論文の公開元へ

書き出し

Refer/BibIX

RIS

BibTeX

TSV

Multimodal Quantitative Gait Assessment Using Minimal Wearable Sensors

Hutabarat Yonatan Christian 東北大学

2022.03.25

概要

Gait assessment is the study of a person’s walking pattern. Walking is a primary function required to perform daily life activities. Although it may appear simple, walking requires precise coordination between several motor, sensory, and cognitive processes. Any alterations in motor, sensory, or cognitive functions induced by disease, trauma, or idiopathic nature may affect the gait pattern of a person. This highlights the importance of performing gait analysis to detect the presence of underlying conditions at an early stage. The current gold standard of gait analysis is performed in a laboratory environment based on gold standard measurements obtained using motion capture and force plate systems. However, the privilege of using specialized instruments is limited to a handful of clinics or research centers. Further, research facilities with space constraints may not be able to capture the natural gait of a person effectively. Recently, flexible, efficient, and inexpensive wearable sensors have been popularized as effective alternatives that can be used to perform gait analysis during daily activities. In this context, this thesis addresses the specific domain of multimodal quantitative gait analysis using wearable sensors.

Chapter 2 of this thesis details the identification and summarizing of the current advances in wearable sensors for gait analysis from various perspectives, such as the application of wearable sensor-based gait analysis, sensor systems and their attachment locations, and the algorithms used. The PRISMA guideline was adopted to find relevant studies from several scientific databases published between 2011 and 2020. In aggregate, 76 articles were selected based on the inclusion and exclusion criteria. The wearable inertial measurement unit (IMU) attached to the lower-limb region was found to be the most commonly utilized sensor–location pair. Temporal, spatial, and spatiotemporal features are the most common quantitative gait features extracted from wearable sensors. Varying performances were observed for each proposed framework, where an increased number of sensors did not necessarily improve the estimation performance metrics. A few studies have integrated various machine learning techniques for classification problems, correction algorithms, cross-checking functions, and scoring functions.

Chapter 3 of the thesis describes the proposed framework for quantitative gait assessment using only two IMU sensors while extracting the maximum number of features. Decreasing the number of sensors negatively affects the gait assessment performance. Based on comparisons with a motion capture setup and previous studies, we verified the potential of the proposed framework to provide a compact sensing system with feature-rich diversity for gait assessment and identified its limitations. The results revealed that the temporal differences were 4.22 ± 15.48 ms (mean ± S.D.) and -8.31 ± 21.02 ms (mean ± S.D.) in the initial contact and toe-off events, respectively. Additionally, with respect to the spatial features, the stride length and heel vertical displacement were overestimated by 7.72 cm and 2.22 cm, respectively, on average. We successfully extracted 17 gait features from two IMUs located on the foot. We also demonstrated that the symmetry index feature can be used to distinguish healthy subjects from subjects with a recent history of lower-limb injury, which is a significant observation for clinical research.

Chapter 4 of the thesis further demonstrates the potential of the proposed framework, we present the following three case studies. The first case study demonstrates an application of the proposed framework to analyze a different type of gait. Detailed gait phases during walking and running were successfully extracted by employing finite state machine (FSM) transition rules. The second case study demonstrates the use of the proposed framework to distinguish cognitive dual-task gait from singletask gait. While visual observation may not objectively identify the differences between single-task and dual-task gait, the proposed framework facilitated the extraction of temporal gait patterns that were later derived into gait indices. Lower motion intensity, a slight increase in double support time, higher gait temporal variability, and worse gait symmetry were observed in the subjects performing dual-task gait in this study. The third case study demonstrates the utilization of the proposed framework in prolonged and outdoor gait experiments. The extraction of various gait index features enabled the objective comparison of the stages of walking as well as the derivation of inter-subject comparisons. In conclusion, all of the presented case studies revealed promising results and established the viability of using the proposed system in the respective applications.

Chapter 5 of this thesis details the integration of several machine learning algorithms to solve various gait-related problems, including gait phase classification, gait activity prediction, and control of the swing phase for prosthetic knees. Three machine learning models, consisting of support vector machines (SVM), AdaBoost, and XGBoost, were trained for gait phase classification. The results indicate that the mean accuracy of XGBoost (84.54%) was higher than those of AdaBoost (82.26%) and SVM (78.31%) models, and the former also exhibited a notably faster processing time compared with SVM. On the other hand, temporal convolutional networks (TCN), recurrent neural networks, and long short-term memory networks were trained for gait activity prediction. The results demonstrate that TCN’s predictions were the best corresponding to 50, 100, and 150-time steps, with an overall best performance of 4.94% mean absolute percentage error (MAPE) on the 100-time step prediction. To control the swing phase, we adopted a model-free reinforcement Q-learning control with a designed reward function as the controller of a semi-active prosthetic knee. The results indicate that the proposed control strategy converges within the desired performance index and is capable of adapting to several walking speeds. Further, the proposed control structure exhibited better overall performance compared to user-adaptive control, while at some walking speeds it outperformed the neural network-based predictive control method.

論文の公開元へ

この論文で使われている画像

参考文献

[1] Y.-S. Lee, C.-S. Ho, Y. Shih, S.-Y. Chang, F. J. R´obert, and T.-Y. Shiang, “Assessment of walking, running, and jumping movement features by using the inertial measurement unit,” Gait Posture, vol. 41, no. 4, pp. 877–881, 2015, issn: 0966-6362. doi: https://doi.org/10.1016/j.gaitpost.2015. 03.007.

[2] Q. Mei, J. Fernandez, W. Fu, N. Feng, and Y. Gu, “A comparative biomechanical analysis of habitually unshod and shod runners based on a foot morphological difference,” Human Movement Science, vol. 42, pp. 38–53, 2015, issn: 0167-9457. doi: https://doi.org/10.1016/j.humov.2015.04.007.

[3] B. H. Dobkin, X. Xu, M. Batalin, S. Thomas, and W. Kaiser, “Reliability and validity of bilateral ankle accelerometer algorithms for activity recognition and walking speed after stroke,” Stroke, vol. 42, no. 8, pp. 2246–2250, 2011, issn: 00392499. doi: 10.1161/STROKEAHA.110.611095.

[4] C. Punin, B. Barzallo, R. Clotet, et al., “A Non-Invasive Medical Device for Parkinson’s Patients with Episodes of Freezing of Gait,” Sensors (Basel, Switzerland), vol. 19, no. 3, 2019, issn: 14248220. doi: 10.3390/s19030737.

[5] S. Del Din, A. Godfrey, and L. Rochester, “Validation of an Accelerometer to Quantify a Comprehensive Battery of Gait Characteristics in Healthy Older Adults and Parkinson’s Disease: Toward Clinical and at Home Use,” IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 3, pp. 838–847, 2016, issn: 21682194. doi: 10.1109/JBHI.2015.2419317.

[6] G. Rescio, A. Leone, and P. Siciliano, “Supervised machine learning scheme for electromyography-based pre-fall detection system,” Expert Systems with Applications, vol. 100, pp. 95–105, Jun. 2018, issn: 09574174. doi: 10.1016/ j.eswa.2018.01.047.

[7] T. Virmani, H. Gupta, J. Shah, and L. Larson-Prior, “Objective measures of gait and balance in healthy non-falling adults as a function of age,” Gait Posture, vol. 65, pp. 100–105, Sep. 2018, issn: 09666362. doi: 10.1016/j. gaitpost.2018.07.167.

