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.

Repetitive lifting of heavy loads increases the risk of back pain and even lumbar vertebral injuries to workers. Active exoskeletons can help workers lift loads by providing power assistance, and therefore reduce the moment and force applied on L5/S1 joint of human body when performing lifting tasks. However, most existing active exoskeletons for lifting assistance are unable to automatically detect user's lift movement, which limits the wide application of active exoskeletons in factories. In this paper, we propose a simple but effective lift detection strategy for exoskeleton control. This strategy uses only exoskeleton integrated sensors, without any extra sensors to capture human motion intentions. This makes the lift detection system more practical for applications in manufacturing environments. Seven healthy subjects participated in this research. Three different sessions were carried out, two for training and one for testing the algorithm. In the two training sessions, subjects were asked to wear a hip exoskeleton, controlled in transparent mode, and perform repetitive lifting and a locomotion circuit; lifting was executed with different techniques. The collected data were used to train the lift detection model. In the testing session, the exoskeleton was controlled in order to deliver torque to assist the lifting action, based on the lift detection made by the trained algorithm. The across-subject average accuracy of lift detection during online test was 97.97 ± 1.39% with subject-dependent model. Offline, the algorithm was trained with data acquired from all subjects to verify its performance for subject-independent detection, and an accuracy of 97.48 ± 1.53% was achieved. In addition, timeliness of the algorithm was quantitatively evaluated and the time delay was <160 ms across different lifting speeds. Surface electromyography was also measured to assess the efficacy of the exoskeleton in assisting subjects in performing load lifting tasks. These results validate the promise of applying the proposed lift detection strategy for exoskeleton control aiming at lift assistance.

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.

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 ( $\bf \pm 5^{\circ}$ , $\bf \pm 10^{\circ}$ , and $\bf \pm 15^{\circ}$ ) 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.

Retinal microsurgery requires precise manipulation of delicate tissue in the interior of the eye. Force sensing instruments can provide surgeons the imperceptible force information, thereby improving the safety of retinal microsurgery. Previous force sensing microneedles have demonstrated robust performance in transverse force measurement. However, in some surgical operations such as puncturing the vessel, the axial force sensing function is critical. In this article, we present a three degrees of freedom force sensing microneedle based on fiber Bragg grating (FBG) sensors. A novel configuration of the FBG sensors is arranged to detect the transverse and axial forces. Three optical fibers with dual FBGs are longitudinally attached along the outside surface of a Nitinol tube. Benefiting from the novel configuration of the FBG sensors, a hollow channel can be used to pass through injection catheter. A new force calculation algorithm was developed to decouple the transverse forces and axial forces. Experimental results demonstrate the proposed force calculation algorithm can achieve a resolution of 0.124 and 0.74 mN for transverse forces and axial forces, respectively. Validation and temperature compensation experiments show the force sensor can provide consistent and accurate measurement of 3-D forces within 35–37 °C temperature variation.