Domestication is not an ancient moment of selection for prosociality: Insights from dogs and modern humans

Domestication is often portrayed as a long-past event, at times even in archaeological literature. The term domestication is also now applied to other processes, including human evolution. In such contexts, domestication means selection for friendliness or prosociality and the bodily results of such selective choices. Both such perspectives are misleading. Using dogs and modern humans as entry points, this paper explores why conceiving of domestication as a threshold event consisting of selection for prosociality is both incomplete and inaccurate. Domestication is an ongoing process, not a moment or an achievement. Selection in breeding, including for prosociality, is a part of many domestication histories, but it alone does not sustain this process over multiple generations. Further, much selection in domestication has little to do with human intention. Care, taming, commensalism, material things, and places are critical in carrying domestic relationships forward.

Gait-stride-and-frequency-based human intention recognition approach and experimental verification on lower limb exoskeleton

In the research of lower extremity exoskeleton, how to achieve synchronization between human and machine is quite significant. The intention recognition, which can be divided into three categories including EMG-based, EEG-based and biomechanics-based, is one of the effective implementation methods. In this paper, a new biomechanics-based method to realize the intention recognition is proposed. Compared with the mainstream, this method identifies the characteristic value of stride and frequency during walking, which describes human intention mathematically and concretizes the intention of human movement, improving the accuracy of recognition result and streamlining the algorithm. In addition, the impedance model is designed to further correct the recognition error. The main contents of this paper can be roughly summarized as follows. Gait feature event points are detected according to the angular signals of exoskeleton joints and the pressure signals of foot sole during the wearer’s walking process. Then the whole gait cycle is segmented by the identified gait feature event points, which is used to identify the wearer’s gait step and frequency in the gait cycle and output the trajectory transformed from standard gait trajectory by the recognized stride and frequency. Moreover, the interactive force signal collected by the three-dimensional force sensors mounted on the four-legged bar is provided as input to the designed impedance controller to adjust the transformed trajectory again. Also, the final trajectory is input to the Proportion Integral and Differential (PID) controller to realize the motion function of the lower extremity exoskeleton based on the wearer’s intention recognition result. Moreover, a simple hardware platform of lower limb exoskeleton is designed and built for practical experimental verification, which involves three kinds of gait respectively having constant stride, constant frequency and time-varying stride and frequency. The feasibility and reliability of the proposed algorithm can be concluded by analyzing the satisfactory experiment result.

Estimation of a Human-Maneuvered Target Incorporating Human Intention

This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states. In the preprocessing, an Interacting Multiple Model (IMM) estimation technique is used to infer the intentions and extract motions, which eventually construct the intention-pattern model. Once the intention-pattern model has been constructed, the proposed approach incorporate the intention-pattern model to estimation using any state estimator including Kalman filter. The proposed approach not only estimates the mean using the human intention more accurately but also updates the covariance using the human intention more precisely. The performance of the proposed approach was investigated through the estimation of a human-maneuvered multirotor. The result of the application has first indicated the effectiveness of the proposed approach for constructing the intention-pattern model. The ability of the proposed approach in state estimation over the conventional technique without intention incorporation has then been demonstrated.