Papers
Topics
Authors
Recent
Search
2000 character limit reached

Inertial Sensors for Human Motion Analysis: A Comprehensive Review

Published 23 Jan 2024 in eess.SY and cs.SY | (2401.12919v1)

Abstract: Inertial motion analysis is having a growing interest during the last decades due to its advantages over classical optical systems. The technological solution based on inertial measurement units allows the measurement of movements in daily living environments, such as in everyday life, which is key for a realistic assessment and understanding of movements. This is why research in this field is still developing and different approaches are proposed. This presents a systematic review of the different proposals for inertial motion analysis found in the literature. The search strategy has been carried out on eight different platforms, including journal articles and conference proceedings, which are written in English and published until August 2022. The results are analyzed in terms of the publishers, the sensors used, the applications, the monitored units, the algorithms of use, the participants of the studies, and the validation systems employed. In addition, we delve deeply into the machine learning techniques proposed in recent years and in the approaches to reduce the estimation error. In this way, we show an overview of the research carried out in this field, going into more detail in recent years, and providing some research directions for future work

Definition Search Book Streamline Icon: https://streamlinehq.com
References (187)
  1. D. Fitzgerald, J. Foody, D. Kelly, T. Ward, C. Markham, J. McDonald, and B. Caulfield, “Development of a wearable motion capture suit and virtual reality biofeedback system for the instruction and analysis of sports rehabilitation exercises,” Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, pp. 4870–4874, 2007.
  2. D. Feil-Seifer and M. Mataric, “Defining socially assistive robotics,” in 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005., pp. 465–468, 2005.
  3. V. Camomilla, E. Bergamini, S. Fantozzi, and G. Vannozzi, “Trends supporting the in-field use of wearable inertial sensors for sport performance evaluation: A systematic review,” Sensors, vol. 18, no. 3, p. 873, 2018.
  4. I. Hussein Lopez-Nava, A. Munoz-Melendez, I. H. López-Nava, and A. Muñoz-Meléndez, “Wearable Inertial Sensors for Human Motion Analysis: A Review,” IEEE Sensors Journal, vol. 16, pp. 7821–7834, nov 2016.
  5. J. Kubicek, K. Fiedorova, D. Vilimek, M. Cerny, M. Penhaker, M. Janura, and J. Rosicky, “Recent trends, construction and applications of smart textiles and clothing for monitoring of health activity: a comprehensive multidisciplinary review,” IEEE Reviews in Biomedical Engineering, 2020.
  6. K. Kudrinko, E. Flavin, X. Zhu, and Q. Li, “Wearable sensor-based sign language recognition: A comprehensive review,” IEEE Reviews in Biomedical Engineering, vol. 14, pp. 82–97, 2020.
  7. I. Weygers, M. Kok, M. Konings, H. Hallez, H. De Vroey, and K. Claeys, “Inertial sensor-based lower limb joint kinematics: A methodological systematic review,” Sensors, vol. 20, no. 3, p. 673, 2020.
  8. V. Joukov, V. Bonnet, M. Karg, G. Venture, and D. Kulić, “Rhythmic Extended Kalman Filter for Gait Rehabilitation Motion Estimation and Segmentation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 2, pp. 407–418, 2018.
  9. L. Adamowicz, R. D. Gurchiek, J. Ferri, A. T. Ursiny, N. Fiorentino, and R. S. McGinnis, “Validation of novel relative orientation and inertial sensor-to-segment alignment algorithms for estimating 3D hip joint angles,” Sensors (Switzerland), vol. 19, no. 23, 2019.
  10. M. J. Page, J. E. McKenzie, P. M. Bossuyt, I. Boutron, T. C. Hoffmann, C. D. Mulrow, L. Shamseer, J. M. Tetzlaff, E. A. Akl, S. E. Brennan, R. Chou, J. Glanville, J. M. Grimshaw, A. Hróbjartsson, M. M. Lalu, T. Li, E. W. Loder, E. Mayo-Wilson, S. McDonald, L. A. McGuinness, L. A. Stewart, J. Thomas, A. C. Tricco, V. A. Welch, P. Whiting, and D. Moher, “The prisma 2020 statement: An updated guideline for reporting systematic reviews,” The BMJ, vol. 372, 3 2021.
  11. E. Allseits, K. J. Kim, C. Bennett, R. Gailey, I. Gaunaurd, and V. Agrawal, “A novel method for estimating knee angle using two leg-mounted gyroscopes for continuous monitoring with mobile health devices,” Sensors (Switzerland), vol. 18, no. 9, 2018.
  12. A. Niijima, O. Mizuno, and T. Tanaka, “A study of gait analysis with a smartphone for measurement of hip joint angle,” in 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014, 2014.
  13. P. Muller, M.-A. Begin, T. Schauer, and T. Seel, “Alignment-Free, Self-Calibrating Elbow Angles Measurement Using Inertial Sensors,” IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 2, pp. 312–319, 2017.
  14. E. Grimpampi, V. Bonnet, A. Taviani, and C. Mazzà, “Estimate of lower trunk angles in pathological gaits using gyroscope data.,” Gait & posture, vol. 38, pp. 523–527, jul 2013.
  15. Q. An, Y. Ishikawa, J. Nakagawa, A. Kuroda, H. Oka, H. Yamakawa, A. Yamashita, and H. Asama, “Evaluation of wearable gyroscope and accelerometer sensor (PocketIMU2) during walking and sit-to-stand motions,” in Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, pp. 731–736, 2012.
  16. F. F. Mohammadzadeh, S. Liu, K. A. Bond, and C. S. Nam, “Feasibility of a Wearable, Sensor-based Motion Tracking System,” Procedia Manufacturing, vol. 3, pp. 192–199, 2015.
  17. T. Watanabe and J. Kodama, “Feasibility study of inertial sensor-based joint moment estimation method during human movements: A test of multi-link modeling of the trunk segment,” in BIOSIGNALS 2016 - 9th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016, pp. 248–255, 2016.