[8] M. B. Nebel, E. L. Sims, F. J. Keefe, et al., “The Relationship of SelfReported Pain and Functional Impairment to Gait Mechanics in Overweight and Obese Persons With Knee Osteoarthritis,” Archives of Physical Medicine and Rehabilitation, vol. 90, no. 11, pp. 1874–1879, Nov. 2009, issn: 00039993. doi: 10.1016/j.apmr.2009.07.010. eprint: NIHMS150003.

[9] M. Benedetti, V. Agostini, M. Knaflitz, V. Gasparroni, M. Boschi, and R. Piperno, “Self-reported gait unsteadiness in mildly impaired neurological patients: an objective assessment through statistical gait analysis,” Journal of NeuroEngineering and Rehabilitation, vol. 9, no. 1, p. 64, 2012, issn: 1743- 0003. doi: 10.1186/1743-0003-9-64.

[10] S. Fritz and M. Lusardi, “White paper: ”walking speed: The sixth vital sign”,” Journal of Geriatric Physical Therapy, vol. 32, no. 2, pp. 2–5, 2009, issn: 15398412. doi: 10.1519/00139143-200932020-00002.

[11] L. C. Benson, C. A. Clermont, E. Boˇsnjak, and R. Ferber, “The use of wearable devices for walking and running gait analysis outside of the lab: A systematic review.,” Gait posture, vol. 63, pp. 124–138, Jun. 2018, issn: 1879-2219 (Electronic). doi: 10.1016/j.gaitpost.2018.04.047.

[12] G. Quer, J. M. Radin, M. Gadaleta, et al., “Wearable sensor data and selfreported symptoms for COVID-19 detection,” Nature Medicine, vol. 27, no. 1, pp. 73–77, Jan. 2021, issn: 1078-8956. doi: 10.1038/s41591-020-1123-x.

[13] J. Dunn, L. Kidzinski, R. Runge, et al., “Wearable sensors enable personalized predictions of clinical laboratory measurements,” Nature Medicine, 2021, issn: 1078-8956. doi: 10.1038/s41591-021-01339-0.

[14] D. Trojaniello, A. Ravaschio, J. M. Hausdorff, and A. Cereatti, “Comparative assessment of different methods for the estimation of gait temporal parameters using a single inertial sensor: application to elderly, post-stroke, Parkinson’s disease and Huntington’s disease subjects,” Gait Posture, vol. 42, no. 3, pp. 310–316, Sep. 2015, issn: 09666362. doi: 10.1016/j.gaitpost.2015. 06.008.

[15] S. Bolink, E. Lenguerrand, L. Brunton, et al., “Assessment of physical function following total hip arthroplasty: Inertial sensor based gait analysis is supplementary to patient-reported outcome measures,” Clinical Biomechanics, vol. 32, pp. 171–179, Feb. 2016, issn: 02680033. doi: 10 . 1016 / j . clinbiomech.2015.11.014.

[16] P. P. Panciani, K. Migliorati, A. Muratori, M. Gelmini, A. Padovani, and M. Fontanella, “Computerized gait analysis with inertial sensor in the management of idiopathic normal pressure hydrocephalus.,” European journal of physical and rehabilitation medicine, vol. 54, no. 5, pp. 724–729, Oct. 2018, issn: 1973-9095 (Electronic). doi: 10.23736/S1973-9087.18.04949-3.

[17] F. De Cillis, F. De Simio, and R. Setola, “Long-term gait pattern assessment using a tri-axial accelerometer,” Journal of Medical Engineering Technology, vol. 41, no. 5, pp. 346–361, Jul. 2017, issn: 0309-1902. doi: 10.1080/ 03091902.2017.1293741.

[18] A. Rodr´ıguez-Molinero, A. Sam`a, C. P´erez-L´opez, et al., “Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales,” Frontiers in Neurology, vol. 8, no. SEP, Sep. 2017, issn: 1664-2295. doi: 10.3389/fneur.2017.00431.

[19] I. Carpinella, D. Cattaneo, G. Bonora, et al., “Wearable Sensor-Based Biofeedback Training for Balance and Gait in Parkinson Disease: A Pilot Randomized Controlled Trial.,” eng, Archives of physical medicine and rehabilitation, vol. 98, no. 4, 622–630.e3, Apr. 2017, issn: 1532-821X (Electronic). doi: 10.1016/j.apmr.2016.11.003.

[20] B. M. Bartels, A. Moreno, M. J. Quezada, H. Sivertson, J. Abbas, and N. Krishnamurthi, “Real-Time Feedback Derived from Wearable Sensors to Improve Gait in Parkinson’s Disease,” Technology Innovation, vol. 20, no. 1, pp. 37–46, Nov. 2018, issn: 1949-8241. doi: 10.21300/20.1-2.2018.37.

[21] L. Angelini, I. Carpinella, D. Cattaneo, et al., “Is a Wearable Sensor-Based Characterisation of Gait Robust Enough to Overcome Differences Between Measurement Protocols? A Multi-Centric Pragmatic Study in Patients with Multiple Sclerosis,” Sensors, vol. 20, no. 1, p. 79, Dec. 2019, issn: 1424-8220. doi: 10.3390/s20010079.

[22] S. Shirai, I. Yabe, I. Takahashi-Iwata, et al., “The Responsiveness of Triaxial Accelerometer Measurement of Gait Ataxia Is Higher than That of the Scale for the Assessment and Rating of Ataxia in the Early Stages of Spinocerebellar Degeneration,” The Cerebellum, vol. 18, no. 4, pp. 721–730, Aug. 2019, issn: 1473-4222. doi: 10.1007/s12311-019-01025-5. [Online]. Available: http://link.springer.com/10.1007/s12311-019-01025-5.

[23] B. Hobbs and P. Artemiadis, “A Review of Robot-Assisted Lower-Limb Stroke Therapy: Unexplored Paths and Future Directions in Gait Rehabilitation,” Frontiers in Neurorobotics, vol. 14, no. April, 2020, issn: 16625218. doi: 10.3389/fnbot.2020.00019.

[24] A. Liberati, D. G. Altman, J. Tetzlaff, et al., “The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration,” Journal of clinical epidemiology, vol. 62, no. 10, e1–34, 2009, issn: 18785921. doi: 10.1016/j. jclinepi.2009.06.006. eprint: arXiv:1011.1669v3.

[25] D. Kobsar, Z. Masood, H. Khan, et al., “Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis—A Scoping Review,” Sensors, vol. 20, no. 24, p. 7143, Dec. 2020, issn: 1424-8220. doi: 10.3390/s20247143.

[26] I. T. G. de Oliveira Gondim, C. d. C. B. de Souza, M. A. B. Rodrigues, I. M. Azevedo, M. d. G. W. de Sales Coriolano, and O. G. Lins, “Portable accelerometers for the evaluation of spatio-temporal gait parameters in people with Parkinson’s disease: an integrative review,” Archives of Gerontology and Geriatrics, vol. 90, p. 104 097, Sep. 2020, issn: 01674943. doi: 10.1016/j. archger.2020.104097.

[27] P. R. F. Junior, R. C. F. de Moura, C. S. Oliveira, and F. Politti, “Use of wearable inertial sensors for the assessment of spatiotemporal gait variables in children: A systematic review,” Motriz: Revista de Educa¸c˜ao F´ısica, vol. 26, no. 3, pp. 1–11, 2020, issn: 1980-6574. doi: 10.1590/s1980-6574202000030139.

[28] S. D´ıaz, J. B. Stephenson, and M. A. Labrador, “Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis,” Applied Sciences, vol. 10, no. 1, p. 234, Dec. 2019, issn: 2076-3417. doi: 10 . 3390 / app10010234.

