Multi-channel Fusion Based Adaptive Gait Trajectory Generation Using Wearable Sensors
Authors: 
Oishee Mazumder, Ananda Sankar Kundu, Prasanna Kumar Lenka, Subhasis BhaumikAuthors Info & Claims
Oishee Mazumder, Ananda Sankar Kundu, Prasanna Kumar Lenka, Subhasis BhaumikAuthors Info & ClaimsPages 335 - 351
Abstract
This paper presents a method to regenerate lower limb joint angle trajectories during gait cycle by judging human intention using wearable sensor system. Myoelectric signals from user are used to detect the intention of gait initiation and gait phases. Multi-channel redundant fusion technique is implemented to obtain a robust stride time and gait phase calculation algorithm. Joint trajectories corresponding to particular gait events and phases are regenerated using a Radial basis neural network. The network is trained with joint angle data measured by Inertial Measurement Unit (IMU) from users with varying anthropomorphic features. Generated trajectory is adaptive to anthropomorphic as well as gait velocity variation. Contribution of this paper is in development of a wearable sensor system, multi-channel redundant fusion to calculate stride time and an adaptive gait trajectory generation algorithm. The proposed method of trajectory generation is used to regenerate lower limb joint motion in sagittal plane for wearable robotic devices like prosthesis and active lower limb exoskeleton.
References
[1]
Tingfang, Y., Marco, C., Calogero, O., Nicola, V.: Review of assistive strategies in powered lower-limb orthoses and exoskeletons. Robot. Auton. Syst. 64, 120---136 (2015)
[2]
Slavka, V., Patrik, K., Marcel, J.: Wearable lower limb robotics: a review. Biocybernatics Biomed. Eng. 3, 96---105 (2013)
[3]
Atushi, T., Ryota, K., Yasuhisa, H., Yoshiyuki, S.: Sit-to-stand and stand-to-sit transfer support for complete paraplegic patients with robot suit HAL. Adv. Robot. 24, 1615---1638 (2010)
[4]
Zoss, A., Kazerooni, H., Chu, A.: Biomechanical design of the Berkeley Lower Extremity Exoskeleton (BLEEX). IEEE/ASME Trans. Mechatron. 11(2), 128---138 (2006)
[5]
Tommaso, L., Maria, C., Sunil, A.: Powered hip exoskeletons can reduce the users hip and ankle muscle activations during walking. IEEE Trans. Neural Syst. Rehabil. Eng. 21(6), 938---948 (2013)
[6]
Jan, V., Rik, K., Edsko, H., Ralf, E., Edwin, V., Herman, V.: Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 15(3), 379---386 (2007)
[7]
Vallery, H., Van, A., Buss, M., Herman, V.: Reference trajectory generation for rehabilitation robots: Complementary limb motion estimation. IEEE Trans. Neural Syst. Rehabil. Eng. 17(1), 23---30 (2009)
[8]
Suzuki, K., Mito, G., Kawamoto, H., Hasegawa, Y., Sankai, Y.: Intention based walking support for paraplegia patients with robot suit HAL. Adv. Robot. 21(12), 1441---1469 (2007)
[9]
Subhash, M.: Wearable Sensors for Human Activity Monitoring: A Review. IEEE Sensors J. 15(3), 13---21 (2015)
[10]
Edwards, J.: Wireless sensors relay medical insight to patients and caregivers {special reports}. IEEE Signal Process. Mag. 29(3), 8---12 (2012)
[11]
Malhi, K., Subhash, M., Schnepper, J., Haefke, M., Ewald, H.: A Zigbee-based wearable physiological parameters monitoring system. IEEE Sensors J. 12(3), 423---430 (2012)
[12]
Weijun, T., Tao, L., Rencheng, Z., Hutian, F.: Gait analysis using wearable sensors. Sensors J. 12, 2255---2283 (2012).
[13]
Shyamal, P., Hyung, P., Paolo, B., Leighton, C., Mary, R.: A review of wearable sensors and systems with application in rehabilitation. J. Neuro Eng. Rehabil. 9(21) (2012). http://www.jneuroengrehab.com/content/9/1/21
[14]
Silva, J., Heim, W., Chau, T.: A self-contained, mechanomyography-driven externally powered prosthesis. Arch. Phys. Med. Rehabil. 86(10), 2066---2070 (2005)
[15]
Fukuda, O., Tsuji, T., Ohtsuka, A., Kaneko, M.: A EMG-based human-robot interface for rehabilitation aid. IEEE Int. Conf. Robot. Autom. 4, 3492---3497 (1998)
[16]
Benedetti, M., Bonato, P., Catani, F., Knaflitz, M., Marcacci, M., Simoncini, L.: Myoelectric activation pattern during gait in total knee replacement: relationship with kinematics, kinetics and clinical outcome. IEEE Trans. Rehabil. Eng. 7, 140---149 (1999)
[17]
Wege, A., Zimmermann, A.: Electromyography sensor based control for a hand exoskeleton. IEEE International Conference on Robotics and Biomimetics. pp 1470 ¿1475 (2007)
[18]
Mulas, M., Folgheraiter, M., Gini, G.: An EMG-controlled exoskeleton for hand rehabilitation. International Conference on Rehabilitation Robotics. pp 371 ¿374 (2005)
[19]
Abdul Hadi, R., Aladin, Z., Rezaul, B., Yufridin, W.: Foot plantar pressure measurement system: a review. Sensors 12, 9884---9912 (2012).