  18. M. Sun, L. Kenney, C. Smith, K. Waring, H. Luckie, A. Liu, and D. Howard, “A novel method of using accelerometry for upper limb FES control,” Medical Engineering and Physics, vol. 38, no. 11, pp. 1244–1250, 2016.
  19. F. Bagalá, V. L. Fuschillo, L. Chiari, and A. Cappello, “Calibrated 2D angular kinematics by single-axis accelerometers: From inverted pendulum to N-Link chain,” IEEE Sensors Journal, vol. 12, no. 3, pp. 479–486, 2012.
  20. D. Laidig and T. Seel, “Deriving kinematic quantities from accelerometer readings for assessment of functional upper limb motions,” Current Directions in Biomedical Engineering, vol. 3, no. 2, pp. 573–576, 2017.
  21. M. Gholami, C. Napier, and C. Menon, “Estimating lower extremity running gait kinematics with a single accelerometer: A deep learning approach,” Sensors (Switzerland), vol. 20, no. 10, 2020.
  22. R. Das, N. Kumar, and A. Kumar, “Inertia-based angle measurement unit for gait assistive device,” International Journal of Medical Engineering and Informatics, vol. 6, no. 3, pp. 266–273, 2014.
  23. J. Ma, H. Kharboutly, A. Benali, F. Benamar, and M. Bouzit, “Joint angle estimation with accelerometers for dynamic postural analysis,” Journal of Biomechanics, vol. 48, no. 13, pp. 3616–3624, 2015.
  24. A. T. M. Willemsen, C. Frigo, and H. B. K. Boom, “Lower Extremity Angle Measurement with Accelerometers—Error and Sensitivity,” IEEE Transactions on Biomedical Engineering, vol. 38, no. 12, pp. 1186–1193, 1991.
  25. M. D. Djurić-Jovičić, N. S. Jovičić, D. B. Popović, and A. R. Djordjević, “Nonlinear optimization for drift removal in estimation of gait kinematics based on accelerometers,” Journal of Biomechanics, vol. 45, no. 16, pp. 2849–2854, 2012.
  26. K. Liu, T. Liu, K. Shibata, Y. Inoue, and R. Zheng, “Novel approach to ambulatory assessment of human segmental orientation on a wearable sensor system,” Journal of Biomechanics, vol. 42, no. 16, pp. 2747–2752, 2009.
  27. H. Lim, B. Kim, and S. Park, “Prediction of lower limb kinetics and kinematics during walking by a single IMU on the lower back using machine learning,” Sensors (Switzerland), vol. 20, no. 1, 2020.
  28. J. D. Zehr, L. M. Tennant, J. M. Buchman-Pearle, and J. P. Callaghan, “Reconstructing an accelerometer-based pelvis segment for three-dimensional kinematic analyses during laboratory simulated tasks with obstructed line-of-sight,” Journal of Biomechanics, vol. 123, 2021.
  29. Z. Zheng, H. Ma, W. Yan, H. Liu, and Z. Yang, “Training Data Selection and Optimal Sensor Placement for Deep-Learning-Based Sparse Inertial Sensor Human Posture Reconstruction.,” Entropy (Basel, Switzerland), vol. 23, may 2021.
  30. G. X. Lee, K. S. Low, and T. Taher, “Unrestrained measurement of arm motion based on a wearable wireless sensor network,” IEEE Transactions on Instrumentation and Measurement, vol. 59, no. 5, pp. 1309–1317, 2010.
  31. A. Watson, A. Lyubovsky, K. Koltermann, and G. Zhou, “Magneto: Joint angle analysis using an electromagnet-based sensing method,” in Proceedings of the 20th International Conference on Information Processing in Sensor Networks, IPSN 2021 (co-located with CPS-IoT Week 2021), pp. 1–14, 2021.
  32. N. Friedman, J. B. Rowe, D. J. Reinkensmeyer, and M. Bachman, “The manumeter: A wearable device for monitoring daily use of the wrist and fingers,” IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 6, pp. 2168–2194, 2014.
  33. Y. Teruyama and T. Watanabe, “A basic study on variable-gain Kalman filter based on angle error calculated from acceleration signals for lower limb angle measurement with inertial sensors,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 3423–3426, 2013.
  34. M. Mundt, W. R. Johnson, W. Potthast, B. Markert, A. Mian, and J. Alderson, “A Comparison of Three Neural Network Approaches for Estimating Joint Angles and Moments from Inertial Measurement Units,” Sensors (Basel, Switzerland), vol. 21, no. 13, 2021.
  35. Y. Ohtaki, K. Sagawa, and H. Inooka, “A method for gait analysis in a daily living environment by body-mounted instruments,” JSME International Journal, Series C: Mechanical Systems, Machine Elements and Manufacturing, vol. 44, no. 4, pp. 1125–1132, 2001.
  36. M. Molnar, M. Kok, T. Engel, H. Kaplick, F. Mayer, and T. Seel, “A Method for Lower Back Motion Assessment Using Wearable 6D Inertial Sensors,” in 2018 21st International Conference on Information Fusion, FUSION 2018, pp. 799–806, 2018.
  37. H. Dejnabadi, B. M. Jolles, and K. Aminian, “A new approach to accurate measurement of uniaxial joint angles based on a combination of accelerometers and gyroscopes,” IEEE Transactions on Biomedical Engineering, vol. 52, no. 8, pp. 1478–1484, 2005.
  38. M. Allen, Q. Zhong, N. Kirsch, A. Dani, W. W. Clark, and N. Sharma, “A nonlinear dynamics-based estimator for functional electrical stimulation: Preliminary results from lower-leg extension experiments,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 12, pp. 2365–2374, 2017.
  39. W. Yin, C. Reddy, Y. Zhou, and X. Zhang, “A Novel Application of Flexible Inertial Sensors for Ambulatory Measurement of Gait Kinematics,” IEEE Transactions on Human-Machine Systems, vol. 51, no. 4, pp. 346–354, 2021.