[29] P. Dasgupta, J. VanSwearingen, A. Godfrey, M. Redfern, M. Montero-Odasso, and E. Sejdic, “Acceleration Gait Measures as Proxies for Motor Skill of Walking: A Narrative Review,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 249–261, 2021, issn: 1534-4320. doi: 10.1109/TNSRE.2020.3044260.

[30] A. Saboor, T. Kask, A. Kuusik, et al., “Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review,” IEEE Access, vol. 8, pp. 167 830–167 864, 2020, issn: 2169-3536. doi: 10.1109/ACCESS.2020.3022818.

[31] W. Tao, T. Liu, R. Zheng, and H. Feng, “Gait analysis using wearable sensors,” Sensors, vol. 12, no. 2, pp. 2255–2283, 2012, issn: 14248220. doi: 10.3390/s120202255.

[32] I. H. Lopez-Nava and A. Munoz-Melendez, “Wearable Inertial Sensors for Human Motion Analysis: A Review,” IEEE Sensors Journal, vol. 16, no. 22, pp. 7821–7834, Nov. 2016, issn: 1530-437X. doi: 10 . 1109 / JSEN . 2016 . 2609392.

[33] R. Caldas, M. Mundt, W. Potthast, F. Buarque de Lima Neto, and B. Markert, “A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms,” Gait and Posture, vol. 57, no. February, pp. 204– 210, 2017, issn: 18792219. doi: 10.1016/j.gaitpost.2017.06.019.

[34] T. Watanabe, H. Saito, E. Koike, and K. Nitta, “A preliminary test of measurement of joint angles and stride length with wireless inertial sensors for wearable gait evaluation system,” Computational Intelligence and Neuroscience, vol. 2011, pp. 1–12, 2011, issn: 1687-5265. doi: 10 . 1155 / 2011 / 975193.

[35] S. Zhu, H. Anderson, and Y. Wang, “A Real-Time On-Chip Algorithm for IMU-Based Gait Measurement,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7674 LNCS, pp. 93–104, 2012. doi: 10.1007/978-3- 642-34778-8_9.

[36] D. McGrath, B. R. Greene, K. J. O’Donovan, and B. Caulfield, “Gyroscopebased assessment of temporal gait parameters during treadmill walking and running,” Sports Engineering, vol. 15, no. 4, pp. 207–213, 2012. doi: 10 . 1007/s12283-012-0093-8.

[37] F. Bugan´e, M. Benedetti, G. Casadio, et al., “Estimation of spatial-temporal gait parameters in level walking based on a single accelerometer: Validation on normal subjects by standard gait analysis,” Computer Methods and Programs in Biomedicine, vol. 108, no. 1, pp. 129–137, Oct. 2012, issn: 01692607. doi: 10.1016/j.cmpb.2012.02.003.

[38] H. Rouhani, J. Favre, X. Crevoisier, and K. Aminian, “Measurement of multisegment foot joint angles during gait using a wearable system.,” Journal of biomechanical engineering, vol. 134, no. 6, p. 61 006, Jun. 2012, issn: 1528- 8951 (Electronic). doi: 10.1115/1.4006674.

[39] A. Dalton, H. Khalil, M. Busse, A. Rosser, R. van Deursen, and G. OLaighin, ´ “Analysis of gait and balance through a single triaxial accelerometer in presymptomatic and symptomatic Huntington’s disease,” Gait Posture, vol. 37, no. 1, pp. 49–54, Jan. 2013, issn: 09666362. doi: 10.1016/j.gaitpost. 2012.05.028.

[40] O. Tirosh, R. Begg, E. Passmore, and N. Knopp-Steinberg, “Wearable textile sensor sock for gait analysis,” in 2013 Seventh International Conference on Sensing Technology (ICST), 2013, pp. 618–622. doi: 10 . 1109 / ICSensT . 2013.6727727.

[41] J. C. Van Den Noort, A. Ferrari, A. G. Cutti, J. G. Becher, and J. Harlaar, “Gait analysis in children with cerebral palsy via inertial and magnetic sensors,” Medical and Biological Engineering and Computing, vol. 51, no. 4, pp. 377–386, 2013. doi: 10.1007/s11517-012-1006-5.

[42] B. Mariani, M. C. Jim´enez, F. J. G. Vingerhoets, and K. Aminian, “OnShoe Wearable Sensors for Gait and Turning Assessment of Patients With Parkinson’s Disease,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 1, pp. 155–158, Jan. 2013, issn: 0018-9294. doi: 10.1109/TBME.2012. 2227317.

[43] S. Tadano, R. Takeda, and H. Miyagawa, “Three Dimensional Gait Analysis Using Wearable Acceleration and Gyro Sensors Based on Quaternion Calculations,” Sensors, vol. 13, no. 7, pp. 9321–9343, Jul. 2013, issn: 1424-8220. doi: 10.3390/s130709321.

[44] F. Mart´ınez-Mart´ı, M. S. Mart´ınez-Garc´ıa, S. G. Garc´ıa-D´ıaz, J. Garc´ıaJim´enez, A. J. Palma, and M. A. Carvajal, “Embedded sensor insole for wireless measurement of gait parameters,” Australasian Physical Engineering Sciences in Medicine, vol. 37, no. 1, pp. 25–35, Mar. 2014, issn: 0158-9938. doi: 10.1007/s13246-013-0236-7.

[45] D. Jarchi, C. Wong, R. M. Kwasnicki, B. Heller, G. A. Tew, and Guang-Zhong Yang, “Gait Parameter Estimation From a Miniaturized Ear-Worn Sensor Using Singular Spectrum Analysis and Longest Common Subsequence,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 4, pp. 1261–1273, Apr. 2014, issn: 0018-9294. doi: 10.1109/TBME.2014.2299772.

[46] A. Br´egou Bourgeois, B. Mariani, K. Aminian, P. Y. Zambelli, and C. J. Newman, “Spatio-temporal gait analysis in children with cerebral palsy using, foot-worn inertial sensors,” Gait and Posture, vol. 39, no. 1, pp. 436–442, Jan. 2014, issn: 0966-6362. doi: 10.1016/j.gaitpost.2013.08.029.

[47] A. Godfrey, S. Del Din, G. Barry, J. C. Mathers, and L. Rochester, “Within trial validation and reliability of a single tri-axial accelerometer for gait assessment,” in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Aug. 2014, pp. 5892–5895, isbn: 978-1-4244-7929-0. doi: 10.1109/EMBC.2014.6944969.

[48] T. Liu, Y. Inoue, K. Shibata, K. Shiojima, and M. M. Han, “Triaxial joint moment estimation using a wearable three-dimensional gait analysis system,” Measurement, vol. 47, pp. 125–129, 2014, issn: 0263-2241. doi: https:// doi.org/10.1016/j.measurement.2013.08.020.

[49] Y. Meng¨u¸c, Y.-L. Park, H. Pei, et al., “Wearable soft sensing suit for human gait measurement,” The International Journal of Robotics Research, vol. 33, no. 14, pp. 1748–1764, Dec. 2014, issn: 0278-3649. doi: 10.1177/ 0278364914543793.

[50] K. Ben Mansour, N. Rezzoug, and P. Gorce, “Analysis of several methods and inertial sensors locations to assess gait parameters in able-bodied subjects,” Gait and Posture, vol. 42, no. 4, pp. 409–414, 2015. doi: 10.1016/j. gaitpost.2015.05.020.

[51] C. M. Kanzler, J. Barth, A. Rampp, et al., “Inertial sensor based and shoe size independent gait analysis including heel and toe clearance estimation,” in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 5424–5427. doi: 10.1109/ EMBC.2015.7319618.

