Showing papers presented at "International Conference on Complex Medical Engineering in 2012"

Development of an amphibious mother spherical robot used as the carrier for underwater microrobots

Abstract: Nowadays, smart materials actuated microrobots are widely used when dealing with complicated missions in limited spaces. But problems still exist in this kind of solutions, such as low locomotion speed and short operating time. To solve these problems, we propose a mother-son multi-robots cooperation system, named GSL system, which included several microrobots as son robots, and a novel designed amphibious spherical robot as the mother robot. The mother robot, called GSLMom, was designed to be able to carry microrobots and provide power supply for them. This paper will talk about the structure and mechanism of the GSLMom robot. The GSLMom robot was designed as an amphibious spherical one. The robot was equipped with a 4 unit locomotion system, and each unit consists of a water-jet propeller and two servo motors. Each servo motor could rotate 90° in horizontal and 120° in vertical direction respectively. When moving in water, servo motors controlled the directions of water jet propellers and the 4 propellers work to actuate the robot. In the ground situation, propellers were used as legs, and servo motors actuated these legs to realize walking mechanism. After discussed structures, experiments were conducted to evaluate performance of the actuators.

The AirBurr: A flying robot that can exploit collisions

Abstract: Research made over the past decade shows the use of increasingly complex methods and heavy platforms to achieve autonomous flight in cluttered environments. However, efficient behaviors can be found in nature where limited sensing is used, such as in insects progressing toward a light at night. Interestingly, their success is based on their ability to recover from the numerous collisions happening along their imperfect flight path. The goal of the AirBurr project is to take inspiration from these insects and develop a new class of flying robots that can recover from collisions and even exploit them. Such robots are designed to be robust to crashes and can take-off again without human intervention. They navigate in a reactive way, bump into obstacles, and unlike conventional approaches, they don't need heavy modeling in order to fly autonomously. We believe that this new paradigm will bring flying robots out of the laboratory and allow them to tackle unstructured, cluttered environments. This paper aims at presenting the vision of the AirBurr project, as well as the latest results in the design of a platform capable of sustaining collisions and self-recovering after crashes.

Wireless Body Area Networks with compressed sensing theory

Abstract: With the rapid advancements of Wireless Sensor Networks (WSNs), wireless communication, and electronic technologies the area of wireless networks has grown significantly supporting a range of applications of Wireless Body Area Networks (WBANs) including Electronic Health (EH) and Mobile health (MH). Wireless Body Area Networks (WBANs) consist of small intelligent wireless sensors attached on or implanted in the body. These wireless sensors are responsible for collecting, processing, and transmitting vital information such as: blood pressure, heart rate, respiration rate, electrocardiographic (ECG), electroencephalography (EEG), oxygenation signals, respiratory rate, and temperature to provide continuous health monitoring with real-time feedback to the users and medical centers. In order to fully exploit the benefits of WBANs for important applications such as EH, MH, and Ambulatory Health Monitoring (AHM), the power consumption must be minimized. Each Wireless Node (WN) in WBANs must be designed to manage its local power supply in order to maximize total network lifetime. With this in mind, we want to employ Compressed Sensing (CS) to WBANs theory as a new sampling procedure to reduce load of sampling rate and minimize power consumption. Our simulation results show that sampling rate can be reduced to 30% of Nyquist Rate (NR) and power consumption to 40% in ECG signals without sacrificing reliability and availability by employing the CS theory to WBANs. This paper presents a novel sampling approach to WBANs using compressive sensing methods to WBANs.

A wireless microrobot with two motions for medical applications

Abstract: This paper discussed the mechanism and control of a wireless robot using magnetic field. We developed wireless microrobot and driver system for the microrobot locomotion. The robot has two motion mechanisms. One is the spiral jet motion, and the other is the fin motion. The spiral jet motion can move by rotating its body. It is developed from the previous spiral motion with the implement of propulsive force and safety. Fin motion can move by vibrating its body. It's like a fin of the fish. Driving systems for the microrobot locomotion is composed of large 3 axes helmholtz coil and high sensitive magnetic sensor. Large 3 axes helmholtz coil system is used as the moving energy of the robot and control method. High sensitive magnetic sensor is used as the position detection of the robot. Based on the experimental results, we confirmed that the microrobot can be moved and controlled by using this system.

