"Krupenevich","year":"2022","journal-title":"Gerontology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1097\/JES.0b013e318279fdc5","article-title":"Mobility decline in old age: A time to intervene","volume":"41","author":"Manini","year":"2013","journal-title":"Exerc. Sport Sci. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"S50","DOI":"10.3961\/jpmph.2013.46.S.S50","article-title":"Promoting mobility in older people","volume":"46","author":"Rantanen","year":"2013","journal-title":"J. Prev. Med. Pub. Health"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"srep46721","DOI":"10.1038\/srep46721","article-title":"An ecologically-controlled exoskeleton can improve balance recovery after slippage","volume":"7","author":"Monaco","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1177\/0733464820932778","article-title":"Relationship between lower limb muscle strength and future falls among community-dwelling older adults with no history of falls: A prospective 1-year study","volume":"40","author":"Porto","year":"2021","journal-title":"J. Appl. Gerontol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1111\/j.1532-5415.2004.52310.x","article-title":"Muscle weakness and falls in older adults: A systematic review and meta-analysis","volume":"52","author":"Moreland","year":"2004","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1152\/japplphysiol.00347.2003","article-title":"Invited review: Aging and sarcopenia","volume":"95","author":"Doherty","year":"2003","journal-title":"J. Appl. Physiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1111\/j.1365-201X.2004.01404.x","article-title":"Changes in triceps surae muscle architecture with sarcopenia","volume":"183","author":"Morse","year":"2005","journal-title":"Acta Physiol. Scand."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s12938-021-00967-4","article-title":"Age-and muscle-specific reliability of muscle architecture measurements assessed by two-dimensional panoramic ultrasound","volume":"21","author":"Hagoort","year":"2022","journal-title":"Biomed. Eng. Online"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1126\/science.aal5054","article-title":"Human-in-the-loop optimization of exoskeleton assistance during walking","volume":"356","author":"Zhang","year":"2017","journal-title":"Science"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"eabj1362","DOI":"10.1126\/scirobotics.abj1362","article-title":"Individualization of exosuit assistance based on measured muscle dynamics during versatile walking","volume":"6","author":"Nuckols","year":"2021","journal-title":"Sci. Robot."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1038\/s41586-022-05191-1","article-title":"Personalizing exoskeleton assistance while walking in the real world","volume":"610","author":"Slade","year":"2022","journal-title":"Nature"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"eabf1078","DOI":"10.1126\/scirobotics.abf1078","article-title":"How adaptation, training, and customization contribute to benefits from exoskeleton assistance","volume":"6","author":"Poggensee","year":"2021","journal-title":"Sci. Robot."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"20190715","DOI":"10.1098\/rsif.2019.0715","article-title":"Estimation of absolute states of human skeletal muscle via standard B-mode ultrasound imaging and deep convolutional neural networks","volume":"17","author":"Cunningham","year":"2020","journal-title":"J. R. Soc. Interface"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1152\/japplphysiol.01430.2011","article-title":"Reliability and validity of ultrasound measurements of muscle fascicle length and pennation in humans: A systematic review","volume":"114","author":"Kwah","year":"2013","journal-title":"J. Appl. Physiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3604","DOI":"10.1038\/s41598-020-60360-4","article-title":"Ultrasound imaging links soleus muscle neuromechanics and energetics during human walking with elastic ankle exoskeletons","volume":"10","author":"Nuckols","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1854","DOI":"10.1109\/TMECH.2022.3171086","article-title":"A Hybrid Knee Exoskeleton Using Real-Time Ultrasound-Based Muscle Fatigue Assessment","volume":"27","author":"Sheng","year":"2022","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"38","DOI":"10.14474\/ptrs.2015.4.1.38","article-title":"Ultrasound imaging for age-related differences of lower extremity muscle architecture","volume":"4","author":"Kim","year":"2015","journal-title":"Phys. Ther. Rehabil. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1152\/jappl.2001.90.4.1349","article-title":"Behavior of fascicles and tendinous structures of human gastrocnemius during vertical jumping","volume":"90","author":"Kurokawa","year":"2001","journal-title":"J. Appl. Physiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1152\/jappl.1998.85.2.398","article-title":"Architectural and functional features of human triceps surae muscles during contraction","volume":"85","author":"Kawakami","year":"1998","journal-title":"J. Appl. Physiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1152\/japplphysiol.00530.2011","article-title":"Automatic tracking of medial gastrocnemius fascicle length during human locomotion","volume":"111","author":"Cronin","year":"2011","journal-title":"J. Appl. Physiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1016\/j.medengphy.2008.11.004","article-title":"Automatic detection method of muscle fiber movement as revealed by ultrasound images","volume":"31","author":"Miyoshi","year":"2009","journal-title":"Med. Eng. Phys."