[20]
Domen, N., Peter, R., Stefano, M., Maria De, R., Marco, D., Janez, P., Tadej, B., Tommaso, L., Nicola, V., Maria, C., Marko, M.: Automated detection of gait initiation and termination using wearable sensors. Med. Eng. Phys. 35, 1713---1720 (2013)
[21]
Laiyin, Q., Hao, M., Wei-Hsin, L.: Insole plantar pressure systems in the gait analysis of post-stroke rehabilitation proceeding of the 2015 IEEE International Conference on Information and Automation. Lijiang, China (2015)
[22]
Morris, S., Paradiso, J.: Shoe-integrated sensor system for wireless gait analysis and real-time feedback gait analysis and real-time feedback, Proceedings of 2nd Joint EMBS/BMES Conference, 2468---2469 (2002)
[23]
Mueller, M., Strube, M.: Generalizability of in-shoe peak pressure measures using the F-scan system. Clin. Biomech. 11(3), 159---164 (1996)
[24]
Ramanathan, A., Kiran, P., Arnold, G., Wang, W., Abboud, R.: Repeatability of the Pedar-X in-shoe pressure measuring system. Foot Ankle Surg. 16(2), 70---73 (2010)
[25]
Thomas, S., Jrg, R., Thomas, S.: IMU-Based joint angle measurement for gait analysis. Sensors 14, 6891---6909 (2014).
[26]
Carlos, C., Ariel, B., Luis, R., Melisa, F., Alfonso, S., Anselmo, F.: Development of a wearable ZigBee sensor system for upper limb rehabilitation robotics. The Fourth IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics Roma Italy (2012)
[27]
Vincent, B., Claudia, M., Philippe, F., Aurelio, C.: Real-time estimate of body kinematics during a planar squat task using a single inertial measurement unit. IEEE Trans. Biomed. Eng. 60(7), 1920---1926 (2013)
[28]
Ju-Won, L., Gun-Ki, L.: Gait angle prediction for lower limb orthotics and prostheses using an EMG signal and neural networks. Int. J. Control. Autom. Syst. 3(2), 152---158 (2005)
[29]
Wang, L., Buchanan, T.: Prediction of joint moments using a neural network mode of activations from EMG signals. IEEE Trans. Rehabilitation Eng. 10(1), 30---37 (2002)
[30]
Genci, C., Yasuo, N., Leonard, B., Kazuhitsa, M.: Real time gait generation for autonomous humanoid robots: A case study for walking. Robotics and Autonomous Systems 42: 107 ¿116 (2013)
[31]
Muhammad, A., Rushdi, A.: Partially-redundant systems: Examples, reliability, and life expectancy. IMACST. 1(1) (2010)
[32]
Biswas, K., Mazumder, O., Kundu, A.: Multichannel fused EMG based biofeedback system with virtual reality for gait rehabilitation. IEEE Proceedings of 4th International Conference on Intelligent Human Computer Interaction, India (2012)
[33]
Kundu, A., Mazumder, O., Chattaraj, R., Bhaumik, S., Lenka, P.: Trajectory generation for myoelectrically controlled lower limb active knee exoskeleton. Seventh International Conference on Contemporary Computing, India (2014)
[34]
Vaughan, C., Davis, B., Connar, J.: Dynamics of human gait, 2nd edition, Kiboho publishers, Capetown, South Africa
[35]
Inaba Rubber Co Ltd. www.inaba-rubber.co.jp/en
[36]
www.sparkfun.com/products/10736
[37]
MATLAB. www.mathwork.com
Recommendations
Muscle fatigue detection through wearable sensors: a comparative study using the myo armband
Interacción '17: Proceedings of the XVIII International Conference on Human Computer InteractionNovel wearable systems allow the measure of very complex physiological phenomena extending their capabilities and maintaining their non-invasiveness. A good example of this is the use of superficial electrodes for recording electromyography signals (...
An ANN Based Approach for Gait Prediction of a Lower-Limb Exoskeleton with Plantar Pressure Sensors
ICIRA 2013: Proceedings of the 6th International Conference on Intelligent Robotics and Applications - Volume 8102This paper proposes an approach based on Artificial Neural Network ANN method for gait prediction of a lower-limb exoskeleton equipped with plantar pressure sensors and a pair of crutches. This approach can be implemented to predict the exact moment to ...
Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease
Parkinsons disease (PD) is a chronic and progressive movement disorder affecting patients in large numbers throughout the world. As PD progresses, the affected person is unable to control movement normally. Individuals affected by Parkinsons disease ...
Comments
Information & Contributors
Information
Published In
Copyright © Copyright © 2017 Springer Science+Business Media Dordrecht.
Publisher
Kluwer Academic Publishers
United States
Publication History
Published: 01 June 2017
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 10 Nov 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in