Non-contact capacitance sensing for continuous locomotion mode recognition: Design specifications and experiments with an amputee

Locomotion mode recognition plays an important role in the control of powered lower-limb prostheses. In this paper, we present a non-contact capacitance sensing system (C-Sens) to measure the interfacial signals between the residual limb and the prosthetic socket. The system includes sensing front-ends, a sensing circuit, a control circuit and foot pressure insoles. In the proposed system, the electrodes are fixed on the inner surface of the socket, which couple with the human body forming capacitors. The foot pressure insoles are built for detecting gait phases. The data sequence is controlled by the control circuit. To evaluate the capacitance sensing system, experiments with a transtibial amputee are carried out and seven kinds of locomotion modes are recorded. With the continuous phase dependent classification method and the quadratic discriminant analysis (QDA) classifier, the average recognition accuracies are 93.8% and 95.0% for the stance phase and the swing phase respectively. The results show the potential of the proposed system for the control of powered lower-limb prostheses.

[1]  Hugh M. Herr,et al.  Powered ankle-foot prosthesis to assist level-ground and stair-descent gaits , 2008, Neural Networks.

[2]  Fan Zhang,et al.  Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion , 2011, IEEE Transactions on Biomedical Engineering.

[3]  Einar Snekkenes,et al.  Gait Recognition Using Wearable Motion Recording Sensors , 2009, EURASIP J. Adv. Signal Process..

[4]  He Huang,et al.  A Strategy for Identifying Locomotion Modes Using Surface Electromyography , 2009, IEEE Transactions on Biomedical Engineering.

[5]  M. Tomizuka,et al.  A Gait Monitoring System Based on Air Pressure Sensors Embedded in a Shoe , 2009, IEEE/ASME Transactions on Mechatronics.

[6]  Long Wang,et al.  Finite-State Control of Powered Below-Knee Prosthesis with Ankle and Toe , 2011 .

[7]  Joseph A. Paradiso,et al.  Gait Analysis Using a Shoe-Integrated Wireless Sensor System , 2008, IEEE Transactions on Information Technology in Biomedicine.

[8]  D. Lefeber,et al.  A pneumatically powered below-knee prosthesis: Design specifications and first experiments with an amputee , 2008, 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.

[9]  Long Wang,et al.  Segmented Foot with Compliant Actuators and Its Applications to Lower-Limb Prostheses and Exoskeletons , 2012 .

[10]  Michael Goldfarb,et al.  Standing Stability Enhancement With an Intelligent Powered Transfemoral Prosthesis , 2011, IEEE Transactions on Biomedical Engineering.

[11]  Long Wang,et al.  A Wearable Plantar Pressure Measurement System: Design Specifications and First Experiments with an Amputee , 2012, IAS.

[12]  Long Wang,et al.  A wearable capacitive sensing system with phase-dependent classifier for locomotion mode recognition , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[13]  Hugh M. Herr,et al.  Powered Ankle--Foot Prosthesis Improves Walking Metabolic Economy , 2009, IEEE Transactions on Robotics.

[14]  S. Jain,et al.  Improving long term myoelectric decoding, using an adaptive classifier with label correction , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[15]  Long Wang,et al.  PANTOE 1: Biomechanical design of powered ankle-foot prosthesis with compliant joints and segmented foot , 2010, 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[16]  Fan Zhang,et al.  Design of a robust EMG sensing interface for pattern classification , 2010, Journal of neural engineering.

[17]  Richard G. Lyons,et al.  Understanding Digital Signal Processing (2nd Edition) , 2004 .

[18]  Guang-Zhong Yang,et al.  Real-Time Activity Classification Using Ambient and Wearable Sensors , 2009, IEEE Transactions on Information Technology in Biomedicine.

[19]  Fan Zhang,et al.  On Design and Implementation of Neural-Machine Interface for Artificial Legs , 2012, IEEE Transactions on Industrial Informatics.

[20]  Richard G. Lyons,et al.  Understanding Digital Signal Processing , 1996 .

[21]  H.A. Varol,et al.  Preliminary Evaluations of a Self-Contained Anthropomorphic Transfemoral Prosthesis , 2009, IEEE/ASME Transactions on Mechatronics.

[22]  Allen Y. Yang,et al.  Distributed recognition of human actions using wearable motion sensor networks , 2009, J. Ambient Intell. Smart Environ..