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Poor balance control and falls are big issues for older adults that due to aging decline have a lower postural balance and directional control in balance performance than younger age groups. The four square step test (FSST) was developed to evaluate rapid stepping that is often required when changing direction and avoiding obstacles while walking. However, previous researchers used only the total time as the assessment in the test. The aim of this letter is to objectively quantify the sequence and direction of the steps in FSST, by using two inertial sensors placed on both feet. An algorithm was developed to automatically segment the steps performed during the test, and calculate the stepping direction from the linear velocity of the foot. Experiments were conducted with 100 Japanese healthy older adults, where sensor data and video of 20 subjects were randomly subtracted for algorithm verification. The results showed that the algorithm succeeded for 71.7% trials in recognizing both the step sequence and step direction in FSST, while 90.2% of the detection failure could be excluded with an auto verification method.

This paper presents an inertial measurement unit-based human gesture recognition system for a robot instrument player to understand the instructions dictated by an orchestra conductor and accordingly adapt its musical performance. It is an extension of our previous publications on natural human–robot musical interaction. With this system, the robot can understand the real-time variations in musical parameters dictated by the conductor’s movements, adding expression to its performance while being synchronized with all the other human partner musicians. The enhanced interaction ability would obviously lead to an improvement of the overall live performance, but also allow the partner musicians, as well as the conductor, to better appreciate a joint musical performance, thanks to the complete naturalness of the interaction.

Automatic and objective detection algorithms for gait events from MEMS Inertial Measurement Units data have been developed to overcome subjective inaccuracy in traditional visual observation. Their accuracy and sensitivity have been verified with healthy older adults, Parkinson's disease and spinal injured patients, using single-task gait exercises, where events are precise as the subject is focusing only on walking. Multi-task walking instead simulates a more realistic and challenging scenario where subjects perform secondary cognitive task while walking, so it is a better benchmark. In this paper, we test two algorithms based on shank and foot angular velocity data in single-task, dual-task and multi-task walking. Results show that both algorithms fail when the subject slows extremely down or pauses due to high cognitive and attentional load, and, in particular, the first stride detection error rate of the foot-based algorithm increases. Stride time is accurate with both algorithms regardless of walking types, but the shank-based algorithm leads to an overestimation on the proportion of swing phase in one gait cycle. Increasing the number of cognitive tasks also causes this error with both algorithms.

Nowadays, with the development of the proportion of the elderly population in the world, several problems caused by the population aging gradually into people's horizons. One of the biggest problems plagued the vast majority of the elderly is hemiplegia, which leads to the vigorous development of the physical therapists. However, these traditional methods of physical therapy mainly rely on the skill of the physical therapists. In order to make up the defects of traditional methods, many research groups have developed different kinds of robots for lower limb rehabilitation training but most of them can only realize passive training which cannot adopt rehabilitation training based on the patients' individual condition effectively and they do not have a rehabilitation evaluation system to assess the real time training condition of the hemiplegic patients effectively. In order to solve the problems above, this paper proposed a lower limb rehabilitation evaluation system which is based on the virtual reality technology. This system has an easy observation of the human-computer interaction interface and the doctor is able to adjust the rehabilitation training direct at different patients in different rehabilitation stage based on this lower limb rehabilitation evaluation system. Compared with current techniques, this novel lower limb rehabilitation evaluation system is expected to have significant impacts in medical rehabilitation robot field.

Neurologic examination takes an important role in the physical examination. It requires abundant knowledge with prominent skills. Normally, the medical staffs, especially novices are trained to master the skills and accumulate experiences with several methods such as watching video, training with the simulated patient (SP), and so on. However, the drawbacks of the above methods, such as lack of multi-symptoms, lack of active interactions, etc, limit the training effects. To make up them, several kinds of medical training simulators have been developed to improve training effectiveness. However, most of these simulators just focus on mimicking the symptoms. The could not simulate the pathology of diseases. In this paper, we will propose an elbow robot named WKE-2(Waseda Kyotokagaku Elbow Robot No.2) to simulate the symptoms of motor nerve system for neurologic examination training on elbow force examination. In this paper, the mechanism of the elbow robot and physiological neurological model is described. As a sample, the performance of lead-pipe symptoms is introduced. Taking advantage of the robot, the trainee can get a full training on the examination skills and knowledge as well as the understanding of disease effection. Finally, several experiments are performed to verify the proposed robot. The results lead to the consideration that the approach is worth following in further research. 1