[52] S. Crea, C. Cipriani, M. Donati, M. C. Carrozza, and N. Vitiello, “Providing time-discrete gait information by wearable feedback apparatus for lower-limb amputees: usability and functional validation.,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 23, no. 2, pp. 250–7, Mar. 2015, issn: 1558-0210. doi: 10.1109/TNSRE.2014.2365548.

[53] S. Shirai, I. Yabe, M. Matsushima, Y. M. Ito, M. Yoneyama, and H. Sasaki, “Quantitative evaluation of gait ataxia by accelerometers.,” Journal of the neurological sciences, vol. 358, no. 1-2, pp. 253–258, Nov. 2015, issn: 1878- 5883 (Electronic). doi: 10.1016/j.jns.2015.09.004.

[54] C. Buckley, B. Galna, L. Rochester, and C. Mazz`a, “Attenuation of Upper Body Accelerations during Gait: Piloting an Innovative Assessment Tool for Parkinson’s Disease,” BioMed Research International, vol. 2015, pp. 1–6, 2015, issn: 2314-6133. doi: 10.1155/2015/865873.

[55] H. Chang, Y. Hsu, S. Yang, J. Lin, and Z. Wu, “A Wearable Inertial Measurement System With Complementary Filter for Gait Analysis of Patients With Stroke or Parkinson’s Disease,” IEEE Access, vol. 4, pp. 8442–8453, 2016. doi: 10.1109/ACCESS.2016.2633304.

[56] S. Byun, J. W. Han, T. H. Kim, and K. W. Kim, “Test-Retest Reliability and Concurrent Validity of a Single Tri-Axial Accelerometer-Based Gait Analysis in Older Adults with Normal Cognition,” PLOS ONE, vol. 11, no. 7, A. Fasano, Ed., e0158956, Jul. 2016, issn: 1932-6203. doi: 10.1371/journal. pone.0158956.

[57] D. Jarchi, B. Lo, C. Wong, E. Ieong, D. Nathwani, and G.-Z. Yang, “Gait Analysis From a Single Ear-Worn Sensor: Reliability and Clinical Evaluation for Orthopaedic Patients,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 8, pp. 882–892, Aug. 2016, issn: 1534-4320. doi: 10.1109/TNSRE.2015.2477720.

[58] W. Chen, Y. Xu, J. Wang, and J. Zhang, “Kinematic Analysis of Human Gait Based on Wearable Sensor System for Gait Rehabilitation,” Journal of Medical and Biological Engineering, vol. 36, no. 6, pp. 843–856, 2016. doi: 10.1007/s40846-016-0179-z.

[59] C. Soaz and K. Diepold, “Step Detection and Parameterization for Gait Assessment Using a Single Waist-Worn Accelerometer,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 5, pp. 933–942, May 2016, issn: 0018- 9294. doi: 10.1109/TBME.2015.2480296.

[60] L. Donath, O. Faude, E. Lichtenstein, G. Pagenstert, C. N¨uesch, and A. M¨undermann, “Mobile inertial sensor based gait analysis: Validity and reliability of spatiotemporal gait characteristics in healthy seniors.,” Gait posture, vol. 49, pp. 371–374, Sep. 2016, issn: 1879-2219 (Electronic). doi: 10.1016/ j.gaitpost.2016.07.269.

[61] A. Pacilli, I. Mileti, M. Germanotta, et al., “A Wearable Setup for Auditory Cued Gait Analysis in Patients with Parkinson’s Disease,” in 2016 IEEE International Symposium on Medical Measurements and Application (MEMEA), ser. IEEE International Symposium on Medical Measurements and Applications Proceedings-MeMeA, IEEE; IEEE Instrumentat Measurement Soc, IEEE, 2016, pp. 551–556, isbn: 978-1-4673-9172-6.

[62] N. Carbonaro, F. Lorussi, and A. Tognetti, “Assessment of a Smart Sensing Shoe for Gait Phase Detection in Level Walking,” Electronics, vol. 5, no. 4, p. 78, Nov. 2016, issn: 2079-9292. doi: 10.3390/electronics5040078.

[63] C. Tunca, N. Pehlivan, N. Ak, B. Arnrich, G. Salur, and C. Ersoy, “Inertial Sensor-Based Robust Gait Analysis in Non-Hospital Settings for Neurological Disorders,” Sensors, vol. 17, no. 4, p. 825, Apr. 2017, issn: 1424-8220. doi: 10.3390/s17040825.

[64] V. Agostini, L. Gastaldi, V. Rosso, M. Knaflitz, and S. Tadano, “A Wearable Magneto-Inertial System for Gait Analysis (H-Gait): Validation on Normal Weight and Overweight/Obese Young Healthy Adults,” Sensors, vol. 17, no. 10, p. 2406, Oct. 2017, issn: 1424-8220. doi: 10.3390/s17102406.

[65] A. Suppa, A. Kita, G. Leodori, et al., “l-DOPA and Freezing of Gait in Parkinson’s Disease: Objective Assessment through a Wearable Wireless System.,” Frontiers in neurology, vol. 8, p. 406, 2017, issn: 1664-2295 (Print). doi: 10.3389/fneur.2017.00406.

[66] S. Qiu, Z. Wang, H. Zhao, and O. Hu, “Heterogeneous data fusion for threedimensional gait analysis using wearable MARG sensors,” International Journal of Computational Science and Engineering, vol. 14, no. 3, p. 222, 2017, issn: 1742-7185. doi: 10.1504/IJCSE.2017.084154.

[67] M. Boutaayamou, V. Den¨oel, O. Br¨uls, et al., “Algorithm for temporal gait analysis using wireless foot-mounted accelerometers,” Communications in Computer and Information Science, vol. 690, pp. 236–254, 2017. doi: 10. 1007/978-3-319-54717-6_14.

[68] Y. Moon, R. S. McGinnis, K. Seagers, et al., “Monitoring gait in multiple sclerosis with novel wearable motion sensors.,” PloS one, vol. 12, no. 2, e0171346, 2017, issn: 1932-6203 (Electronic). doi: 10.1371/journal.pone.0171346.

[69] K. Orlowski, F. Eckardt, F. Herold, N. Aye, J. Edelmann-Nusser, and K. Witte, “Examination of the reliability of an inertial sensor-based gait analysis system.,” Biomedizinische Technik. Biomedical engineering, vol. 62, no. 6, pp. 615–622, Nov. 2017, issn: 1862-278X (Electronic). doi: 10.1515/bmt2016-0067.

[70] S. A. Moore, A. Hickey, S. Lord, S. Del Din, A. Godfrey, and L. Rochester, “Comprehensive measurement of stroke gait characteristics with a single accelerometer in the laboratory and community: a feasibility, validity and reliability study,” Journal of NeuroEngineering and Rehabilitation, vol. 14, no. 1, p. 130, Dec. 2017, issn: 1743-0003. doi: 10.1186/s12984-017-0341-z.

[71] X. Chen, S. Liao, S. Cao, D. Wu, and X. Zhang, “An Acceleration-Based Gait Assessment Method for Children with Cerebral Palsy,” Sensors, vol. 17, no. 5, p. 1002, May 2017, issn: 1424-8220. doi: 10.3390/s17051002.

[72] S. Rogan, R. de Bie, and E. Douwe de Bruin, “Sensor-based foot-mounted wearable system and pressure sensitive gait analysis,” Zeitschrift f¨ur Gerontologie und Geriatrie, vol. 50, no. 6, pp. 488–497, Aug. 2017, issn: 0948-6704. doi: 10.1007/s00391-016-1124-z.

