Abstract
During last decades, many technological alternatives for posture assessment has been proposed for people with balance problems caused by several pathologies, these alternatives are commonly based on center of pressure (COP) analysis using strength platforms, being the most used quantitative technique in the current clinical environment. Nevertheless, COP is not the only parameter interfering in balance, recent related investigations on this subject has analyzed the center of mass (COM) in static and dynamic state of balance problems people.
Nowadays, inertial sensors based tools has been proposed for postural control of people on static and dynamic state, which have provided a suitable clinical use in non-specialized environments, this technology has been used to provide space-time parameters related to human movement. In order to improve COM’s analysis and its effect in postural control many bio-mechanical models has been proposed, considering sensor arrays on upper only, lower only, or entire body. However, a suitable stabilometric analysis is still on research. The aim of this systematic review is to summarize the main Inertial sensors based stabilometric analysis for postural control in elderly people studies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Richards, J.: The Comprehensive Textbook of Clinical Biomechanics, 2nd edn. Elsevier, Preston, UK (2018)
Kirtley, C.: Clinical Gait Analysis. Theory and Practice, Churchill Living-Stone, United Kingdom (2006)
Kotas, R., Janc, M., Kamiski, M., Marciniak, P., Zamysowska-Szmytke, E., Tylman, W.: Evaluation of agreement between static posturography methods employing tensometers and inertial sensors. IEEE Access 7, 164120–164126 (2019). https://doi.org/10.1109/ACCESS.2019.2952496
Veeravelli, S., Najafi, B., Marin, I., Blumenkron, F., Smith, S., Klotz, S.A.: Exergaming in older people living with HIV improves balance, mobility and ameliorates some aspects of frailty. J. Vis. Exp. 2016 (2016). https://doi.org/10.3791/54275
Zhou, H., et al.: Hemodialysis impact on motor function beyond aging and diabetes- objectively assessing gait and balance by wearable technology. Sensors (Basel) 18 (2018). https://doi.org/10.3390/s18113939
Hasegawa, N., Shah, V.V., Carlson-Kuhta, P., Nutt, J.G., Horak, F.B., Mancini, M.: How to select balance measures sensitive to Parkinson’s disease from body-worn inertial sensors—separating the trees from the forest. Sensors 19(15), 3320 (2019). https://doi.org/10.3390/s19153320
Soangra, R., Lockhart, T.: Inertial sensor-based variables are indicators of frailty and adverse post-operative outcomes in cardiovascular disease patients. Sensors 18(6), 1792 (2018). https://doi.org/10.3390/s18061792
Schwenk, M., Grewal, G.S., Holloway, D., Muchna, A., Garland, L., Najafi, B.: Interactive sensor based balance training in older cancer patients with chemotherapy induced peripheral neuropathy: a randomized controlled trial. Gerontology 62, 553–563 (2016). https://doi.org/10.1159/000442253
Bonora, G., et al.: Investigation of anticipatory postural adjustments during one leg stance using inertial sensors: evidence from subjects with parkinsonism. Front. Neurol. 8 (2017). https://doi.org/10.3389/fneur.2017.00361
Agurto, C., Heisig, S., Abrami, A., Ho, B.K., Caggiano, V.: Parkinson’s disease medication state and severity assessment based on coordination during walking. PLoS ONE 16(2), e0244842 (2021). https://doi.org/10.1371/journal.pone.0244842
Fino, P.C., Mancini, M.: Phase dependent effects of closed loop tactile feedback on gait stability in Parkinson’s disease. IEEE Trans. Neural Syst. Rehabil. Eng. 28, 1636–1641 (2020). https://doi.org/10.1109/TNSRE.2020.2997283
Rodríguez, D., Samà, A., Pérez-López, C., Cabestany, J., Català, A., Rodríguez Molinero, A.: Posture transition identification on PD patients through a SVM-based technique and a single waist-worn accelerometer. Neurocomputing 164, 144–153 (2015). https://doi.org/10.1016/j.neucom.2014.09.084
Schwenk, M., et al.: Sensor based balance training with motion feedback in people with mild cognitive impairment. J. Rehabil. Res. Dev. 53, 945–958 (2016). https://doi.org/10.1682/JRRD.2015.05.0089
Grewal, G.S., et al.: Sensor based interactive balance training with visual joint movement feedback for improving postural stability in diabetics with peripheral neuropathy: a randomized controlled trial. Gerontology 61, 567–574 (2015). https://doi.org/10.1159/000371846
