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Qining Wang - One of the best experts on this subject based on the ideXlab platform.

  • Controlling a Robotic Hip Exoskeleton With Noncontact Capacitive Sensors
    IEEE ASME Transactions on Mechatronics, 2019
    Co-Authors: Simona Crea, Silvia Manca, Andrea Parri, Enhao Zheng, Raffaele Molino Lova, Nicola Vitiello, Qining Wang
    Abstract:

    For partial lower-limb exoskeletons, an accurate real-time estimation of the gait phase is paramount to provide timely and well-tailored assistance during gait. To this end, dedicated wearable sensors separated from the exoskeletons mechanical structure may be preferable because they are typically isolated from movement artifacts that often result from the transient dynamics of the physical human-robot interaction. Moreover, wearable sensors that do not require time-consuming calibration procedures are more easily acceptable by users. In this paper, a robotic hip orthosis was controlled using Capacitive sensors placed in orthopedic cuffs on the shanks. The Capacitive signals are zeroed after donning the cuffs and do not require any further calibration. The Capacitive-Sensing-based controller was designed to perform online estimation of the gait cycle phase via adaptive oscillators, and to provide a phase-locked assistive torque. Two experimental activities were carried out to validate the effectiveness of the proposed control strategy. Experiments conducted with seven healthy subjects walking on a treadmill at different speeds demonstrated that the controller can estimate the gait phase with an average error of 4%, while also providing hip flexion assistance. Moreover, experiments carried out with four healthy subjects showed that the Capacitive-Sensing-based controller could reduce the metabolic expenditure of subjects compared to the unassisted condition (mean ± SEM, - 3.2% ± 1.1).

  • Gait Phase Estimation Based on Noncontact Capacitive Sensing and Adaptive Oscillators
    IEEE Transactions on Biomedical Engineering, 2017
    Co-Authors: Enhao Zheng, Silvia Manca, Andrea Parri, Nicola Vitiello, Qining Wang
    Abstract:

    This paper presents a novel strategy aiming to acquire an accurate and walking-speed-adaptive estimation of the gait phase through noncontact Capacitive Sensing and adaptive oscillators (AOs). The Capacitive Sensing system is designed with two Sensing cuffs that can measure the leg muscle shape changes during walking. The system can be dressed above the clothes and free human skin from contacting to electrodes. In order to track the capacitance signals, the gait phase estimator is designed based on the AO dynamic system due to its ability of synchronizing with quasi-periodic signals. After the implementation of the whole system, we first evaluated the offline estimation performance by experiments with 12 healthy subjects walking on a treadmill with changing speeds. The strategy achieved an accurate and consistent gait phase estimation with only one channel of capacitance signal. The average root-mean-square errors in one stride were 0.19 rad (3.0% of one gait cycle) for constant walking speeds and 0.31 rad (4.9% of one gait cycle) for speed transitions even after the subjects rewore the Sensing cuffs. We then validated our strategy in a real-time gait phase estimation task with three subjects walking with changing speeds. Our study indicates that the strategy based on Capacitive Sensing and AOs is a promising alternative for the control of exoskeleton/orthosis.

  • A Noncontact Capacitive Sensing System for Recognizing Locomotion Modes of Transtibial Amputees
    IEEE Transactions on Biomedical Engineering, 2014
    Co-Authors: Enhao Zheng, Long Wang, Qining Wang
    Abstract:

    This paper presents a noncontact Capacitive Sensing system (C-Sens) for locomotion mode recognition of transtibial amputees. C-Sens detects changes in physical distance between the residual limb and the prosthesis. The Sensing front ends are built into the prosthetic socket without contacting the skin. This novel signal source improves the usability of locomotion mode recognition systems based on electromyography (EMG) signals and systems based on capacitance signals obtained from skin contact. To evaluate the performance of C-Sens, we carried out experiments among six transtibial amputees with varying levels of amputation when they engaged in six common locomotive activities. The capacitance signals were consistent and stereotypical for different locomotion modes. Importantly, we were able to obtain sufficiently informative signals even for amputees with severe muscle atrophy (i.e., amputees lacking of quality EMG from shank muscles for mode classification). With phase-dependent quadratic classifier and selected feature set, the proposed system was capable of making continuous judgments about locomotion modes with an average accuracy of 96.3% and 94.8% for swing phase and stance phase, respectively (Experiment 1). Furthermore, the system was able to achieve satisfactory recognition performance after the subjects redonned the socket (Experiment 2). We also validated that C-Sens was robust to load bearing changes when amputees carried 5-kg weights during activities (Experiment 3). These results suggest that noncontact Capacitive Sensing is capable of circumventing practical problems of EMG systems without sacrificing performance and it is, thus, promising for automatic recognition of human motion intent for controlling powered prostheses.