Development of a capacitive coupling electrode for bioelectrical signal measurements and assistive device use

Abstract: The human body is known to produce several different electrical patterns and signals derived from bioelectrical activity. Bioelectrical signals such as the cardioelectrical and myoelectrical signals have several medical applications. In order to acquire those signals, low impedance contact electrodes are commonly used. However, because these sensors require skin preparation or application of conductive gels, placing these sensors can be difficult, time consuming and uncomfortable task for the patient. Moreover the presence of body hair and sweat can be a source of noise and cause signal depletion. This paper proposes a noncontact capacitively coupled electrode based on high impedance input, required to detect weak electrical fields. Noncontact capacitively coupled electrodes rely on reacting to electrical field variations caused by bioelectrical activity, therefore eliminating the need to maintain resistive contact between the skin and the electrode. The sensor high impedance input of 1 teraohms allowed only 0.3 to 1 picoampere input current to flow between the sensor plate and the preamplifier unit. Experiments focused on the recording of electromyogram and electrocardiograms over clothing. Data was collected and compared with data from resistive electrodes. We confirmed that our electrodes are capable of collecting bioelectrical data that can be used for assistive devices interfacing without direct contact with the skin of the subject. Such electrodes can replace currently used resistive contact electrodes improving the reliability of collected signals and increasing user-friendliness of systems that require daily, long term bioelectrical signal monitoring.

Semi-automatic P300-based brain-controlled wheelchair

Abstract: The self mobility is a dream of many persons who suffered with disability. To realize their hope, brain-controlled wheelchair (BCW) will be a powerful system for mobility improvement. P300 is the reliably brain phenomenon that can be used for interpreting brain commands as well as eyes-blink artifact signal. This paper aims to propose a prototype of BCW that allows the persons with severe disabilities to practically control the electric wheelchair in their home environment. The combination of EEG signal between P300 phenomenon and eyes-blink artifact were used as a hybrid BCW system. Furthermore, this hybrid BCW can be operated in automatic navigation and normal control mode. These two modes delivered 4 destination commands in automatic navigation control and 4 direction commands (forward, backward, turn left and turn right) in normal control mode. These commands were selected via P300 processing system. The different pattern of eyes blinking was used for fast stop, on/off P300 system, and mode changing command (2 times, 3 times and 4 times of eyes-blinking respectively). The result show that the prototype BCW can be operated both automatic navigation and brain control mode with safe by 2 times confirmation of P300 command. Without assistant, user can operate this system by them self via defined different pattern of eyes blinking command. 100% accuracy can be achieved in eyes-blinking detection algorithm. With our new design of LED based P300 stimulator, 95% averaged accuracy with the transfer rate of 3.52command/minute can be achieved by simple and timesaving P300 detection algorithm.

Development of Airway Management training system WKA-4: Provide useful feedback of trainee performance to trainee during Airway Management

Abstract: The emerging field of medical robotics is aimed at introducing intelligent tools. More recently, thanks to the innovations in robot technology (RT), advanced medical training systems have been introduced to improve the skills of trainees. The principal challenges of developing efficient medical training systems is that they must simulate real-world conditions of the task, provide objective assessments of training progress, and provide useful feedback to trainees. So far, many medical training systems have been developed; however, those training system do not fulfill the three conditions of the Active Training system. For the proof of concept of an effective Active Training system, we proposed WKA-4 (Waseda Kyotokagaku Airway No.4) which satisfies all of the requirements of the Active Training system. In this paper, we present the hardware configuration of the WKA-4 briefly, which has 11 embedded actuators and 44 embedded sensors, and simulates real-world condition of the task. In addition, in order to verify the usefulness of feedback of the WKA-4 for medical training, a set of the experiments were carried out to a doctor group and two novice groups. One novice group is provided by the feedback of the embedded sensors of the WKA-4 while the other novice group is not. From the comparisons of the results of the experiments, we verify the effectiveness of our proposed Active Training system.