[73] J. L. Lanovaz, A. R. Oates, T. T. Treen, J. Unger, and K. E. Musselman, “Validation of a commercial inertial sensor system for spatiotemporal gait measurements in children,” Gait Posture, vol. 51, pp. 14–19, Jan. 2017, issn: 09666362. doi: 10.1016/j.gaitpost.2016.09.021.

[74] S. Khandelwal and N. Wickstr¨om, “Evaluation of the performance of accelerometer based gait event detection algorithms in different real-world scenarios using the MAREA gait database,” Gait Posture, vol. 51, pp. 84–90, Jan. 2017, issn: 09666362. doi: 10.1016/j.gaitpost.2016.09.023.

[75] G. Pacini Panebianco, M. C. Bisi, R. Stagni, and S. Fantozzi, “Analysis of the performance of 17 algorithms from a systematic review: Influence of sensor position, analysed variable and computational approach in gait timing estimation from IMU measurements,” Gait Posture, vol. 66, pp. 76–82, Oct. 2018, issn: 09666362. doi: 10.1016/j.gaitpost.2018.08.025.

[76] R. Mc Ardle, R. Morrisa, A. Hickey, et al., “Gait in Mild Alzheimer’s Disease: Feasibility of Multi-Center Measurement in the Clinic and Home with BodyWorn Sensors: A Pilot Study,” Journal of Alzheimer’s Disease, vol. 63, no. 4, pp. 1557–1557, May 2018, issn: 13872877. doi: 10.3233/JAD-189003.

[77] H. De Vroey, F. Staes, I. Weygers, et al., “The implementation of inertial sensors for the assessment of temporal parameters of gait in the knee arthroplasty population,” Clinical Biomechanics, vol. 54, pp. 22–27, 2018. doi: 10.1016/j.clinbiomech.2018.03.002.

[78] C. Caramia, D. Torricelli, M. Schmid, et al., “IMU-Based Classification of Parkinson’s Disease From Gait: A Sensitivity Analysis on Sensor Location and Feature Selection,” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 6, pp. 1765–1774, Nov. 2018, issn: 2168-2194. doi: 10.1109/ JBHI.2018.2865218.

[79] S. Qiu, L. Liu, H. Zhao, Z. Wang, and Y. Jiang, “MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion.,” Micromachines, vol. 9, no. 9, Sep. 2018, issn: 2072-666X (Print). doi: 10. 3390/mi9090442.

[80] W. Teufl, M. Lorenz, M. Miezal, et al., “Towards inertial sensor based mobile gait analysis: Event-detection and spatio-temporal parameters,” Sensors (Switzerland), vol. 19, no. 1, Jan. 2018, issn: 1424-8220. doi: 10 . 3390 / s19010038.

[81] M. Fusca, F. Negrini, P. Perego, L. Magoni, F. Molteni, and G. Andreoni, “Validation of a wearable IMU system for gait analysis: Protocol and application to a new system,” Applied Sciences (Switzerland), vol. 8, no. 7, 2018. doi: 10.3390/app8071167.

[82] N. Roth, C. F. Martindale, H. Gaßner, Z. Kohl, J. Klucken, and B. M. Eskofer, “Synchronized sensor insoles for clinical gait analysis in home-monitoring applications,” Current Directions in Biomedical Engineering, vol. 4, no. 1, pp. 433–437, 2018. doi: 10.1515/cdbme-2018-0103.

[83] E. Papi, Y. N. Bo, and A. H. McGregor, “A flexible wearable sensor for knee flexion assessment during gait,” Gait Posture, vol. 62, pp. 480–483, May 2018, issn: 09666362. doi: 10.1016/j.gaitpost.2018.04.015.

[84] A. R. Anwary, H. Yu, and M. Vassallo, “Optimal Foot Location for Placing Wearable IMU Sensors and Automatic Feature Extraction for Gait Analysis,” IEEE Sensors Journal, vol. 18, no. 6, pp. 2555–2567, Mar. 2018, issn: 1530437X. doi: 10.1109/JSEN.2017.2786587.

[85] A. M. Keppler, T. Nuritidinow, A. Mueller, et al., “Validity of accelerometry in step detection and gait speed measurement in orthogeriatric patients.,” PloS one, vol. 14, no. 8, e0221732, 2019, issn: 1932-6203 (Electronic). doi: 10.1371/journal.pone.0221732.

[86] S. Qiu, L. Liu, Z. Wang, et al., “Body Sensor Network-Based Gait Quality Assessment for Clinical Decision-Support via Multi-Sensor Fusion,” IEEE Access, vol. 7, pp. 59 884–59 894, 2019. doi: 10.1109/ACCESS.2019.2913897.

[87] S. Qiu, Z. Wang, H. Zhao, et al., “Body Sensor Network-Based Robust Gait Analysis: Toward Clinical and at Home Use,” IEEE Sensors Journal, vol. 19, no. 19, pp. 8393–8401, 2019. doi: 10.1109/JSEN.2018.2860938.

[88] P. Pierleoni, A. Belli, L. Palma, et al., “Validation of a Gait Analysis Algorithm for Wearable Sensors,” in 2019 International Conference on Sensing and Instrumentation in IoT Era (ISSI), IEEE, Aug. 2019, pp. 1–6, isbn: 978-1-7281-1022-6. doi: 10.1109/ISSI47111.2019.9043647.

[89] F. Garc´ıa-Pinillos, P. A. Latorre-Rom´an, V. M. Soto-Hermoso, ´ et al., “Agreement between the spatiotemporal gait parameters from two different wearable devices and high-speed video analysis,” PLOS ONE, vol. 14, no. 9, D. Boullosa, Ed., e0222872, Sep. 2019, issn: 1932-6203. doi: 10 . 1371 / journal . pone.0222872.

[90] L. Meng, U. Martinez-Hernandez, C. Childs, A. A. Dehghani-Sanij, and A. Buis, “A Practical Gait Feedback Method Based on Wearable Inertial Sensors for a Drop Foot Assistance Device,” IEEE Sensors Journal, vol. 19, no. 24, pp. 12 235–12 243, Dec. 2019, issn: 1530-437X. doi: 10.1109/JSEN.2019. 2938764.

[91] N. Lefeber, M. Degelaen, C. Truyers, I. Safin, and D. Beckwee, “Validity and Reproducibility of Inertial Physilog Sensors for Spatiotemporal Gait Analysis in Patients With Stroke,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 9, pp. 1865–1874, Sep. 2019, issn: 1534-4320. doi: 10.1109/TNSRE.2019.2930751.

[92] Wang, Kim, Shin, and Min, “Preliminary Clinical Application of Textile Insole Sensor for Hemiparetic Gait Pattern Analysis,” Sensors, vol. 19, no. 18, p. 3950, Sep. 2019, issn: 1424-8220. doi: 10.3390/s19183950.

[93] R. De Ridder, J. Lebleu, T. Willems, C. De Blaiser, C. Detrembleur, and P. Roosen, “Concurrent Validity of a Commercial Wireless Trunk Triaxial Accelerometer System for Gait Analysis.,” Journal of sport rehabilitation, vol. 28, no. 6, Aug. 2019, issn: 1543-3072 (Electronic). doi: 10.1123/jsr. 2018-0295.

[94] J. Yang, T.-H. Huang, S. Yu, et al., “Machine Learning Based Adaptive Gait Phase Estimation Using Inertial Measurement Sensors,” in 2019 Design of Medical Devices Conference, vol. 19, American Society of Mechanical Engineers, Apr. 2019, pp. 12 235–12 243, isbn: 978-0-7918-4103-7. doi: 10.1115/DMD2019-3266.

