Towards understanding and synthesis of contact-rich anthropomorphic motions through interactive cyber-physical human.

This article presents perspective on the research challenge of understanding and synthesizing anthropomorphic whole-body contact motions through a platform called "interactive cyber-physical human (iCPH)" for data collection and augmentation. The iCPH platform combines humanoid robots as "physical twins" of human and "digital twins" that simulates humans and robots in cyber-space. Several critical research topics are introduced to address this challenge by leveraging the advanced model-based analysis together with data-driven learning to exploit collected data from the integrated platform of iCPH. Definition of general description is identified as the first topic as a common basis of contact motions compatible to both humans and humanoids. Then, we set continual learning of a feasible contact motion network as the second challenge by benefiting from model-based approach and machine learning bridged by the efficient analytical gradient computation developed by the author and his collaborators. The final target is to establish a high-level symbolic system allowing automatic understanding and generation of contact motions in unexperienced environments. The proposed approaches are still under investigation, and the author expects that this article triggers discussions and further collaborations from different research communities, including robotics, artificial intelligence, neuroscience, and biomechanics.

Estimating Muscle Activity from the Deformation of a Sequential 3D Point Cloud.

Estimation of muscle activity is very important as it can be a cue to assess a person's movements and intentions. If muscle activity states can be obtained through non-contact measurement, through visual measurement systems, for example, muscle activity will provide data support and help for various study fields. In the present paper, we propose a method to predict human muscle activity from skin surface strain. This requires us to obtain a 3D reconstruction model with a high relative accuracy. The problem is that reconstruction errors due to noise on raw data generated in a visual measurement system are inevitable. In particular, the independent noise between each frame on the time series makes it difficult to accurately track the motion. In order to obtain more precise information about the human skin surface, we propose a method that introduces a temporal constraint in the non-rigid registration process. We can achieve more accurate tracking of shape and motion by constraining the point cloud motion over the time series. Using surface strain as input, we build a multilayer perceptron artificial neural network for inferring muscle activity. In the present paper, we investigate simple lower limb movements to train the network. As a result, we successfully achieve the estimation of muscle activity via surface strain.

Galvanic Vestibular Stimulation-Based Prediction Error Decoding and Channel Optimization.

A significant problem in brain-computer interface (BCI) research is decoding - obtaining required information from very weak noisy electroencephalograph signals and extracting considerable information from limited data. Traditional intention decoding methods, which obtain information from induced or spontaneous brain activity, have shortcomings in terms of performance, computational expense and usage burden. Here, a new methodology called prediction error decoding was used for motor imagery (MI) detection and compared with direct intention decoding. Galvanic vestibular stimulation (GVS) was used to induce subliminal sensory feedback between the forehead and mastoids without any burden. Prediction errors were generated between the GVS-induced sensory feedback and the MI direction. The corresponding prediction error decoding of the front/back MI task was validated. A test decoding accuracy of 77.83-78.86% (median) was achieved during GVS for every 100[Formula: see text]ms interval. A nonzero weight parameter-based channel screening (WPS) method was proposed to select channels individually and commonly during GVS. When the WPS common-selected mode was compared with the WPS individual-selected mode and a classical channel selection method based on correlation coefficients (CCS), a satisfactory decoding performance of the selected channels was observed. The results indicated the positive impact of measuring common specific channels of the BCI.

Adaptive attention-based human machine interface system for teleoperation of industrial vehicle.

This study proposes a Human Machine Interface (HMI) system with adaptive visual stimuli to facilitate teleoperation of industrial vehicles such as forklifts. The proposed system estimates the context/work state during teleoperation and presents the optimal visual stimuli on the display of HMI. Such adaptability is supported by behavioral models which are developed from behavioral data of conventional/manned forklift operation. The proposed system consists of two models, i.e., gaze attention and work state transition models which are defined by gaze fixations and operation pattern of operators, respectively. In short, the proposed system estimates and shows the optimal visual stimuli on the display of HMI based on temporal operation pattern. The usability of teleoperation system is evaluated by comparing the perceived workload elicited by different types of HMI. The results suggest the adaptive attention-based HMI system outperforms the non-adaptive HMI, where the perceived workload is consistently lower as responded by different categories of forklift operators.

Statistical Human Body Shape Model including Elderly People.