  40. G. Ligorio and A. M. Sabatini, “A Novel Kalman Filter for Human Motion Tracking With an Inertial-Based Dynamic Inclinometer,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 8, pp. 2033–2043, 2015.
  41. S. Choi, Y. B. Shin, S.-Y. Kim, and J. Kim, “A novel sensor-based assessment of lower limb spasticity in children with cerebral palsy,” Journal of NeuroEngineering and Rehabilitation, vol. 15, no. 1, 2018.
  42. 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, 2011.
  43. S. Q. Liu, J. C. Zhang, and R. Zhu, “A Wearable Human Motion Tracking Device Using Micro Flow Sensor Incorporating a Micro Accelerometer,” IEEE Transactions on Biomedical Engineering, vol. 67, no. 4, pp. 940–948, 2020.
  44. G. Ligorio, E. Bergamini, L. Truppa, M. Guaitolini, M. Raggi, A. Mannini, A. M. Sabatini, G. Vannozzi, and P. Garofalo, “A Wearable Magnetometer-Free Motion Capture System: Innovative Solutions for Real-World Applications,” IEEE Sensors Journal, vol. 20, no. 15, pp. 8844–8857, 2020.
  45. F. Feldhege, A. Mau-Moeller, T. Lindner, A. Hein, A. Markschies, U. K. Zettl, and R. Bader, “Accuracy of a custom physical activity and knee angle measurement sensor system for patients with neuromuscular disorders and gait abnormalities,” Sensors (Switzerland), vol. 15, no. 5, pp. 10734–10752, 2015.
  46. K. Liu, T. Liu, K. Shibata, and Y. Inoue, “Ambulatory measurement and analysis of the lower limb 3D posture using wearable sensor system,” in 2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009, pp. 3065–3069, 2009.
  47. M. S. Karunarathne, S. W. Ekanayake, and P. N. Pathirana, “An adaptive complementary filter for inertial sensor based data fusion to track upper body motion,” in 2014 7th International Conference on Information and Automation for Sustainability: ”Sharpening the Future with Sustainable Technology”, ICIAfS 2014, 2014.
  48. W. Hu, E. Charry, M. Umer, A. Ronchi, and S. Taylor, “An inertial sensor system for measurements of tibia angle with applications to knee valgus/varus detection,” in IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings, 2014.
  49. J. Y. Goulermas, A. H. Findlow, C. J. Nester, P. Liatsis, X.-J. Zeng, L. P. J. Kenney, P. Tresadern, S. B. Thies, and D. Howard, “An instance-based algorithm with auxiliary similarity information for the estimation of gait kinematics from wearable sensors,” IEEE Transactions on Neural Networks, vol. 19, no. 9, pp. 1574–1582, 2008.
  50. C. Mazzà, M. Donati, J. McCamley, P. Picerno, and A. Cappozzo, “An optimized Kalman filter for the estimate of trunk orientation from inertial sensors data during treadmill walking.,” Gait & posture, vol. 35, pp. 138–142, jan 2012.
  51. V. H. Flores-Morales, B. G. Contreras-Bermeo, F. L. Bueno-Palomeque, and L. J. Serpa-Andrade, “Analysis of a mobile system to register the kinematic parameters in ankle, knee, and hip based in inertial sensors,” in icSPORTS 2016 - Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support, pp. 29–33, 2016.
  52. T. Watanabe and K. Ohashi, “Angle measurements during 2D and 3D movements of a rigid body model of lower limb: Comparison between integral-based and quaternion-based methods,” in BIOSIGNALS 2014 - 7th Int. Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014, pp. 35–44, 2014.
  53. M. Mundt, A. Koeppe, F. Bamer, S. David, and B. Markert, “Artificial neural networks in motion analysis—applications of unsupervised and heuristic feature selection techniques,” Sensors (Switzerland), vol. 20, no. 16, pp. 1–15, 2020.
  54. L. K. Tham, N. A. A. Osman, M. A. Kouzbary, and K. Aminian, “Biomechanical Ambulatory Assessment of 3D Knee Angle Using Novel Inertial Sensor-Based Technique,” IEEE Access, vol. 9, pp. 36559–36570, 2021.
  55. V. Joukov, J.-S. Lin, and D. Kulic, “Closed-chain pose estimation from wearable sensors,” in IEEE-RAS International Conference on Humanoid Robots, vol. 2019-Octob, pp. 594–600, 2019.
  56. E. Dorschky, M. Nitschke, C. F. Martindale, A. J. van den Bogert, A. D. Koelewijn, and B. M. Eskofier, “CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data,” Frontiers in Bioengineering and Biotechnology, vol. 8, 2020.
  57. T. Watanabe, Y. Teruyama, and K. Ohashi, “Comparison of angle measurements between integral-based and quaternion-based methods using inertial sensors for gait evaluation,” Communications in Computer and Information Science, vol. 511, pp. 274–288, 2015.
  58. H. Eom, B. Choi, and J. Noh, “Data-Driven Reconstruction of Human Locomotion Using a Single Smartphone,” Computer Graphics Forum, vol. 33, no. 7, pp. 11–19, 2014.
  59. E. Charry, M. Umer, and S. Taylor, “Design and validation of an ambulatory inertial system for 3-D measurements of low back movements,” in Proceedings of the 2011 7th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2011, pp. 58–63, 2011.
  60. R. Williamson and B. J. Andrews, “Detecting absolute human knee angle and angular velocity using accelerometers and rate gyroscopes,” Medical and Biological Engineering and Computing, vol. 39, no. 3, pp. 294–302, 2001.
  61. S. Bakhshi, M. H. Mahoor, and B. S. Davidson, “Development of a body joint angle measurement system using IMU sensors,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 6923–6926, 2011.
  62. Y. Chen, C. Fu, W. S. W. Leung, and L. Shi, “Drift-Free and Self-Aligned IMU-Based Human Gait Tracking System with Augmented Precision and Robustness,” IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4671–4678, 2020.
