ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC Information Science and Technology 08 June 2023

Dual-modality smart shoes for quantitative assessment of hemiplegic patients’ lower limb muscle strength

Cite this:
https://doi.org/10.52396/JUSTC-2022-0161
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  • Author Bio:

    Huajun Long received his master’s degree from the University of Science and Technology of China in 2023. He is particularly interested in sports analysis based on smart shoes

    Jingyuan Cheng is a Professor at the School of Computer Science and Technology, University of Science and Technology of China (USTC). She received her B.S. degree in Applied Physics and Ph.D. in Electronic Science and Technology from USTC in 2002 and 2007, respectively. Her research interests lie in human-centered large-scale sensing matrices and their applications, especially in the format of smart textiles

  • Corresponding author: E-mail: jingyuan@ustc.edu.cn
  • Received Date: 14 November 2022
  • Accepted Date: 09 May 2023
  • Available Online: 08 June 2023
  • Stroke can lead to the impaired motor function in patients’ lower limbs and hemiplegia. Accurate assessment of lower limb motor ability is important for diagnosis and rehabilitation. To digitalize such assessments so that each test can be traced back at any time and subjectivity can be avoided, we test how dual-modality smart shoes equipped with pressure-sensitive insoles and inertial measurement units can be used for this purpose. A 5 m walking test protocol, including the left and right turns, is designed. The data are collected from 23 patients and 17 healthy subjects. For the lower limbs’ motor ability, the tests are performed by two physicians and assessed using the five-grade Medical Research Council scale for muscle examination. The average of two physicians’ scores for the same patient is used as the ground truth. Using the feature set we developed, 100% accuracy is achieved in classifying the patients and healthy subjects. For patients’ muscle strength, a mean absolute error of 0.143 and a maximum error of 0.395 are achieved using our feature set and the regression method; these values are closer to the ground truth than the scores from each physician (mean absolute error: 0.217, maximum error: 0.5). We thus validate the possibility of using such smart shoes to objectively and accurately evaluate the muscle strength of the lower limbs of stroke patients.
    The overall framework for assessing lower extremity muscle strength using walking data.
    Stroke can lead to the impaired motor function in patients’ lower limbs and hemiplegia. Accurate assessment of lower limb motor ability is important for diagnosis and rehabilitation. To digitalize such assessments so that each test can be traced back at any time and subjectivity can be avoided, we test how dual-modality smart shoes equipped with pressure-sensitive insoles and inertial measurement units can be used for this purpose. A 5 m walking test protocol, including the left and right turns, is designed. The data are collected from 23 patients and 17 healthy subjects. For the lower limbs’ motor ability, the tests are performed by two physicians and assessed using the five-grade Medical Research Council scale for muscle examination. The average of two physicians’ scores for the same patient is used as the ground truth. Using the feature set we developed, 100% accuracy is achieved in classifying the patients and healthy subjects. For patients’ muscle strength, a mean absolute error of 0.143 and a maximum error of 0.395 are achieved using our feature set and the regression method; these values are closer to the ground truth than the scores from each physician (mean absolute error: 0.217, maximum error: 0.5). We thus validate the possibility of using such smart shoes to objectively and accurately evaluate the muscle strength of the lower limbs of stroke patients.
    • We propose a two-step method to assess lower limb muscle strength in hemiplegic patients. The result is even closer to the ground truth than the scores of individual physicals.
    • We propose the dual-modality fusion features and prove the importance of these newly proposed features.
    • We extend the 5 m walk test including the left and right turns, and demonstrate the muscle strength’s regression results that this extension is essential.

