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This paper presents a time-domain vibration observer and controller for physical Human-Robot Interaction (pHRI). The proposed observer/controller aims at reducing or eliminating vibrations that may occur in stiff interactions. The vibration observer algorithm first detects minima and maxima of a given signal with robustness in regards to noise. Based on these extrema, a vibration index is computed and then used by an adaptive controller to adjust the control gains in order to reduce vibrations. The controller is activated only when the amplitude of the vibrations exceeds a given threshold and thus it does not influence the performance in normal operation. Also, the observer does not require a model and can analyze a wide time frame with only a few computations. Finally, the algorithm is implemented on two different prototypes that use an admittance controller. [ABSTRACT FROM AUTHOR]

• Pattern recognition based on a HD-sEMG sensor and spatial features to characterize the muscular distribution. • Higher accuracy classification and more robust to the muscular contraction variations than time-domain features. • Real-time efficient control of an assistive robotic arm by simultaneously providing both the direction and the velocity. To enable an efficient alternative control of an assistive robotic arm using electromyographic (EMG) signals, the control method must simultaneously provide both the direction and the velocity. However, the contraction variations of the forearm muscles, used to proportionally control the device's velocity using a regression method, can disturb the accuracy of the classification used to estimate its direction at the same time. In this paper, the original set of spatial features takes advantage of the 2D structure of an 8 × 8 high-density surface EMG (HD-sEMG) sensor to perform a high accuracy classification while improving the robustness to the contraction variations. Based on the HD-sEMG sensor, different muscular activity images are extracted by applying different spatial filters. In order to characterize their distribution specific to each movement, instead of the EMG signals' amplitudes, these muscular images are divided in sub-images upon which the proposed spatial features, such as the centers of the gravity coordinates and the percentages of influence, are computed. These features permits to achieve average accuracies of 97% and 96.7% to detect respectively 16 forearm movements performed by a healthy subject with prior experience with the control approach and 10 movements by ten inexperienced healthy subjects. Compared with the time-domain features, the proposed method exhibits significant higher accuracies in presence of muscular contraction variations, requires less training data and is more robust against the time of use. Furthermore, two fine real-time tasks illustrate the potential of the proposed approach to efficiently control a robotic arm. [ABSTRACT FROM AUTHOR]

Many applications of physical human–robot interaction (pHRI) seek to minimize the impedance felt by the operator. For large-scale operations, such as industrial material handling, two architectures have been proposed. The first uses a rigid robotic manipulator with force sensing and an admittance controller, such as the class of Intelligent Assist Devices (IADs). The second utilizes an underactuated macro-mini manipulator system, such as the uMan Assist Device. Given an application with a large payload, which of these two systems can offer the lowest inertia interaction? This work analyzes the effective inertia experienced by the operator in each of the systems. It compares the conditions under which each provides the lightest manipulation, conditions that depend on the payload as well as the frequency of manipulation. The results are validated on a large overhead robotic system configured alternatively in each of the configurations. [ABSTRACT FROM AUTHOR]

This paper discusses the motion control problem of kinematically redundant hybrid parallel robots that were recently proposed. The kinematic and dynamic models are firstly reviewed. It is pointed out that the robot can be decomposed into two parts and that each part can be analysed independently. A new hybrid approach is proposed based on this property of the robot. This approach includes an adapted computed-torque control scheme for the legs that operates in the joint space as well as a compensation of the errors of the platform applied in the Cartesian space. The convergence of this proposed approach is also verified using the Lyapunov stability theory. Two example architectures of kinematically redundant robots are built and experiments are conducted. Finally, the results are compared and analysed in order to validate the improvements provided by the proposed control method. It is shown that the proposed control scheme significantly reduces the position error. Potential extensions of this work are also discussed. [ABSTRACT FROM AUTHOR]

• Speed constrained with large attentional demand changes the walk ratio. • A LEDs strip to control walking speed results in a more natural gait pattern. • Common peak sagittal kinematic was not significantly impacted. [ABSTRACT FROM AUTHOR]

Gait retraining as a non-invasive prospective approach to restore mechanical loading at the knee joint and slowing down knee osteoarthritis (OA) progression shows great promise. However, the impact of gait modifications such as an increase in foot progression angle (FPA) or lateral trunk lean (LTL) on the ankle and hip is not yet well understood. Thus, the goal of this study is to provide insight on the impact of FPA and LTL on the sagittal and frontal external moments at the ankle and hip of healthy participants. We hypothesize that there is an optimum, for which an increase in FPA and/or LTL minimize the knee adduction moment (KAM) without increasing significantly the frontal and sagittal external moments at the ankle and hip during gait. To test this hypothesis, 23 participants performed walking trials with modified FPA and/or LTL angles following a real-time visual feedback. The hypothesis was not confirmed and while not all the gait modifications performed by the participants in this study reduced the KAM, they significantly increased the sagittal moment at the ankle and the frontal moment at the hip. This study highlights the importance to consider the biomechanical consequences of gait modifications on the ankle and hip before considering a clinical application of gait retraining approaches. [ABSTRACT FROM AUTHOR]