  63. M. Falbriard, F. Meyer, B. Mariani, G. P. Millet, and K. Aminian, “Drift-Free Foot Orientation Estimation in Running Using Wearable IMU,” Frontiers in Bioengineering and Biotechnology, vol. 8, 2020.
  64. I. Weygers, M. Kok, H. De Vroey, T. Verbeerst, M. Versteyhe, H. Hallez, and K. Claeys, “Drift-Free Inertial Sensor-Based Joint Kinematics for Long-Term Arbitrary Movements,” IEEE Sensors Journal, vol. 20, no. 14, pp. 7969–7979, 2020.
  65. L. Meng, B. Li, C. Childs, A. Buis, F. He, and D. Ming, “Effect of walking variations on complementary filter based inertial data fusion for ankle angle measurement,” in 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2019 - Proceedings, 2019.
  66. V. Joukov, J. Cesic, K. Westermann, I. Markovic, I. Petrovic, and D. Kulic, “Estimation and Observability Analysis of Human Motion on Lie Groups,” IEEE Transactions on Cybernetics, vol. 50, no. 3, pp. 1321–1332, 2020.
  67. E. Dorschky, M. Nitschke, A.-K. Seifer, A. J. van den Bogert, and B. M. Eskofier, “Estimation of gait kinematics and kinetics from inertial sensor data using optimal control of musculoskeletal models,” Journal of Biomechanics, vol. 95, 2019.
  68. M. Mundt, A. Koeppe, S. David, T. Witter, F. Bamer, W. Potthast, and B. Markert, “Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network,” Frontiers in Bioengineering and Biotechnology, vol. 8, 2020.
  69. E. Rapp, S. Shin, W. Thomsen, R. Ferber, and E. Halilaj, “Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework,” Journal of Biomechanics, vol. 116, 2021.
  70. D. Young, S. D’Orey, R. Opperman, C. Hainley, and D. J. Newman, “Estimation of lower limb joint angles during walking using extended kalman filtering,” in IFMBE Proceedings, vol. 31 IFMBE, pp. 1319–1322, 2010.
  71. T. Lee, I. Kim, and S.-H. Lee, “Estimation of the continuous walking angle of knee and ankle (Talocrural joint, subtalar joint) of a lower‐limb exoskeleton robot using a neural network,” Sensors, vol. 21, no. 8, 2021.
  72. C. Jakob, P. Kugler, F. Hebenstreit, S. Reinfelder, U. Jensen, D. Schuldhaus, M. Lochmann, and B. M. Eskofier, “Estimation of the knee flexion-extension angle during dynamic sport motions using body-worn inertial sensors,” in Proceedings of the 8th International Conference on Body Area Networks, BodyNets 2013, pp. 289–295, 2013.
  73. R. Takeda, S. Tadano, A. Natorigawa, M. Todoh, and S. Yoshinari, “Gait posture estimation using wearable acceleration and gyro sensors,” Journal of Biomechanics, vol. 42, no. 15, pp. 2486–2494, 2009.
  74. C. Xu, J. He, X. Zhang, C. Yao, and P.-H. Tseng, “Geometrical kinematic modeling on human motion using method of multi-sensor fusion,” Information Fusion, vol. 41, pp. 243–254, 2018.
  75. D. Cehajic, S. Erhart, and S. Hirche, “Grasp pose estimation in human-robot manipulation tasks using wearable motion sensors,” in IEEE International Conference on Intelligent Robots and Systems, vol. 2015-Decem, pp. 1031–1036, 2015.
  76. K. Kitano, A. Ito, and N. Tsujiuchi, “Hand Motion Measurement using Inertial Sensor System and Accurate Improvement by Extended Kalman Filter,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 6405–6408, 2019.
  77. J. Musić, R. Kamnik, V. Zanchi, and M. Munih, “Human body model based inertial measurement of sit-to-stand motion kinematics,” WSEAS Transactions on Systems, vol. 7, no. 3, pp. 156–164, 2008.
  78. J.-S. Lin and D. Kulic, “Human pose recovery for rehabilitation using ambulatory sensors,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 4799–4802, 2013.
  79. J. F. S. Lin and D. Kulić, “Human pose recovery using wireless inertial measurement units,” Physiological Measurement, vol. 33, no. 12, pp. 2099–2115, 2012.
  80. T. Seel, J. Raisch, and T. Schauer, “IMU-Based joint angle measurement for gait analysis,” Sensors, pp. 6891–6909, 2014.
  81. C. T. M. Baten, P. Oosterhoff, I. Kingma, P. H. Veltink, and H. J. Hermens, “Inertial sensing in ambulatory back load estimation,” in Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, vol. 2, pp. 497–498, 1996.
  82. G. Cooper, I. Sheret, L. McMillian, K. Siliverdis, N. Sha, D. Hodgins, L. Kenney, and D. Howard, “Inertial sensor-based knee flexion/extension angle estimation,” Journal of Biomechanics, vol. 42, no. 16, pp. 2678–2685, 2009.
  83. B. Fasel, J. Sporri, J. Chardonnens, J. Kroll, E. Muller, and K. Aminian, “Joint Inertial Sensor Orientation Drift Reduction for Highly Dynamic Movements,” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 1, pp. 77–86, 2018.
  84. C. L. Bennett, C. Odom, and M. Ben-Asher, “Knee angle estimation based on imu data and artificial neural networks,” in Proceedings - 29th Southern Biomedical Engineering Conference, SBEC 2013, pp. 111–112, 2013.
  85. D. Mayorca-Torres, J. C. Caicedo-Eraso, and D. H. Peluffo-Ordóñez, “Knee joint angle measuring portable embedded system based on inertial measurement units for gait analysis,” International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 2, pp. 430–437, 2020.
  86. V. Hernandez, D. Dadkhah, V. Babakeshizadeh, and D. Kulić, “Lower body kinematics estimation from wearable sensors for walking and running: A deep learning approach.,” Gait & posture, vol. 83, pp. 185–193, jan 2021.