Ankle rehabilitation training takes an important role in the hemiplegic rehabilitation training. Traditional training requires the physical therapist treating the patient as one to one. The training process is repeating and needs a long period. The performances of training rely heavily on the skill of therapists. The training effectiveness depends on the skills of the therapist. To improve the training effectiveness, there are many ankle rehabilitation robots are proposed. However all of them are only focusing on providing a passive training to the patient. In this paper, an ankle rehabilitation robot with three degrees is proposed. This robot can not only realizes the passive training but also has much more sensors to detect the movements of the ankle, especially realizes the patient active training. In this paper, the detail design of the mechanism is introduced. The mechanical structure includes pedal parts, thigh fixing parts, cross slider parts, driving unit and sensing unit. Comparing with current ankle rehabilitation researches, this robot uses linear motion in horizontal and vertical plane instead of rotary motion to realize dorsiflexion/plantar flexion and abduction/adduction. Finally, we present ankle motion space analysis and ankle robot workspace analysis of ankle robot to verify the feasibility of the robot.

Current training for laparoscopy focuses only on the enhancement of manual skill and does not give advice on improving trainees’ posture. However, a poor posture can result in increased static muscle loading, faster fatigue, and impaired psychomotor task performance. In this paper, the authors propose a method, named subliminal persuasion, which gives the trainee real-time advice for correcting the upper limb posture during laparoscopic training like the expert but leads to a lower increment in the workload. A 9-axis inertial measurement unit was used to compute the upper limb posture, and a Detection Reaction Time device was developed and used to measure the workload. A monitor displayed not only images from laparoscope, but also a visual stimulus, a transparent red cross superimposed to the laparoscopic images, when the trainee had incorrect upper limb posture. One group was exposed, when their posture was not correct during training, to a short (about 33 ms) subliminal visual stimulus. The control group instead was exposed to longer (about 660 ms) supraliminal visual stimuli. We found that subliminal visual stimulation is a valid method to improve trainees’ upper limb posture during laparoscopic training. Moreover, the additional workload required for subconscious processing of subliminal visual stimuli is less than the one required for supraliminal visual stimuli, which is processed instead at the conscious level. We propose subliminal persuasion as a method to give subconscious real-time stimuli to improve upper limb posture during laparoscopic training. Its effectiveness and efficiency were confirmed against supraliminal stimuli transmitted at the conscious level: Subliminal persuasion improved upper limb posture of trainees, with a smaller increase on the overall workload.

Current prosthetic hands are basically simple grippers with one or two degrees of freedom, which barely restore the capability of the thumb-index pinch. Although most amputees consider this performance as acceptable for usual tasks, there is ample room for improvement by exploiting recent progresses in mechatronics design and technology. We are developing a novel prosthetic hand featured by multiple degrees of freedom, tactile sensing capabilities, and distributed control. Our main goal is to pursue an integrated design approach in order to fulfill critical requirements such as cosmetics, controllability, low weight, low energy consumption and noiselessness. This approach can be synthesized by the definition "biomechatronic design", which means developing mechatronic systems inspired by living beings and able to work harmoniously with them.This paper describes the first implementation of one single finger of a future biomechatronic hand. The finger has a modular design, which allows to obtain hands with different degrees of freedom and grasping capabilities. Current developments include the implementation of a hand comprising three fingers (opposing thumb, index and middle) and an embedded controller.

