https://doi.org/10.4081/ejtm.2020.9002
Validation of a portable system for spatial-temporal gait parameters based on a single inertial measurement unit and a mobile application
Submitted: 1 April 2020
Accepted: 23 May 2020
Published: 22 June 2020
Accepted: 23 May 2020
Abstract Views: 754
PDF: 456
HTML: 55
HTML: 55
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
There is a lack of commercially available low-cost technologies to assess gait clinically in non-controlled environments. As a consequence of this, there has been poor massification of motion measurement technologies that are both objective and reliable in nature. Advances about the study of gait and its interpretation in recent years using inertial sensors have allowed proposing acceptable alternatives for the development of portable and low-cost systems that contribute to people’s health in places and institutions that cannot acquire or maintain the operation of commercially available systems. A system based on a custom single Inertial Measurement Unit and a mobile application is proposed. Thus, an investigation is carried out using methodologies and algorithms found in the literature in order to get the main gait events and the spatial-temporal gait parameters. Twenty healthy Chilean subjects were assessed using a motion capture system simultaneously with the proposed tool. The results show that it is possible to estimate temporal gait parameters with slight differences respect gold--standard. We reach maximum mean differences of -2.35±5.02[step/min] for cadence, 0.03±0.04[sec] for stride time,0.02±0.03[sec] for step time, ±0.02[sec] for a single support time, 0.01±0.02[sec] for double support time and 0.01±0.03[m] for step length. As a result of experimental findings, we propose a new technological tool that can perform gait analysis. Our proposed system is user-friendly, low-cost, and portable. Therefore, we suggest that it could be an attractive technological tool that healthcare professionals could harness to objectively measure gait in environments that are either within the community or controlled. We also suggest that the tool could be used in countries where advanced clinical tools cannot be acquired. Therefore, we propose in this paper that our system is an attractive, alternative system that can be used for gait analysis by health professionals worldwide.
Bright, T.; Wallace, S.; Kuper, H. A systematic review of access to rehabilitation for people with disabilities in low-and middle-income countries. International journal of environmental research and public health 2018, 15, 2165.
DOI: https://doi.org/10.3390/ijerph15102165
Flory, S. The prospects and challenges of bringing physical therapy to the physically disabled population in developing countries 2009.
Echeverry, L.L.G.; Henao, A.M.J.; Molina, M.A.R.; Restrepo, S.M.V.; Velásquez, C.A.P.; Bolívar, G.J.S. Human motion capture and analysis systems: a systematic review/Sistemas de captura y análisis de movimiento cinemático humano: una revisión sistemática. Prospectiva 2018, 16, 24–34
DOI: https://doi.org/10.15665/rp.v16i2.1587
Giannini S. Gait analysis: methodologies and clinical applications. Amsterdam: IOS Press for B.T.S. Bioengineering Technology & Systems; 1994.
Watson MJ. Refining the Ten-metre Walking Test for Use with Neurologically Impaired People. Physiotherapy. 2002;88(7):386–97.
DOI: https://doi.org/10.1016/S0031-9406(05)61264-3
Mayagoitia RE, Nene AV, Veltink PH. Accelerometer and rate gyroscope measurement of kinematics: an inexpensive alternative to optical motion analysis systems. Journal of Biomechanics. 2002;35(4):537–42.
DOI: https://doi.org/10.1016/S0021-9290(01)00231-7
Lau H, Tong K. The reliability of using accelerometer and gyroscope for gait event identification on persons with dropped foot. Gait & Posture. 2008;27(2):248–57.
DOI: https://doi.org/10.1016/j.gaitpost.2007.03.018
Yang C-C, Hsu Y-L. A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring. Sensors. 2010;10(8):7772–88.
DOI: https://doi.org/10.3390/s100807772
Buckley C, Galna B, Rochester L, Mazzà C. Quantification of upper body movements during gait in older adults and in those with Parkinson’s disease: impact of acceleration realignment methodologies. Gait & Posture. 2017; 52:265–71.
DOI: https://doi.org/10.1016/j.gaitpost.2016.11.047
Jasiewicz JM, Allum JH, Middleton JW, Barriskill A, Condie P, Purcell B, et al. Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals. Gait & Posture. 2006;24(4):502–9.
DOI: https://doi.org/10.1016/j.gaitpost.2005.12.017
Kim JW, Jang HJ, Hwang D-H, Park C. A Step, Stride and Heading Determination for the Pedestrian Navigation System. Journal of Global Positioning Systems. 2004;3(1&2):273–9.
DOI: https://doi.org/10.5081/jgps.3.1.273
Sayeed T, Sama A, Catala A, Cabestany J. Comparison and adaptation of step length and gait speed estimators from single belt worn accelerometer positioned on lateral side of the body. 2013 IEEE 8th International Symposium on Intelligent Signal Processing. 2013;14-20.
DOI: https://doi.org/10.1109/WISP.2013.6657475
Salarian A, Burkhard PR, Vingerhoets FJG, Jolles BM, Aminian K. A Novel Approach to Reducing Number of Sensing Units for Wearable Gait Analysis Systems. IEEE Transactions on Biomedical Engineering. 2013;60(1):72–7.
DOI: https://doi.org/10.1109/TBME.2012.2223465
Glowinski, S.; Blazejewski, A.; Krzyzynski,T. Humangaitfeaturedetectionusinginertialsensorswavelets. In Wearable Robotics: Challenges and Trends; Springer, 2017; pp. 397–401
DOI: https://doi.org/10.1007/978-3-319-46532-6_65
Jee H. Review of researches on smartphone applications for physical activity promotion in healthy adults. Journal of Exercise Rehabilitation. 2017;13(1):3–11.
