Ambient floor vibration sensing advances the accessibility of functional gait assessments for children with muscular dystrophies.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
11 May 2024
11 May 2024
Historique:
received:
09
08
2023
accepted:
18
04
2024
medline:
11
5
2024
pubmed:
11
5
2024
entrez:
10
5
2024
Statut:
epublish
Résumé
Muscular dystrophies (MD) are a group of genetic neuromuscular disorders that cause progressive weakness and loss of muscles over time, influencing 1 in 3500-5000 children worldwide. New and exciting treatment options have led to a critical need for a clinical post-marketing surveillance tool to confirm the efficacy and safety of these treatments after individuals receive them in a commercial setting. For MDs, functional gait assessment is a common approach to evaluate the efficacy of the treatments because muscle weakness is reflected in individuals' walking patterns. However, there is little incentive for the family to continue to travel for such assessments due to the lack of access to specialty centers. While various existing sensing devices, such as cameras, force plates, and wearables can assess gait at home, they are limited by privacy concerns, area of coverage, and discomfort in carrying devices, which is not practical for long-term, continuous monitoring in daily settings. In this study, we introduce a novel functional gait assessment system using ambient floor vibrations, which is non-invasive and scalable, requiring only low-cost and sparsely deployed geophone sensors attached to the floor surface, suitable for in-home usage. Our system captures floor vibrations generated by footsteps from patients while they walk around and analyzes such vibrations to extract essential gait health information. To enhance interpretability and reliability under various sensing scenarios, we translate the signal patterns of floor vibration to pathological gait patterns related to MD, and develop a hierarchical learning algorithm that aggregates insights from individual footsteps to estimate a person's overall gait performance. When evaluated through real-world experiments with 36 subjects (including 15 patients with MD), our floor vibration sensing system achieves a 94.8% accuracy in predicting functional gait stages for patients with MD. Our approach enables accurate, accessible, and scalable functional gait assessment, bringing MD progressive tracking into real life.
Identifiants
pubmed: 38729999
doi: 10.1038/s41598-024-60034-5
pii: 10.1038/s41598-024-60034-5
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
10774Subventions
Organisme : National Science Foundation
ID : NSF-CMMI-2026699
Informations de copyright
© 2024. The Author(s).
Références
Bushby, K. et al. Diagnosis and management of Duchenne muscular dystrophy, part 1: Diagnosis, and pharmacological and psychosocial management. Lancet Neurol. 9(1), 77–93 (2010).
pubmed: 19945913
doi: 10.1016/S1474-4422(09)70271-6
Parsons, E. P., Clarke, A. J. & Bradley, D. M. Developmental progress in Duchenne muscular dystrophy: Lessons for earlier detection. Eur. J. Paediatr. Neurol. 8(3), 145–153 (2004).
pubmed: 15120686
doi: 10.1016/j.ejpn.2004.01.009
Amoasii, L. et al. Single-cut genome editing restores dystrophin expression in a new mouse model of muscular dystrophy. Sci. Transl. Med. 9(418), 756–760 (2017).
doi: 10.1126/scitranslmed.aan8081
Landfeldt, E. et al. Life expectancy at birth in Duchenne muscular dystrophy: A systematic review and meta-analysis. Eur. J. Epidemiol. 35, 643–653 (2020).
pubmed: 32107739
pmcid: 7387367
doi: 10.1007/s10654-020-00613-8
Yiu, E. M. & Kornberg, A. J. Duchenne muscular dystrophy. J. Paediatr. Child Health 51(8), 759–764 (2015).
pubmed: 25752877
doi: 10.1111/jpc.12868
Bushby, K. et al. Diagnosis and management of Duchenne muscular dystrophy, part 1: Diagnosis, and pharmacological and psychosocial management. Lancet Neurol. 9(1), 77–93 (2010).
pubmed: 19945913
doi: 10.1016/S1474-4422(09)70271-6
Angelini, C. The role of corticosteroids in muscular dystrophy: A critical appraisal. Muscle Nerve Off. J. Am. Assoc. Electrodiagn. Med. 36(4), 424–435 (2007).