  87. V. Bonnet, V. Joukov, D. Kulić, P. Fraisse, N. Ramdani, and G. Venture, “Monitoring of Hip and Knee Joint Angles Using a Single Inertial Measurement Unit during Lower Limb Rehabilitation,” IEEE Sensors Journal, vol. 16, no. 6, pp. 1557–1564, 2016.
  88. V. Joukov, M. Karg, and D. Kulic, “Online tracking of the lower body joint angles using IMUs for gait rehabilitation,” 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, pp. 2310–2313, 2014.
  89. K. Liu, Y. Inoue, and K. Shibata, “Physical-Sensor and Virtual-Sensor Based Method for Estimation of Lower Limb Gait Posture Using Accelerometers and Gyroscopes,” Journal of Biomechanical Science and Engineering, vol. 5, no. 4, pp. 472–483, 2010.
  90. A. Findlow, J. Y. Goulermas, C. Nester, D. Howard, and L. P. J. Kenney, “Predicting lower limb joint kinematics using wearable motion sensors,” Gait and Posture, vol. 28, no. 1, pp. 120–126, 2008.
  91. M. Mundt, W. Thomsen, T. Witter, A. Koeppe, S. David, F. Bamer, W. Potthast, and B. Markert, “Prediction of lower limb joint angles and moments during gait using artificial neural networks,” Medical and Biological Engineering and Computing, vol. 58, no. 1, pp. 211–225, 2020.
  92. A. Caroselli, F. Bagalà, and A. Cappello, “Quasi-real time estimation of angular kinematics using single-axis accelerometers,” Sensors (Switzerland), vol. 13, no. 1, pp. 918–937, 2013.
  93. V. Bonnet, C. Mazza, P. Fraisse, and A. Cappozzo, “Real-time estimate of body kinematics during a planar squat task using a single inertial measurement unit,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 7, pp. 1920–1926, 2013.
  94. E. Villeneuve, W. Harwin, W. Holderbaum, B. Janko, and R. S. Sherratt, “Reconstruction of angular kinematics from wrist-worn inertial sensor data for smart home healthcare,” IEEE Access, vol. 5, pp. 2351–2363, 2017.
  95. H. Zhou and H. Hu, “Reducing drifts in the inertial measurements of wrist and elbow positions,” IEEE Transactions on Instrumentation and Measurement, vol. 59, no. 3, pp. 575–585, 2010.
  96. V. Joukov, V. Bonnet, M. Karg, G. Venture, and D. Kulić, “Rhythmic EKF for pose estimation during gait,” in IEEE-RAS International Conference on Humanoid Robots, vol. 2015-Decem, pp. 1167–1172, 2015.
  97. D. Alvarado, L. Corona, S. Muñoz, and J. Aquino, “Sensorial system for obtaining the angles of the human movement in the coronal and sagittal anatomical planes,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10061 LNAI, pp. 535–547, 2017.
  98. A. Alizadegan and S. Behzadipour, “Shoulder and elbow joint angle estimation for upper limb rehabilitation tasks using low-cost inertial and optical sensors,” Journal of Mechanics in Medicine and Biology, vol. 17, no. 2, 2017.
  99. M. El-Gohary and J. McNames, “Shoulder and elbow joint angle tracking with inertial sensors,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 9, pp. 2635–2641, 2012.
  100. F.-Y. Liang, F. Gao, and W.-H. Liao, “Synergy-based knee angle estimation using kinematics of thigh,” Gait and Posture, vol. 89, pp. 25–30, 2021.
  101. B. S. M. Sharma, S. Vidhya, and N. Kumar, “System for measurement of joint range of motion using inertial sensors,” Biomedical Research (India), vol. 28, no. 8, pp. 3699–3704, 2017.
  102. Z. Ding, C. Yang, J. Ma, J. G. Wei, and F. Jiang, “The online estimation of the joint angle based on the gravity acceleration using the accelerometer and gyroscope in the wireless networks,” Multimedia Tools and Applications, vol. 79, no. 23-24, pp. 16265–16279, 2020.
  103. M. Sharifi Renani, A. M. Eustace, C. A. Myers, and C. W. Clary, “The use of synthetic IMU signals in the training of deep learning models significantly improves the accuracy of joint kinematic predictions,” Sensors, vol. 21, no. 17, 2021.
  104. S. Tadano, R. Takeda, and H. Miyagawa, “Three dimensional gait analysis using wearable acceleration and gyro sensors based on quaternion calculations,” Sensors (Switzerland), vol. 13, no. 7, pp. 9321–9343, 2013.
  105. S. Kumar, K. Gopinath, L. Rocchi, P. T. Sukumar, S. Kulkarni, and J. Sampath, “Towards a portable human gait analysis & monitoring system,” in 2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings, pp. 174–180, 2018.
  106. M. El-Gohary, L. Holmstrom, J. Huisinga, E. King, J. McNames, and F. Horak, “Upper limb joint angle tracking with inertial sensors,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 5629–5632, 2011.
  107. H. Zhou, T. Stone, H. Hu, and N. Harris, “Use of multiple wearable inertial sensors in upper limb motion tracking,” Medical Engineering and Physics, vol. 30, no. 1, pp. 123–133, 2008.
  108. S. Salehi and D. Stricker, “Validation of a low-cost inertial exercise tracker,” in SENSORNETS 2020 - Proceedings of the 9th International Conference on Sensor Networks, pp. 97–104, 2020.
  109. J. Figueiredo, S. P. Carvalho, J. P. Vilas-Boas, L. M. Gonçalves, J. C. Moreno, and C. P. Santos, “Wearable inertial sensor system towards daily human kinematic gait analysis: Benchmarking analysis to MVN BIOMECH,” Sensors (Switzerland), vol. 20, no. 8, 2020.
  110. R. Pellois and O. Brüls, “An inertial human upper limb motion tracking method for robot programming by demonstration,” Robotics and Autonomous Systems, vol. 156, 2022. Export Date: 31 August 2022.