In this study, we present a human body shape statistical model including elderly people, which is constructed using principal component analysis (PCA) on 3D body scan data of approximately 130 people. As a pre-process step, a template human body mesh model is fitted to 3D scan data using a coarse-to-fine surface registration technique based on a conformal deformation method, in order to establish correspondences between the scans of different subjects possibly in different poses. To change body style by a small set of parameters, such as "age", "weight" and "height" or the easily measurable anthropometric parameters like "shoulder width", the linear transformations between these attributes and the first 10 principal component scores are obtained. We design a simple user interface to use this deformation model to generate different body styles easily. As a result, we were able to produce and show body styles capturing the characteristics of elderly people whose shoulders fell and back bent. Finally, as an application, we used our deformation method to generate different body types, performed forward dynamics simulations in an assistive device setting and visualized the differences in contact pressure distributions due to body shape changes.

Evaluation of the Anti-Adenoviral Activity of ALTANT, an Ozonated Alcohol Disinfectant.

Seven human mastadenovirus (HAdV) species (A-G) are known with more than 100 reported types. HAdV is highly resistant to common hand sanitizers. Epidemic keratoconjunctivitis and pharyngoconjunctival fever are caused by HAdV, which can be explosively transmitted in a confined space, resulting in outbreaks, such as nosocomial infections. Given the absence of an antiviral agent against the HAdV infection, it is important to prevent the spread of the infection by using disinfectants. Ozone has already been well-known for its bactericidal and virucidal effects. ALTANT is an ozonated alcohol preparation developed by E-TECH Co., Ltd. (Kobe, Hyogo, Japan). In this study, we mixed ALTANT with different HAdV types at a ratio of 9:1 and determined HAdV viability after instantaneous reactions for varying periods (flash to 5 minutes) using the TCID50 assay. The assay results demonstrated that the HAdV viability decreased by 1/10 to 1/100 within 1 minute after the reaction; additionally, slight differences in the reactivity were observed among the HAdV types. HAdV viability decreased by a factor of > 4log10, and the virus was eliminated within 3 minutes. This study demonstrated the potent HAdV disinfection effect of ALTANT.

The where of handovers by humans: Effect of partner characteristics, distance and visual feedback.

Object handovers between humans are common in our daily life but the mechanisms underlying handovers are still largely unclear. A good understanding of these mechanisms is important not only for a better understanding of human social behaviors, but also for the prospect of an automatized society in which machines will need to perform similar objects exchanges with humans. In this paper, we analyzed how humans determine the location of object transfer during handovers- to determine whether they can predict the preferred handover location of a partner, the variation of this prediction in 3D space, and to examine how much of a role vision plays in the whole process. For this we developed a paradigm that allows us to compare handovers by humans with and without on-line visual feedback. Our results show that humans have the surprising ability to modulate their handover location according to partners they have just met such that the resulting handover errors are in the order of few centimeters, even in the absence of vision. The handover errors are least along the axis joining the two partners, suggesting a limited role for visual feedback in this direction. Finally, we show that the handover locations are explained very well by a linear model considering the heights, genders and social dominances of the two partners, and the distance between them. We developed separate models for the behavior of 'givers' and 'receivers' and discuss how the behavior of the same individual changes depending on his role in the handover.

Utilizing sensory prediction errors for movement intention decoding: A new methodology.

We propose a new methodology for decoding movement intentions of humans. This methodology is motivated by the well-documented ability of the brain to predict sensory outcomes of self-generated and imagined actions using so-called forward models. We propose to subliminally stimulate the sensory modality corresponding to a user's intended movement, and decode a user's movement intention from his electroencephalography (EEG), by decoding for prediction errors-whether the sensory prediction corresponding to a user's intended movement matches the subliminal sensory stimulation we induce. We tested our proposal in a binary wheelchair turning task in which users thought of turning their wheelchair either left or right. We stimulated their vestibular system subliminally, toward either the left or the right direction, using a galvanic vestibular stimulator and show that the decoding for prediction errors from the EEG can radically improve movement intention decoding performance. We observed an 87.2% median single-trial decoding accuracy across tested participants, with zero user training, within 96 ms of the stimulation, and with no additional cognitive load on the users because the stimulation was subliminal.

Risk estimation for intervertebral disc pressure through musculoskeletal joint reaction force simulation.