Toosizadeh, N., Mohler, J., Armstrong, D.G., Talal, T.K., Najafi, B.: The influence of diabetic peripheral neuropathy on local postural muscle and central sensory feedback balance control. PLoS ONE 10(8), e0135255 (2015). https://doi.org/10.1371/journal.pone.0135255
Delrobaei, M., Memar, S., Pieterman, M., Stratton, T.W., McIsaac, K., Jog, M.: Towards remote monitoring of Parkinson’s disease tremor using wearable motion capture systems. J. Neurol. Sci. 384, 38–45 (2018). https://doi.org/10.1016/j.jns.2017.11.004
Schwenk, M., et al.: Wearable sensor-based in-home assessment of gait, balance, and physical activity for discrimination of frailty status: baseline results of the Arizona frailty cohort study. Gerontology 61, 258–267 (2015). https://doi.org/10.1159/000369095
Mosquera-Lopez, C., et al.: Automated Detection of Real-World Falls: Modeled from People with Multiple Sclerosis. IEEE J. Biomed. Health Inform. 25(6), 1975–1984 (2021). https://doi.org/10.1109/JBHI.2020.3041035
Moon, S., et al.: Classification of Parkinson’s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach. J. Neuroeng. Rehabil. 17 (2020). https://doi.org/10.1186/s12984-020-00756-5
Morris, R., et al.: Cognitive associations with comprehensive gait and static balance measures in Parkinson’s disease. Park. Relat. Disord. 69, 104–110 (2019). https://doi.org/10.1016/j.parkreldis.2019.06.014
Perez, D., Gonzalez-Sanchez, M., Cuesta-Vargas, A.I.: differences in kinematic variables in single leg stance between patients with stroke and healthy elderly people measured with inertial sensors: a cross-sectional study. J. Stroke Cerebrovasc. Dis. 27, 229–239 (2018). https://doi.org/10.1016/j.jstrokecerebrovasdis.2017.08.024
King, L.A., Mancini, M., Priest, K., Salarian, A., Rodrigues-de-Paula, F., Horak, F.: Do clinical scales of balance reflect turning abnormalities in people with Parkinson’s disease? J. Neurol. Phys. Ther. 36(1), 25–31 (2012). https://doi.org/10.1097/NPT.0b013e31824620d1
Curtze, C., Nutt, J.G., Carlson-Kuhta, P., Mancini, M., Horak, F.B.: Levodopa is a double-edged sword for balance and gait in people with Parkinson’s disease. Mov. Disord. 30, 1361–1370 (2015). https://doi.org/10.1002/mds.26269
Parvaneh, S., Mohler, J., Toosizadeh, N., Grewal, G.S., Najafi, B.: Postural transitions during activities of daily living could identify frailty status: application of wearable technology to identify frailty during unsupervised condition. Gerontology 63, 479–487 (2017). https://doi.org/10.1159/000460292
Wuest, S., Masse, F., Aminian, K., Gonzenbach, R., de Bruin, E.D.: Reliability and validity of the inertial sensor-based Timed “Up and Go” test in individuals affected by stroke. J. Rehabil. Res. Dev. 53, 599–610 (2016). https://doi.org/10.1682/JRRD.2015.04.0065
Gavilánez, E.L., Chedraui, P., Franco, K.G., Blum, D.M., Riofrío, J.P., Bajaña, A.S.: Osteoporotic hip fractures in older adults in Ecuador 2016. Rev. Osteoporos. Metab. Miner. 10, 63–70 (2018). https://doi.org/10.4321/s1889-836x2018000200002
Gillies, G.E., Pienaar, I.S., Vohra, S., Qamhawi, Z.: Sex differences in Parkinson’s disease. Front. Neuroendocrinol. 35, 370–384 (2014). https://doi.org/10.1016/j.yfrne.2014.02.002
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Byron Zapata, José Bucheli and Fabian Narvaez were funded by the bio-mechatronics and bioengineer (GiByB) research group from Salesian Polytechnic University, Quito - Ecuador.
The funding group did not influence the collection, analysis and interpretation of data presented in this document, also the group does not influence in the approval or disapproval of this publication.
There was no interest conflict involved in this project.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zapata Chancusig, B.R., Bucheli Naranjo, J.L., Narváez Espinoza, F.R. (2023). Inertial Sensors Based on Stabilometric Analysis for Postural Control in Elderly People: A Systematic Review. In: Robles-Bykbaev, V., Mula, J., Reynoso-Meza, G. (eds) Intelligent Technologies: Design and Applications for Society. CITIS 2022. Lecture Notes in Networks and Systems, vol 607. Springer, Cham. https://doi.org/10.1007/978-3-031-24327-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-24327-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24326-4
Online ISBN: 978-3-031-24327-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)