[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
|
[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
|