  • locomotion mode classification using a wearable Capacitive Sensing system
    International Conference of the IEEE Engineering in Medicine and Biology Society, 2013
    Co-Authors: Baojun Chen, Enhao Zheng, Qining Wang, Xiaodan Fan, Tong Liang, Kunlin Wei, Long Wang
    Abstract:

    Locomotion mode classification is one of the most important aspects for the control of powered lower-limb prostheses. We propose a wearable Capacitive Sensing system for recognizing locomotion modes as an alternative solution to popular electromyography (EMG)-based systems, aiming to overcome drawbacks of the latter. Eight able-bodied subjects and five transtibial amputees were recruited for automatic classification of six common locomotion modes. The system measured ten channels of capacitance signals from the shank, the thigh, or both. With a phase-dependent linear discriminant analysis classifier and selected time-domain features, the system can achieve a satisfactory classification accuracy of 93.6% ±0.9% and 93.4% ±0.8% for able-bodied subjects and amputee subjects, respectively. The classification accuracy is comparable with that of EMG-based systems. More importantly, we verify that neuro-mechanical delay inherent in Capacitive Sensing does not affect the timeliness of classification decisions as the system, similar to EMG-based systems, can make multiple judgments during a gait cycle. Experimental results also indicate that capacitance signals from the thigh alone are sufficient for mode classification for both able-bodied and transtibial subjects. Our investigations demonstrate that Capacitive Sensing is a promising alternative to myoelectric Sensing for real-time control of powered lower-limb prostheses.

Enhao Zheng - One of the best experts on this subject based on the ideXlab platform.

  • Controlling a Robotic Hip Exoskeleton With Noncontact Capacitive Sensors
    IEEE ASME Transactions on Mechatronics, 2019
    Co-Authors: Simona Crea, Silvia Manca, Andrea Parri, Enhao Zheng, Raffaele Molino Lova, Nicola Vitiello, Qining Wang
    Abstract:

    For partial lower-limb exoskeletons, an accurate real-time estimation of the gait phase is paramount to provide timely and well-tailored assistance during gait. To this end, dedicated wearable sensors separated from the exoskeletons mechanical structure may be preferable because they are typically isolated from movement artifacts that often result from the transient dynamics of the physical human-robot interaction. Moreover, wearable sensors that do not require time-consuming calibration procedures are more easily acceptable by users. In this paper, a robotic hip orthosis was controlled using Capacitive sensors placed in orthopedic cuffs on the shanks. The Capacitive signals are zeroed after donning the cuffs and do not require any further calibration. The Capacitive-Sensing-based controller was designed to perform online estimation of the gait cycle phase via adaptive oscillators, and to provide a phase-locked assistive torque. Two experimental activities were carried out to validate the effectiveness of the proposed control strategy. Experiments conducted with seven healthy subjects walking on a treadmill at different speeds demonstrated that the controller can estimate the gait phase with an average error of 4%, while also providing hip flexion assistance. Moreover, experiments carried out with four healthy subjects showed that the Capacitive-Sensing-based controller could reduce the metabolic expenditure of subjects compared to the unassisted condition (mean ± SEM, - 3.2% ± 1.1).

