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Purpose Laparoscopy is used in many surgical specialties. Subjective reports have suggested that performing laparoscopic surgery in patients with a high body mass index (BMI) is leading to increased prevalence of musculoskeletal symptoms in surgeons. The aim of this study was to objectively quantify the impact on surgeon upper body kinematics and dynamic workload when performing simulated laparoscopy at different BMI levels. Methods Upper body kinematics and dynamic workload of novice, intermediate and expert surgeons were calculated based on measurements from inertial measurement units positioned on upper body segments. Varying thicknesses of foam were used to simulate patient BMIs of 20, 30, 40 and 50 kg/m2 during laparoscopic training. Results Significant increases in the jerkiness, angular speed and cumulative displacement of the head, torso and upper arms were found within all experience groups when subject to the 40 and 50 kg/m2 models. Novice surgeons were found to have less controlled kinematics and larger dynamic workloads compared to the more experienced surgeons. Conclusions Our findings indicate that performing laparoscopic surgery on a high BMI model worsens upper body motion efficiency and efficacy, and increases dynamic workload, producing conditions that are more physically demanding when compared to operating on a 20 kg/m2 model. These findings also suggest that the head, torso, and upper arm segments are especially affected by high BMI models and therefore exposure to patients with high BMIs may increase the risk of musculoskeletal injury when performing laparoscopic surgery.

The use of technology has been suggested as a means of allowing continued autonomous living for older adults, while reducing the burden on caregivers and aiding decision-making relating to healthcare. However, more clarity is needed relating to the Activities of Daily Living (ADL) recognised, and the types of technology included within current monitoring approaches. This review aims to identify these differences and highlight the current gaps in these systems. A scoping review was conducted in accordance with PRISMA-ScR, drawing on PubMed, Scopus, and Google Scholar. Articles and commercially available systems were selected if they focused on ADL recognition of older adults within their home environment. Thirty-nine ADL recognition systems were identified, nine of which were commercially available. One system incorporated environmental and wearable technology, two used only wearable technology, and 34 used only environmental technologies. Overall, 14 ADL were identified but there was variation in the specific ADL recognised by each system. Although the use of technology to monitor ADL of older adults is becoming more prevalent, there is a large variation in the ADL recognised, how ADL are defined, and the types of technology used within monitoring systems. Key stakeholders, such as older adults and healthcare workers, should be consulted in future work to ensure that future developments are functional and useable.

The socio-economic and health consequences of our ageing population are well documented, with older adults living in long-term care facilities amongst the frailest possessing specific and significant healthcare and social care needs. These needs may be exacerbated through the sedentary behaviour which is prevalent within care home settings. Reducing sedentary time can reduce the risk of many diseases and improve functional health, implying that improvements in health may be gained by simply helping older adults substitute time spent sitting with time spent standing or in light-intensity ambulation. This study identified the impact of 1 year of lifestyle intervention in a group of older adults living in a long-term care setting in Italy. One hundred and eleven older adults (mean age, 82.37 years, SD = 10.55 years) participated in the study. Sixty-nine older adults were in the intervention group (35 without severe cognitive decline and 34 with dementia) and 42 older adults were in the control group. Data on physical functioning, basic activities of daily living (BADL) and mood were collected 4 times, before, during (every four months) and after the 1 year of intervention. The lifestyle intervention focused on improving the amount of time spent every week in active behaviour and physical activity (minimum 150 min of weekly activities). All participants completed the training program and no adverse events, related to the program, occurred. The intervention group showed steady and significant improvements in physical functioning and a stable situation in BADL and mood following the intervention in older adults with and without dementia, whilst the control group exhibited a significant decline over time. These results suggest that engagement in a physical activity intervention may benefit care home residents with and without dementia both physically and mentally, leading to improved social care and a reduced burden on healthcare services.

Laparoscopy is a cornerstone of modern surgical care. Despite clear advantages for the patients, it has been associated with inducing upper body musculoskeletal disorders amongst surgeons due to the propensity of non-neutral postures. Furthermore, there is a perception that patients with obesity exacerbate these factors. Therefore, novice, intermediate and expert surgeon upper body posture was objectively quantified using inertial measurement units and the LUBA ergonomic framework was used to assess the subsequent postural data during laparoscopic training on patient models that simulated BMI’s of 20, 30, 40 and 50 kg/m2. In all experience groups, the posture of the upper body significantly worsened during simulated surgery on the BMI 50 kg/m2 model as compared to on the baseline BMI model of 20 kg/m2. These findings suggest that performing laparoscopic surgery on patients with severe obesity increases the prevalence of non-neutral upper body posture and may further increase the risk of musculoskeletal disorders in surgeons.

