A Novel Method for Stride Length Estimation Using Wireless Foot Sensor Module

References

• A. Rodríguez-Molinero, A. Herrero-Larrea, A. Miñarro, L. Narvaiza, C. Gálvez-Barrón, N. G. León, E. Valldosera, E. de Mingo, O. Macho, and D. Aivar, “The spatial parameters of gait and their association with falls, functional decline and death in older adults: a prospective study,” Sci. Rep., Vol. 9, no. 1, pp. 1–9, Jun. 2019.
   PubMedGoogle Scholar
• G. Chen, C. Patten, D. H. Kothari, and F. E. Zajac, “Gait differences between individuals with post-stroke hemiparesis and non-disabled controls at matched speeds,” Gait Posture, Vol. 22, no. 1, pp. 51–6, Aug. 2005.
   PubMed Web of Science ®Google Scholar
• J. M. Hausdorff, “Gait dynamics, fractals and falls: finding meaning in the stride-to-stride fluctuations of human walking,” Hum. Mov. Sci., Vol. 26, no. 4, pp. 555–89, Aug. 2007.
   PubMed Web of Science ®Google Scholar
• R. W. Kressig, R. J. Gregor, A. Oliver, D. Waddell, W. Smith, M. O’Grady, A. T. Curns, M. Kutner, and S. L. Wolf, “Temporal and spatial features of gait in older adults transitioning to frailty,” Gait Posture, Vol. 20, no. 1, pp. 30–5, Aug. 2004.
   PubMed Web of Science ®Google Scholar
• J. M. Hausdorff, D. A. Rios, and H. K. Edelberg, “Gait variability and fall risk in community-living older adults: a 1-year prospective study,” Arch. Phys. Med. Rehabil., Vol. 82, no. 8, pp. 1050–6, Aug. 2001.
   PubMed Web of Science ®Google Scholar
• J. Verghese, R. Holtzer, R. B. Lipton, and C. Wang, “Quantitative gait markers and incident fall risk in older adults,” J. Gerontol. A, Vol. 64, no. 8, pp. 896–901, Aug. 2009.
   Google Scholar
• B. Bloem, J. Gussekloo, A. Lagaay, E. Remarque, J. Haan, and R. Westendorp, “Idiopathic senile gait disorders are signs of subclinical disease,” J. Am. Geriatr. Soc., Vol. 48, no. 9, pp. 1098–1101, Apr. 2015.
   Google Scholar
• C. Rosano, J. Brach, W. T. Longstreth Jr, and A. B. Newman, “Quantitative measures of gait characteristics indicate prevalence of underlying subclinical structural brain abnormalities in high-functioning older adults,” Neuroepidemiology, Vol. 26, no. 1, pp. 52–60, 2006.
   PubMed Web of Science ®Google Scholar
• G. A. Van Kan, Y. Rolland, S. Andrieu, J. Bauer, O. Beauchet, M. Bonnefoy, M. Cesari, L. Donini, S. Gillette-Guyonnet, and M. Inzitari, “Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) task force,” J. Nutr. Health Aging, Vol. 13, no. 10, pp. 881–9, Feb. 2010.
   Web of Science ®Google Scholar
• D. E. Anderson, and M. L. Madigan, “Healthy older adults have insufficient hip range of motion and plantar flexor strength to walk like healthy young adults,” J. Biomech., Vol. 47, no. 5, pp. 1104–9, Mar. 2014.
   PubMed Web of Science ®Google Scholar
• L. W. Lee, K. Zavarei, J. Evans, J. J. Lelas, P. O. Riley, and D. C. Kerrigan, “Reduced hip extension in the elderly: dynamic or postural?,” Arch. Phys. Med. Rehabil., Vol. 86, no. 9, pp. 1851–4, Sep. 2005.
   PubMed Web of Science ®Google Scholar
• A. Muro-De-La-Herran, B. Garcia-Zapirain, and A. Mendez-Zorrilla, “Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications,” Sensors, Vol. 14, no. 2, pp. 3362–94, Feb. 2014.