This research proposes a novel method that evaluates joint reaction forces by motion analysis using a musculoskeletal model. While general muscle tension estimations minimize the sum of the muscle tensions, the proposed method utilizes the joint reaction forces themselves in the objective function of the optimization problem in addition to conventional method. This method can estimate a pattern of the muscle tensions that maximizes or minimizes a specific joint force. As a typical outcome, the proposed method allows evaluating intervertebral disc compressive force caused by co-contraction of muscles while avoiding risk underestimation. We analyzed the actual lifting motion as an example and confirmed that the method can estimate the muscle tension distribution under different tension conditions.

Simulation-based design for robotic care device: Optimizing trajectory of transfer support robot.

This paper presents a framework of simulation-based design for robotic care devices developed to reduce the burden of caregiver and care receivers. First, physical interaction between the user and device is quantitatively estimated by using a digital human simulator. Then we introduce a method for optimizing the design parameters according to given evaluation criteria. An example of trajectory optimization of transfer support robot is provided to demonstrate the effectiveness of the proposed method.

Leg stiffness of older and younger individuals over a range of hopping frequencies.

The purpose of this study was to compare spring-mass behavior between older and younger individuals at a range of hopping frequencies. A total of 14 elderly and 14 young subjects performed in-place hopping in time with a metronome at frequencies of 2.2, 2.6, and 3.0 Hz. Using a spring-mass model, leg stiffness was calculated as the ratio of maximum ground reaction force to maximum center of mass displacement at the middle of the stance phase during ground contact. The lower extremities of both groups behaved like a simple spring-mass system at all three hopping frequencies. Further, statistical analysis revealed the existence of a significant interaction between hopping frequency and age group on leg stiffness. These results suggest that the sensitivity of leg stiffness to accommodate for variations in hopping frequency is likely to differ between elderly and young individuals.

Forward dynamics simulation of human figures on assistive devices using geometric skin deformation model.

We present a forward dynamics (FD) simulation technique for human figures when they are supported by assistive devices. By incorporating a geometric skin deformation model, called linear blend skinning (skinning), into rigid-body skeleton dynamics, we can model a time-varying geometry of body surface plausibly and efficiently. Based on the skinning model, we also derive a Jacobian (a linear mapping) that maps contact forces exerted on the skin to joint torques, which is the main technical contribution of this paper. This algorithm allows us to efficiently simulate dynamics of human body that interacts with assistive devices. Experimental results showed that the proposed approach can generate plausible motions and can estimate pressure distribution that is roughly comparable to the tactile sensor data.

Cloning, sequencing, and expression of the gene from bacillus circulans that codes for a heparinase that degrades both heparin and heparan sulfate.

The gene, designated hep, coding for a heparinase that degrades both heparin and heparan sulfate, was cloned from Bacillus circulans HpT298. Nucleotide sequence analysis showed that the open reading frame of the hep gene consists of 3,150 bp, encoding a precursor protein of 1,050 amino acids with a molecular mass of 116.5 kDa. A homology search found that the deduced amino acid sequence has partial similarity with enzymes belonging to the family of acidic polysaccharide lyases that degrade chondroitin sulfate and hyaluronic acid. Recombinant mature heparinase (111.2 kDa) was produced by the addition of IPTG from Escherichia coli harboring pETHEP with an open reading frame of the mature hep gene and was purified to homogeneity by SDS-polyacrylamide gel electrophoresis. Analyses of substrate specificity and degraded disaccharides indicated that the recombinant enzyme acts on both heparin and HS, as does heparinase purified from the wild-type strain.

Purification and characterization of heparinase that degrades both heparin and heparan sulfate from Bacillus circulans.

A heparinase that degrades both heparin and heparan sulfate (HS) was purified to homogeneity from the cell-free extract of Bacillus circulans HpT298. The purified enzyme had a single band on SDS-polyacrylamide gel electrophoresis with an estimated molecular mass of 111,000. The enzyme showed optimal activity at pH 7.5 and 45 degrees C, and its activity was stimulated in the presence of 5 mM CaCl2, BaCl2, or MgCl2. Analysis of substrate specificity and degraded disaccharides demonstrated that the enzyme acts on both heparin and HS, similar to heparinase II from Flavobacterium heparinum.