A brain-computer interface in the context of a head up display system

Abstract: In this paper, we propose a new P300-based brain-computer interface (BCI) with visual stimuli being displayed on a car windshield by a Head Up Display (HUD) system. A 3*3 matrix of characters representing nine predefined locations were developed as P300 stimuli. A linear discriminant analysis (LDA) classifier with the features selected from EEG potentials by principal component analysis (PCA) was used to recognize the P300 and thus determine the desired locations. Experimental results of three healthy participants with no subject screening being conducted indicate that the average accuracy of the new BCI system is above 75% when fifty rounds of EEG data were used, showing that the proposed BCI system is feasible. The potential benefit of this system is that it can cause little distraction when applied to control mobile robots or automobiles in a known environment.

Internet based remote control for a robotic catheter manipulating system

Abstract: Endovascular intervention is expected to become increasingly popular in medical practice, both for diagnosis and for surgery. Accordingly, researches of robotic systems for endovascular surgery assistant have been carried out widely. Robotic system takes advantages of higher precision, can be controlled remotely etc. In this paper a novel robotic catheter manipulation system is presented. The developed system consists of two parts, one is the controller and the other one is the catheter manipulator. The controller is designed to simulate the surgeon's operating procedure, and the catheter manipulator takes the same movement motion with the controller. A internet based communication between the controller and the catheter manipulator has been build. A server client structure is em- ployed to realize the communication. Two-way remote control experiments are carried out between China and Japan.

A new vision for biomedicine: A systems approach

Abstract: While it is widely recognised that, on the one hand, reductionistic approaches are inadequate to deal with the multifactorial and complex nature of health and disease, and on the other hand, a system level understanding of the normal and pathological functioning of biological systems is sorely needed; no clear procedure have been put forth about the actual implementation of such a program. In this paper we review the tools and the mode of thinking that systems biology has set forward in the last twenty years, pinpointing its key methodological and epistemic aspects, together with the technical obstacles and conceptual limitations that it is faced with. A new approach conducive to a system level understanding of biomedical systems is thus, proposed.

Fundamental deliberation on exploring mental health through social interaction pattern

Abstract: In order to clarify the relationship between human social interaction and mental health, we measured face-to-face communication pattern for a few months and conducted a questionnaire on mental health in two organizations in real world. Face-to-face interaction data were measured using wearable sensing system in two organizations in Japan. We extracted some feature values from those two kind data and investigated correlation between them. In this study, we reconsidered social network centrality which means the connection strength between people. We classified the group size of simultaneous interaction and duration of interaction. As results, we found that these factors affected the correlation coefficients between face-to-face interaction and degree of stress.

A hybrid Brain-Computer Interface for improving the usability of a smart home control

Abstract: A Brain-computer interface (BCI) provides a new communication channel for a human without using any muscle activities. Within this study we propose hybrid BCI based on the P300 evoked potential and steady-state visually evoked potential (SSVEP) approach to control a smart home environment in an asynchronous way. Firstly a P300 based BCI system was developed and tested in a virtual smart home environment implementation to work with a high accuracy and a high degree of freedom. Secondly, in order to initiate and stop the operation of the P300 BCI, a SSVEP based toggle switch was implemented. Results indicate that a P300 based system is very well suitable for applications with several controllable devices and where a discrete control command is desired. A SSVEP based system is more suitable if a continuous control signal is needed and the number of commands is rather limited. The combination of a SSVEP based BCI as a toggle switch allows to arbitrarily initiate and stop the P300 selection and yielded in all subjects very high reliability and accuracy.