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  • [1]
    Feigin V L, Stark B A, Johnson C O, et al. Global, regional, and national burden of stroke and its risk factors, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet Neurology, 2021, 20: 795–820. doi: 10.1016/S1474-4422(21)00252-0
    [2]
    National Center for Chronic Disease Prevention and Health Promotion. Division of nutrition, physical activity, and obesity. data, trend and maps. CDC, 2018. https://www.cdc.gov/nccdphp/dnpao/data-trends-maps/index.html. Accessed October 14, 2022.
    [3]
    Andrews A W, Bohannon R W. Short-term recovery of limb muscle strength after acute stroke. Archives of Physical Medicine and Rehabilitation, 2003, 84: 125–130. doi: 10.1053/apmr.2003.50003
    [4]
    Gregson J M, Leathley M J, Moore A P, et al. Reliability of measurements of muscle tone and muscle power in stroke patients. Age and Ageing, 2000, 29 (3): 223–228. doi: 10.1093/ageing/29.3.223
    [5]
    Mentiplay B F, Perraton L G, Bower K J, et al. Assessment of lower limb muscle strength and power using hand-held and fixed dynamometry: A reliability and validity study. PLoS One, 2015, 10: e0140822. doi: 10.1371/journal.pone.0140822
    [6]
    Rastegarpanah A, Scone T, Saadat M, et al. Targeting effect on gait parameters in healthy individuals and post-stroke hemiparetic individuals. Journal of Rehabilitation and Assistive Technologies Engineering, 2018, 5: 2055668318766710. doi: https://doi.org/10.1177/2055668318766710
    [7]
    Wang J, Qiao L, Yu L, et al. Effect of customized insoles on gait in post-stroke hemiparetic individuals: A randomized controlled trial. Biology, 2021, 10: 1187. doi: 10.3390/biology10111187
    [8]
    Yang S, Zhang J T, Novak A C, et al. Estimation of spatio-temporal parameters for post-stroke hemiparetic gait using inertial sensors. Gait & Posture, 2013, 37: 354–358. doi: https://doi.org/10.1016/j.gaitpost.2012.07.032
    [9]
    Moticon. Rego sensor insoles: The assessment lab in your shoe. 2022. https://moticon.com/rego/sensor-insoles.
    [10]
    de Fazio R, Perrone E, Velázquez R, et al. Development of a self-powered piezo-resistive smart insole equipped with low-power BLE connectivity for remote gait monitoring. Sensors, 2021, 21: 4539. doi: 10.3390/s21134539
    [11]
    Tekscan. F-Scan Go System: Ultra-thin, in-shoe sensors capture timing pressure information for foot function gait analysis. 2022. https://www.tekscan.com/products-solutions/systems/f-scan-system. Accessed October 14, 2022.
    [12]
    Lin F, Wang A, Zhuang Y, et al. Smart insole: A wearable sensor device for unobtrusive gait monitoring in daily life. IEEE Transactions on Industrial Informatics, 2016, 12: 2281–2291. doi: 10.1109/TII.2016.2585643
    [13]
    Digitsole. When your mobility watches for your health daily. 2022. https://digitsole.com/. Accessed October 14, 2022.
    [14]
    Chen G, Patten C, Kothari D H, et al. Gait differences between individuals with post-stroke hemiparesis and non-disabled controls at matched speeds. Gait & Posture, 2005, 22: 51–56. doi: https://doi.org/10.1016/j.gaitpost.2004.06.009
    [15]
    Laudanski A, Brouwer B, Li Q. Measurement of lower limb joint kinematics using inertial sensors during stair ascent and descent in healthy older adults and stroke survivors. Journal of Healthcare Engineering, 2013, 4: 555–576. doi: 10.1260/2040-2295.4.4.555
    [16]
    Hodt-Billington C, Helbostad J L, Moe-Nilssen R. Should trunk movement or footfall parameters quantify gait asymmetry in chronic stroke patients? Gait & Posture, 2008, 27: 552–558. doi: https://doi.org/10.1016/j.gaitpost.2007.07.015
    [17]
    Bonnyaud C, Pradon D, Vuillerme N, et al. Spatiotemporal and kinematic parameters relating to oriented gait and turn performance in patients with chronic stroke. PLoS One, 2015, 10: e0129821. doi: 10.1371/journal.pone.0129821
    [18]
    Galli M, Cimolin V, Rigoldi C, et al. Gait patterns in hemiplegic children with Cerebral Palsy: Comparison of right and left hemiplegia. Research in Developmental Disabilities, 2010, 31: 1340–1345. doi: 10.1016/j.ridd.2010.07.007
    [19]
    Wang L, Sun Y, Li Q, et al. IMU-based gait normalcy index calculation for clinical evaluation of impaired gait. IEEE Journal of Biomedical and Health Informatics, 2021, 25: 3–12. doi: 10.1109/JBHI.2020.2982978
    [20]
    Kazutaka, Echigoya, Okada K, Wakasa M, et al. Changes to foot pressure pattern in post-stroke individuals who have started to walk independently during the convalescent phase. Gait & Posture, 2021, 90: 307–312. doi: https://doi.org/10.1016/j.gaitpost.2021.09.181
    [21]
    Chisholm A E, Perry S D, McIlroy W E. Inter-limb centre of pressure symmetry during gait among stroke survivors. Gait & Posture, 2011, 33: 238–243. doi: https://doi.org/10.1016/j.gaitpost.2010.11.012
    [22]
    Sundholm M, Cheng J, Zhou B, et al. Smart-mat: Recognizing and counting gym exercises with low-cost resistive pressure sensing matrix. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York: ACM, 2014: 373–382.
    [23]
    Zhou B, Lukowicz P. TPM feature set: A universal algorithm for spatial-temporal pressure mapping imagery data. In: The Thirteenth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies. Porto, Portugal: IARIA, 2019: 1.
    [24]
    Guo T, Huang Z, Cheng J. LwTool: A data processing toolkit for building a real-time pressure mapping smart textile software system. Pervasive and Mobile Computing, 2022, 80: 101540. doi: 10.1016/j.pmcj.2022.101540
    [25]
    Hu M K. Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 1962, 8: 179–187. doi: 10.1109/TIT.1962.1057692.
    [26]
    Guyon I, Elisseeff A. An introduction to variable and feature selection. Journal of Machine Learning Research, 2003, 3: 1157–1182. doi: https://dl.acm.org/doi/10.5555/944919.944968
  • 加载中