Laparoscopy is a cornerstone of modern surgical care, with clear advantages for the patients. However, it has also been associated with inducing upper body musculoskeletal disorders amongst surgeons due to their propensity to assume non-neutral postures. Further, there is a perception that patients with high body mass indexes (BMI) exacerbate these factors. Therefore, surgeon upper body postures were objectively quantified using inertial measurement units and the LUBA ergonomic framework was used to assess posture during laparoscopic training on patient models that simulated BMIs of 20, 30, 40 and 50 kg/m2. In all surgeons the posture of the upper body significantly worsened during simulated laparoscopic surgery on the BMI 50 kg/m2 model as compared to the baseline BMI model of 20 kg/m2. These findings suggest that performing laparoscopic surgery on patients with high BMIs increases the prevalence of non-neutral posture and may further increase the risk of musculoskeletal disorders in surgeons.

This paper is closed access. This is an abstract from the Fourth International Workshop on Chemical Science and Pharmaceutical Research (CSPR2017), 17–20 October 2017, Guangzhou, China.

Work-related musculoskeletal symptoms (WMSs) are reported to be increasing in surgeons performing minimally invasive procedures. Therefore, we investigated the use of inertial measurement units (IMUs) and electromyography (EMG) sensor recorders to record real-time information on the muscle movement/activity required to perform training exercises in simulated in normal and high body mass index (BMI) models.Prospective study.University hospital.Four consultant gynecologic oncology surgeons experienced in complex straight-stick (SS) laparoscopic and robotically assisted (RA) surgery.Three exercises (hoops onto pegs and wire chase) using SS and RA surgery on 2 abdominal models: normal BMI and high BMI.We measured time to complete exercise and surgeon muscle movement/activity. The time to complete all exercises was significantly lower for RA surgery as compared with SS laparoscopy (p.05 or better). The movement of the surgeons' core was significantly greater in high BMI SS laparoscopy compared with normal BMI SS laparoscopy for exercises 1 and 2 (p.001). Muscle usage, as determined by EMG peak, was significantly higher in normal BMI SS laparoscopy and even higher in high BMI SS laparoscopy but was generally flat for all normal and high BMI RA surgery exercises (p.05 or better).Detailed real-time information can be collected through IMUs/EMG sensors. Our results indicate that RA surgery requires less surgeon movements and muscle activity to complete tasks compared with SS laparoscopy, particularly in a high BMI model. The implications of these results are that RA surgery in high BMI patients may therefore have less physical impact on the surgeon compared with SS laparoscopy and may result in lower WMS rates.

Neuromotor training program are recommended as part of essential activity programs for older adults. With the growth of wearable sensor technology, wearable sensor provides a low-cost monitoring method for objective exercise assessment. Customized remote sensors combination with off-the-shelf smart phone provides a high performance and low-cost monitoring platform. A connection-oriented sensor network with Bluetooth Low Energy (BLE) provides high bandwidth encrypted communication. With multiple remotes sensors, synchronization between sensors is fundamental. However, the accuracy with the generic BLE time service is limited. In this paper, we present a novel method that can potentially synchronize multiple sensors with an unmodified Android Device. The method is based on timestamping the Connection Event (Anchor Point) on the Slave side. The results show an absolute error is below 1 ms across two sensors, which is ideal for motion sensing.

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.

Dementia is a global issue which is rising rapidly, with 46.8 million people worldwide living with dementia in 2015. This number is expected to increase to 74.7 million by 2030. Some researches show that some kinds of exercises, such as resistance band exercises, would help to improve the memory and other abilities in patients with dementia. However, the true effectiveness of these activities is not really known due to the lack of objective assessment of efforts done during the long intervention time. Moreover, there is no way of measuring the actual activities performed by the patient without direct observation. As a consequence, it is impossible to measure the concordance between the patient and the therapist.

The commercially available Perception Neuron motion capture (Mo-Cap) system is a cost effective and easy to use option for motion analysis. However, the accuracy of this system in a practical setting is unknown and needs to be evaluated if it is to be considered for applications that require a specific level of measurement precision. Therefore, the validity of the Mo-Cap system for estimating postural angular kinematics of the upper body was assessed. Upper body motion was evaluated through three-dimensional analysis of functional movements performed by the neck, thorax and shoulders. Range of motion (RoM) estimates were compared to Vicon using Bland-Altman analysis. Systematic biases in neutral to peak RoM differences were all ≤4.5° and random biases ≤±4.5° except for neck extension where the values were larger. The present findings suggest that the Mo-Cap system is a valid method for assessing the majority of upper body ROM to within 5°.

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.

