This paper describes a novel approach for autonomous and incremental learning of motion pattern primitives by observation of human motion. Human motion patterns are abstracted into a dynamic ...stochastic model, which can be used for both subsequent motion recognition and generation, analogous to the mirror neuron hypothesis in primates. The model size is adaptable based on the discrimination requirements in the associated region of the current knowledge base. A new algorithm for sequentially training the Markov chains is developed, to reduce the computation cost during model adaptation. As new motion patterns are observed, they are incrementally grouped together using hierarchical agglomerative clustering based on their relative distance in the model space. The clustering algorithm forms a tree structure, with specialized motions at the tree leaves, and generalized motions closer to the root. The generated tree structure will depend on the type of training data provided, so that the most specialized motions will be those for which the most training has been received. Tests with motion capture data for a variety of motion primitives demonstrate the efficacy of the algorithm.
An electro-hydrostatic actuator (EHA) has high backdrivability and is suitable for robots that interact with the environment, including human. However, the challenging problem in its mechanical ...design is that it is difficult to achieve compact size and high power transmission efficiency, compared with standard gear reducers. To tackle this problem, this study presents the design method by macro- and micro-scale designs. A linear-type EHA consists of a cylinder and a hydraulic pump. The former includes cylinder parameters that are dominant in the total size, and the latter includes gap width in the gear pump that is important for reducing power loss. We propose the hierarchical design to determine these parameters. In the macro-scale design, we determine the cylinder radius and differential pressure to maximize the efficiency-to-volume ratio. Based on the result of the macro-scale design, the micro-scale design determines the width of internal gaps to minimize the power loss in the gear pump. We derive mathematical formulations for the designs and develop the EHA by utilizing modular design and 3D printing. Moreover, we evaluate the fundamental properties of the developed EHA module, focusing on its power transmission efficiency and backdrivability.
Mitochondria may be transferred from cell to cell in the central nervous system and this process may help defend neurons against injury and disease. But how mitochondria maintain their functionality ...during the process of release into extracellular space remains unknown. Here, we report that mitochondrial protein O-GlcNAcylation is a critical process to support extracellular mitochondrial functionality. Activation of CD38-cADPR signaling in astrocytes robustly induced protein O-GlcNAcylation in mitochondria, while oxygen-glucose deprivation and reoxygenation showed transient and mild protein modification. Blocking the endoplasmic reticulum – Golgi trafficking with Brefeldin A or slc35B4 siRNA reduced O-GlcNAcylation, and resulted in the secretion of mitochondria with decreased membrane potential and mtDNA. Finally, loss-of-function studies verified that O-GlcNAc-modified mitochondria demonstrated higher levels of neuroprotection after astrocyte-to-neuron mitochondrial transfer. Collectively, these findings suggest that post-translational modification by O-GlcNAc may be required for supporting the functionality and neuroprotective properties of mitochondria released from astrocytes.
Pineal parenchymal tumor of intermediate differentiation (PPTID) is a WHO grade II and III tumor arising from pineal parenchymal cells. PPTID is a rare tumor accounting for less than 1% of all ...primary central nervous system neoplasms. Therefore, reports describing the clinical characteristics and biological features of PPTID are lacking. Moreover, the therapeutic strategy remains controversial. The current study aimed to evaluate treatment results and problems of contemporary therapeutic modalities of PPTID based on its features compared with other pineal parenchymal tumors. A comprehensive systematic literature review of 69 articles was performed, including articles on PPTID (389 patients) and similar tumors. Patient demographics, disease presentation, imaging characteristics, biological features, and current therapeutic options and their results were reviewed. We found that histopathological findings based on current WHO classification are well associated with survival; however, identifying and treating aggressive PPTID cases with uncommon features could be problematic. A molecular and genetic approach may help improve diagnostic accuracy. Therapeutic strategy, especially for grade III and aforementioned uncommon and aggressive tumors, remains controversial. A combination therapy involving maximum tumor resection, chemotherapy, and radiotherapy could be the first line of treatment. However, although challenging, a large prospective study would be required to identify ways to improve the clinical results of PPTID treatment.
An interactive loop between motion recognition and motion generation is a fundamental mechanism for humans and humanoid robots. We have been developing an intelligent framework for motion recognition ...and generation based on symbolizing motion primitives. The motion primitives are encoded into Hidden Markov Models (HMMs), which we call “motion symbols”. However, to determine the motion primitives to use as training data for the HMMs, this framework requires a manual segmentation of human motions. Essentially, a humanoid robot is expected to participate in daily life and must learn many motion symbols to adapt to various situations. For this use, manual segmentation is cumbersome and impractical for humanoid robots. In this study, we propose a novel approach to segmentation, the Real-time Unsupervised Segmentation (RUS) method, which comprises three phases. In the first phase, short human movements are encoded into feature HMMs. Seamless human motion can be converted to a sequence of these feature HMMs. In the second phase, the causality between the feature HMMs is extracted. The causality data make it possible to predict movement from observation. In the third phase, movements having a large prediction uncertainty are designated as the boundaries of motion primitives. In this way, human whole-body motion can be segmented into a sequence of motion primitives. This paper also describes an application of RUS to AUtonomous Symbolization of motion primitives (AUS). Each derived motion primitive is classified into an HMM for a motion symbol, and parameters of the HMMs are optimized by using the motion primitives as training data in competitive learning. The HMMs are gradually optimized in such a way that the HMMs can abstract similar motion primitives. We tested the RUS and AUS frameworks on captured human whole-body motions and demonstrated the validity of the proposed framework.