doi: 10.1002/mus.20812
McDonald, C. M. et al. The 6-minute walk test as a new outcome measure in Duchenne muscular dystrophy. Muscle Nerve 41(4), 500–510 (2010).
pubmed: 19941337
doi: 10.1002/mus.21544
D’Angelo, M. G. et al. Gait pattern in Duchenne muscular dystrophy. Gait Posture 29(1), 36–41 (2009).
pubmed: 18656361
doi: 10.1016/j.gaitpost.2008.06.002
Abinaya, B., Latha, V. & Suchetha, M. An advanced gait monitoring system based on air pressure sensor embedded in a shoe. Procedia Eng. 38(3), 1634–1643 (2012).
doi: 10.1016/j.proeng.2012.06.199
Lin, F., Wang, A., Zhuang, Y., Tomita, M. R. & Wenyao, X. Smart insole: A wearable sensor device for unobtrusive gait monitoring in daily life. IEEE Trans. Ind. Inf. 12(6), 2281–2291 (2016).
doi: 10.1109/TII.2016.2585643
de Carvalho, E. V., Hukuda, M. E., Escorcio, R., Voos, M. C. & Caromano, F. A. Development and reliability of the functional evaluation scale for Duchenne muscular dystrophy, gait domain: A pilot study. Physiother. Res. Int. 20(3), 135–146 (2015).
pubmed: 25521365
doi: 10.1002/pri.1605
Dong, Y., Zou, J. J., Liu, J., Fagert, J., Mirshekari, M., Lowes, L., Iammarino, M., Zhang, P. & Noh, H. Y. Md-vibe: physics-informed analysis of patient-induced structural vibration data for monitoring gait health in individuals with muscular dystrophy. In Adjunct proceedings of the 2020 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2020 ACM international symposium on wearable computers, 525–531 (2020).
Van Iersel, M. B. & Mulley, G. P. What is a waddling gait?. Disabil. Rehabil. 26(11), 678–682 (2004).
pubmed: 15204507
doi: 10.1080/09638280410001672526
National Library of Medicine. Waddling gait, https://www.ncbi.nlm.nih.gov/medgen/66667 (2024).
Pan, S., Wang, N., Qian, Y., Velibeyoglu, I., Noh, H. Y. & Zhang, P. Indoor person identification through footstep induced structural vibration. HotMobile 2015 - 16th International Workshop on Mobile Computing Systems and Applications, 81–86 (2015).
Karg, M., Kühnlenz, K. & Buss, M. Recognition of affect based on gait patterns. IEEE Trans. Syst. Man Cybern. Part B (Cybernet.) 40(4), 1050–1061 (2010).
doi: 10.1109/TSMCB.2010.2044040
Michalak, J., Burg, J. & Heidenreich, T. Don’t forget your body: Mindfulness, embodiment, and the treatment of depression. Mindfulness 3, 190–199 (2012).
doi: 10.1007/s12671-012-0107-4
Dodge, H. H., Mattek, N. C., Austin, D., Hayes, T. L. & Kaye, J. A. In-home walking speeds and variability trajectories associated with mild cognitive impairment. Neurology 78(24), 1946–1952 (2012).
pubmed: 22689734
pmcid: 3369505
doi: 10.1212/WNL.0b013e318259e1de
Alfano, L. N. et al. The 100-meter timed test: Normative data in healthy males and comparative pilot outcome data for use in duchenne muscular dystrophy clinical trials. Neuromuscul. Disord. 27(5), 452–457 (2017).
pubmed: 28279570
doi: 10.1016/j.nmd.2017.02.007
Vicon motion capture system.