A dry electrode concept for SMR, P300 and SSVEP based BCIs

Abstract: A BCI enables a new communication channel that bypasses the standard neural pathways and output channels and in order to control an external device. Recently BCI systems are used for communication purposes, to control robotic devices to control games or for rehabilitation. A limiting factor in the wide-spread application is the usage of abrasive gel and conductive paste to mount EEG electrodes. Therefore many research groups are now working on the practical usability of dry electrodes to completely avoid the usage of electrode gel. In this chapter results for endogenous and exogenous BCI approaches are presented and discussed based on the g.SAHARA dry electrode sensor concept. Power spectra, time courses of evoked potentials, ERD/ERS values and BCI accuracy are compared for three BCI setups based on P300, SMR and SSVEP BCIs. Although the focus in this study was set to P300 evoked potentials it could be demonstrated that the used electrode concept works well for P300, SMR and SSVEP based BCIs.

Tongue elasticity sensing with muscle contraction monitoring

Abstract: This paper discusses a elasticity sensing system that is able to evaluate tongue elasticity in vivo. We utilize an equivalent Young's modulus of tongue, which is defined by the applied pressure divided by the tissue strain. We also introduce an index of muscle contraction by using an electromyographic (EMG) signal for judging whether the increase in elasticity is caused by a muscle contraction or a disease. By using a experimental system composed of a gripper-type elasticity sensing system and an EMG sensor, we confirm the validity of the proposed method. Through experiments, we show that the equivalent Young's modulus of tongue under the muscle contraction is four times larger than that under no muscle contraction.

Improvement study for measurement accuracy on wireless shoe-type measurement device to support walking rehabilitation

Abstract: We have developed a shoe-type measurement device which is able to measure gait information such as step length, width and pressure distribution while daily living. We hypothesized that a walking rehabilitation system could be realized by combining shoe-type device and comprehensively display which showed analytical results for gait with real time operation. From evaluation to first trial manufacture, it was found that our system was effective to let the patients, physicians and physical therapists know quantitative gait information. However, it was also found that there were some problems such as insufficient measurement area. Then, hardware of device was redesigned to enlarge measurement area but measurement accuracy decreased. Therefore, improvement method which could decrease measurement error was proposed and the efficacy of proposition was revealed by experimental results.

Visual motion sensing onboard a 50-g helicopter flying freely under complex VICON-lighting conditions

Abstract: In previous studies, we described how complicated tasks such as ground avoidance, terrain following, takeoff and landing can be performed using optic flow sensors mounted on a tethered flying robot called OCTAVE. In the present study, a new programmable visual motion sensor connected to a lightweight Bluetooth module was mounted on a free-flying 50-gram helicopter called TwinCoax. This small helicopter model equipped with 3 IR-reflective markers was flown in a dedicated room equipped with a VICON system to record its trajectory. The results of this study show that despite the complex, adverse lighting conditions, the optic flow measured onboard matched the ground-truth optic flow generated by the free-flying helicopter's trajectory quite exactly.

EEG activity over frontal regions during positive and negative emotional experience

Abstract: It has been shown that frontal EEG activity has potential for estimating emotional state. When one emotional state changes into another, it is likely that the time course and dominance of emotion play an important role in this process. Therefore, the present study investigated the correspondence between emotional state, such as pleasantness or unpleasantness, and EEG activity. In addition, we used different sensory modalities of emotional stimuli, such as odor and image, to examine whether EEG activity fluctuates with the time course of emotion. Fifteen university students participated in both the image and odor stimulus sessions. They were instructed to adjust a dial to rate their subjective emotional intensity during the period between the onset of image/odor presentation and the offset of a blank screen presentation. Standard 21 channel electroencephalograms were recorded and analyzed, after which laterality, coherence and frequency fluctuation of the alpha band of frontal EEG signals were compared among control, pleasantness and unpleasantness conditions. Our results indicated that both the laterality model and the frequency fluctuation model were able to estimate emotional state from frontal alpha band EEG activity. However, the sensory modality of the stimulation influenced the laterality of frontal cortical activity, as indicated by a difference in the laterality index between the image and odor sessions.