  111. M. S. B. Hossain, J. Dranetz, H. Choi, and Z. Guo, “Deepbbwae-net: A cnn-rnn based deep superlearner for estimating lower extremity sagittal plane joint kinematics using shoe-mounted imu sensors in daily living,” IEEE Journal of Biomedical and Health Informatics, 2022. Export Date: 31 August 2022.
  112. M. A. Zandbergen, J. Reenalda, R. P. van Middelaar, R. I. Ferla, J. H. Buurke, and P. H. Veltink, “Drift-free 3d orientation and displacement estimation for quasi-cyclical movements using one inertial measurement unit: Application to running,” Sensors, vol. 22, 2022. Cited By :1¡br/¿¡br/¿Export Date: 31 August 2022.
  113. S. Wang, Y. Cai, K. Hase, K. Uchida, D. Kondo, T. Saitou, and S. Ota, “Estimation of knee joint angle during gait cycle using inertial measurement unit sensors: a method of sensor-to-clinical bone calibration on the lower limb skeletal model,” Journal of Biomechanical Science and Engineering, vol. 17, pp. 1–15, 2022. Export Date: 31 August 2022.
  114. H. Yang, Y. Wang, H. Wang, Y. Shi, L. Zhu, Y. Kuang, and Y. Yang, “Multi-inertial sensor-based arm 3d motion tracking using elman neural network,” Journal of Sensors, vol. 2022, 2022. Export Date: 31 August 2022.
  115. J.-S. Tan, S. Tippaya, T. Binnie, P. Davey, K. Napier, J. P. Caneiro, P. Kent, A. Smith, P. O’sullivan, and A. Campbell, “Predicting knee joint kinematics from wearable sensor data in people with knee osteoarthritis and clinical considerations for future machine learning models,” Sensors, vol. 22, 2022. Cited By :1¡br/¿¡br/¿Export Date: 31 August 2022.
  116. J. K. Lee and E. J. Park, “A fast quaternion-based orientation optimizer via virtual rotation for human motion tracking,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 5, pp. 1574–1582, 2009.
  117. M. Nazarahari and H. Rouhani, “A Full-State Robust Extended Kalman Filter for Orientation Tracking during Long-Duration Dynamic Tasks Using Magnetic and Inertial Measurement Units,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 1280–1289, 2021.
  118. C. W. Kang, H. J. Kim, and C. G. Park, “A Human Motion Tracking Algorithm Using Adaptive EKF Based on Markov Chain,” IEEE Sensors Journal, vol. 16, no. 24, pp. 8953–8962, 2016.
  119. R. Abbasi-Kesbi and A. Nikfarjam, “A Miniature Sensor System for Precise Hand Position Monitoring,” IEEE Sensors Journal, vol. 18, no. 6, pp. 2577–2584, 2018.
  120. Y. Duan, X. Zhang, and Z. Li, “A new quaternion-based kalman filter for human body motion tracking using the second estimator of the optimal quaternion algorithm and the joint angle constraint method with inertial and magnetic sensors,” Sensors (Switzerland), vol. 20, no. 21, pp. 1–19, 2020.
  121. L. Peppoloni, A. Filippeschi, E. Ruffaldi, and C. A. Avizzano, “A novel 7 degrees of freedom model for upper limb kinematic reconstruction based on wearable sensors,” in SISY 2013 - IEEE 11th International Symposium on Intelligent Systems and Informatics, Proceedings, pp. 105–110, 2013.
  122. E. Ruffaldi, L. Peppoloni, A. Filippeschi, and C. A. Avizzano, “A novel approach to motion tracking with wearable sensors based on Probabilistic Graphical Models,” in Proceedings - IEEE International Conference on Robotics and Automation, no. May, pp. 1247–1252, IEEE, 2014.
  123. J. Cockcroft, J. H. Muller, and C. Scheffer, “A novel complimentary filter for tracking hip angles during cycling using wireless inertial sensors and dynamic acceleration estimation,” IEEE Sensors Journal, vol. 14, no. 8, pp. 2864–2871, 2014.
  124. B. Fang, F. Sun, H. Liu, and D. Guo, “A novel data glove for fingers motion capture using inertial and magnetic measurement units,” in 2016 IEEE International Conference on Robotics and Biomimetics, ROBIO 2016, pp. 2099–2104, 2016.
  125. Z.-Q. Zhang and J.-K. Wu, “A novel hierarchical information fusion method for three-dimensional upper limb motion estimation,” IEEE Transactions on Instrumentation and Measurement, vol. 60, no. 11, pp. 3709–3719, 2011.
  126. R. Abbasi-Kesbi, A. Nikfarjam, and H. Memarzadeh-Tehran, “A patient-centric sensory system for in-home rehabilitation,” IEEE Sensors Journal, vol. 17, no. 2, pp. 524–533, 2017.
  127. D. Meng, T. Shoepe, and G. Vejarano, “Accuracy Improvement on the Measurement of Human-Joint Angles,” IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 2, pp. 498–507, 2016.
  128. M. Nazarahari and H. Rouhani, “Adaptive Gain Regulation of Sensor Fusion Algorithms for Orientation Estimation with Magnetic and Inertial Measurement Units,” IEEE Transactions on Instrumentation and Measurement, vol. 70, 2021.
  129. L. Kun, Y. Inoue, K. Shibata, and C. Enguo, “Ambulatory estimation of knee-joint kinematics in anatomical coordinate system using accelerometers and magnetometers,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 2, pp. 435–442, 2011.
  130. T. McGrath, R. Fineman, and L. Stirling, “An auto-calibrating knee flexion-extension axis estimator using principal component analysis with inertial sensors,” Sensors (Switzerland), vol. 18, no. 6, 2018.
  131. E. Mazomenos, A. Cranny, D. Biswas, N. Harris, and K. Maharatna, “An investigation into the accuracy of calculating upper body joint angles using MARG sensors,” in Procedia Engineering, vol. 87, pp. 1330–1333, 2014.