Merriaux, P., Dupuis, Y., Boutteau, R., Vasseur, P. & Savatier, X. A study of vicon system positioning performance. Sensors 17(7), 1591 (2017).
pubmed: 28686213
pmcid: 5551098
doi: 10.3390/s17071591
Windolf, M., Götzen, N. & Morlock, M. Systematic accuracy and precision analysis of video motion capturing systems-exemplified on the vicon-460 system. J. Biomech. 41(12), 2776–2780 (2008).
pubmed: 18672241
doi: 10.1016/j.jbiomech.2008.06.024
Vicon plug-in gait lower-body model.
Fagert, J., Mirshekari, M., Zhang, P. & Noh, H. Y. Recursive sparse representation for identifying multiple concurrent occupants using floor vibration sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6(1), 1–33 (2022).
doi: 10.1145/3517229
Doglio, L. et al. Early signs of gait deviation in Duchenne muscular dystrophy. Eur. J. Phys. Rehabil. Med. 47(4), 587–94 (2011).
pubmed: 21912365
Osoba, M. Y., Rao, A. K., Agrawal, S. K. & Lalwani, A. K. Balance and gait in the elderly: A contemporary review. Laryngoscope Investig. Otolaryngol. 4(1), 143–153 (2019).
pubmed: 30828632
pmcid: 6383322
doi: 10.1002/lio2.252
Katz-Leurer, M., Rotem, H., Lewitus, H., Keren, O. & Meyer, S. Relationship between balance abilities and gait characteristics in children with post-traumatic brain injury. Brain Inj. 22(2), 153–159 (2008).
pubmed: 18240044
doi: 10.1080/02699050801895399
Heckmatt, J. Z. et al. Prolongation of walking in Duchenne muscular dystrophy with lightweight orthoses; Review of 57 cases. Dev. Med. Child Neurol. 27(2), 149–154 (1985).
pubmed: 3996772
doi: 10.1111/j.1469-8749.1985.tb03763.x
Cornelio, F. et al. Functional evaluation of Duchenne muscular dystrophy: Proposal for a protocol. Ital. J. Neurol. Sci. 3, 323–330 (1982).
pubmed: 6762370
doi: 10.1007/BF02043581
Pan, S., Ramirez, C. G., Mirshekari, M., Fagert, J., Chung, A. J, Hu, C. C., Shen, J. P., Noh, H. Y. & Zhang, P. Surfacevibe: Vibration-based tap & swipe tracking on ubiquitous surfaces. In Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, 197–208 (2017).
Sutherland, D. H. et al. The pathomechanics of gait in Duchenne muscular dystrophy. Dev. Med. Child Neurol. 23(1), 3–22 (1981).
pubmed: 7202868
doi: 10.1111/j.1469-8749.1981.tb08442.x
Sienko Thomas, S. et al. Classification of the gait patterns of boys with Duchenne muscular dystrophy and their relationship to function. J. Child Neurol. 25(9), 1103–1109 (2010).
pubmed: 20587736
doi: 10.1177/0883073810371002
MCDonald, C. M. et al. The 6-minute walk test in Duchenne/Becker muscular dystrophy: Longitudinal observations. Muscle Nerve 42(6), 966–974 (2010).
pubmed: 21038378
doi: 10.1002/mus.21808
D’Angelo, M. G. et al. Gait pattern in Duchenne muscular dystrophy. Gait Posture 29(1), 36–41 (2009).
pubmed: 18656361
doi: 10.1016/j.gaitpost.2008.06.002
Ruzbarsky, J. J., Scher, D. & Dodwell, E. Toe walking: Causes, epidemiology, assessment, and treatment. Curr. Opin. Pediatr. 28(1), 40–46 (2016).
pubmed: 26709689
doi: 10.1097/MOP.0000000000000302
Hsu, J. D. & Furumasu, J. Gait and posture changes in the Duchenne muscular dystrophy child. Clin. Orthop. Relat. Res. 1976–2007(288), 122–125 (1993).