Detecting emergency situations by monitoring drivers' states from EEG

Abstract: This paper proposes a new method to detect pedestrian sudden occurrence, as an example of emergency situations, by monitoring drivers' state from EEG. Three drivers attended the experiment in a driving simulator with virtual driving environments with EEG signals being collected at twenty standard locations on the scalp. The (LDA) classifier with power spectrum of EEG potentials as input features of the detection model was used to recognize the emergency situation, and (ROC) was used to determine the threshold of the classifier. The experimental results of three healthy subjects indicate that the detection model can recognize the emergency situation within one second (shorter than the response time of drivers) with an accuracy of about 70%, showing that it is feasible to detect emergency situations by monitoring driver's states from EEG.

Development of compact rehabilitation robot for a wrist using biological signal

Abstract: The authors developed the rehabilitation robot which detects and analyzes the intention that man tries to move a wrist with biological signal, such as muscle potential, and makes a wrist exercise as one's intention. It consists of a grip for wrists, an actuator, a biological signal primary means, biological signal processing part, and a rehabilitation controller. It's easy to carry because size is compact, rehabilitation can be performed in the small space at the hospital or home.

Modular organization of intrinsic brain networks: A graph theoretical analysis of resting-state fMRI

Abstract: Recently, modular organization of intrinsic brain networks has been revealed by the graph theoretical analysis of resting-state functional MRI (rs-fMRI). In this paper, we introduce the concept of the graph theoretical analysis and modular organization. Then, we present the results of our analysis. In the graph theoretical analysis, intrinsic brain networks measured by rs-fMRI are modeled as the graphs (nodes linked by edges). Then, a module is defined as a group of highly inter-connected nodes which have relatively sparse connections to nodes in other modules. Recently, effective module detection methods have been proposed, and applied to rs-fMRI. In our study, rs-fMRI data were collected from 18 healthy young participants, and we detected the modules from a group level graph with fine spatial resolution. As a result, we found 6 dominant modules (default-mode, fronto-parietal, cingulo-opercular, sensorimotor, visual, and auditory). These modules were also detected when another module detection method was applied. Then, nodes were classified according to their roles based on their intra-module and inter-module connections. We found that majority of brain regions were classified as peripheral nodes which mostly connect with nodes within their modules. Interestingly, fronto-parietal module which consists of transmodal higher-order brain regions had more connector nodes (connecting with other modules) than unimodal visual and sensorimotor modules. This suggested that modular organization in intrinsic brain networks can reflect functional properties of brain systems.

Design of a miniature UHF PIFA for DBS implants

Abstract: This paper presents design and simulation of a miniature rectangular spiral planar inverted-F antenna (PIFA) at UHF RFID band (902.75 – 927.25 MHz) for integration in batteryless deep brain stimulation implants. Operation in the UHF band offers small antenna size and longer transmission range. The proposed antenna has the dimensions of 10 mm × 11.5 mm × 1.6 mm, resonance frequency of 920 MHz with a bandwidth of 18 MHz at return loss of −10dB. A dielectric substrate of FR-4 of e r = 4.5 and δ = 0.018 with thickness of 1.5644 mm is used in this design. The resonance, radiation characteristics as well as the specific absorption rate distribution induced by the designed antenna within a four layer spherical head model is evaluated by using electromagnetic modeling software which employs the finite element method.