Emotion display through facial expressions is an important channel of communication. However, between humans there are differences in the way a meaning to facial cues is assigned, depending on the background culture. This leads to a gap in recognition rates of expressions: this problem is present when displaying a robotic face too, as a robot’s facial expression recognition is often hampered by a cultural divide, and poor scores of recognition rate may lead to poor acceptance and interaction. It would be desirable if robots could switch their output facial configuration flexibly, adapting to different cultural backgrounds. To achieve this, we made a generation system that produces facial expressions and applied it to the 24 degrees of freedom head of the humanoid social robot KOBIAN-R, and thanks to the work of illustrators and cartoonists, the system can generate two versions of the same expression, in order to be easily recognisable by both Japanese and Western subjects. As a tool for making recognition easier, the display of Japanese comic symbols on the robotic face has also been introduced and evaluated. In this work, we conducted a cross-cultural study aimed at assessing this gap in recognition and finding solutions for it. The investigation was extended to Egyptian subjects too, as a sample of another different culture. Results confirmed the differences in recognition rates, the effectiveness of customising expressions, and the usefulness of symbols display, thereby suggesting that this approach might be valuable for robots that in the future will interact in a multi-cultural environment.

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.

In the last years there has been increasing interest on Inertial Measurement Units (IMUs) in several fields, because they can provide a measurement of the orientation, velocity, and acceleration at low cost with high performance. The main reason of the low cost is the possibility to find inertial sensors, as accelerometers, gyroscopes and magnetometers, based on Micro-Electro-Mechanical Systems (MEMS) made by silicon, with further advantages of small dimensions and mass production from the main semiconductor suppliers. In this paper, we present the development of the ultra miniaturized wireless IMU, WB-4 (Waseda Bioinstrumentation number 4), with a 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. Additionally, we illustrate, together with results, the algorithms used for the data filtering and calibration, with particular emphasis on a new methodology for the online calibration of the digital magnetometer.

Up to now, the number of hemiplegia rehabilitation devices is increasing quickly along with hemiplegic patients'. But most of hand rehabilitation training just limit to the fingers flexible training of patients' affected hand. They not only ignore the importance of functional training of hand, but also wrist cooperative training during rehabilitation process. In our new research, we proposed a novel hand and wrist rehabilitation robot to achieve grasp functional training of hand except thumb and intorsion/extorsion and dorsiflexion/plantar flexion of wrist, which provides a creative hand rehabilitation way for hemiplegic patients. In this paper, we will introduce the detail design of the robot. It mainly includes two rehabilitation units —wrist rehabilitation unit and hand rehabilitation unit, which can realize separate motion or cooperate motion of hand and wrist based on patients' willingness. What's more, the torque sensor unit is purposely designed to detect feedback torque of related motion instead of available ones in the market, which makes the whole mechanical structure more compact. In a word, this novel hand and wrist rehabilitation robot will have a promising prospect.

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.

Up to now, the number of hemiplegia rehabilitation devices is increasing quickly. But most of ankle rehabilitation training just limit to the training of patients' affected side. They ignore the importance of patients' subjective participation in the recovery of central nervous system. In our new research, we proposed a new method that making use of the movements of the sound side to realize the passive training of the affected side. Therefore, an ankle motion detecting mechanism is proposed to detect the ankle motion of patients' sound side. In this paper, we will introduce the detail design of the mechanism. It includes three detecting units for recording the rotations of ankle. Comparing with other Body-movement information detection method, such as optical measurement method and biological signals measuring method, it can gather the real-time information of three rotations of ankle with a simple structure. This ankle motion detecting mechanism can not only detects the ankle motion but also helps patients do affected ankle strength training. Finally, the workspace analysis of this detecting mechanism is carried out to verify the ability of the designed mechanism.

Nowadays, the technologies for detecting, processing and interpreting bioelectrical signals have improved tremendously. In particular, surface electromyography (sEMG) has gained momentum in a wide range of applications in various fields. However, sEMG sensing has several shortcomings, the most important being: measurements are heavily sensible to individual differences, sensors are difficult to position and very expensive. In this paper, the authors will present an innovative muscle contraction sensing device (MC sensor), aiming to replace sEMG sensing in the field of muscle movement analysis. Compared with sEMG, this sensor is easier to position, setup and use, less dependent from individual differences, and less expensive. Preliminary experiments, described in this paper, confirm that MC sensing is suitable for muscle contraction analysis, and compare the results of sEMG and MC sensor for the measurement of forearm muscle contraction.

Robots, especially humanoids, are expected to perform human-like actions and adapt to our ways of communication in order to facilitate their acceptance in human society. Among humans, rules of communication change depending on background culture: greetings are a part of communication in which cultural differences are strong. Robots should adapt to these specific differences in order to communicate effectively, being able to select the appropriate manner of greeting for different cultures depending on the social context. In this paper, we present the modelling of social factors that influence greeting choice, and the resulting novel culture-dependent greeting gesture and words selection system. An experiment with German participants was run using the humanoid robot ARMAR-IIIb. Thanks to this system, the robot, after interacting with Germans, can perform greeting gestures appropriate to German culture in addition to a repertoire of greetings appropriate to Japanese culture.

