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A recurrent integrator proportional integral derivative (PID) model that has been used to account for cerebrocerebellar stabilization and scaling of transcortical proprioceptive feedback in the control of horizontal planar arm movements has been augmented with long-loop force~feedback and gainscheduling to describe the control of human upright balance. The cerebellar component of the controller is represented by two sets of gains that each provide linear scaling of same-joint and interjoint long-loop stretch responses between ankle, knee, and hip. The cerebral component of the model includes a single set of same-joint linear force feedback gains. Responses to platform translations of a three-segment body model operating under this hybrid proprioception and force-based long-loop control were simulated. With low-velocity platform disturbances, ‘‘ankle-strategy’’-type postural recovery kinematics and electromyogram (EMG) patterns were generated using the first set of cerebeller control gains. With faster disturbances, balance was maintained by including the second set of gains cerebellar control gains that yielded ‘‘mixed ankle-hip strategy’’-type kinematics and EMG patterns. The addition of small amounts of simulated muscular coactivation improved the fit to certain human datasets. It is proposed that the cerebellum switches control gainsets as a function of sensed body kinematic state. Reduction of cerebellar gains with a compensatory increase in muscular stiffness yielded posture recovery with abnormal motions consistent with those found in cerebellar disease. The model demonstrates that stabilized hybrid long-loop feedback with scheduling of linear gains may afford realistic balance control in the absence of explicit internal dynamics models and suggests that the cerebellum and cerebral cortex may contribute to balance control by such a mechanism.

We have investigated properties of Antimony implanted Si such as crystalline defect, dopant distribution, and sheet resistance before and after annealing using TEM, SIMS, and 4-point probe. The sheet resistance of Sb implanted Si shows 35% lower than Arsenic, even though junction depth is 25% shallower. Electrical behaviors of Sorce/Drain extension implanted with Sb show 10% improvement of Ion/Ioff characteristics without degradation of drain-induced barrier lowering compared to the As.

The design of a bipedal walker that enables a human-like, compliant walking motions with simple control commands is presented. The design includes a passive knee bending/stretching mechanism with a latch hinge and a parted foot structure with compliant spring-based actuation. In addition, the leg posture, asymmetric lateral spring placement, round ankles, active hip sway, pelvic tilt actuation, and provisions for simple control were designed to implement the desired walking motion. The prototype bipedal walker was built with a combination of passive and actuated joints, utilizing springs around the joints for further compliancy. Experiments were conducted using the prototype bipedal walker in order to evaluate the design.

In this paper, we proposes a hybrid brain computer interface (BCI) system of the steady state visually evoked potential (SSVEP), and event-related de-synchronization (ERD) for humanoid navigation. This study also aims to demonstrate the possibility of using a low-cost hybrid BCI system. An experimental scenario is proposed in which a subject remotely controls a humanoid robot in a properly sized maze. Results show the performance of the BCI system were comparable to a keyboard-based interface. This study presents that hybrid approaches of simple BCI protocols extend usability and controllability of the low-cost BCI systems.

Undifferentiated carcinoma of the pancreas with osteoclast-like giant cells (OGCs) is very rare, less than 1% of all pancreatic malignancies, and shows worse prognosis than that of invasive ductal adenocarcinoma of the pancreas. We present a case of en bloc resection for a huge undifferentiated carcinoma with OGCs that invaded the stomach and transverse mesocolon. A 67-year female was admitted for left upper quadrant pain and computed tomography demonstrated a mass occupying the lesser sac and abutting the stomach and pancreas. There were no distant metastases and the patient underwent subtotal pancreatectomy with splenectomy, total gastrectomy, and segmental resection of the transverse colon. Histopathological examination confirmed an 11 cm-sized undifferentiated carcinoma of the pancreas with OGCs. Immunohistochemical staining revealed reactivity with pan-cytokeratin in adenocarcinoma component, with vimentin in neoplastic multi-nucleated cells, with CD45/CD68 in OGCs, and with p53 in tumor cells, respectively. The patient had suffered from multiple bone metastases and survived 9 mo after surgery. This case supports the ductal epithelial origin of undifferentiated carcinoma with OGCs and early diagnosis could result in favorable surgical outcomes. Investigations on the surgical role and prognostic factors need to be warranted in this tumor.