  132. K. Kawano, S. Kobashi, M. Yagi, K. Kondo, S. Yoshiya, and Y. Hata, “Analyzing 3D knee kinematics using accelerometers, gyroscopes and magnetometers,” in 2007 IEEE International Conference on System of Systems Engineering, SOSE, 2007.
  133. F. Fei, S. Xian, X. Xie, C. Wu, D. Yang, K. Yin, and G. Zhang, “Development of a wearable glove system with multiple sensors for hand kinematics assessment,” Micromachines, vol. 12, no. 4, 2021.
  134. J. Conte Alcaraz, S. Moghaddamnia, and J. Peissig, “Efficiency of deep neural networks for joint angle modeling in digital gait assessment,” Eurasip Journal on Advances in Signal Processing, vol. 2021, no. 1, 2021.
  135. L. W. Sy, N. H. Lovell, and S. J. Redmond, “Estimating Lower Body Kinematics Using a Lie Group Constrained Extended Kalman Filter and Reduced IMU Count,” IEEE Sensors Journal, vol. 21, no. 18, pp. 20969–20979, 2021.
  136. L. Sy, M. Raitor, M. D. Rosario, H. Khamis, L. Kark, N. H. Lovell, and S. J. Redmond, “Estimating Lower Limb Kinematics Using a Reduced Wearable Sensor Count,” IEEE Transactions on Biomedical Engineering, vol. 68, no. 4, pp. 1293–1304, 2021.
  137. A. Atrsaei, H. Salarieh, A. Alasty, and M. Abediny, “Human Arm Motion Tracking by Inertial/Magnetic Sensors Using Unscented Kalman Filter and Relative Motion Constraint,” Journal of Intelligent and Robotic Systems: Theory and Applications, vol. 90, no. 1-2, pp. 161–170, 2018.
  138. S. Slajpah, R. Kamnik, and M. Munih, “Human Motion Kinematics Assessment Using Wearable Sensors,” in ADVANCES IN ROBOT KINEMATICS (Lenarcic, J and Khatib, O, ed.), pp. 171–180, Springer, 2014.
  139. Z.-B. Wang, L. Yang, Z.-P. Huang, J.-K. Wu, Z.-Q. Zhang, and L.-X. Sun, “Human motion tracking based on complementary Kalman filter,” in 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2017, pp. 55–58, 2017.
  140. H. T. Butt, M. Pancholi, M. Musahl, P. Murthy, M. A. Sanchez, and D. Stricker, “Inertial Motion Capture Using Adaptive Sensor Fusion and Joint Angle Drift Correction,” in FUSION 2019 - 22nd International Conference on Information Fusion, 2019.
  141. C. Schiefer, R. P. Ellegast, I. Hermanns, T. Kraus, E. Ochsmann, C. Larue, and A. Plamondon, “Optimization of Inertial Sensor-Based Motion Capturing for Magnetically Distorted Field Applications,” Journal of Biomechanical Engineering, vol. 136, no. 12, 2014.
  142. A. Saito, S. Kizawa, Y. Kobayashi, and K. Miyawaki, “Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output,” ROBOMECH Journal, vol. 7, no. 1, 2020.
  143. H. Yang, S. Liu, T. Luo, H. Liang, J. Zou, and L. Zhao, “Research on Human Motion Monitoring Method Based on Multi-Joint Constraint Filter Model,” IEEE Sensors Journal, vol. 21, no. 9, pp. 10989–10999, 2021.
  144. CRC Press, 2017.
  145. P. N. Pathirana, M. S. Karunarathne, G. L. Williams, P. T. Nam, and H. Durrant-Whyte, “Robust and Accurate Capture of Human Joint Pose Using an Inertial Sensor,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 6, 2018.
  146. N. Sim, C. Gavriel, W. W. Abbott, and A. A. Faisal, “The head mouse - Head gaze estimation ’In-the-Wild’ with low-cost inertial sensors for BMI use,” in International IEEE/EMBS Conference on Neural Engineering, NER, pp. 735–738, 2013.
  147. F. J. Wouda, M. Giuberti, N. Rudigkeit, B.-J. van Beijnum, M. Poel, and P. H. Veltink, “Time coherent full-body poses estimated using only five inertial sensors: Deep versus shallow learning,” Sensors (Switzerland), vol. 19, no. 17, 2019.
  148. D. Nagaraj, R. Dobinson, and D. Werth, “Towards kinematically constrained real time human pose estimation using sparse IMUs,” in CEUR Workshop Proceedings, vol. 2846, 2021.
  149. Z.-Q. Zhang, W.-C. Wong Sr., and J.-K. Wu, “Ubiquitous human upper-limb motion estimation using wearable sensors,” IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 4, pp. 513–521, 2011.
  150. D. Álvarez, J. C. Alvarez, R. C. González, and A. M. López, “Upper limb joint angle measurement in occupational health,” Computer Methods in Biomechanics and Biomedical Engineering, vol. 19, no. 2, pp. 159–170, 2016.
  151. K. Liu, T. Liu, K. Shibata, and Y. Inoue, “Visual estimation of lower limb motion using physical and virtual sensors,” in 2010 IEEE International Conference on Information and Automation, ICIA 2010, pp. 179–184, 2010.
  152. J. Li, X. Liu, Z. Wang, H. Zhao, T. Zhang, S. Qiu, X. Zhou, H. Cai, R. Ni, and A. Cangelosi, “Real-time human motion capture based on wearable inertial sensor networks,” IEEE Internet of Things Journal, vol. 9, pp. 8953–8966, 2022. Cited By :1¡br/¿¡br/¿Export Date: 31 August 2022.
  153. M. Zabat, N. Ouadahi, A. Youyou, A. Ababou, and N. Ababou, “Digital inclinometer for joint angles measurements with a real-time 3D-animation,” in 12th International Multi-Conference on Systems, Signals and Devices, SSD 2015, 2015.