The use of inertial sensors for the gait event detection during a long-distance walking, for example, on different surfaces and with different walking patterns, is important to evaluate the human locomotion. Previous studies demonstrated that gyroscopes on the shank or foot are more reliable than accelerometers and magnetometers for the event detection in case of normal walking. However, these studies did not link the events with the temporal parameters used in the clinical practice; furthermore, they did not clearly verify the optimal position for the sensors depending on walking patterns and surface conditions. The event detection quality of the sensors is compared with video, used as ground truth, according to the parameters proposed by the Gait and Clinical Movement Analysis Society. Additionally, the performance of the sensor on the foot is compared with the one on the shank. The comparison is performed considering both normal walking and deviations to the walking pattern, on different ground surfaces and with or without constraints on movements. The preliminary results show that the proposed methodology allows reliable detection of gait events, even in case of abnormal footfall and in slipping surface conditions, and that the optimal location to place the sensors is the shank.

Neurologic examination takes an important role in the physical examination. By now, several methods have been carried out for medical training which bring the benefits for trainee to master the skills and accumulate experiences. However, because of the limits of these methods, the training effectiveness is limited. With the developments of technology, more and more simulators have been launched to improve medical training effectiveness. However, most of these simulators only focus on mimicking the symptoms, not simulating the pathology of diseases. In this paper, we propose an improved motor nerve model which is used in the elbow robot named WKE-2(Waseda Kyotokagaku Elbow Robot No.2). This robot is designed to simulate the disorders of motor nerves to give the trainee a full training on the elbow examination. The motor nerve model mimics the real motor nerve structure. And the functions of each part are designed from analysis of the real functions of each organ. In this robot, the effects of motor nerve system, and various symptoms related to the examination of elbow force, biceps tendon reflex, involuntary action are simulated. Making use of this robot, a systematic training on the skills as well as the understanding of knowledge is provided. Finally, several experiments are performed to verify our proposed system. The experimental results lead to the consideration that the approach is worth following in further research.

Cranial nerve examination takes an important role in the physical examination. All the medical staffs need to be trained to master the skills for examination. By now, several training methods have been carried out for medical training including training with the simulated patient (SP). However, considering the characters of eyeball, the involuntary actions cannot be simulated by Sp. With the developments of technology, more and more robot heads have been launched. However, these simulators only focus on mimicking the healthy human abilities, not simulating disorders for medical training. In this paper, we propose a novel eyeball motion nerve model which is used in the head robot named WKH-2(Waseda Kyotokagaku Head Robot No.2). This robot is designed to simulate the disorders of eyeball movements to give the trainee a full training on cranial examination. The motor nerve model is carried out from mimicking the real eyeball nerve structure. And the functions of each part are designed from analysis of the real functions of each organ. In this robot, the effects of eyeball motion nerve system, and various symptoms are simulated. Making use of this robot, a systematic training on the skills as well as the understanding of medical knowledge is provided. Finally, several experiments are carried out to verify our proposed system. The experimental results show that the approach is worth following in further research.

Endotracheal intubation (ETI) is a difficult technique and requires a great deal of practice to master. Research on the difference in movements between experts and novices performing the procedure has shown that experts perform movements more precisely than novices. Experts keep a fixed posture and use the upper arm muscles and wrist joints more effectively. These studies were conducted using optical motion capture systems and surface electromyography (sEMG), which are measurement systems that require a long setup time and expensive equipment. In this paper, we propose a novel method to measure the biomechanical performance of doctors during ETI using an innovative muscle contraction sensing device (MC sensor) and inertial measurement units (IMUs). We performed several experiments to measure the movements of both experts and novices performing ETI and then analysed and compared the obtained data. The results clearly showed that our system, comprising an MC sensor and IMUs, allows for an objective evaluation of ETI skills and highlighted the major differences between the movements of novices and experts.

Different types of sensors are being used to study deglutition and mastication. These often suffer from problems related to portability, cost, reliability, comfort etc. that make it difficult to use for long term studies. An inertial measurement based sensor seems a good fit in this application; however its use has not been explored much for the specific application of deglutition research. In this paper, we present a system comprised of an IMU and EMG sensor that are integrated together as a single system. With a preliminary experiment, we determine that the system can be used for measuring the head-neck posture during swallowing in addition to other parameters during the swallowing phase. The EMG sensor may not always be a reliable source of physiological data especially for small clustered muscles like the ones responsible for swallowing. In this case, we explore the possibility of using gyroscopic data for the recognition of deglutition events.