This work addresses surface electromyogram (sEMG)-based muscle pattern classification using a generative model. By using a hierarchical Bayesian model, the proposed approach constructs an overall process model of recorded sEMG signals. By inferring probabilistically latent neural states which governs a collection of training sEMG data, classification is realized. To validate the approach, eight-class classification using four sEMG sensors on the limb actions is tested with five subjects. The proposed model achieves an overall 95% accuracy in the classification experiment. The results support that the proposed approach is very promising for sEMG pattern classification.

This work addresses the development of a low-cost hybrid interface with eye tracking and brain signals. Eye movement detection is used for search task and EEG-based brain computer interface (BCI) for selection task. Multi-directional target selection experiments with the hybrid interface device were conducted with five subjects to evaluate the proposed hybrid interface scheme. The task asked each user to move a cursor onto a circular target among twelve possible positions and select it. Using the Fitts' law, the interface performance was compared with the computer mouse. With two BCI selection confirmation schemes, the hybrid interface attained 2-2.7 bit/s overall. Based on the results, the potential of the proposed hybrid interface was discussed.

This paper proposes the feasibility of a stiffness measurement for muscle contraction force estimation under muscle fatigue conditions. Bioelectric signals have been widely studied for the estimation of the contraction force for physical human-robot interactions, but the correlation between the biosignal and actual motion is decreased under fatigue conditions. Muscle stiffness could be a useful contraction force estimator under fatigue conditions because it measures the same physical quantity as the muscle contraction that generates the force. Electromyography (EMG), mechanomyography (MMG), and a piezoelectric resonance-based active muscle stiffness sensor were used to analyze the biceps brachii under isometric muscle fatigue conditions with reference force sensors at the end of the joint. Compared to EMG and MMG, the change in the stiffness signal was smaller (p < 0.05) in the invariable contraction force generation test until failure. In addition, in the various contraction level force generation tests, the stiffness signal under the fatigue condition changed

This paper describes a hybrid brain-computer interface (BCI) technique that combines the P300 potential, the steady state visually evoked potential (SSVEP), and event related de-synchronization (ERD) to solve a complicated multi-task problem consisting of humanoid robot navigation and control along with object recognition using a low-cost BCI system. Our approach enables subjects to control the navigation and exploration of a humanoid robot and recognize a desired object among candidates. This study aims to demonstrate the possibility of a hybrid BCI based on a low-cost system for a realistic and complex task. It also shows that the use of a simple image processing technique, combined with BCI, can further aid in making these complex tasks simpler. An experimental scenario is proposed in which a subject remotely controls a humanoid robot in a properly sized maze. The subject sees what the surrogate robot sees through visual feedback and can navigate the surrogate robot. While navigating, the robot encounters objects located in the maze. It then recognizes if the encountered object is of interest to the subject. The subject communicates with the robot through SSVEP and ERD-based BCIs to navigate and explore with the robot, and P300-based BCI to allow the surrogate robot recognize their favorites. Using several evaluation metrics, the performances of five subjects navigating the robot were quite comparable to manual keyboard control. During object recognition mode, favorite objects were successfully selected from two to four choices. Subjects conducted humanoid navigation and recognition tasks as if they embodied the robot. Analysis of the data supports the potential usefulness of the proposed hybrid BCI system for extended applications. This work presents an important implication for the future work that a hybridization of simple BCI protocols provide extended controllability to carry out complicated tasks even with a low-cost system.

We develop an algorithm for 3D human-pose tracking through a monocular vision. The algorithm is based on body-silhouette shape matching combined with particle-filter-based selected-region tracking in the 2D view. The selected-region tracking, combined with human-body structural data, restricts the temporal interpretation of 3D human poses to those best corresponding to the 2D silhouette shapes. The experimental results demonstrate that our approach performs real-time human-motion tracking with good quality and reasonable robustness.

We propose an algorithm for object recognition in indoor service robots. The problem of object recognition is one of the key challenges in the creation of realistic robotic services. Despite great advancements in the past, sufficiently accurate object recognition for service robots in real-world environments remains problematic. Our algorithm uses image and range data information that is available on a service robotic platform to execute the segmentation and classification steps. The segmentation decision rule is applied to correctly segment objects even in overlapped placements. In the classification step, the bag of words is employed with feature descriptors that are constructed from image and range information of segmented regions. In experiments, a working service robotic platform recognizes objects of similar shapes and colors. In addition, we test the recognition capability of overlapped objects. The results demonstrate the feasibility of the proposed algorithm.