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.cmpb.2016.02.016","article-title":"UltraTrack: Software for semi-automated tracking of muscle fascicles in sequences of B-mode ultrasound images","volume":"128","author":"Farris","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1474","DOI":"10.1016\/j.ultrasmedbio.2008.02.009","article-title":"Estimation of muscle fiber orientation in ultrasound images using revoting hough transform (RVHT)","volume":"34","author":"Zhou","year":"2008","journal-title":"Ultrasound Med. Biol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TPAMI.2005.126","article-title":"Radon transform orientation estimation for rotation invariant texture analysis","volume":"27","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"S188","DOI":"10.1016\/S0720-048X(98)00061-8","article-title":"Ultrasound transducers","volume":"27","author":"Rizzatto","year":"1998","journal-title":"Eur. J. Radiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/TBME.2011.2144593","article-title":"Automatic tracking of muscle fascicles in ultrasound images using localized radon transform","volume":"58","author":"Zhao","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_30","first-page":"313","article-title":"Automated regional analysis of B-mode ultrasound images of skeletal muscle movement","volume":"112","author":"Darby","year":"2012","journal-title":"J. Appl. Physiol. Bethesda Md 1985"},{"doi-asserted-by":"crossref","unstructured":"van der Zee, T.J., and Kuo, A.D. (2022). TimTrack: A drift-free algorithm for estimating geometric muscle features from ultrasound images. PLoS ONE, 17.","key":"ref_31","DOI":"10.1371\/journal.pone.0265752"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e7120","DOI":"10.7717\/peerj.7120","article-title":"An automatic fascicle tracking algorithm quantifying gastrocnemius architecture during maximal effort contractions","volume":"7","author":"Drazan","year":"2019","journal-title":"PeerJ."},{"doi-asserted-by":"crossref","unstructured":"Rosa, L.G., Zia, J.S., Inan, O.T., and Sawicki, G.S. (2021). Machine learning to extract muscle fascicle length changes from dynamic ultrasound images in real-time. PloS ONE, 16.","key":"ref_33","DOI":"10.1101\/2021.01.25.428061"},{"doi-asserted-by":"crossref","unstructured":"Katakis, S., Barotsis, N., Kakotaritis, A., Economou, G., Panagiotopoulos, E., and Panayiotakis, G. (2022). Automatic Extraction of Muscle Parameters with Attention UNet in Ultrasonography. Sensors, 22.","key":"ref_34","DOI":"10.3390\/s22145230"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA.","key":"ref_35","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"800698","DOI":"10.3389\/fnagi.2022.800698","article-title":"Hoffmann Reflex Measured from Lateral Gastrocnemius Is More Reliable than from Soleus among Elderly with Peripheral Neuropathy","volume":"14","author":"Song","year":"2022","journal-title":"Front. Aging Neurosci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1152\/japplphysiol.00128.2015","article-title":"In vivo behavior of the human soleus muscle with increasing walking and running speeds","volume":"118","author":"Lai","year":"2015","journal-title":"J. Appl. Physiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"doi-asserted-by":"crossref","unstructured":"Gionfrida, L., Rusli, W.M., Kedgley, A.E., and Bharath, A.A. (2022). A 3DCNN-LSTM Multi-Class Temporal Segmentation for Hand Gesture Recognition. Electronics, 11.","key":"ref_39","DOI":"10.20944\/preprints202206.0368.v1"},{"unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). {TensorFlow}: A system for {Large-Scale} machine learning. Proceedings of the Osdi, Savannah, GA, USA. Available online: https:\/\/www.usenix.org\/system\/files\/conference\/osdi16\/osdi16-abadi.pdf.","key":"ref_40"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1093\/ageing\/afz079","article-title":"Assessment of core and lower limb muscles for static\/dynamic balance in the older people: An ultrasonographic study","volume":"48","author":"Kara","year":"2019","journal-title":"Age Ageing"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e1052","DOI":"10.7717\/peerj-cs.1052","article-title":"Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models","volume":"8","author":"Leiva","year":"2022","journal-title":"PeerJ. Comput. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"100565","DOI":"10.1016\/j.imu.2021.100565","article-title":"Conditional sliding windows: An approach for handling data limitation in colorectal histopathology image classification","volume":"23","author":"Haryanto","year":"2021","journal-title":"Inform. Med. Unlocked"},{"doi-asserted-by":"crossref","unstructured":"Frey, S., Vostrikov, S., Benini, L., and Cossettini, A. (2022, January 10\u201313). WULPUS: A Wearable Ultra Low-Power Ultrasound probe for multi-day monitoring of carotid artery and muscle activity. Proceedings of the 2022 IEEE International Ultrasonics Symposium (IUS), Venice, Italy.","key":"ref_44","DOI":"10.1109\/IUS54386.2022.9958156"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1109\/TMRB.2022.3172680","article-title":"Sparse Sonomyography-based Estimation of Isometric Force: A Comparison of Methods and Features","volume":"4","author":"Kamatham","year":"2022","journal-title":"IEEE Trans. Med. Robot. Bionics"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1670\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:22:55Z","timestamp":1760120575000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1670"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,3]]},"references-count":45,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031670"],"URL":"https:\/\/doi.org\/10.3390\/s23031670","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,2,3]]}}}