  • Gait Phase Estimation Based on Noncontact Capacitive Sensing and Adaptive Oscillators
    IEEE Transactions on Biomedical Engineering, 2017
    Co-Authors: Enhao Zheng, Silvia Manca, Andrea Parri, Nicola Vitiello, Qining Wang
    Abstract:

    This paper presents a novel strategy aiming to acquire an accurate and walking-speed-adaptive estimation of the gait phase through noncontact Capacitive Sensing and adaptive oscillators (AOs). The Capacitive Sensing system is designed with two Sensing cuffs that can measure the leg muscle shape changes during walking. The system can be dressed above the clothes and free human skin from contacting to electrodes. In order to track the capacitance signals, the gait phase estimator is designed based on the AO dynamic system due to its ability of synchronizing with quasi-periodic signals. After the implementation of the whole system, we first evaluated the offline estimation performance by experiments with 12 healthy subjects walking on a treadmill with changing speeds. The strategy achieved an accurate and consistent gait phase estimation with only one channel of capacitance signal. The average root-mean-square errors in one stride were 0.19 rad (3.0% of one gait cycle) for constant walking speeds and 0.31 rad (4.9% of one gait cycle) for speed transitions even after the subjects rewore the Sensing cuffs. We then validated our strategy in a real-time gait phase estimation task with three subjects walking with changing speeds. Our study indicates that the strategy based on Capacitive Sensing and AOs is a promising alternative for the control of exoskeleton/orthosis.

  • A Noncontact Capacitive Sensing System for Recognizing Locomotion Modes of Transtibial Amputees
    IEEE Transactions on Biomedical Engineering, 2014
    Co-Authors: Enhao Zheng, Long Wang, Qining Wang
    Abstract:

    This paper presents a noncontact Capacitive Sensing system (C-Sens) for locomotion mode recognition of transtibial amputees. C-Sens detects changes in physical distance between the residual limb and the prosthesis. The Sensing front ends are built into the prosthetic socket without contacting the skin. This novel signal source improves the usability of locomotion mode recognition systems based on electromyography (EMG) signals and systems based on capacitance signals obtained from skin contact. To evaluate the performance of C-Sens, we carried out experiments among six transtibial amputees with varying levels of amputation when they engaged in six common locomotive activities. The capacitance signals were consistent and stereotypical for different locomotion modes. Importantly, we were able to obtain sufficiently informative signals even for amputees with severe muscle atrophy (i.e., amputees lacking of quality EMG from shank muscles for mode classification). With phase-dependent quadratic classifier and selected feature set, the proposed system was capable of making continuous judgments about locomotion modes with an average accuracy of 96.3% and 94.8% for swing phase and stance phase, respectively (Experiment 1). Furthermore, the system was able to achieve satisfactory recognition performance after the subjects redonned the socket (Experiment 2). We also validated that C-Sens was robust to load bearing changes when amputees carried 5-kg weights during activities (Experiment 3). These results suggest that noncontact Capacitive Sensing is capable of circumventing practical problems of EMG systems without sacrificing performance and it is, thus, promising for automatic recognition of human motion intent for controlling powered prostheses.

  • locomotion mode classification using a wearable Capacitive Sensing system
    International Conference of the IEEE Engineering in Medicine and Biology Society, 2013
    Co-Authors: Baojun Chen, Enhao Zheng, Qining Wang, Xiaodan Fan, Tong Liang, Kunlin Wei, Long Wang
    Abstract:

    Locomotion mode classification is one of the most important aspects for the control of powered lower-limb prostheses. We propose a wearable Capacitive Sensing system for recognizing locomotion modes as an alternative solution to popular electromyography (EMG)-based systems, aiming to overcome drawbacks of the latter. Eight able-bodied subjects and five transtibial amputees were recruited for automatic classification of six common locomotion modes. The system measured ten channels of capacitance signals from the shank, the thigh, or both. With a phase-dependent linear discriminant analysis classifier and selected time-domain features, the system can achieve a satisfactory classification accuracy of 93.6% ±0.9% and 93.4% ±0.8% for able-bodied subjects and amputee subjects, respectively. The classification accuracy is comparable with that of EMG-based systems. More importantly, we verify that neuro-mechanical delay inherent in Capacitive Sensing does not affect the timeliness of classification decisions as the system, similar to EMG-based systems, can make multiple judgments during a gait cycle. Experimental results also indicate that capacitance signals from the thigh alone are sufficient for mode classification for both able-bodied and transtibial subjects. Our investigations demonstrate that Capacitive Sensing is a promising alternative to myoelectric Sensing for real-time control of powered lower-limb prostheses.