This paper presents a new algorithm of encoding dynamic movements through pattern-preserving optimization by a physical robot. This research follows a recent robot programming approach called learning from demonstration in which the motion trajectory is learned from human demonstrations. The motivation of this work is to deal with major challenges in learning from demonstration such as embodiment mapping, generalization, adaptation, robustness to perturbations, stability, pattern-preserving, and parameter tuning. We propose a new method that can deal with those problems and present empirical results to support our insistence.

This study proposes an asynchronous noninvasive Brain Computer Interface (BCI) -based navigation system for a humanoid robot, which can behave similarly to a human. In the experimental procedure, each subject is asked to undertake three different sessions: offline training, an online feedback test, and real-time control of a humanoid robot in an indoor maze. During the offline training session, amplitude features from the EEG are extracted using auto-regressive frequency analysis with a Laplacian filter. The optimal feature components are selected by using the Fisher ratio and the linear discriminant analysis (LDA) distance metric. Two classifiers are hierarchically set to build the asynchronous BCI system. During the online test session, the trained BCI system translates a subject's ongoing EEG into four mental states: rest, left-hand imagery, right-hand imagery, and foot imagery. Event-by-event analysis is applied to evaluate the performance of the BCI system. If the test performance is consistently satisfactory, the subject executes the real-time control experiments. During the navigation experiments, the subject controls the robot in an indoor maze using the BCI system while surveying the environment through visual feedback. The results show that BCI control was comparable to manual control with a performance ratio of 81%. The evaluation of the results validates the feasibility and power of the proposed system.

Brain-actuated robotic systems have been proposed as a new control interface to translate different human intentions into appropriate motion commands for robotic applications. This study proposes a brain-actuated humanoid robot navigation system that uses an EEG-BCI. The experimental procedures consisted of offline training sessions, online feedback test sessions, and real-time control sessions. During the offline training sessions, amplitude features from the EEGs were extracted using band power analysis, and the informative feature components were selected using the Fisher ratio and the linear discriminant analysis (LDA) distance metric. The Intentional Activity Classifier (IAC) and the Motor Direction Classifier (MDC) were hierarchically structured and trained to build an asynchronous BCI system. During the navigation experiments, the subject controlled the humanoid robot in an indoor maze using the BCI system with real-time images from the camera on the robot's head. The results showed that three subjects successfully navigated the indoor maze using the proposed brain-actuated humanoid robot navigation system.

Purpose: In spite of the recent improved results of hepatectomy for huge hepatocellular carcinomas (HCC), the prognosis of patients with huge HCCs is still poor compared to that of patients with small HCCs. This study was performed to compare the results of hepatectomy between patients with huge HCCs and those with small HCCs, to identify the prognostic factors in patients with huge HCCs, and to determine the preoperative selection criteria. Methods: We retrospectively analyzed 51 patients who underwent hepatectomy, between July 1994 and February 2009 at Dankook University Hospital. Patients with HCC≥10 cm were classified in large (L) group and others were classified in small (S) group. The clinicopathological features, operative procedures, and postoperative outcome were compared between both groups and various prognostic factors were investigated in group L. Results: Eleven patients were classified in group L. Tumor size, vascular invasion, and tumor stage were higher in group L. Postoperative morbidity was higher in group L, but mortality was not different between the groups. Disease-free survivals were significantly lower in group L than in group S (36.4%, and 24.2% vs. 72.0%, and 44.0% for 1- and 3-year), but overall survival rates were similar in both groups (45.5%, and 15.2% in group L vs. 60.3%, and 41.3% in group S for 3- and 5-year). Presence of satellite nodules was the only prognostic factor in multivariate analysis after surgery for huge HCC. Conclusion: Regardless of tumor size, huge HCCs deserve consideration for surgery in patients with preserved liver function. Furthermore, the effect of surgery could be maximized with appropriate selection criteria, such as huge HCC without satellite nodules.

This work addresses the problem of behavioral performance of multi-robots corresponding to human drawing inputs in the sense of friendly human-robot interaction. We propose a drawing interface algorithm with multi-robots based on the centroidal Voronoi tessellation and the continuous-time Lloyd algorithms which have popularly been used for sensing and coverage control of multi-robots. Multi-robots can perform some meaningful behaviors through the realtime density functional update which reflects human drawings. Three drawing modes (distribution, following, and dancing modes) are implemented. Simulation tests verify the feasibility of the proposed algorithm.