[95] D. Phan, N. Nguyen, P. N. Pathirana, M. Horne, L. Power, and D. Szmulewicz, “A Random Forest Approach for Quantifying Gait Ataxia With Truncal and Peripheral Measurements Using Multiple Wearable Sensors,” IEEE Sensors Journal, vol. 20, no. 2, pp. 723–734, 2020. doi: 10 . 1109 / JSEN.2019.2943879.

[96] M. Lueken, L. Mueller, M. G. Decker, C. Bollheimer, S. Leonhardt, and C. Ngo, “Evaluation and Application of a Customizable Wireless Platform: A Body Sensor Network for Unobtrusive Gait Analysis in Everyday Life.,” Sensors (Basel, Switzerland), vol. 20, no. 24, Dec. 2020, issn: 1424-8220 (Electronic). doi: 10.3390/s20247325.

[97] N. Muthukrishnan, J. J. Abbas, and N. Krishnamurthi, “A Wearable Sensor System to Measure Step-Based Gait Parameters for Parkinson’s Disease Rehabilitation,” Sensors, vol. 20, no. 22, p. 6417, Nov. 2020, issn: 1424-8220. doi: 10.3390/s20226417.

[98] N. Mani, P. Haridoss, and B. George, “A Wearable Ultrasonic based Ankle Angle and Toe Clearance Sensing System for Gait Analysis,” IEEE Sensors Journal, p. 1, 2020. doi: 10.1109/JSEN.2020.3047900.

[99] S. Qiu, H. Wang, J. Li, et al., “Towards Wearable-Inertial-Sensor-Based Gait Posture Evaluation for Subjects with Unbalanced Gaits,” Sensors, vol. 20, no. 4, p. 1193, Feb. 2020, issn: 1424-8220. doi: 10.3390/s20041193.

[100] Y. Hutabarat, D. Owaki, and M. Hayashibe, “Quantitative Gait Assessment With Feature-Rich Diversity Using Two IMU Sensors,” IEEE Transactions on Medical Robotics and Bionics, vol. 2, no. 4, pp. 639–648, 2020. doi: 10. 1109/TMRB.2020.3021132.

[101] F. Amitrano, A. Coccia, C. Ricciardi, et al., “Design and Validation of an E-Textile-Based Wearable Sock for Remote Gait and Postural Assessment.,” Sensors (Basel, Switzerland), vol. 20, no. 22, Nov. 2020, issn: 1424-8220 (Electronic). doi: 10.3390/s20226691.

[102] J. Figueiredo, S. P. Carvalho, J. P. Vilas-Boas, L. M. Gon¸calves, J. C. Moreno, and C. P. Santos, “Wearable Inertial Sensor System towards Daily Human Kinematic Gait Analysis: Benchmarking Analysis to MVN BIOMECH,” Sensors, vol. 20, no. 8, p. 2185, Apr. 2020, issn: 1424-8220. doi: 10 . 3390 / s20082185.

[103] D. Renggli, C. Graf, N. Tachatos, et al., “Wearable Inertial Measurement Units for Assessing Gait in Real-World Environments.,” Frontiers in physiology, vol. 11, p. 90, 2020, issn: 1664-042X (Print). doi: 10.3389/fphys. 2020.00090.

[104] L. Zhou, C. Tunca, E. Fischer, et al., “Validation of an IMU Gait Analysis Algorithm for Gait Monitoring in Daily Life Situations,” in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC), 2020, pp. 4229–4232. doi: 10.1109/EMBC44109.2020.9176827.

[105] I. A. Vajs, V. N. Bobi´c, M. D. uri´c-Joviˇci´c, and M. M. Jankovi´c, “Opensource application for real-time gait analysis using inertial sensors,” in 2020 28th Telecommunications Forum (TELFOR), 2020, pp. 1–4. doi: 10.1109/ TELFOR51502.2020.9306636.

[106] H. Zhao, Z. Wang, S. Qiu, J. Li, F. Gao, and J. Wang, “Evaluation of Inertial Sensor Configurations for Wearable Gait Analysis,” Studies in Computational Intelligence, vol. 844, pp. 197–212, 2020. doi: 10.1007/978-3-030-24405- 7_13.

[107] S. V´ıteˇckov´a, H. Hor´akov´a, K. Pol´akov´a, R. Krupiˇcka, E. R˚uˇziˇcka, and H. Broˇzov´a, “Agreement between the GAITRite® System and the Wearable Sensor BTS G-Walk® for measurement of gait parameters in healthy adults and Parkinson’s disease patients.,” PeerJ, vol. 8, e8835, 2020, issn: 2167-8359 (Print). doi: 10.7717/peerj.8835.

[108] C. Pradeau, N. Sturbois-Nachef, and E. Allart, “Concurrent validity of the ZeroWire® footswitch system for the measurement of temporal gait parameters.,” Gait posture, vol. 82, pp. 133–137, Oct. 2020, issn: 1879-2219 (Electronic). doi: 10.1016/j.gaitpost.2020.09.003.

[109] L.-F. Shi, C.-X. Qiu, D.-J. Xin, and G.-X. Liu, “Gait recognition via random forests based on wearable inertial measurement unit,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 11, pp. 5329–5340, Nov. 2020, issn: 1868-5137. doi: 10.1007/s12652-020-01870-x.

[110] W. Zijlstra and A. L. Hof, “Assessment of spatio-temporal gait parameters from trunk accelerations during human walking,” Gait and Posture, vol. 18, no. 2, pp. 1–10, 2003, issn: 09666362. doi: 10.1016/S0966-6362(02)00190- X.

[111] K. Aminian, B. Najafi, C. B¨ula, P.-F. Leyvraz, and P. Robert, “Spatiotemporal parameters of gait measured by an ambulatory system using miniature gyroscopes,” Journal of Biomechanics, vol. 35, no. 5, pp. 689–699, May 2002, issn: 00219290. doi: 10 . 1016 / S0021 - 9290(02 ) 00008 - 8. eprint: arXiv:1011.1669v3.

[112] S. Studenski, S. Perera, K. Patel, et al., “Gait speed and survival in older adults.,” JAMA, vol. 305, no. 1, pp. 50–8, Jan. 2011, issn: 1538-3598. doi: 10.1001/jama.2010.1923.

[113] K. Aminian, F. Dadashi, B. Mariani, C. Lenoble-Hoskovec, B. Santos-Eggimann, and C. J. B¨ula, “Gait analysis using shoe-worn inertial sensors: How is foot clearance related to walking speed?” In UbiComp 2014 - Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014, pp. 481–485. doi: 10.1145/2632048.2632071.

[114] R. de Souza Baptista, A. P. L. Bo, and M. Hayashibe, “Automatic Human Movement Assessment With Switching Linear Dynamic System: Motion Segmentation and Motor Performance,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 6, pp. 628–640, Jun. 2017, issn: 1534-4320. doi: 10.1109/TNSRE.2016.2591783.

[115] M. Sharifi Renani, C. A. Myers, R. Zandie, M. H. Mahoor, B. S. Davidson, and C. W. Clary, “Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors,” Sensors, vol. 20, no. 19, p. 5553, Sep. 2020, issn: 1424-8220. doi: 10.3390/s20195553.

[116] A. Tura, L. Rocchi, M. Raggi, A. G. Cutti, and L. Chiari, “Recommended number of strides for automatic assessment of gait symmetry and regularity in above-knee amputees by means of accelerometry and autocorrelation analysis,” Journal of NeuroEngineering and Rehabilitation, vol. 9, no. 1, p. 11, Feb. 2012, issn: 1743-0003. doi: 10.1186/1743-0003-9-11.

