ABSTRACT
We introduce a footstep-induced floor vibration sensing system that enables us to quantify the gait pattern of individuals with Muscular Dystrophy (MD) in non-clinical settings. MD is a neuromuscular disorder causing progressive loss of muscle, which leads to symptoms in gait patterns such as toe-walking, frequent falls, balance difficulty, etc. Existing systems that are used for progressive tracking include pressure mats, wearable devices, or direct observation by healthcare professionals. However, they are limited by operational requirements including dense deployment, users' device carrying, special training, etc. To overcome these limitations, we introduce a new approach that senses floor vibrations induced by human footsteps. Gait symptoms in these footsteps are reflected by the vibration signals, which enables monitoring of gait health for individuals with MD. Our approach is non-intrusive, unrestricted by line-of-sight, and thus suitable for in-home deployment. To develop our approach, we characterize the gait pattern of individuals with MD using vibration signals, and infer the health state of the patients based on both symptom-based and signal-based features. However, there are two main challenges: 1) different aspects of human gaits are mixed up in footstep-induced floor vibrations; and 2) structural heterogeneity distorts vibration propagation and attenuation through the floor medium. To overcome the first challenge, we characterize the symptom-based gait features of the footstep-induced floor vibration specific to MD. To minimize the performance inconsistency across different sensing locations in the building, we reduce the structural effects by removing the free-vibration phase due to structural damping. With these two challenges addressed, we evaluate our system performance by conducting a real-world experiment with six patients with MD and seven healthy participants. Our approach achieved 96% accuracy in predicting whether the footstep was from a patient with MD.
- Leonela Amoasii, Chengzu Long, Hui Li, Alex A. Mireault, John M. Shelton, Efrain Sanchez-Ortiz, John R. McAnally, Samadrita Bhattacharyya, Florian Schmidt, Dirk Grimm, Stephen D. Hauschka, Rhonda Bassel-Duby, and Eric N. Olson. Single-cut genome editing restores dystrophin expression in a new mouse model of muscular dystrophy. Science Translational Medicine, 9(418):756--760, 2017.Google ScholarCross Ref
- Katharine Bushby, Richard Finkel, David J. Birnkrant, Laura E. Case, Paula R. Clemens, Linda Cripe, Ajay Kaul, Kathi Kinnett, Craig McDonald, Shree Pandya, James Poysky, Frederic Shapiro, Jean Tomezsko, and Carolyn Constantin. Diagnosis and management of Duchenne muscular dystrophy, part 1: diagnosis, and pharmacological and psychosocial management. The Lancet Neurology, 9(1):77--93, 2010.Google ScholarCross Ref
- Evelyn P. Parsons, Angus J. Clarke, and Don M. Bradley. Developmental progress in Duchenne muscular dystrophy: Lessons for earlier detection. European Journal of Paediatric Neurology, 8(3):145--153, 2004.Google ScholarCross Ref
- Emma Ciafaloni, Deborah J. Fox, Shree Pandya, Christina P. Westfield, Soman Puzhankara, Paul A. Romitti, Katherine D. Mathews, Timothy M. Miller, Dennis J. Matthews, Lisa A. Miller, Christopher Cunniff, Charlotte M. Druschel, and Richard T. Moxley. Delayed Diagnosis in Duchenne Muscular Dystrophy: Data from the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet). Journal of Pediatrics, 155(3):380--385, 2009.Google ScholarCross Ref
- Craig M. McDonald, Erik K. Henricson, Jay J. Han, R. Ted Abresch, Alina Nicorici, Gary L. Elfring, Leone Atkinson, Allen Reha, Samit Hirawat, and Langdon L. Miller. The 6-minute walk test as a new outcome measure in duchenne muscular dystrophy. Muscle and Nerve, 41(4):500--510, 2010.Google ScholarCross Ref
- Maria Grazia D'Angelo, Matteo Berti, Luigi Piccinini, Marianna Romei, Michela Guglieri, Sara Bonato, Alessandro Degrate, Anna Carla Turconi, and Nereo Bresolin. Gait pattern in Duchenne muscular dystrophy. Gait and Posture, 29(1):36--41, 2009.Google ScholarCross Ref
- B. Abinaya, V. Latha, and M. Suchetha. An advanced gait monitoring system based on air pressure sensor embedded in a shoe. Procedia Engineering, 38(3):1634--1643, 2012.Google ScholarCross Ref
- Feng Lin, Aosen Wang, Yan Zhuang, Machiko R. Tomita, and Wenyao Xu. Smart Insole: A Wearable Sensor Device for Unobtrusive Gait Monitoring in Daily Life. IEEE Transactions on Industrial Informatics, 12(6):2281--2291, 2016.Google ScholarCross Ref
- Shijia Pan, Susu Xu, Mostafa Mirshekari, Pei Zhang, and Hae Young Noh. Collaboratively adaptive vibration sensing system for high-fidelity monitoring of structural responses induced by pedestrians. Frontiers in Built Environment, 3:28, 2017.Google ScholarCross Ref
- Shijia Pan, Amelie Bonde, Jie Jing, Lin Zhang, Pei Zhang, and Hae Young Noh. Boes: building occupancy estimation system using sparse ambient vibration monitoring. In Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2014, volume 9061, page 90611O. International Society for Optics and Photonics, 2014.Google Scholar