Development of a novel underwater biomimetic microrobot with two motion attitudes

Abstract: In the last few years, various microrobots were applied more and more in the fields of biomedical engineering and underwater operation. By having a compact structure, low driving voltage and a simple control system, microrobots could do a variety of underwater missions, especially in limited spaces. To realize the purpose of multifunction of the microrobot aiming at adapting to the complex underwater environment, we proposed a walking biomimetic microrobot which had two kinds of motion attitudes. The microrobot used eleven ICPF (ionic conducting polymer film) actuators for locomotion and missions and two SMA (shape memory alloy) actuators for attitude change. In lying structure, the microrobot could implement stick insect-inspired walking/rotating motions by using eight ICPF legs, fish-like swimming motion by using one ICPF tail fin, horizontal grasping motion by using two ICPF fingers, and floating motion by electrolyzing water. In standing structure, it could implement inchworm-inspired crawling motion along two directions and vertical grasping motion by using inside four legs. Then we developed a prototype of multi-functional biomimetic microrobot and evaluated the walking speed and floating speed experimentally for performance testing.

Preliminary study on upper limb movement identification based on sEMG signal

Abstract: Stroke has become a very prevalent disease, especially in elder people. Many researches have focused on developing advanced and intelligent robotic system to assist the treatment of patients. For this field, Electromyography (EMG) is widely used for its benefit to get valuable information about the neuromuscular activity of a muscle. In this paper, wavelet packet decomposition method which is a kind of time-frequency domain is used for movement identification. Appropriate coefficients between three important movements for ADLs and sEMG signal will be extracted with wavelet packet decomposition method. These coefficients could be used as the input of BP neural network for movement identification. Experimental results proved that this method is effective off-line. Whereas the on-line identification rate should be improved in the future works.

Plasmon eignevalues as a function of nano-spheroids size and elongation

Abstract: In this paper, the effects of silver nano-spheroid size and elongation on plasmon wavelength are investigated, and the plasmon eigenvalues are formulated as a function of the radius and aspect ratio of the nano-particles. These can be used in eigenmode plasmonic interaction method to study interaction of nano-particles on each other at dipole resonance frequencies‥ It is demonstrated that plasmon eigenvalues are partially linear with respect to radius and aspect ratio of the nano-spheroids. In addition, it is shown that the maximum enhancement occurs in the direction of the polarization angle.

Adaptive hemodynamic response function to optimize differential temporal information of hemoglobin signals in functional near-infrared spectroscopy

Abstract: It has been nearly twenty years since functional near-infrared spectroscopy (fNIRS) was first applied to assessing human brain functions It has now become widely accepted as a common functional imaging modality with more than 100 publications of fNIRS-related scientific literature annually However, universal analytical methods for fNIRS data have yet to be established Although not frequently mentioned, temporal analysis of fNIRS data also poses a technical challenge: how oxygenated and deoxygenated hemoglobin (Hb) signals should be treated With its analogy to fMRI, a general linear model (GLM) with regression to a canonical hemodynamic response function (HRF) has often been used However, the Hb parameters do not necessarily follow the same behavior as the BOLD signal: rather, we often encounter different temporal profiles for the two Hb signals Here we introduce adaptive methods to find the optimal HRF for temporal analysis of fNIRS data Application of the GLM with regression to a temporally optimized HRF on the functional activation data during an overt confrontation naming task revealed different temporal structures for oxy-Hb and deoxy-Hb signals, with the latter having substantial temporal delay However, when the temporally optimized HRF was used, the two parameters yielded reasonably compatible activation patterns including activation in classical language-related areas of the left hemisphere These results suggest the potential use of the GLM with regression to an adaptive HRF to fully utilize temporal information of both Hb parameters

The spatial pattern of basal ganglia network: A resting state fMRI study

Abstract: Basal ganglia, consisting of the putamen, caudate nucleus, pallidum, substantia nigra and subthalamic nucleus, are associated with a variety of functions, including motor, cognitive, motivational, and emotional processes, and play an important role in numerous neurological and psychiatric disorders Here, we utilized two kinds of resting state functional connectivity techniques: independent component analysis (ICA) and regions of interest (ROI) based correlation analysis, to find the spatial pattern of basal ganglia network through functional magnetic resonance imaging (fMRI) examination during rest fMRI data were acquired in twenty-one healthy subjects The results identified the existence of resting state network in basal ganglia and thalamus Meantime, our study highlighted that caudate nucleus was also a part of this network Compared with ROI-based analysis, the approach of ICA was more suited to detect this network