[117] V. Belluscio, E. Bergamini, M. Tramontano, R. Formisano, M. G. Buzzi, and G. Vannozzi, “Does Curved Walking Sharpen the Assessment of Gait Disorders? An Instrumented Approach Based on Wearable Inertial Sensors,” Sensors, vol. 20, no. 18, p. 5244, Sep. 2020, issn: 1424-8220. doi: 10.3390/ s20185244.

[118] L. Sloot, M. van der Krogt, and J. Harlaar, “Self-paced versus fixed speed treadmill walking,” Gait Posture, vol. 39, no. 1, pp. 478–484, Jan. 2014, issn: 09666362. doi: 10.1016/j.gaitpost.2013.08.022.

[119] M. D. Chang, S. Shaikh, and T. Chau, “Effect of treadmill walking on the stride interval dynamics of human gait,” Gait Posture, vol. 30, no. 4, pp. 431– 435, Nov. 2009, issn: 09666362. doi: 10.1016/j.gaitpost.2009.06.017.

[120] D. Owaki, Y. Sekiguchi, K. Honda, A. Ishiguro, and S. I. Izumi, “Shortterm effect of prosthesis transforming sensory modalities on walking in stroke patients with hemiparesis,” Neural Plasticity, vol. 2016, 2016, issn: 16875443. doi: 10.1155/2016/6809879.

[121] S. Biesmans and P. Markopoulos, “Design and evaluation of sonis, a wearable biofeedback system for gait retraining,” Multimodal Technologies and Interaction, vol. 4, no. 3, pp. 1–13, 2020. doi: 10.3390/mti4030060.

[122] K. R. Sheerin, D. Reid, D. Taylor, and T. F. Besier, “The effectiveness of realtime haptic feedback gait retraining for reducing resultant tibial acceleration with runners,” Physical Therapy in Sport, vol. 43, pp. 173–180, May 2020, issn: 1466853X. doi: 10.1016/j.ptsp.2020.03.001.

[123] E. Allseits, V. Agrawal, J. Luˇcarevi´c, R. Gailey, I. Gaunaurd, and C. Bennett, “A practical step length algorithm using lower limb angular velocities,” Journal of Biomechanics, vol. 66, pp. 137–144, Jan. 2018, issn: 00219290. doi: 10.1016/j.jbiomech.2017.11.010.

[124] L. Wang, Y. Sun, Q. Li, and T. Liu, “Estimation of Step Length and Gait Asymmetry Using Wearable Inertial Sensors,” IEEE Sensors Journal, vol. 18, no. 9, pp. 3844–3851, May 2018, issn: 1530-437X. doi: 10.1109/JSEN.2018. 2815700. [

125] F. A. Storm, C. J. Buckley, and C. Mazz`a, “Gait event detection in laboratory and real life settings: Accuracy of ankle and waist sensor based methods,” Gait and Posture, vol. 50, pp. 42–46, 2016, issn: 18792219. doi: 10.1016/j. gaitpost.2016.08.012.

[126] A. Sabatini, C. Martelloni, S. Scapellato, and F. Cavallo, “Assessment of Walking Features From Foot Inertial Sensing,” IEEE Transactions on Biomedical Engineering, vol. 52, no. 3, pp. 486–494, Mar. 2005, issn: 0018-9294. doi: 10.1109/TBME.2004.840727.

[127] S. O. Madgwick, A. J. Harrison, and R. Vaidyanathan, “Estimation of IMU and MARG orientation using a gradient descent algorithm,” IEEE International Conference on Rehabilitation Robotics, pp. 0–6, 2011, issn: 19457898. doi: 10.1109/ICORR.2011.5975346.

[128] Y. Thong, M. Woolfson, J. Crowe, B. Hayes-Gill, and D. Jones, “Numerical double integration of acceleration measurements in noise,” Measurement, vol. 36, no. 1, pp. 73–92, Jul. 2004, issn: 02632241. doi: 10 . 1016 / j . measurement.2004.04.005.

[129] M. Zhang and A. Sawchuk, “A Feature Selection-Based Framework for Human Activity Recognition Using Wearable Multimodal Sensors,” in Proceedings of the 6th International ICST Conference on Body Area Networks, vol. 1, ACM, 2011, pp. 92–98, isbn: 978-1-936968-29-9. doi: 10.4108/icst. bodynets.2011.247018.

[130] A. Derungs, C. Schuster-Amft, and O. Amft, “Physical Activity Comparison Between Body Sides in Hemiparetic Patients Using Wearable Motion Sensors in Free-Living and Therapy: A Case Series,” Frontiers in Bioengineering and Biotechnology, vol. 6, no. OCT, Oct. 2018, issn: 2296-4185. doi: 10.3389/ fbioe.2018.00136.

[131] R. Selles, M. Formanoy, J. Bussmann, P. Janssens, and H. Stam, “Automated estimation of initial and terminal contact timing using accelerometers; development and validation in transtibial amputees and controls,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 13, no. 1, pp. 81–88, Mar. 2005, issn: 1534-4320. doi: 10.1109/TNSRE.2004.843176.

[132] B. R. Greene, D. McGrath, R. O’Neill, K. J. O’Donovan, A. Burns, and B. Caulfield, “An adaptive gyroscope-based algorithm for temporal gait analysis,” Medical Biological Engineering Computing, vol. 48, no. 12, pp. 1251– 1260, Dec. 2010, issn: 0140-0118. doi: 10.1007/s11517-010-0692-0.

[133] N. Kitagawa and N. Ogihara, “Estimation of foot trajectory during human walking by a wearable inertial measurement unit mounted to the foot,” Gait Posture, vol. 45, pp. 110–114, Mar. 2016, issn: 09666362. doi: 10.1016/j. gaitpost.2016.01.014.

[134] J. Hannink, T. Kautz, C. F. Pasluosta, et al., “Mobile Stride Length Estimation With Deep Convolutional Neural Networks,” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 2, pp. 354–362, Mar. 2018, issn: 2168-2194. doi: 10.1109/JBHI.2017.2679486. eprint: 1609.03321.

[135] K. Aminian, C. Trevisan, B. Najafi, et al., “Evaluation of an ambulatory system for gait analysis in hip osteoarthritis and after total hip replacement,” Gait Posture, vol. 20, no. 1, pp. 102–107, Aug. 2004, issn: 09666362. doi: 10.1016/S0966-6362(03)00093-6.

[136] A. Salarian, P. R. Burkhard, F. J. G. Vingerhoets, B. M. Jolles, and K. Aminian, “A Novel Approach to Reducing Number of Sensing Units for Wearable Gait Analysis Systems,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 1, pp. 72–77, 2013.

[137] A. Rotstein, O. Inbar, T. Berginsky, and Y. Meckel, “Preferred transition speed between walking and running: Effects of training status,” Medicine and Science in Sports and Exercise, vol. 37, no. 11, pp. 1864–1870, 2005, issn: 01959131. doi: 10.1249/01.mss.0000177217.12977.2f.

[138] R. de Souza Baptista, A. P. L. Bo, and M. Hayashibe, “Automatic Human Movement Assessment With Switching Linear Dynamic System: Motion Segmentation and Motor Performance,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 6, pp. 628–640, Jun. 2017, issn: 1534-4320. doi: 10.1109/TNSRE.2016.2591783.

[139] L. L. Long and M. Srinivasan, “Walking, running, and resting under time, distance, and average speed constraints: Optimality of walk-run-rest mixtures,” Journal of the Royal Society Interface, vol. 10, no. 81, 2013, issn: 17425662. doi: 10.1098/rsif.2012.0980.