A common problem among elderly people is the loss of motor ability. Rehabilitation exercises can help these people recover strength and maintain a good level of mobility. However, high costs and the need for special equipment make professional rehabilitation impractical for regular use in daily life, precluding elderly the possibility to perform focalized training at home. The idea of telerehabilitation is becoming more and more concrete with the rapid development of internet technology. Telerehabilitation would allow the user to perform exercises at home with online professional direction from the doctor. However, at the present state, the doctor cannot obtain real-time and quantitative data from the user, and this limits the training effectiveness. To overcome this problem, an extremely miniaturized, portable motion capture system, named WB-4R, has been developed. Calibration and real-time link orientation reconstruction are very important to improve the accuracy in real-time measurement. In this paper, using the positive results of preliminary experiments on lower limbs, the authors will show the feasibility of the method and confirm the effectiveness of the developed system.

The increasing age of the world population is posing new challenges to our society, such as how to keep this aging population healthy and active despite of the age. In recent years, there has been a lot of interest for gait analysis for rehabilitation purposes as well as for performance assessment of this aging population. While current systems work well, they still have several limitations. Cost, need for specialized personnel, need to be used in a research center, and sporadic measurement prevent these systems from being widely used. The authors propose the use of extremely miniaturized, portable measurement systems, which can be worn by the users during their everyday life, and can monitor their gait over a long timespan. This paper presents the preliminary experiments with such a system.

Physical therapy helps patients to restore the use of the musculoskeletal and the nervous systems through the use of specifics techniques and exercises. The introduction of measurement systems for patient assessment may allow detection of initial stage of diseases, an objective severity assessment, and efficient delivery of drugs and therapies. In rehabilitation centers, sometimes there are specific devices and methodologies available for the locomotion assessment. However, the measurements are usually carried out in a short time slot and this could lead to an overestimation of the walking abilities. The authors propose a system, named WB-4R, which can provide a fast and objective walking assessment using a set of Inertial Measurement Units (IMUs). The WB-4R can be used for the gait analysis in rehabilitation centers or at home because it is compact and relatively maintenance-free. In this paper, it will be shown that our system is able to reconstruct the joint angles of lower limbs and build a foot phase space diagram during straight line walking. Furthermore, we compared the results with an optical system used in the clinical practice.

Neurologic examination procedures require not only abundant knowledge but also prominent skills. During medical education and training, several methods will be put to use such as watching video, reading books, making use of the simulated patient (SP), and so on. These can help medical staffs, especially novices, to master the skills and accumulate experiences. To make up for the drawbacks of the above methods, such as lack of active interactions, limitation of multi-symptom reproductions, etc, the medical training simulators have been developed to improve training effectiveness. However, most of these simulators only mimic the symptoms. They have no the abilities to show the pathology of diseases. This limits the training effectiveness. In this paper, we propose an elbow robot named WKE-1(Waseda Kyotokagaku Elbow Robot No.1) for neurologic examination training as one part of the whole body patient robot named WKP (Waseda Kyotokagaku Patient). In this robot, we simulate various symptoms occurring during the examination of elbow force, biceps tendon reflex, involuntary action, and also make a physiological neurological model to simulate the pathology of the nervous system. Taking advantage of this robot, the trainee can get a systematic training on both the skills and knowledge. Finally, we take a set of experiments to verify our proposed mechanism and system. The experimental results lead to the consideration that the approach is worth following in further research.

A device for the gait analysis during a long-distance walking is important for the correct assessment of patients during the rehabilitation. This device should be able to measure all gait parameters in a single unit. In addition, it is required that the measurement system is not spatially constrained. In our group, we have been developing a new wireless system, namely WB-4R, composed of Inertial Measurement Units, to be used in rehabilitation centers for gait analysis that is cheap, small, and relatively maintenance-free. This paper presents the results of a pilot study conducted with healthy subjects. The system was able to detect the gait phase and provide frequency analysis of the angular velocity and acceleration of the lower limbs.