This paper presents a design of a thermal SMA based simple small-sized and low-weight crawling robot, mimicking the crawling motion mechanism of Caenorhabditis elegans (C. elegans). Properties of the thermal SMA are similar to those of C. elegans muscle, which enables us to generate biologically relevant undulating motions. Each of 12 body segments composed of a pair of actuators is designed to be serially connected via a link that includes a motion control unit. Microcontroller is used to implement a simple sequential mode-based motion control scheme. Computer simulation and experimental results with a four segment prototype demonstrate the feasibility of the proposed robot design and control mechanism.

This work presents the mechanical human gait-based 3D bipedal walking robot. The robot mimics human walking through the push-off mechanism at the ankle, the passive knee bending mechanism, and the simple lateral balance control. To generate walking, it uses simple waveforms which are derived from human gait data. The preliminary experiment demonstrates that the robot walks stably.

This work presents that, only using non-invasively captured brain signals, a person can navigate an electric wheelchair with no serious training for a long term. Only two electrodes are set on the scalp non-invasively to detect a P300 EEG signal and a reference signal. A simple signal processing interprets the measured signals to decide a movement direction of the wheelchair. The whole devices are loaded on the wheelchair. No external system is required. The experimental results demonstrate the feasibility of the simple BCI processing to achieve reasonable performance.

This chapter discusses potential features of neural systems that may provide insights into biomimetic system design. Recent modeling studies on the cerebrocerebellar and spinal level systems have been of huge interests because they motivate advanced biologically inspired approaches to solve various complex engineering problems in the area of control, automation, and learning. In the view of control scheme, we argue that the cerebrum loosely specifies the control space, while the cerebellum implements modular controllers that are designated to task and performance specifications. The translation of the control commands from the central nervous system to muscles is in the single-to-multiple manner. The neural circuits operate in a simple control space and distribute the commands through synergies rather than directly treat the whole redundant set of actuators. Furthermore, considering a motion task as a sequence of sub-motions controlled by multiple modules of cerebrocerebellar systems , the movement control, which can be seen as a hybrid combination of sequential command generation mechanism coupled with state dependent gainscheduled controllers, proposes interesting biomimetic design schemes. We summarize two possible such schemes: Sequential module-based or parallel module-based gain scheduling approaches. Control variables used in neural motor control may be interpreted as a hybrid of kinematic and kinetic quantities and be chosen to simplify the control dimensionality. Furthermore, mirror neuron studies show majority of those neurons are multi-modal, i.e., they appear to respond to shapes and kinematics of objects, such as limbs, of other subjects and also respond similarly to internally generated motor command for execution. Thus more biological extensions to the proposed schemes become enable to learn from imitations more efficiently. By introducing conceptual computational neural models, we suggest several functionally important in vivo neural circuitry and its connection which are very useful for designing biomimetic systems. 4

Background Because intraductal papillary mucinous neoplasm of the bile duct (IPMN-B) is believed to show a better clinical course than non-papillary biliary neoplasms, it is important to make a precise diagnosis and to perform complete surgical resection. Case presentation We herein report a case of malignant IPMN-B treated by right trisectionectomy with caudate lobectomy and extrahepatic bile duct resection. Radiologic images showed marked dilatation of the left medial sectional bile duct (B4) resulting in a bulky cystic mass with multiple internal papillary projections. Duodenal endoscopic examination demonstrated very patulous ampullary orifice with mucin expulsion and endoscopic retrograde cholangiogram confirmed marked cystic dilatation of B4 with luminal filling defects. These findings suggested IPMN-B with malignancy potential. The functional volume of the left lateral section was estimated to be 45%. A planned extensive surgery was successfully performed. The remnant bile ducts were also dilated but had no macroscopic intraluminal tumorous lesion. The histopathological examination yielded the diagnosis of mucin-producing oncocytic intraductal papillary carcinoma of the bile duct with poorly differentiated carcinomas showing neuroendocrine differentiation. The tumor was 14.0 × 13.0 cm-sized and revealed no stromal invasiveness. Resection margins of the proximal bile duct and hepatic parenchyma were free of tumor cell. The patient showed no postoperative complication and was discharged on 10th postoperative date. He has been regularly followed at outpatient department with no evidence of recurrence. Conclusion Considering a favorable prognosis of IPMN-B compared to non-papillary biliary neoplasms, this tumor can be a good indication for aggressive surgical resection regardless of its tumor size.