Catalog

    Figure  1.  Our smart shoe prototype, plus a mobile phone for data storage.

    Figure  2.  The feature extraction workflow.

    Figure  3.  The relation between the gait cycle and the total pressure.

    Figure  4.  The predicted results based on left-turn and right-turn experiments, compared with the evaluation values of two doctors.

    Figure  5.  The experimental results using forward search algorithm.

    [1]
    Feigin V L, Stark B A, Johnson C O, et al. Global, regional, and national burden of stroke and its risk factors, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet Neurology, 2021, 20: 795–820. doi: 10.1016/S1474-4422(21)00252-0
    [2]
    National Center for Chronic Disease Prevention and Health Promotion. Division of nutrition, physical activity, and obesity. data, trend and maps. CDC, 2018. https://www.cdc.gov/nccdphp/dnpao/data-trends-maps/index.html. Accessed October 14, 2022.
    [3]
    Andrews A W, Bohannon R W. Short-term recovery of limb muscle strength after acute stroke. Archives of Physical Medicine and Rehabilitation, 2003, 84: 125–130. doi: 10.1053/apmr.2003.50003
    [4]
    Gregson J M, Leathley M J, Moore A P, et al. Reliability of measurements of muscle tone and muscle power in stroke patients. Age and Ageing, 2000, 29 (3): 223–228. doi: 10.1093/ageing/29.3.223
    [5]
    Mentiplay B F, Perraton L G, Bower K J, et al. Assessment of lower limb muscle strength and power using hand-held and fixed dynamometry: A reliability and validity study. PLoS One, 2015, 10: e0140822. doi: 10.1371/journal.pone.0140822
    [6]
    Rastegarpanah A, Scone T, Saadat M, et al. Targeting effect on gait parameters in healthy individuals and post-stroke hemiparetic individuals. Journal of Rehabilitation and Assistive Technologies Engineering, 2018, 5: 2055668318766710. doi: https://doi.org/10.1177/2055668318766710
    [7]
    Wang J, Qiao L, Yu L, et al. Effect of customized insoles on gait in post-stroke hemiparetic individuals: A randomized controlled trial. Biology, 2021, 10: 1187. doi: 10.3390/biology10111187
    [8]
    Yang S, Zhang J T, Novak A C, et al. Estimation of spatio-temporal parameters for post-stroke hemiparetic gait using inertial sensors. Gait & Posture, 2013, 37: 354–358. doi: https://doi.org/10.1016/j.gaitpost.2012.07.032
    [9]
    Moticon. Rego sensor insoles: The assessment lab in your shoe. 2022. https://moticon.com/rego/sensor-insoles.
    [10]
    de Fazio R, Perrone E, Velázquez R, et al. Development of a self-powered piezo-resistive smart insole equipped with low-power BLE connectivity for remote gait monitoring. Sensors, 2021, 21: 4539. doi: 10.3390/s21134539
    [11]
    Tekscan. F-Scan Go System: Ultra-thin, in-shoe sensors capture timing pressure information for foot function gait analysis. 2022. https://www.tekscan.com/products-solutions/systems/f-scan-system. Accessed October 14, 2022.
    [12]
    Lin F, Wang A, Zhuang Y, et al. Smart insole: A wearable sensor device for unobtrusive gait monitoring in daily life. IEEE Transactions on Industrial Informatics, 2016, 12: 2281–2291. doi: 10.1109/TII.2016.2585643
    [13]