In this paper, the shape of the medical robot which can move in the colon is suggested for its gate control. In this system, the current shape information plays a sensing role in order to control the gate of robot in the colon. In order to find current shape of robot, we construct sensor network system which composed of several electronic compass units. This unit makes use of chip which includes pair of 3 axis accelerometer and 3 axis magnetometer. From this signals, orientation is evaluated after filtering noise. Then, based on the kinematic chain model, the shape of the flexible robot is calculated using orientation information. The resulting trajectory shows that this method cans percept shape of flexible robot well.

In this paper we propose the basic concept of a tele-presence system for two (or more) anthropomorphic robots located in remote locations. As one robot interacts with a user, it acquires knowledge about the user's behavior and transfers this knowledge to the network. The robot in the remote location accesses this knowledge and according to this information emulates the behavior of the remote user when interacting with its partner.

Performing laparoscopic surgery requires several skills which have never been required for conventional open surgery, surgeons experience difficulties in learning and mastering these techniques. Various training methods and metrics have been developed in order to assess and improve surgeon's operative abilities. While these training metrics are currently widely being used, skill evaluation methods are still far from being objective in the regular laparoscopic skill education. This paper proposes a methodology of defining a model to objectively evaluate surgical performance and skill expertise in the routine laparoscopic training course. Our approach is based on the processing of kinematic data describing the movements of surgeon's upper limbs. An ultra-miniaturized wearable motion capture system (Waseda Bioinstrumentation system WB-3), therefore, has been developed to measure and analyze these movements. The skill evaluation model was trained by using the subjects' motion features acquired from WB-3 system and further validated to classify the expertise levels of the subjects with different laparoscopic experience. An experiment for training fundamental laparoscopic psychomotor skill was elaborated by using laparoscopic box trainer. Preliminary results show that, the proposed methodology can be efficiently used both for quantitative assessment of surgical ability, and for the discrimination between expert surgeons and novices.

Mastication analysis can provide an objective basis for studying and diagnosing jaw musculoskeletal disorders. Therefore, the use and development of devices for quantitatively measuring and analyzing jaw movement have become very popular in the consulting room. This paper proposes a simple to be used jaw tracking prototype by using the new miniaturized wireless Inertial Measurement Unit (IMU) named WB-4. The WB-4 EVTU primarily contains a mother board, a Bluetooth module and a Li-Polymer battery. The mother board is provided with a microcontroller and 9-axis inertial sensors (miniaturized MEMS accelerometer, gyroscope and magnetometer) to measure the jaw motion. The data transmission between WB-4 and PC is based on the Bluetooth module to realize the wireless communication. The IMU's extremely reduced weight and size allows it to be easily attached to mandible during normal mastication tests without physical restriction to the subjects. Three pilot experiments for mastication analysis of chewing gum were elaborated. A group of 9 healthy subjects kindly participated in the experiment. The preliminary results show that WB-4 IMU can efficiently evaluate the jaw movement and mastication pattern of different subjects, which could provide quantitative information to the doctors for the diagnosing and clinical treatment of different jaw diseases.

This paper presents the comparison of the WB-3 ultra-miniaturized inertial measurement unit (IMU), developed by our group, with the commercial IMU IntertiaCube by InterSense and the Vicon motion capture system. WB-3 is provided with a 32-bit microcontroller and 9-axis inertial sensors (miniaturized MEMS accelerometer, gyroscope and magnetometer). We implemented an Extended Kalman Filter (EKF) based on quaternion for the sensor fusion to retrieve the attitude. A new methodology for the automatic estimation of the measurement covariance matrix on the EKF is presented (R-Adaptive Algorithm). The preliminary results showed that WB-3 performance is comparable with Vicon. The R-Adaptive Algorithm improves the attitude reconstruction in case of non-static condition compared with the commercial IMU.