  154. K. Liu, Y. Inoue, and K. Shibata, “Visual and quantitative analysis of lower limb 3D gait posture using accelerometers and magnetometers,” in 2010 IEEE International Conference on Mechatronics and Automation, ICMA 2010, pp. 1420–1425, 2010.
  155. H. T. Butt, B. Taetz, M. Musahl, M. A. Sanchez, P. Murthy, and D. Stricker, “Magnetometer robust deep human pose regression with uncertainty prediction using sparse body worn magnetic inertial measurement units,” IEEE Access, vol. 9, pp. 36657–36673, 2021.
  156. John Wiley & Sons, 2006.
  157. R. G. Brown, “Integrated Navigation Systems and Kalman Filtering: A Perspective,” Navigation, vol. 19, no. 4, pp. 355–362, 1972.
  158. C. E. Rasmussen, “Gaussian processes in machine learning,” in Summer school on machine learning, pp. 63–71, Springer, 2003.
  159. L. Rokach and O. Maimon, “Decision trees,” in Data mining and knowledge discovery handbook, pp. 165–192, Springer, 2005.
  160. MIT press, 2002.
  161. B. Schölkopf, A. J. Smola, R. C. Williamson, and P. L. Bartlett, “New support vector algorithms,” Neural Computation, vol. 12, no. 5, pp. 1207–1245, 2000.
  162. A. K. Jain, J. Mao, and K. M. Mohiuddin, “Artificial neural networks: A tutorial,” Computer, vol. 29, no. 3, pp. 31–44, 1996.
  163. M. F. Alghifari, T. S. Gunawan, and M. Kartiwi, “Speech emotion recognition using deep feedforward neural network,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 10, no. 2, pp. 554–561, 2018.
  164. Pearson Educación, 1997.
  165. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
  166. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016.
  167. B. A. Pearlmutter, “Learning state space trajectories in recurrent neural networks,” Neural Computation, vol. 1, no. 2, pp. 263–269, 1989.
  168. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
  169. K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014.
  170. T. Lin, B. G. Horne, P. Tino, and C. L. Giles, “Learning long-term dependencies in narx recurrent neural networks,” IEEE Transactions on Neural Networks, vol. 7, no. 6, pp. 1329–1338, 1996.
  171. OpenSim, “Opensim.” https://opensim.stanford.edu/ Last visit: 2020-05-23, 2020.
  172. J. Uicker, J. Denavit, and R. Hartenberg, “An iterative method for the displacement analysis of spatial mechanisms,” Journal of Applied Mechanics, vol. 31, no. 2, pp. 309–314, 1964.
  173. J. B. Lee, B. Burkett, R. B. Mellifont, and D. A. James, “The use of micro-electro-mechanical-systems technology to assess gait characteristics,” in Impact of Technology on Sport II, pp. 181–186, 2008.
  174. L. W. F. Sy, N. H. Lovell, and S. J. Redmond, “Estimating lower limb kinematics using a lie group constrained extended Kalman filter with a reduced wearable IMU count and distance measurements,” Sensors (Switzerland), vol. 20, no. 23, pp. 1–28, 2020.
  175. Vicon, “Vicon Motion Capture.” https://www.vicon.com/ Last visit: 2020-01-28, 2020.
  176. OptiTrack, “Optitrack - motive - optical motion capture software.” https://optitrack.com/software/motive/ Last visit: 2020-06-05, 2020.
  177. “Xsens - movella.” https://www.movella.com/., 2023. Accessed on March 23, 2023.
  178. M. Crabolu, D. Pani, L. Raffo, M. Conti, P. Crivelli, and A. Cereatti, “In vivo estimation of the shoulder joint center of rotation using magneto-inertial sensors: MRI-based accuracy and repeatability assessment,” BioMedical Engineering Online, vol. 16, no. 1, pp. 1–18, 2017.
  179. S. G. de Villa, A. J. Martín, J. J. G. Domínguez, A. J. Martin, and J. J. G. Dominguez, “Implementation of a lower-limb model for monitoring exercises in rehabilitation,” in Medical Measurements and Applications, MeMeA 2019 - Symposium Proceedings, pp. 1–6, 2019.
  180. E. Frick and S. Rahmatalla, “Joint center estimation using single-frame optimization: Part 1: Numerical simulation,” Sensors (Switzerland), vol. 18, no. 4, pp. 1–17, 2018.
  181. E. Frick and S. Rahmatalla, “Joint center estimation using single-frame optimization: Part 2: Experimentation,” Sensors (Switzerland), vol. 18, no. 8, pp. 1–22, 2018.
  182. S. García-de Villa, A. Jiménez-Martín, and J. J. García-Domínguez, “Novel imu-based adaptive estimator of the center of rotation of joints for movement analysis,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–11, 2021.
  183. S. García-de-Villa, A. Jiménez-Martín, and J. J. García-Domínguez, “Adaptive IMU-based Calibration of the Center of Joints for Movement Analysis: One Case Study,” in IEEE International Symposium on Medical Measurements and Applications, (Bari - online), pp. 1–10, 2020.
  184. C. J. Lee and J. K. Lee, “Wearable immu-based relative position estimation between body segments via time-varying segment-to-joint vectors,” Sensors, vol. 22, no. 6, p. 2149, 2022.
  185. S. García-de Villa, A. Jiménez-Martín, and J. J. García-Domínguez, “A database of physical therapy exercises with variability of execution collected by wearable sensors,” Scientific Data, vol. 9, no. 1, pp. 1–13, 2022.
  186. G. Santos, M. Wanderley, T. Tavares, and A. Rocha, “A multi-sensor human gait dataset captured through an optical system and inertial measurement units,” 2021.
  187. T. Liu, Y. Inoue, and K. Shibata, “Development of a wearable sensor system for quantitative gait analysis,” Measurement: Journal of the International Measurement Confederation, vol. 42, no. 7, pp. 978–988, 2009.
Citations (16)
View on Semantic Scholar

Summary

No one has generated a summary of this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Summarize

Paper to Video (Beta)

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up