- Shijia Pan, Tong Yu, Mostafa Mirshekari, Jonathon Fagert, Amelie Bonde, Ole Mengshoel, Hae Young Noh, and Pei Zhang. FootprintID: Indoor Pedestrian Identification through Ambient Structural Vibration Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(3):1--31, 2017.Google ScholarDigital Library
- Mostafa Mirshekari, Shijia Pan, Pei Zhang, and Hae Young Noh. Characterizing wave propagation to improve indoor step-level person localization using floor vibration. Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2016, 9803(April):980305, 2016.Google Scholar
- Jonathon Fagert, Mostafa Mirshekari, Shijia Pan, Pei Zhang, and Hae Young Noh. Gait health monitoring through footstep-induced floor vibrations. IPSN 2019 - Proceedings of the 2019 Information Processing in Sensor Networks, pages 319--320, 2019.Google ScholarDigital Library
- Mostafa Mirshekari, Jonathon Fagert, Shijia Pan, Pei Zhang, and Hae Young Noh. Step-Level Occupant Detection across Different Structures through Footstep-Induced Floor Vibration Using Model Transfer. Journal of Engineering Mechanics, 146(3):1--18, 2020.Google ScholarCross Ref
- Shijia Pan, Mario Berges, Juleen Rodakowski, Pei Zhang, and Hae Young Noh. Fine-grained recognition of activities of daily living through structural vibration and electrical sensing. BuildSys 2019 - Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pages 149--158, 2019.Google ScholarDigital Library
- V. Racic, A. Pavic, and J. M.W. Brownjohn. Experimental identification and analytical modelling of human walking forces: Literature review. Journal of Sound and Vibration, 326(1--2):1--49, 2009.Google ScholarCross Ref
- Chris Kirtley. Chapter 1 - The temporal-spatial parameters. In Chris Kirtley, editor, Clinical Gait Analysis, pages 15--37. Churchill Livingstone, Edinburgh, 2006.Google ScholarCross Ref
- Jonathon Fagert, Mostafa Mirshekari, Shijia Pan, Pei Zhang, and Hae Young Noh. Characterizing left-right gait balance using footstep-induced structural vibrations. Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2017, 10168(724):1016819, 2017.Google Scholar
- Alexander Ekimov and James M. Sabatier. Vibration and sound signatures of human footsteps in buildings. The Journal of the Acoustical Society of America, 120(2):762--768, 2006.Google ScholarCross Ref
- Jonathon Fagert, Mostafa Mirshekari, Shijia Pan, Pei Zhang, and Hae Young Noh. Structural Property Guided Gait Parameter Estimation Using Footstep-Induced Floor Vibrations. In Shamim Pakzad, editor, Dynamics of Civil Structures, Volume 2, pages 191--194, Cham, 2020. Springer International Publishing.Google ScholarCross Ref
- S. H. Cho, J. M. Park, and O. Y. Kwon. Gender differences in three dimensional gait analysis data from 98 healthy Korean adults. Clinical Biomechanics, 19(2):145--152, 2004.Google ScholarCross Ref
- Karen T. Nozoe, Ricardo T. Akamine, Diego R. Mazzotti, Daniel N. Polesel, Luís F. Grossklauss, Sergio Tufik, Monica L. Andersen, and Gustavo A. Moreira. Phenotypic contrasts of Duchenne Muscular Dystrophy in women: Two case reports. Sleep Science, 9(3):129--133, 2016.Google ScholarCross Ref
- Jun Ye. Cosine similarity measures for intuitionistic fuzzy sets and their applications. Mathematical and Computer Modelling, 53(1--2):91--97, 2011.Google Scholar
- A Simple Method and For Estimating. A Simple Method For Estimating Conditional Probabilities For SVMs. 2004.Google Scholar
- Anil K Chopra. Dynamics of Structures. Pearson Higher Ed, 2015, 4 edition, 2015.Google Scholar
- Shijia Pan, Ningning Wang, Yuqiu Qian, Irem Velibeyoglu, Hae Young Noh, and Pei Zhang. Indoor person identification through footstep induced structural vibration. HotMobile 2015 - 16th International Workshop on Mobile Computing Systems and Applications, pages 81--86, 2015.Google Scholar
Index Terms
- MD-Vibe: physics-informed analysis of patient-induced structural vibration data for monitoring gait health in individuals with muscular dystrophy
Recommendations
Human Gait Monitoring Using Footstep-Induced Floor Vibrations Across Different Structures
UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable ComputersIn this paper, we present a structure-adaptive approach for monitoring human gait using footstep-induced floor vibrations. Human gait information is critical for timely and accurate assessment and diagnosis of many health conditions. Footstep-induced ...
FootprintID: Indoor Pedestrian Identification through Ambient Structural Vibration Sensing
We present FootprintID, an indoor pedestrian identification system that utilizes footstep-induced structural vibration to infer pedestrian identities for enabling various smart building applications. Previous studies have explored other sensing methods, ...
MyoBuddy: Detecting Barbell Weight Using Electromyogram Sensors
DigitalBiomarkers '17: Proceedings of the 1st Workshop on Digital BiomarkersMuscular dystrophy is a group of genetic diseases that cause the loss of muscles and hence weakening the muscle strength. A typical treatment for muscular dystrophy patients is routinely performing weight exercise to slow down the loss in muscles. Thus, ...
Comments