   PubMed Web of Science ®Google Scholar
• F. Petraglia, L. Scarcella, G. Pedrazzi, L. Brancato, R. Puers, and C. Costantino, “Inertial sensors versus standard systems in gait analysis: a systematic review and meta-analysis,” Eur. J. Phys. Rehabil. Med., Vol. 55, no. 2, pp. 265–80, Apr. 2019.
   PubMed Web of Science ®Google Scholar
• E. Ledoux, “Inertial sensing for gait event detection and transfemoral prosthesis control strategy,” IEEE Trans. Biomed. Eng., Vol. 65, no. 12, pp. 2704–2712, Dec. 2018.
   PubMed Web of Science ®Google Scholar
• H. F. Maqbool, M. A. B. Husman, M. I. Awad, A. Abouhossein, P. Mehryar, N. Iqbal, and A. A. Dehghani-Sanij, “Real-time gait event detection for lower limb amputees using a single wearable sensor,” in 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Oct. 2016, pp. 5067–70.
   Google Scholar
• J. K. Lee, and E. J. Park, “Quasi real-time gait event detection using shank-attached gyroscopes,” Med. Biol. Eng. Comput., Vol. 49, no. 6, pp. 707–12, Jan. 2011.
   PubMed Web of Science ®Google Scholar
• M. K. O’Brien, M. D. Hidalgo-Araya, C. K. Mummidisetty, H. Vallery, R. Ghaffari, J. A. Rogers, R. Lieber, and A. Jayaraman, “Augmenting clinical outcome measures of gait and balance with a single inertial sensor in age-ranged healthy adults,” Sensors, Vol. 19, no. 20, pp. 4537, Oct. 2019.
   PubMed Web of Science ®Google Scholar
• D. Gouwanda, A. A. Gopalai, and B. H. Khoo, “A low cost alternative to monitor human gait temporal parameters – wearable wireless gyroscope,” IEEE Sensors J., Vol. 16, no. 24, pp. 9029–35, Oct. 2016.
   Web of Science ®Google Scholar
• S. R. Hundza, W. R. Hook, C. R. Harris, S. V. Mahajan, P. A. Leslie, C. A. Spani, L. G. Spalteholz, B. J. Birch, D. T. Commandeur, and N. J. Livingston, “Accurate and reliable gait cycle detection in Parkinson's disease,” IEEE Trans. Neural Syst. Rehabil. Eng., Vol. 22, no. 1, pp. 127–37, Oct. 2013.
   PubMed Web of Science ®Google Scholar
• P. C. Formento, R. Acevedo, S. Ghoussayni, and D. Ewins, “Gait event detection during stair walking using a rate gyroscope,” Sensors, Vol. 14, no. 3, pp. 5470–85, Mar. 2014.
   PubMed Web of Science ®Google Scholar
• J. R. Nistler, and M. F. Selekwa, “Gravity compensation in accelerometer measurements for robot navigation on inclined surfaces,” Procedia. Comput. Sci., Vol. 6, pp. 413–8, Oct. 2011.
   Google Scholar
• K. Tuck. “Tilt sensing using linear accelerometers,” Freescale semiconductor application note AN3107, Apr. 2007.
   Google Scholar
• S. Patel, C. Mancinelli, J. Healey, M. Moy, and P. Bonato. “Using wearable sensors to monitor physical activities of patients with copd: A comparison of classifier performance,” in Sixth International Workshop on Wearable and Implantable Body Sensor Networks, Aug. 2009, pp. 234–9.
   Google Scholar
• M. H. Cole, W. Van Den Hoorn, J. K. Kavanagh, S. Morrison, P. W. Hodges, J. E. Smeathers, and G. K. Kerr, “Concurrent validity of accelerations measured using a tri-axial inertial measurement unit while walking on firm, compliant and uneven surfaces,” PLoS ONE, Vol. 9, no. 5, pp. e98395, May 2014.