Rijken, N. H. M. et al. Skeletal muscle imaging in facioscapulohumeral muscular dystrophy, pattern and asymmetry of individual muscle involvement. Neuromuscul. Disord. 24(12), 1087–1096 (2014).
pubmed: 25176503
doi: 10.1016/j.nmd.2014.05.012
Song, T.-J., Lee, K.-A., Kang, S.-W., Cho, H. & Choi, Y.-C. Three cases of manifesting female carriers in patients with Duchenne muscular dystrophy. Yonsei Med. J. 52(1), 192–195 (2011).
pubmed: 21155054
doi: 10.3349/ymj.2011.52.1.192
Thompson, R. & Straub, V. Limb-girdle muscular dystrophies-international collaborations for translational research. Nat. Rev. Neurol. 12(5), 294–309 (2016).
pubmed: 27033376
doi: 10.1038/nrneurol.2016.35
Dong, Y. & Noh, H. Y. Structure-agnostic gait cycle segmentation for in-home gait health monitoring through footstep-induced structural vibrations.
Dong, Y., Liu, J. & Noh, H. Y. Gaitvibe+: Enhancing structural vibration-based footstep localization using temporary cameras for in-home gait analysis. In Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems, SenSys ’22, 1168-1174 (Association for Computing Machinery, New York, NY, USA, 2023).
Dong, Y., Fagert, J. & Noh, H. Y. Characterizing the variability of footstep-induced structural vibrations for open-world person identification. Mech. Syst. Signal Process. 204, 110756 (2023).
doi: 10.1016/j.ymssp.2023.110756
Dong, Y., Zhu, J. & Noh, H. Y. Re-vibe: vibration-based indoor person re-identification through cross-structure optimal transport. In Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, 348–352 (2022).
Pan, S. et al. FootprintID: Indoor pedestrian identification through ambient structural vibration sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1(3), 1–31 (2017).
doi: 10.1145/3130954
Miller, N. F. et al. Natural history of steroid-treated young boys with Duchenne muscular dystrophy using the NSAA, 100 m, and timed functional tests. Pediatr. Neurol. 113, 15–20 (2020).
pubmed: 32979653
doi: 10.1016/j.pediatrneurol.2020.08.013
Bohannon, R. W. Reference values for the timed up and go test: A descriptive meta-analysis. J. Geriatr. Phys. Ther 29(2), 64–68 (2006).
pubmed: 16914068
doi: 10.1519/00139143-200608000-00004
Petian-Alonso, D. C., de Castro, A. C., de Queiroz Davoli, G. B., Martinez, E. Z. & Mattiello-Sverzut, A. C. Defining ambulation status in patients with Duchenne muscular dystrophy using the 10-metre walk test and the motor function measure scale. Disabil. Rehabil. 45(18), 2984–2988 (2023).
pubmed: 35980858
doi: 10.1080/09638288.2022.2112098
James, M. K. et al. Validation of the north star assessment for limb-girdle type muscular dystrophies. Phys. Ther. 102(10), pzac113 (2022).
pubmed: 35932452
pmcid: 9586158
doi: 10.1093/ptj/pzac113
Racic, V., Pavic, A. & Brownjohn, J. M. W. Experimental identification and analytical modelling of human walking forces: Literature review. J. Sound Vib. 326(1–2), 1–49 (2009).
doi: 10.1016/j.jsv.2009.04.020
Szandała, T. Review and comparison of commonly used activation functions for deep neural networks. In Bio-inspired Neurocomputing. 203–224 (2021).
doi: 10.1007/978-981-15-5495-7_11
Liu, W., Wen, Y., Yu, Z. & Yang, M. Large-margin softmax loss for convolutional neural networks, arXiv preprintarXiv:1612.02295 (2016).
Goutte, C. & Gaussier, E. A probabilistic interpretation of precision, recall and f-score, with implication for evaluation. In Advances in Information Retrieval: 27th European Conference on IR Research, ECIR 2005, Santiago de Compostela, Spain, March 21-23, 2005. Proceedings 27, 345–359 (Springer, 2005).
Willmott, C. J. & Matsuura, K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res. 30(1), 79–82 (2005).
doi: 10.3354/cr030079