Performing laparoscopic surgery requires several skills which have never been required for conventional open surgery, surgeons experience difficulties in learning and mastering these techniques. Various training methods and metrics have been developed in order to assess and improve surgeon's operative abilities. While these training metrics are currently widely being used, skill evaluation methods are still far from being objective in the regular laparoscopic skill education. This study proposes a methodology of defining a processing model to objectively evaluate surgical performance and skill expertise in the routine laparoscopic training course. Our approach is based on the analysis of kinematic data describing the movements of surgeon's upper limbs. An ultra-miniaturized wearable motion capture system (Waseda Bioinstrumentation system WB-3), therefore, has been developed to measure and analyze these movements. The skill evaluation model was trained by using the subjects' motion features acquired from WB-3 system and further validated to classify the expertise levels of the subject with different laparoscopic experience. Experimental results show that, the proposed methodology can be efficiently used both for quantitative assessment of surgical performance, and for the discrimination between expert surgeons and novices.

In recent years there has been an ever increasing amount of research and development of technologies and methods to improve the quality and the performance of advanced surgery. In other fields several training methods and metrics have been proposed, both to improve the surgeon's abilities and also to assess her/his skills. For neurosurgery, however, the extremely small movements and sizes involved have prevented until now the development of similar methodologies and systems. In this paper we present the development of an ultra-miniaturized Inertial Measurement Unit and its application for the evaluation of the performance in a simple pick and place scenario. This analysis is a preliminary yet fundamental step to realize a better training/evaluation system for neurosurgeons, and to objectively evaluate and understand how the neurosurgery is performed.

In recent years there has been an ever increasing amount of research and development of technologies and methods to improve the quality and the performance of advanced surgery. In several fields, such as laparoscopy, various training methods and metrics have been proposed, both to improve the surgeon's abilities and also to assess her/his skills. For neurosurgery, however, the extremely small movements and target operating space involved have prevented until now the development of similar methodologies and systems. In this paper we present the development of an ultra-miniaturized Inertial Measurement Unit (IMU) and its application for neurosurgery skill assessment in a simple pick and place scenario. This analysis is a preliminary yet fundamental step to realize a better training/evaluation system for neurosurgeons, and to objectively evaluate and understand how the neurosurgery is performed.

There has been an ever increasing amount of research and development of technologies and methods to improve the safety of surgery such as Minimally Invasive Surgery (MIS). While these new technologies and methods have many advantages for patients, they often require surgeons to undergo long and difficult training. In this context, several training methods and metrics have been proposed, both to improve the surgeon's abilities and also to assess his/her skills. Our research is aimed at using WB-2R (Waseda Bioinstrumentation system NO.2 Refined), to investigate and analyze a surgeon's movements and performance. In this paper, we analyze the effects of two days of laparoscopic training on a novice subject. By using the Inertial Measurement Unit (IMU) of WB-2R it is possible to evaluate the novice's ability and improvement to handle surgical instruments and perform some knots of basic C-Loop suture. The preliminary analysis of the data acquired during the experiments (the mean and standard deviation of acceleration; 95% cumulated distribution of acceleration; the path length of the movements of hands and the execution time completing the knots) clearly shows the novice's improvements after the training. WB-2R system could provide additional information improving to help assess the experience and performance of surgeons, and to show the effectiveness of laparoscopic training. These analyses and modeling are an important step to realize a better training/evaluation system for surgeons during MIS, to understand better how the surgery is performed.

In recent years there has been an ever increasing amount of research and development of technologies and methodologies aimed at improving the safety of advanced surgery. In this context, several training methods and metrics have been proposed, in particular for laparoscopy, both to improve the surgeon's abilities and also to assess her/his skills. For neurosurgery, however, the extremely small movements and sizes involved have prevented until now the development of similar methodologies and systems. In this paper we present the development of the ultra-miniaturized Inertial Measurement Unit WB3 (at present the smallest, lightest, and best performing in the world) for practical application in neurosurgery as skill assessment tool. This paper presents the feasibility study for quantitative discrimination of movements of experienced surgeons and beginners in a simple pick and place scenario.

