Abstract
Human limb motion monitoring has been a challenging task for robotic applications in unstructured environments, because traditional sensing methods are limited in conforming to compliant human bodies and adapting to unpredictable external disturbances. Motivated by the need to capture rotation angles of lower-limb joints in real time, this paper proposes a soft wearable sensing system based on a network of soft capacitive sensors that can be stretched with joint bending. The capacitance of the sensing module changes with the sensor deformations thus its deviated value from the initial installation state is closely related to the joint rotation angle. The sensor fabrication is developed with shape deposition molding, and the sensing electronics are designed to improve signal transmission and sensing robustness. The sensing system is calibrated with machine vision and its performance is evaluated in walking tests with different speeds. An illustrative example is presented to verify the proposed method capable to monitor human lower-limb motions in practice.
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References
Zhou, H., Hu, H.: Human motion tracking for rehabilitation—a survey. Biomed. Signal Process. Control 3(1), 1–18 (2008)
Asaeda, M., Kuwahara, W., Fujita, N., Yamasaki, T., Adachi, N.: Validity of motion analysis using the Kinect system to evaluate single leg stance in patients with hip disorders. Gait Posture 62, 458–462 (2018)
Clark, R.A., et al.: Reliability and concurrent validity of the Microsoft Xbox One Kinect for assessment of standing balance and postural control. Gait Posture 42(2), 210–213 (2015)
Jahanandish, M.H., Fey, N.P., Hoyt, K.: Lower-limb motion estimation using ultrasound imaging: a framework for assistive device control. IEEE J. Biomed. Health Inf. 1 (2019). https://doi.org/10.1109/JBHI.2019.2891997
Filippeschi, A., Schmitz, N., Miezal, M., Bleser, G., Ruffaldi, E., Stricker, D.: Survey of motion tracking methods based on inertial sensors: a focus on upper limb human motion. Sensors 17(6), 1257 (2017)
Bartlett, H.L., Goldfarb, M.: A phase variable approach for IMU-Based locomotion activity recognition. IEEE Trans. Biomed. Eng. 65(6), 1330–1338 (2018)
Yun, X., Bachmann, E.R.: Design, implementation, and experimental results of a quaternion-based Kalman filter for human body motion tracking. IEEE Trans. Robot. 22(6), 1216–1227 (2006)
Tao, Y., Hu, H., Zhou, H.: Integration of vision and inertial sensors for 3D arm motion tracking in home-based rehabilitation. Int. J. Robot. Res. 26(6), 607–624 (2007)
De Rossi, D., Veltink, P.H.: Wearable technology for biomechanics: e-textile or micromechanical sensors? IEEE Eng. Med. Biol. Mag. 29(3), 37–43 (2010)
Mengüç, Y., et al.: Wearable soft sensing suit for human gait measurement. Int. J. Robot. Res. 33(14), 1748–1764 (2014)
Votzke, C., Daalkhaijav, U., Mengüç, Y., Johnston, M.L.: Highly-stretchable biomechanical strain sensor using printed liquid metal paste. In: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), 17–19 October 2018, pp. 1–4 (2018)
Kim, S., Jeong, D., Oh, J., Park, W., Bae, J.: A novel All-in-One manufacturing process for a soft sensor system and its application to a soft sensing glove. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1–5 October 2018, pp. 7004–7009 (2018)
Xiloyannis, M., Cappello, L., Binh, K.D., Antuvan, C.W., Masia, L.: Preliminary design and control of a soft exosuit for assisting elbow movements and hand grasping in activities of daily living. 4 (2017). https://doi.org/10.1177/2055668316680315
Asbeck, A.T., Schmidt, K., Walsh, C.J.: Soft exosuit for hip assistance. Robot. Auton. Syst. 73, 102–110 (2015)
Muth, J.T., et al.: Embedded 3D Printing of strain sensors within highly stretchable elastomers. Adv. Mater. 26(36), 6307–6312 (2014)
Rosset, S., Shea, H.R.: Flexible and stretchable electrodes for dielectric elastomer actuators. Appl. Phys. A 110(2), 281–307 (2013)
Cheung, Y.-N., Zhu, Y., Cheng, C.-H., Chao, C., Leung, W.W.-F.: A novel fluidic strain sensor for large strain measurement. Sens. Actuators A 147(2), 401–408 (2008)
Chossat, J., Park, Y., Wood, R.J., Duchaine, V.: A soft strain sensor based on ionic and metal liquids. IEEE Sens. J. 13(9), 3405–3414 (2013)
Larson, C., et al.: Highly stretchable electroluminescent skin for optical signaling and tactile sensing. Science 351(6277), 1071–1074 (2016)
Sun, J.-Y., Keplinger, C., Whitesides, G.M., Suo, Z.: Ionic skin. Adv. Mater. 26(45), 7608–7614 (2014)
Acknowledgements
This research was supported by the National Natural Science Foundation of China (Grant 51875221, 51505164, U1713204) and the International Science & Technology Cooperation Program of China (Grant 2016YFE0113600).
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Ma, X., Guo, J., Lee, KM., Yang, L., Chen, M. (2019). A Soft Capacitive Wearable Sensing System for Lower-Limb Motion Monitoring. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11743. Springer, Cham. https://doi.org/10.1007/978-3-030-27538-9_40
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DOI: https://doi.org/10.1007/978-3-030-27538-9_40
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