Wang Qining - One of the best experts on this subject based on the ideXlab platform.

  • Gait Phase Estimation Based on Noncontact Capacitive Sensing and Adaptive Oscillators
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2017
    Co-Authors: Zheng Enhao, Manca Silvia, Yan Tingfang, Parri Andrea, Vitiello Nicola, Wang Qining
    Abstract:

    This paper presents a novel strategy aiming to acquire an accurate and walking-speed-adaptive estimation of the gait phase through noncontact Capacitive Sensing and adaptive oscillators (AOs). The Capacitive Sensing system is designed with two Sensing cuffs that can measure the leg muscle shape changes during walking. The system can be dressed above the clothes and free human skin from contacting to electrodes. In order to track the capacitance signals, the gait phase estimator is designed based on the AO dynamic system due to its ability of synchronizing with quasi-periodic signals. After the implementation of the whole system, we first evaluated the offline estimation performance by experiments with 12 healthy subjects walking on a treadmill with changing speeds. The strategy achieved an accurate and consistent gait phase estimation with only one channel of capacitance signal. The average root-meansquare errors in one stride were 0.19 rad (3.0% of one gait cycle) for constant walking speeds and 0.31 rad (4.9% of one gait cycle) for speed transitions even after the subjects rewore the Sensing cuffs. We then validated our strategy in a real-time gait phase estimation task with three subjects walking with changing speeds. Our study indicates that the strategy based on Capacitive Sensing and AOs is a promising alternative for the control of exoskeleton/orthosis.Beijing Disabled Persons' Federation; National Natural Science Foundation of China [91648207]; Beijing Municipal Science and Technology Project [Z151100000915073]; Beijing Nova Program [Z141101001814001]; EU within the CYBERLEGs [FP7-ICT-2011-2.1, 287894]; Fondazione Pisa within the IUVO [154/11]SCI(E)ARTICLE102419-24306

Andrea Parri - One of the best experts on this subject based on the ideXlab platform.

  • Controlling a Robotic Hip Exoskeleton With Noncontact Capacitive Sensors
    IEEE ASME Transactions on Mechatronics, 2019
    Co-Authors: Simona Crea, Silvia Manca, Andrea Parri, Enhao Zheng, Raffaele Molino Lova, Nicola Vitiello, Qining Wang
    Abstract:

    For partial lower-limb exoskeletons, an accurate real-time estimation of the gait phase is paramount to provide timely and well-tailored assistance during gait. To this end, dedicated wearable sensors separated from the exoskeletons mechanical structure may be preferable because they are typically isolated from movement artifacts that often result from the transient dynamics of the physical human-robot interaction. Moreover, wearable sensors that do not require time-consuming calibration procedures are more easily acceptable by users. In this paper, a robotic hip orthosis was controlled using Capacitive sensors placed in orthopedic cuffs on the shanks. The Capacitive signals are zeroed after donning the cuffs and do not require any further calibration. The Capacitive-Sensing-based controller was designed to perform online estimation of the gait cycle phase via adaptive oscillators, and to provide a phase-locked assistive torque. Two experimental activities were carried out to validate the effectiveness of the proposed control strategy. Experiments conducted with seven healthy subjects walking on a treadmill at different speeds demonstrated that the controller can estimate the gait phase with an average error of 4%, while also providing hip flexion assistance. Moreover, experiments carried out with four healthy subjects showed that the Capacitive-Sensing-based controller could reduce the metabolic expenditure of subjects compared to the unassisted condition (mean ± SEM, - 3.2% ± 1.1).