DOI: https://doi.org/10.12965/jer.1732928.464
Muro-De-La-Herran A, Garcia-Zapirain B, Mendez-Zorrilla A. Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications. Sensors. 2014;14(2):3362–94.
DOI: https://doi.org/10.3390/s140203362
Paraschiv-Ionescu A, Newman CJ, Carcreff L, Gerber CN, Armand S, Aminian K. Correction to: Locomotion and cadence detection using a single trunk-fixed accelerometer: validity for children with cerebral palsy in daily life-like conditions. Journal of NeuroEngineering and Rehabilitation. 2019Dec;16(1).
DOI: https://doi.org/10.1186/s12984-019-0498-8
Carcreff L, Gerber C, Paraschiv-Ionescu A, Coulon GD, Newman C, Armand S, et al. What is the Best Configuration of Wearable Sensors to Measure Spatiotemporal Gait Parameters in Children with Cerebral Palsy? Sensors. 2018;18(2):394.
DOI: https://doi.org/10.3390/s18020394
Trojaniello D, Cereatti A, Croce UD. Accuracy, sensitivity and robustness of five different methods for the estimation of gait temporal parameters using a single inertial sensor mounted on the lower trunk. Gait & Posture. 2014;40(4):487–92.
DOI: https://doi.org/10.1016/j.gaitpost.2014.07.007
González RC, López AM, Rodriguez-Uría J, Álvarez D, Alvarez JC. Real-time gait event detection for normal subjects from lower trunk accelerations. Gait & Posture. 2010;31(3):322–5.
Doheny, E.P.; Foran, T.G.; Greene, B.R. A single gyroscope method for spatial gait analysis. Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE. IEEE, 2010, pp. 1300–1303
DOI: https://doi.org/10.1109/IEMBS.2010.5626397
Sejdic E, Lowry KA, Bellanca J, Perera S, Redfern MS, Brach JS. Extraction of Stride Events From Gait Accelerometry During Treadmill Walking. IEEE Journal of Translational Engineering in Health and Medicine. 2016;4:1–11.
DOI: https://doi.org/10.1109/JTEHM.2015.2504961
González, R.C.; López, A.M.; Rodriguez-Uría, J.; Alvarez, D.; Alvarez, J.C. Real-time gait event detection for normal subjects from lower trunk accelerations. Gait & posture 2010, 31, 322–325
DOI: https://doi.org/10.1016/j.gaitpost.2009.11.014
Kose A, Cereatti A, Croce UD. Daily life activity classification using a single inertial measurement unit attached to the waist. Gait & Posture. 2012;35.
DOI: https://doi.org/10.1016/j.gaitpost.2011.09.050
Mccamley J, Donati M, Grimpampi E, Mazzà C. An enhanced estimate of initial contact and final contact instants of time using lower trunk inertial sensor data. Gait & Posture. 2012;36(2):316–8.
DOI: https://doi.org/10.1016/j.gaitpost.2012.02.019
Aminian K, Rezakhanlou K, Andres ED, Fritsch C, Leyvraz P-F, Robert P. Temporal feature estimation during walking using miniature accelerometers: an analysis of gait improvement after hip arthroplasty. Medical & Biological Engineering & Computing. 1999;37(6):686–91.
DOI: https://doi.org/10.1007/BF02513368
Sabatini A, Martelloni C, Scapellato S, Cavallo F. Assessment of Walking Features From Foot Inertial Sensing. IEEE Transactions on Biomedical Engineering. 2005;52(3):486–94.
DOI: https://doi.org/10.1109/TBME.2004.840727
Aminian K, Najafi B, Büla C, Leyvraz P-F, Robert P. Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. Journal of Biomechanics. 2002;35(5):689–99.
DOI: https://doi.org/10.1016/S0021-9290(02)00008-8
Zijlstra W, Hof AL. Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait & Posture. 2003;18(2):1–10.
DOI: https://doi.org/10.1016/S0966-6362(02)00190-X
Alvarez JC, Alvarez D, López A, González RC. Pedestrian Navigation Based on a Waist-Worn Inertial Sensor. Sensors. 2012Mar;12(8):10536–49.
DOI: https://doi.org/10.3390/s120810536
Godfrey, A.; Del Din, S.; Barry, G.; Mathers, J.; Rochester, L. Instrumenting gait with an accelerometer: a system and algorithm examination. Medical engineering & physics 2015, 37, 400–407 [32] BNO055 Intelligent 9-Axis Absolute Sensor [Internet]. Mouser Electronics - Electronic Components Distributor. 2019 [cited 2020 Feb 25]. Available from: https://www.mouser.com/new/bosch/bosch-bno55-sensor/
DOI: https://doi.org/10.1016/j.medengphy.2015.02.003
Ang M, Tourassis V. Singularities of Euler and Roll-Pitch-Yaw Representations. IEEE Transactions on Aerospace and Electronic Systems. 1987; AES-23(3):317–24.
DOI: https://doi.org/10.1109/TAES.1987.310828
Davis RB, Õunpuu S, Tyburski D, Gage JR. A gait analysis data collection and reduction technique. Human Movement Science. 1991;10(5):575–87.
DOI: https://doi.org/10.1016/0167-9457(91)90046-Z
Supporting Agencies
ANID Chile, Preclinical and Clinical Sciences Department, Faculty of Medicine, Universidad Católica de la Santísima ConcepciónHow to Cite
Aqueveque, P., Gómez, B. A., Saavedra, F., Canales, C., Contreras, S., Ortega-Bastidas, P., & Cano-de-la-Cuerda, R. (2020). Validation of a portable system for spatial-temporal gait parameters based on a single inertial measurement unit and a mobile application. European Journal of Translational Myology, 30(2), 268–276. https://doi.org/10.4081/ejtm.2020.9002
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.