A deficit of dorsal stream function in patients with mild cognitive impairment and Alzheimer's disease

Abstract: In humans, motion information is mainly processed by the dorsal visual stream. This stream consists of two functional streams: the ventro-dorsal (v-d) and dorso-dorsal (d-d) streams. Patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) exhibit an impairment in motion perception. By using visual event-related potentials (ERPs), we have previously demonstrated that v-d function related to optic flow (OF) perception is selectively impaired in the dorsal stream in MCI patients. The present study is aimed at examining the difference in the changes in two functional dorsal streams among MCI and AD patients and healthy controls. Therefore, we recorded ERPs in response to OF and horizontal (HO) motion stimuli in patients with AD and MCI, and healthy controls. In all groups, motion-related N170 (V5/middle temporal (MT) origin) and OF-specific P200 (v-d origin) were elicited as major components. MCI patients showed a prolonged latency of P200 for OF but not of N170 for both stimuli compared with healthy controls. In contrast, the latencies of N170 for both stimuli and P200 for OF were significantly prolonged in AD patients compared with other groups. These findings indicate that MCI patients exhibit a selective impairment in the v-d stream, whereas AD patients have impairments in distributed dorsal stream function. These ERP results may reflect the progression of pathological changes in the course of the disease. Therefore, motion-related ERPs are useful to detect and track changes in the brain function of patients with MCI and AD.

Imitation movement reduces the phantom limb pain caused by the abnormality of body schema

Abstract: Phantom limb pain is caused by the abnormality of the body schema. It is known that the mirror therapy is one of the most effective ways for reducing the phantom limb pain by using the visual information of the intact limb as the visual feedback of the amputated one, with looking through the mirror. This mirror therapy, however, works only for amputees with single limb amputation, because mirror therapy needs the vision of intact limb. Here, we tried to use the vision of the limb of another person as the visual feedback of the amputated limb by imitation movement and we reports that imitation movement could reduce the phantom limb pain of the amputee with bilateral lower limbs and left upper limb amputation by performing imitation movements.

Control system for maxillofacial surgery robot: Master-slave, motion control and safety design

Abstract: As the complex anatomical structure of the maxillofacial region, operation in this area is of high hazard and difficult to implement. Thus, a multi-arm medical robot assisting maxillofacial surgery, which can improve surgical precision and reduce surgeons' strain, was proposed. Firstly, overview of the whole robot system is introduced. Then this paper focuses on the control system which mainly consists of master-slave, motion control and safety design. Finally, the prototype was established and experiments were carried out to show the robot's smooth running and accurate positioning.

Evaluation of severity of Parkinson's disease using stride interval variability

Abstract: Parkinson's disease (PD) is a neurodegenerative disorder by degeneration of dopamine neurons, affecting motor controls related to basal ganglia. Because severe movement disorders such as gait disturbances are often observed, evaluation from gait analysis is useful. From such a background, Coefficient of Variation (CV) and Detrended Fluctuation Analysis (DFA) comes to be used as one of the methods for analyzing the variability of the stride interval in recent years. However classification of the severity of PD by stride interval variability has not been reached to practical use enough. In this paper, in order to clarify the difference in age and the severity of PD patients, variability of stride interval were analyzed by CV and DFA. As a first step, we performed analysis of stride interval in three minutes' walk of 17 PD patients, 13 healthy elderly and 12 healthy young people. Particularly, we divided PD patients based on the Hoehn and Yahr (HY) scale into an HY2 group (n=9) and an HY3 group (n=8) in order to examine the relation to disease severity. Results indicate that CV seemed to distinguish PD patients from healthy people and that DFA fractal exponent tended to be related to the age and the disease severity. From these results, gait analysis using both CV and DFA is suggested to classify participants into healthy young, healthy elderly, HY2 and HY3 groups. For future direction, there are possibilities for seeing the progression from healthy people to PD patients.