•This paper proposes a framework for real-time unsupervised segmentation of human motions and automatic symbolization of the motions.•The segmentation is based on prediction uncertainty and symbolization is based on competitive learning of human motion.•Their integration was verified on the human motion datasets.
In this paper, we propose a motion model that focuses on the discriminative parts of the human body related to target motions to classify human motions into specific categories, and apply this model ...to multi-class daily motion classifications. We extend this model to a motion recognition system which generates multiple sentences associated with human motions. The motion model is evaluated with the following four datasets acquired by a Kinect sensor or multiple infrared cameras in a motion capture studio: UCF-kinect; UT-kinect; HDM05-mocap; and YNL-mocap. We also evaluate the sentences generated from the dataset of motion and language pairs. The experimental results indicate that the motion model improves classification accuracy and our approach is better than other state-of-the-art methods for specific datasets, including human–object interactions with variations in the duration of motions, such as daily human motions. We achieve a classification rate of 81.1% for multi-class daily motion classifications in a non cross-subject setting. Additionally, the sentences generated by the motion recognition system are semantically and syntactically appropriate for the description of the target motion, which may lead to human–robot interaction using natural language.
Abstract This study investigated how baseball players generate large angular velocity at each joint by coordinating the joint torque and velocity-dependent torque during overarm throwing. Using a ...four-segment model (i.e., trunk, upper arm, forearm, and hand) that has 13 degrees of freedom, we conducted the induced acceleration analysis to determine the accelerations induced by these torques by multiplying the inverse of the system inertia matrix to the torque vectors. We found that the proximal joint motions (i.e., trunk forward motion, trunk leftward rotation, and shoulder internal rotation) were mainly accelerated by the joint torques at their own joints, whereas the distal joint motions (i.e., elbow extension and wrist flexion) were mainly accelerated by the velocity-dependent torques. We further examined which segment motion is the source of the velocity-dependent torque acting on the elbow and wrist accelerations. The results showed that the angular velocities of the trunk and upper arm produced the velocity-dependent torque for initial elbow extension acceleration. As a result, the elbow joint angular velocity increased, and concurrently, the forearm angular velocity relative to the ground also increased. The forearm angular velocity subsequently accelerated the elbow extension and wrist flexion. It also accelerated the shoulder internal rotation during the short period around the ball-release time. These results indicate that baseball players accelerate the distal elbow and wrist joint rotations by utilizing the velocity-dependent torque that is originally produced by the proximal trunk and shoulder joint torques in the early phase.
This study develops a multi-level neuromuscular model consisting of topological pools of spiking motor, sensory and interneurons controlling a bi-muscular model of the human arm. The spiking output ...of motor neuron pools were used to drive muscle actions and skeletal movement via neuromuscular junctions. Feedback information from muscle spindles were relayed via monosynaptic excitatory and disynaptic inhibitory connections, to simulate spinal afferent pathways. Subject-specific model parameters were identified from human experiments by using inverse dynamics computations and optimization methods. The identified neuromuscular model was used to simulate the biceps stretch reflex and the results were compared to an independent dataset. The proposed model was able to track the recorded data and produce dynamically consistent neural spiking patterns, muscle forces and movement kinematics under varying conditions of external forces and co-contraction levels. This additional layer of detail in neuromuscular models has important relevance to the research communities of rehabilitation and clinical movement analysis by providing a mathematical approach to studying neuromuscular pathology.
Humanoid robots are expected to be integrated into daily life. This requires the robots to perform human-like actions that are easily understandable by humans. Learning by imitation is an effective ...framework that enables the robots to generate the same motions that humans do. However, it is generally not useful for the robots to generate motions that are precisely the same as learned motions because the environment is likely to be different from the environment where the motions were learned. The humanoid robot should synthesize motions that are adaptive to the current environment by modifying learned motions. Previous research encoded captured human whole-body motions into hidden Markov models, which are hereafter referred to as motion primitives, and generated human-like motions based on the acquired motion primitives. The contact between the body and the environment also needs to be controlled, so that the humanoid robot’s whole-body motion can be realized in its current environment. This paper proposes a novel approach to synthesizing kinematic data using the motion primitive and controlling the torques of all the joints in the humanoid robot to achieve the desired whole-body motions and contact forces. The experiments demonstrate the validity of the proposed approach to synthesizing and controlling whole-body motions by humanoid robots.
•This paper proposes the synthesis of whole body motions from stochastic motion primitives.•The joint torques are computed during preserving the profile of the synthesized motion and controlling the reaction forces.•Simulation demonstrates the validity of the motion synthesis and force control.
This study investigates the compatibility between the soft deformation and high stiffness through the development of a soft robotic gripper for a human-scale payload. Softness is important for ...robotic systems that physically interact with the environments, especially for adaptive grasping or manipulation of unknown objects. Pursuing only softness would not achieve them either, and creating a certain stiffness is also an essential function in many human-scale applications. Soft robotics is unique in that it employs soft materials for the structure, and will find a lot more applications if it gains the human-scale specifications of force or the equivalent stiffness. We discuss the compatibility of the soft deformation and high stiffness based on a numerical analysis, and then present the design of a soft robotic gripper actuated by high oil-pressure, reporting its experimental validations.
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