  • Gait Phase Estimation Based on Noncontact Capacitive Sensing and Adaptive Oscillators
    IEEE Transactions on Biomedical Engineering, 2017
    Co-Authors: Enhao Zheng, Silvia Manca, Andrea Parri, Nicola Vitiello, Qining Wang
    Abstract:

    This paper presents a novel strategy aiming to acquire an accurate and walking-speed-adaptive estimation of the gait phase through noncontact Capacitive Sensing and adaptive oscillators (AOs). The Capacitive Sensing system is designed with two Sensing cuffs that can measure the leg muscle shape changes during walking. The system can be dressed above the clothes and free human skin from contacting to electrodes. In order to track the capacitance signals, the gait phase estimator is designed based on the AO dynamic system due to its ability of synchronizing with quasi-periodic signals. After the implementation of the whole system, we first evaluated the offline estimation performance by experiments with 12 healthy subjects walking on a treadmill with changing speeds. The strategy achieved an accurate and consistent gait phase estimation with only one channel of capacitance signal. The average root-mean-square errors in one stride were 0.19 rad (3.0% of one gait cycle) for constant walking speeds and 0.31 rad (4.9% of one gait cycle) for speed transitions even after the subjects rewore the Sensing cuffs. We then validated our strategy in a real-time gait phase estimation task with three subjects walking with changing speeds. Our study indicates that the strategy based on Capacitive Sensing and AOs is a promising alternative for the control of exoskeleton/orthosis.

Nicola Vitiello - One of the best experts on this subject based on the ideXlab platform.

  • Controlling a Robotic Hip Exoskeleton With Noncontact Capacitive Sensors
    IEEE ASME Transactions on Mechatronics, 2019
    Co-Authors: Simona Crea, Silvia Manca, Andrea Parri, Enhao Zheng, Raffaele Molino Lova, Nicola Vitiello, Qining Wang
    Abstract:

    For partial lower-limb exoskeletons, an accurate real-time estimation of the gait phase is paramount to provide timely and well-tailored assistance during gait. To this end, dedicated wearable sensors separated from the exoskeletons mechanical structure may be preferable because they are typically isolated from movement artifacts that often result from the transient dynamics of the physical human-robot interaction. Moreover, wearable sensors that do not require time-consuming calibration procedures are more easily acceptable by users. In this paper, a robotic hip orthosis was controlled using Capacitive sensors placed in orthopedic cuffs on the shanks. The Capacitive signals are zeroed after donning the cuffs and do not require any further calibration. The Capacitive-Sensing-based controller was designed to perform online estimation of the gait cycle phase via adaptive oscillators, and to provide a phase-locked assistive torque. Two experimental activities were carried out to validate the effectiveness of the proposed control strategy. Experiments conducted with seven healthy subjects walking on a treadmill at different speeds demonstrated that the controller can estimate the gait phase with an average error of 4%, while also providing hip flexion assistance. Moreover, experiments carried out with four healthy subjects showed that the Capacitive-Sensing-based controller could reduce the metabolic expenditure of subjects compared to the unassisted condition (mean ± SEM, - 3.2% ± 1.1).

  • Gait Phase Estimation Based on Noncontact Capacitive Sensing and Adaptive Oscillators
    IEEE Transactions on Biomedical Engineering, 2017
    Co-Authors: Enhao Zheng, Silvia Manca, Andrea Parri, Nicola Vitiello, Qining Wang
    Abstract:

    This paper presents a novel strategy aiming to acquire an accurate and walking-speed-adaptive estimation of the gait phase through noncontact Capacitive Sensing and adaptive oscillators (AOs). The Capacitive Sensing system is designed with two Sensing cuffs that can measure the leg muscle shape changes during walking. The system can be dressed above the clothes and free human skin from contacting to electrodes. In order to track the capacitance signals, the gait phase estimator is designed based on the AO dynamic system due to its ability of synchronizing with quasi-periodic signals. After the implementation of the whole system, we first evaluated the offline estimation performance by experiments with 12 healthy subjects walking on a treadmill with changing speeds. The strategy achieved an accurate and consistent gait phase estimation with only one channel of capacitance signal. The average root-mean-square errors in one stride were 0.19 rad (3.0% of one gait cycle) for constant walking speeds and 0.31 rad (4.9% of one gait cycle) for speed transitions even after the subjects rewore the Sensing cuffs. We then validated our strategy in a real-time gait phase estimation task with three subjects walking with changing speeds. Our study indicates that the strategy based on Capacitive Sensing and AOs is a promising alternative for the control of exoskeleton/orthosis.