This paper proposes the online Support Vector Regression (SVR) based value function approximation method for Reinforcement Learning (RL). This approach conserves the Support Vector Machine (SVM)'s good property, the generalization which is a key issue of function approximation. Online SVR can do incremental learning and automatically track variation of environment with time-varying characteristics. Using the online SVR, we can obtain the fast and good estimation of value function and achieve RL objective efficiently. Throughout simulation tests, the feasibility and usefulness of the proposed approach is demonstrated by comparison with SARSA and Q-learning.

Human gaits in a sagittal plane are simulated throughout a musculoskeletal model with a simple feedback postural controller. Hill-type muscletendon models generate joint motions over each leg. Typical human gait data about joint torques and angles is used to estimate muscular activation signal profiles by a simple optimization technique. A simple feedback controller updates the activation profiles to achieve stable dynamical walking simulation through optimization with a genetic algorithm. Simulation study demonstrates the proposed approach achieves realistic human walking behavior with neurophysiologically plausible muscular activations.

A hypothetical neuromusculoskeletal model is developed to simulate human normal walking and its modulated behaviors. A small set of neural periodic patterns drive spinal muscle synergies which in turn lead to specific pattern of muscle activation and supraspinal feedback systems maintain postural balance during walking. Then, the model demonstrates modulated behaviors by superimposing voluntary perturbations on the underlying walking pattern. Motions of kicking a ball and obstacle avoidance during walking are simulated as examples. The superposition of the new pulse command to a set of invariant pulses representing spino-locomotor is sufficient to achieve the coordinated behaviors. Also, forward bent walking motion is demonstrated by applying similar superposition. The composition of activations avoids a complicated computation of motor program for a specific task and presents a simple control scheme for different walking patterns.

This paper proposes a human mimetic neuro-musculo-skeletal model to simulate the recovery reactions from perturbations during walking. The computational model incorporates nonlinear viscoelastic muscular mechanics, supraspinal control of the center-of-mass, spinal pattern generator including muscle synergy network, spinal reflexes, and long-loop reflexes. Especially the long-loop reflexes specify recovery strategies based on the experimental observations [Schillings, A.M., van Wezel, B.M.H., Mulder, T.H., Duysen, J., 2000. Muscular responses and movement strategies during stumbling over obstacles. J. Neurophysiol. 83, 2093-2102; Eng, J.J., Winter, D.A., Patla, A.E., 1994. Strategies for recovery from a trip in early and late swing during human walking. Exp. Brain Res. 102, 339-349]. The model demonstrates two typical recovery strategies, i.e., elevating and lowering strategies against pulling over a swing leg. Sensed perturbation triggers a simple tonic pulse from the cortex. Depending on the swing phase, the tonic pulse activates a different compound of muscles over lower limbs. The compound induces corresponding recovery strategies. The reproduction of principal recovery behaviors may support the model's proposed functional and/or anatomical correspondence.

The effect of glutamine (Gln) supplementation in patients undergoing a major operation has not been conclusively established. This study was designed to elucidate the effect of Gln supplementation on the surgical outcome after a pancreaticoduodenectomy (PD) for periampullary tumors. A prospective, randomized, double-blind, and controlled clinical trial was undertaken for patients who underwent a classical PD or a pylorus-preserving PD for periampullary tumors. The Gln and control groups received isonitrogenous amino acid, with a 0.2 g/kg per day Gln regimen administered to the Gln group. The surgical outcome was compared in light of length of postoperative hospital stay, nutritional and chemical profiles, and complication rate between the Gln and control groups. Sixty of the consecutive 143 patients who were admitted to undergo operation for periampullary tumors were enrolled in our study; 32 were in the Gln group and 28 in the control group. The two groups were comparable prior to and during the operation. The median length of the postoperative hospital stay and the postoperative nutritional and chemical profiles were not different between two groups. The overall and PD-related complication rates of the Gln group (37.5% and 25.0%) and the control group (28.6% and 14.3%) were not statistically different. No significant beneficial effect of Gln supplementation with a low-dose parenteral regimen was demonstrated on the surgical outcome after a PD for periampullary tumors. Therefore, we should be prudent in using Gln as a routine pharmacologic supplement to the standard nutrition in patients who undergo major operations.