    Digitsole. When your mobility watches for your health daily. 2022. https://digitsole.com/. Accessed October 14, 2022.
    [14]
    Chen G, Patten C, Kothari D H, et al. Gait differences between individuals with post-stroke hemiparesis and non-disabled controls at matched speeds. Gait & Posture, 2005, 22: 51–56. doi: https://doi.org/10.1016/j.gaitpost.2004.06.009
    [15]
    Laudanski A, Brouwer B, Li Q. Measurement of lower limb joint kinematics using inertial sensors during stair ascent and descent in healthy older adults and stroke survivors. Journal of Healthcare Engineering, 2013, 4: 555–576. doi: 10.1260/2040-2295.4.4.555
    [16]
    Hodt-Billington C, Helbostad J L, Moe-Nilssen R. Should trunk movement or footfall parameters quantify gait asymmetry in chronic stroke patients? Gait & Posture, 2008, 27: 552–558. doi: https://doi.org/10.1016/j.gaitpost.2007.07.015
    [17]
    Bonnyaud C, Pradon D, Vuillerme N, et al. Spatiotemporal and kinematic parameters relating to oriented gait and turn performance in patients with chronic stroke. PLoS One, 2015, 10: e0129821. doi: 10.1371/journal.pone.0129821
    [18]
    Galli M, Cimolin V, Rigoldi C, et al. Gait patterns in hemiplegic children with Cerebral Palsy: Comparison of right and left hemiplegia. Research in Developmental Disabilities, 2010, 31: 1340–1345. doi: 10.1016/j.ridd.2010.07.007
    [19]
    Wang L, Sun Y, Li Q, et al. IMU-based gait normalcy index calculation for clinical evaluation of impaired gait. IEEE Journal of Biomedical and Health Informatics, 2021, 25: 3–12. doi: 10.1109/JBHI.2020.2982978
    [20]
    Kazutaka, Echigoya, Okada K, Wakasa M, et al. Changes to foot pressure pattern in post-stroke individuals who have started to walk independently during the convalescent phase. Gait & Posture, 2021, 90: 307–312. doi: https://doi.org/10.1016/j.gaitpost.2021.09.181
    [21]
    Chisholm A E, Perry S D, McIlroy W E. Inter-limb centre of pressure symmetry during gait among stroke survivors. Gait & Posture, 2011, 33: 238–243. doi: https://doi.org/10.1016/j.gaitpost.2010.11.012
    [22]
    Sundholm M, Cheng J, Zhou B, et al. Smart-mat: Recognizing and counting gym exercises with low-cost resistive pressure sensing matrix. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York: ACM, 2014: 373–382.
    [23]
    Zhou B, Lukowicz P. TPM feature set: A universal algorithm for spatial-temporal pressure mapping imagery data. In: The Thirteenth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies. Porto, Portugal: IARIA, 2019: 1.
    [24]
    Guo T, Huang Z, Cheng J. LwTool: A data processing toolkit for building a real-time pressure mapping smart textile software system. Pervasive and Mobile Computing, 2022, 80: 101540. doi: 10.1016/j.pmcj.2022.101540
    [25]
    Hu M K. Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 1962, 8: 179–187. doi: 10.1109/TIT.1962.1057692.
    [26]
    Guyon I, Elisseeff A. An introduction to variable and feature selection. Journal of Machine Learning Research, 2003, 3: 1157–1182. doi: https://dl.acm.org/doi/10.5555/944919.944968

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