Baojun Chen

Locomotion Mode Classification Using a Wearable Capacitive Sensing System

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.

Adaptive Slope Walking With a Robotic Transtibial Prosthesis Based on Volitional EMG Control

Allowing amputees to volitionally control robotic prostheses can improve the adaptability to terrain changes. In this paper, we propose a myoelectric controller for a robotic transtibial prosthesis to realize adaptive slope walking. It works together with the intrinsic controller, enabling amputee users to pay relatively less attention on myoelectric control during walking. Amputee users convey the information of ground slope to prostheses by consciously performing dorsiflexion and plantar flexion of the “phantom ankle” with different intensities at the beginning of the swing phase. Two channels of surface electromyographic signals are measured from the residual shank, and they are mapped to the inclination angle of the slope. Control parameters of the intrinsic controller are then calculated according to the estimated inclination angle. In this preliminary study, two transtibial amputee subjects were recruited. They were asked to convey six targeted inclination angles (±5°, ±10°, and ±15°) to the prosthesis with the trained myoelectric controller during level-ground walking, and satisfactory control performance was achieved. This experiment was designed to simulate the scenario of transiting from level-ground walking to slope walking. Experimental results of controlling the robotic prosthesis to walk on level ground and slopes further verified that it is promising for amputees to adaptively walk on the ground with varied inclination angles in daily life.

Modeling and gait selection of passivity-based seven-link bipeds with dynamic series of walking phases

This paper presents a seven-link dynamic walking model that is more close to human beings than other passivity-based dynamic walking models. We add hip actuation, upper body, flat feet, and ankle joints with torsional springs to the model. Walking sequence of flat-feet walkers has several substreams, which forms bipedal walking with dynamic series of phases. We investigate the effects of ankle stiffness on gait selection and evaluate different gaits in walking velocity, efficiency, and stability. Experimental results indicate that ankle stiffness plays different roles in different gaits and the gaits, which are more close to human walking with moderate speed, achieve better motion characteristics.

Energetic efficiency and stability of dynamic bipedal walking gaits with different step lengths

This paper presents a seven-link dynamic walking model that is more close to human beings. We add hip actuation, upper body, flat feet and compliant ankle joints to the model. Walking sequence of the flat-foot walker has several sub-streams that form bipedal walking with dynamic series of phases, which is different with the motion of round-foot and point-foot models. We investigate the characteristics of three different walking gaits with different step lengths. Comparison of these walking gaits in walking velocity, efficiency and stability reveals the relation between step length and walking performance. Experimental results indicate that the gait which is more close to human normal walking achieves higher stability and energetic efficiency.

A Locomotion Intent Prediction System Based on Multi-Sensor Fusion

Locomotion intent prediction is essential for the control of powered lower-limb prostheses to realize smooth locomotion transitions. In this research, we develop a multi-sensor fusion based locomotion intent prediction system, which can recognize current locomotion mode and detect locomotion transitions in advance. Seven able-bodied subjects were recruited for this research. Signals from two foot pressure insoles and three inertial measurement units (one on the thigh, one on the shank and the other on the foot) are measured. A two-level recognition strategy is used for the recognition with linear discriminate classifier. Six kinds of locomotion modes and ten kinds of locomotion transitions are tested in this study. Recognition accuracy during steady locomotion periods (i.e., no locomotion transitions) is 99.71% ± 0.05% for seven able-bodied subjects. During locomotion transition periods, all the transitions are correctly detected and most of them can be detected before transiting to new locomotion modes. No significant deterioration in recognition performance is observed in the following five hours after the system is trained, and small number of experiment trials are required to train reliable classifiers.

Lower Limb Wearable Capacitive Sensing and Its Applications to Recognizing Human Gaits

In this paper, we present an approach to sense human body capacitance and apply it to recognize lower limb locomotion modes. The proposed wearable sensing system includes sensing bands, a signal processing circuit and a gait event detection module. Experiments on long-term working stability, adaptability to disturbance and locomotion mode recognition are carried out to validate the effectiveness of the proposed approach. Twelve able-bodied subjects are recruited, and eleven normal gait modes are investigated. With an event-dependent linear discriminant analysis classifier and feature selection procedure, four time-domain features are used for pattern recognition and satisfactory recognition accuracies (97.3% ± 0.5%, 97.0% ± 0.4%, 95.6% ± 0.9% and 97.0% ± 0.4% for four phases of one gait cycle respectively) are obtained. The accuracies are comparable with that from electromyography-based systems and inertial-based systems. The results validate the effectiveness of the proposed lower limb capacitive sensing approach in recognizing human normal gaits.

On the Design of a Wearable Multi-sensor System for Recognizing Motion Modes and Sit-to-stand Transition

Locomotion mode recognition is one of the key aspects of control of intelligent prostheses. This paper presents a wireless wearable multi-sensor system for locomotion mode recognition. The sensor suit of the system includes three inertial measurement units (IMUs) and eight force sensors. The system was built to measure both kinematic (tilt angles) and dynamic (ground contact forces) signals of human gaits. To evaluate the recognition performance of the system, seven motion modes and sit-to-stand transition were monitored. With a linear discriminant analysis (LDA) classifier, the proposed system can accurately classify the current states. The overall motion mode recognition accuracy was 99.9% during the stance phase and 98.5% during the swing phase. For sit-to-stand transition recognition, the average accuracy was 99.9%. These promising results show the potential of the designed system for the control of intelligent prostheses.

A new strategy for parameter optimization to improve phase-dependent locomotion mode recognition

Phase-dependent recognition strategy is an effective approach for lower-limb locomotion mode recognition. However, in previous studies, classifiers, feature sets and other parameters for the classification are the same for all the phases. The potential of this method could therefore be limited, as movement characteristics of different phases are not the same. In this paper, we aim to further improve phase-dependent recognition by proposing a new parameter optimization strategy which optimizes classifier, feature set and window size individually for each phase. Seven able-bodied subjects and one transtibial amputee subject are recruited in this research and they are required to perform six kinds of locomotion tasks. Signals recorded from two inertial measurement units and one pressure insole of the measured side are used for feature set calculation. And phase-dependent recognition method with four phases defined is applied for locomotion mode identification. The proposed strategy for parameter optimization is proved to be more efficient than the conventional optimization strategy by providing better overall recognition performance and lower computation burden.

Adding segmented feet to passive dynamic walkers

This paper presents a passive dynamic walking model with segmented feet. The model extends the Simplest Walking Model with the addition of flat feet and torsional springs based compliance on ankle joints and toe joints, to achieve stable walking on a slope driven by gravity. The push-off phase includes foot rotations around toe joint and around toe tip, which shows a great resemblance to human normal walking. The segmented foot model is compared with single rigid foot model in simulations to study the advantages of adding toe joints to passive dynamic walkers. We investigate the effects of segmented foot structure on dynamic bipedal walking. Experimental results show that the model achieves efficient and stable walking on even or uneven slope.

Dynamic Walking on Uneven Terrains with Passivity-Based Bipedal Robots

In this paper, we present an approach for passivity-based bipedal robots to achieve stable dynamic walking on uneven terrain. A powered two-dimensional seven-link walking model with flat feet and compliant ankles has been proposed to analyze and simulate the walking dynamics. We further describe a Particle Swarm Optimization based method, which uses optimized hip actuation and ankle compliance as control parameters of bipedal walking. Satisfactory results of simulations and real robot experiments show that the passivity-based walker can achieve stable bipedal walking with larger ground disturbance by the proposed method in view of stability and efficiency.