Personal robots and robot technology (RT)- based assistive devices are expected to play a major role in Japan's elderly-dominated society, both for joint activities with their human partners and for participation in community life. These new devices should be capable of smooth and natural adaptation and interaction with their human partners and the environment, should be able to communicate naturally with humans, and should never have a negative effect on their human partners, neither physical nor emotional. To achieve this smooth and natural integration between humans and robots, we need first to investigate and clarify how these interactions are carried out. Therefore, we developed the portable bioinstrumentation system WB-2 (Waseda bioinstrumentation system No.2), which can measure the movements of the head, the arms, and the hands (position, velocity, and acceleration), as well as several physiological parameters (electrical activity of the heart respiration, perspiration, pulse wave, and so on), to objectively measure and understand the physical and physiological effects of the interaction between robots and humans. In this paper we present our development of the Inertial Measurement Unit, which is at the heart of our new motion-capture system that replaces the system used in the Waseda bioinstrumentation system No.1 refined (WB-1R). Some preliminary results of experiments with the unit are also presented and analyzed.

Minimally invasive surgery (MIS) has become very common in recent years, thanks to the many advantages it provides for patients. Since it is difficult for surgeons to learn and master this technique, several training methods and metrics have been proposed, both to improve the surgeon's abilities and also to assess his/her skills. This paper presents the use of the WB-1R (Waseda bioinstrumentation system no.1 refined), which was developed at Waseda University, Tokyo, to investigate and analyze a surgeon's movements and performance. Specifically, the system can measure the movements of the head, the arms, and the hands, as well as several physiological parameters. In this paper we present our experiment to evaluate a surgeon's ability to handle surgical instruments and his/her depth perception using a laparoscopic view. Our preliminary analysis of a subset of the acquired data (i.e. comfort of the subjects; the amount of time it took o complete each exercise; and respiration) clearly shows that the expert surgeon and the group of medical students perform very differently. Therefore, WB-1R (or, better, a newer version tailored specifically for use in the operating room) could provide important additional information to help assess the experience and performance of surgeons, thus leading to the development of a global performance index for surgeons during MIS. These analyses and modeling, moreover, are an important step towards the automatization and the robotic assistance of the surgical gesture.

Personal Robots and Robot Technology (RT)-based assistive devices are expected to play a major role in Japan's elderly-dominated society, both for joint activities with their human partners and for participation in community life. These new devices should be capable of smooth and natural adaptation and interaction with their human partners and the environment, should be able to communicate naturally with humans, and should never have a negative effect on their human partners, neither physical nor emotional. To achieve this smooth and natural integration between humans and robots, we need first to investigate and clarify how these interactions are carried out. Therefore, we developed the portable Bioinstrumentation System WB-1R (Waseda Bioinstrumentation system no.l Refined), which can measure the movements of the head, the arms, the hands (position, velocity, and acceleration), as well as several physiological parameters (electrocardiogram, respiration, perspiration, pulse wave, and so on), to objectively measure and understand the physical and physiological effects of the interaction between robots and humans. In this paper we present our development of the head and hands motion capture systems as additional modules for the Waseda Bioinstrumentation system No.1 (WB-1). The preliminary experimental results, given the inexpensiveness of the systems, are good for our purposes.

This paper presents the preliminary performance evaluation of our new wireless ultra-miniaturized inertial measurement unit (IMU) WB-4 by compared with the Vicon motion capture system. The WB-4 IMU primarily contains a mother board for motion sensing, a Bluetooth module for wireless data transmission with PC, and a Li-Polymer battery for power supply. The mother board is provided with a microcontroller and 9-axis inertial sensors (miniaturized MEMS accelerometer, gyroscope and magnetometer) to measure orientation. A quaternion-based extended Kalman filter (EKF) integrated with an R-Adaptive algorithm for automatic estimation of the measurement covariance matrix is implemented for the sensor fusion to retrieve the attitude. The experimental results showed that the wireless ultra-miniaturized WB-4 IMU could provide high accuracy performance at the angles of roll and pitch. The yaw angle which has reasonable performance needs to be further evaluated.

WABIAN-2R is a bipedal humanoid robot which is capable of stretched knee walking with heel-contact and toe-off motions due to the presence of a 2-DoF waist and a passive joint for bending toe motion, and can thus mimic human-like walking. This paper aims to determine the similarity between the walking gait of WABIAN-2R to the natural walking gait of a human of comparable size and stature, on the factors (i) pelvic tilt, (ii) pelvic rotation, (iii) swing and stance percentage, and (iv) double limb support and single limb support percentage using inertial measurement units and force-torque sensors.