[140] N. U. Ahamed, D. Kobsar, L. C. Benson, C. A. Clermont, S. T. Osis, and R. Ferber, “Subject-specific and group-based running pattern classification using a single wearable sensor,” Journal of Biomechanics, vol. 84, pp. 227– 233, Feb. 2019, issn: 00219290. doi: 10.1016/j.jbiomech.2019.01.001.

[141] W. A. Sparrow, E. J. Bradshaw, E. Lamoureux, and O. Tirosh, “Ageing effects on the attention demands of walking,” Human Movement Science, vol. 21, no. 5-6, pp. 961–972, 2002, issn: 01679457. doi: 10.1016/S0167- 9457(02)00154-9.

[142] V. Dubost, R. W. Kressig, R. Gonthier, et al., “Relationships between dualtask related changes in stride velocity and stride time variability in healthy older adults,” Human Movement Science, vol. 25, no. 3, pp. 372–382, Jun. 2006, issn: 01679457. doi: 10.1016/j.humov.2006.03.004. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0167945706000285.

[143] J. Howcroft, E. D. Lemaire, J. Kofman, and W. E. McIlroy, “Dual-Task Elderly Gait of Prospective Fallers and Non-Fallers: A Wearable-Sensor Based Analysis.,” Sensors (Basel, Switzerland), vol. 18, no. 4, Apr. 2018, issn: 1424- 8220 (Electronic). doi: 10.3390/s18041275.

[144] J. Nonnekes, V. Dibilio, C. Barthel, T. Solis-Escalante, B. R. Bloem, and V. Weerdesteyn, “Understanding the dual-task costs of walking: a StartReact study,” Experimental Brain Research, vol. 238, no. 5, pp. 1359–1364, 2020, issn: 14321106. doi: 10.1007/s00221- 020- 05817- 8. [Online]. Available: https://doi.org/10.1007/s00221-020-05817-8.

[145] I. Hillel, E. Gazit, A. Nieuwboer, et al., “Is every-day walking in older adults more analogous to dual-task walking or to usual walking? Elucidating the gaps between gait performance in the lab and during 24/7 monitoring,” European Review of Aging and Physical Activity, vol. 16, no. 1, pp. 1–12, 2019, issn: 18137253. doi: 10.1186/s11556-019-0214-5.

[146] Y. Hutabarat, D. Owaki, and M. Hayashibe, “Recent Advances in Quantitative Gait Analysis using Wearable Sensors: A Review,” IEEE Sensors Journal, vol. 21, no. 23, pp. 26 470–26 487, 2021, issn: 15581748. doi: 10. 1109/JSEN.2021.3119658.

[147] W. Pitt and L.-S. Chou, “Reliability and practical clinical application of an accelerometer-based dual-task gait balance control assessment.,” eng, Gait posture, vol. 71, pp. 279–283, Jun. 2019, issn: 1879-2219 (Electronic). doi: 10.1016/j.gaitpost.2019.05.014.

[148] J. T. Coulthard, T. T. Treen, A. R. Oates, and J. L. Lanovaz, “Evaluation of an inertial sensor system for analysis of timed-up-and-go under dual-task demands.,” eng, Gait posture, vol. 41, no. 4, pp. 882–887, May 2015, issn: 1879-2219 (Electronic). doi: 10.1016/j.gaitpost.2015.03.009.

[149] J. Howcroft, J. Kofman, E. D. Lemaire, and W. E. McIlroy, “Analysis of dual-task elderly gait in fallers and non-fallers using wearable sensors.,” eng, Journal of biomechanics, vol. 49, no. 7, pp. 992–1001, May 2016, issn: 1873- 2380 (Electronic). doi: 10.1016/j.jbiomech.2016.01.015.

[150] Y. Freund and R. E. Schapire, “A Decision-Theoretic Generalization of OnLine Learning and an Application to Boosting,” Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119–139, 1997, issn: 00220000. doi: 10. 1006/jcss.1997.1504.

[151] T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” pp. 785–794, Aug. 2016. doi: 10 . 1145 / 2939672 . 2939785. arXiv: 1603 . 02754.

[152] A. Althnian, D. AlSaeed, H. Al-Baity, et al., “Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain,” Applied Sciences, vol. 11, no. 2, p. 796, Jan. 2021, issn: 2076-3417. doi: 10.3390/app11020796.

[153] S. Bai, J. Z. Kolter, and V. Koltun, “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,” arXiv, 2018, issn: 23318422. arXiv: 1803.01271.

[154] J. Yan, L. Mu, L. Wang, R. Ranjan, and A. Y. Zomaya, “Temporal Convolutional Networks for the Advance Prediction of ENSO,” Scientific Reports, vol. 10, no. 1, p. 8055, Dec. 2020, issn: 2045-2322. doi: 10.1038/s41598- 020-65070-5.

[155] L. A. Gemein, R. T. Schirrmeister, P. Chrabaszcz, et al., “Machine-learningbased diagnostics of EEG pathology,” NeuroImage, vol. 220, no. December 2019, p. 117 021, Oct. 2020, issn: 10538119. doi: 10.1016/j.neuroimage. 2020.117021. eprint: 2002.05115.

[156] J. Chen, D. Chen, and G. Liu, “Using temporal convolution network for remaining useful lifetime prediction,” Engineering Reports, vol. 3, no. 3, pp. 1– 17, 2021, issn: 2577-8196. doi: 10.1002/eng2.12305.

[157] P. Zhang, X. Wang, J. Chen, W. You, and W. Zhang, “Spectral and Temporal Feature Learning with Two-Stream Neural Networks for Mental Workload Assessment,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 6, pp. 1149–1159, 2019, issn: 15580210. doi: 10.1109/TNSRE.2019.2913400.

[158] Y. Hutabarat, K. Ekkachai, M. Hayashibe, and W. Kongprawechnon, “Reinforcement q-learning control with reward shaping function for swing phase control in a semi-active prosthetic knee,” Frontiers in Neurorobotics, vol. 14, pp. 1–10, November Nov. 2020, issn: 1662-5218. doi: 10.3389/fnbot.2020. 565702.

[159] H. Herr and A. Wilkenfeld, “User-adaptive control of a magnetorheological prosthetic knee,” Industrial Robot: An International Journal, vol. 30, no. 1, pp. 42–55, Feb. 2003, issn: 0143-991X. doi: 10.1108/01439910310457706.

[160] K. Ekkachai and I. Nilkhamhang, “Swing Phase Control of Semi-Active Prosthetic Knee Using Neural Network Predictive Control With Particle Swarm Optimization,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 11, pp. 1169–1178, Nov. 2016, issn: 1534-4320. doi: 10.1109/TNSRE.2016.2521686.

[161] Y. Wen, J. Si, A. Brandt, X. Gao, and H. H. Huang, “Online Reinforcement Learning Control for the Personalization of a Robotic Knee Prosthesis,” IEEE Transactions on Cybernetics, vol. 50, no. 6, pp. 2346–2356, Jun. 2020, issn: 2168-2267. doi: 10.1109/TCYB.2019.2890974.

[162] L. Rochester, C. Mazz`a, A. Mueller, et al., “A Roadmap to Inform Development, Validation and Approval of Digital Mobility Outcomes: The Mobilise-D Approach,” Digital Biomarkers, vol. 4, no. 1, pp. 13–27, 2020, issn: 2504- 110X. doi: 10.1159/000512513.

参考文献をもっと見る

分野

大学

学位論文種類・取得年

言語

Multimodal Quantitative Gait Assessment Using Minimal Wearable Sensors

概要

この論文で使われている画像

参考文献

分野

大学

学位論文種類・取得年

言語

コピーが完了しました

URLをコピーしました

Multimodal Quantitative Gait Assessment Using Minimal Wearable Sensors

概要

この論文で使われている画像

参考文献