Although diagnosis and surgical treatment for distal common bile duct cancer have enormously advanced, survival is not satisfactory and its prognostic factors are still being debated. Thus, we evaluated the outcomes and prognostic factors after major resection for distal extrahepatic cholangiocarcinoma (dCC).One hundred and fifty-four patients who underwent major resection such as pancreatic oduodenectomy for dCC were retrospectively analyzed. We investigated clinical features, postoperative complications, survival, and prognostic factors of dCC.One hundred and three (66.9%) male and 51 (33.1%) female patients were enrolled and their mean age was 59.6 (31-78) years. Among them, 97 patients (63.0%) underwent Whipple's procedure, 45(29.2%) pylorus-preserving pancreatic oduodenectomy, 7 (4.5%) total pancreatectomy, and 5 (3.3%) hepatopancreaticoduodenectomy, respectively. Mean follow-up duration was 26.6 (0.4-108.5) months. The postoperative morbidity and mortality were 42.2% and 1.3%, respectively. Five-year survival rate was 32.8% and mean survival duration was 47.2 (39.1-55.3) months. Type of biliary drainage (percutaneous transhepatic biliary drainage), lymph node status (positive), and cellular differentiation (moderate or poor) were significant indicators for death in multivariate analysis of resectable dCC.Moderate or poor cellular differentiation and lymph node metastasis maybe independent poor prognostic factors for resectable dCC.

Inter-RAT handover technique from the WCDMA network to the CDMA network is developed. Two systems are linked and inter-worked with each other. The UE which gets out of the WCDMA area changes its radio network to the CDMA system and maintains the call without seams. Through the field test in the commercial network we got a high success rate of the handover

AIM: To evaluate the impact of surgical volume on nationwide hospital mortality after pancreaticoduodenectomy (PD) for periampullary tumors in South Korea. METHODS: Periampullary cancer patients who underwent PD between 2005 and 2008 were analyzed from the database of the Health Insurance Review and Assessment Service of South Korea. A total of 126 hospitals were divided into 5 categories, each similar in terms of surgical volume for each category. We used hospital mortality as a quality indicator, which was defined as death during the hospital stay for PD, and calculated adjusted mortality through multivariate logistic models using several confounder variables. RESULTS: A total of eligible 4975 patients were enrolled in this study. Average annual surgical volume of hospitals was markedly varied, ranging from 215 PDs in the very-high-volume hospital to < 10 PDs in the very-low-volume hospitals. Admission route, type of medical security, and type of operation were significantly different by surgical volume. The overall hospital mortality was 2.1% and the observed hospital mortality by surgical volume showed statistical difference. Surgical volume, age, and type of operation were independent risk factors for hospital death, and adjusted hospital mortality showed a similar difference between hospitals with observed mortality. The result of the Hosmer-Lemeshow test was 5.76 (P = 0.674), indicating an acceptable appropriateness of our regression model. CONCLUSION: The higher-volume hospitals showed lower hospital mortality than the lower-volume hospitals after PD in South Korea, which were clarified through the nationwide database.

Presented is a wheelchair control using a head pose free eye-gaze estimation. The direction of the wheelchair is computed from a user's eye-gaze pointing. The eye-gaze estimation system includes a 3D orientation sensor to take into account the head pose. Therefore, the user can change his or her pose with comfort during navigation. In experiments, its performance was evaluated in comparison with a manual keyboard control.

Inspired by its simple musculature, actuation and motion mechanisms, we have developed a small crawling robot that closely mimics the model organism of our choice: Caenorhabditis elegans. A thermal shape memory alloy (SMA) was selected as an actuator due to the similarities of its properties to C. elegans muscles. Based on the anatomy of C. elegans, a 12-unit robot was designed to generate a sinusoidal undulating motion. Each body unit consisting of a pair of SMA actuators is serially connected by rigid links with an embedded motion control circuit. A simple binary operation-based motion control mechanism was implemented using a microcontroller. The assembled robot can execute C. elegans-like motion with a 0.17 Hz undulation frequency. Its motion is comparable to that of a real worm.