   PubMed Web of Science ®Google Scholar
• A. Rampp, J. Barth, S. Schülein, K.-G. Gaßmann, J. Klucken, and B. M. Eskofier, “Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients,” IEEE Trans. Biomed. Eng., Vol. 62, no. 4, pp. 1089–97, Nov. 2014.
   Web of Science ®Google Scholar
• T. Watanabe, T. Miyazawa, and J. Shibasaki, “A study on IMU-based stride length estimation for motor disabled subjects: a comparison under different calculation methods of rotation matrix,” in IEEE EMBS International Conference on biomedical & health informatics (BHI), Apr. 2018, pp. 70–3.
   Google Scholar
• E. Macias, D. Torres, and S. Ravindran. “Nine-axis sensor fusion using the direction cosine matrix algorithm on the msp430f5xx family,” Application Report, Feb. 2012.
   Google Scholar
• R. Takeda, G. Lisco, T. Fujisawa, L. Gastaldi, H. Tohyama, and S. Tadano, “Drift removal for improving the accuracy of gait parameters using wearable sensor systems,” Sensors, Vol. 14, no. 12, pp. 23230–47, Dec. 2014.
   PubMed Web of Science ®Google Scholar
• M. Hao, K. Chen, and C. Fu, “Smoother-based 3D foot trajectory estimation using inertial sensors,” in IEEE Transactions on Biomedical engineering, Dec. 2019.
   Google Scholar
• S. Yang, and Q. Li, “Inertial sensor-based methods in walking speed estimation: A systematic review,” Sensors, Vol. 12, no. 5, pp. 6102–16, May. 2012.
   PubMed Web of Science ®Google Scholar
• R. Das, N. Hooda, and N. Kumar, “A novel approach for real-time gait events detection using developed wireless foot sensor module,” IEEE Sens. Lett., Vol. 3, no. 6, pp. 1–4, May 2019.
   Google Scholar
• N. Hooda, R. Das, and N. Kumar, “Fusion of EEG and EMG signals for classification of unilateral foot movements,” Biomed. Signal. Process. Control., Vol. 60, pp. 101990, Jul. 2020.
   Web of Science ®Google Scholar
• Y. Liu, S. Li, C. Mu, and Y. Wang, “Step length estimation based on D-ZUPT for pedestrian dead-reckoning system,” Electron. Lett., Vol. 52, no. 11, pp. 923–4, May 2016.
   Web of Science ®Google Scholar
• Z. Zhu, and S. Wang, “A novel step length estimator based on foot-mounted MEMS sensors,” Sensors, Vol. 18, no. 12, pp. 4447, Dec. 2018.
   PubMed Web of Science ®Google Scholar
• F. Woyano, S. Lee, and S. Park, “Evaluation and comparison of performance analysis of indoor inertial navigation system based on foot mounted IMU,” in 18th International Conference on Advanced Communication Technology (ICACT), Mar. 2016, pp. 792–8.
   Google Scholar
• J. W. Kim, H. J. Jang, D.-H. Hwang, and C. Park, “A step, stride and heading determination for the pedestrian navigation system,” J. Glob. Position. Syst., Vol. 1, no. 08, pp. 273–9, Feb. 2005.
   Google Scholar
• Y. Zhang, Y. Li, C. Peng, D. Mou, M. Li, and W. Wang, “The height-adaptive parameterized step length measurement method and experiment based on motion parameters,” Sensors, Vol. 18, no. 4, pp. 1039, Mar. 2018.
   PubMed Web of Science ®Google Scholar
• A. Köse, A. Cereatti, and U. Della Croce, “Bilateral step length estimation using a single inertial measurement unit attached to the pelvis,” J. Neuroeng. Rehabil., Vol. 9, no. 1, pp. 9, Feb. 2012.
   PubMedGoogle Scholar