An "ideal" upper limb prosthesis should be perceived as part of the natural body by the amputee and should replicate sensory-motor capabilities of the amputated limb. However, such an ideal "cybernetic" prosthesis is still far from reality: current prosthetic hands are simple grippers with one or two degrees of freedom (DOF), which barely restore the capability of the thumb-index pinch. This paper describes the development of a novel prosthetic hand based on a "biomechatronic" design. The proposed hand is designed to augment the dexterity of traditional prosthetic hands while maintaining approximately the same dimension and weight. Our approach is aimed at providing enhanced grasping capabilities and "natural" sensory-motor coordination to the amputee, by integrating miniature mechanisms, sensors, actuators, and embedded control. A biomechatronic hand prototype with three fingers and a total of six independent DOFs has been designed and fabricated. The paper is focused on the actuators system, which is based on miniature electromagnetic motors.

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 human hand is a complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the efforts required to the user to carry out the different movements is quite small (albeit after an appropriate and lengthy training). On the contrary, prosthetic hands are just a pale replication of the natural hand, with significantly reduced grasping capabilities and no sensory information delivered back to the user. Several attempts have been carried out to develop multifunctional prosthetic devices controlled by electromyographic (EMG) signals (myoelectric hands), harness (kinematic hands), dimensional changes in residual muscles, and so forth, but none of these methods permits the "natural" control of more than two DoFs. This article presents a review of the traditional methods used to control artificial hands by means of EMG signal, in both the clinical and research contexts, and introduces what could be the future developments in the control strategy of these devices.

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.

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.

This paper present a method for measuring the posture of a human body during different phases of sit to stand motion using inertial sensors. The proposed method fuses data from inertial sensors placed in trunk and thigh using Adaptive Neuro-Fuzzy Inference System (ANFIS) followed by a Kalman Filter (KF). The ANFIS attempts to estimate the position of shoulder of the human, at each sampling instant when measurement update step is carried out. The Kalman filter supervises the performance of the ANFIS with the aim of reducing the mismatch between the estimated and actual. The performance of the method is verified by measurements from VICON (motion analysis system). The obtained results show the effectiveness of the proposed algorithm in prediction the human shoulder position with root mean square error 0.018 m and 0.016 m in the x and y direction, respectively.

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.

Humanoid robots have this formidable advantage to possess a body quite similar in shape to humans. This body grants them, obviously, locomotion but also a medium to express emotions without even needing a face. In this paper we propose to study the effects of emotional gaits from our biped humanoid robot on the subjects' perception of the robot (recognition rate of the emotions, reaction time, anthropomorphism, safety, likeness, etc.). We made the robot walk towards the subjects with different emotional gait patterns. We assessed positive (Happy) and negative (Sad) emotional gait patterns on 26 subjects divided in two groups (whether they were familiar with robots or not). We found that even though the recognition of the different types of patterns does not differ between groups, the reaction time does. We found that emotional gait patterns affect the perception of the robot. The implications of the current results for Human Robot Interaction (HRI) are discussed.

This paper describes the bipedal humanoid robot that makes human laugh with its whole body expression and affect human’s psychological state. In order to realize "Social interaction" between human and robot, the robot has to affect human’s psychological state actively. We focused on "laugh" because it can be thought as a typical example for researching "Social interaction". Looking through a Japanese comedy style called "manzai" or the art of conversation, we picked out several methods for making human laugh. Then we made several skits with the advice of comedians, and made the whole body humanoid robot perform them. Results of experimental evaluation with these skits shows that the robot’s behavior made subjects laugh and change their psychological state seen as a decrease of "Depression" and "Anger".

A complex relationship exists between national cultural background and interaction with robots, and many earlier studies have investigated how people from different cultures perceive the inclusion of robots into society. Conversely, very few studies have investigated how robots, speaking and using gestures that belong to a certain national culture, are perceived by humans of different cultural background. The purpose of this work is to prove that humans may better accept a robot that can adapt to their specific national culture. This experiment of Human-Robot Interaction was performed in Egypt. Participants (native Egyptians versus Japanese living in Egypt) were shown two robots greeting them and speaking respectively in Arabic and Japanese, through a simulated video conference. Spontaneous reactions of the human subjects were measured in different ways, and participants completed a questionnaire assessing their preferences and their emotional state. Results suggested that Egyptians prefer the Arabic version of the robot, while they report discomfort when interacting with the Japanese version. These findings confirm the importance of a culture-specific customisation of robots in the context of Human-Robot Interaction.

A tightly-coupled stereo vision-aided inertial navigation system is proposed in this work, as a synergistic incorporation of vision with other sensors. In order to avoid loss of information possibly resulting by visual preprocessing, a set of feature-based motion sensors and an inertial measurement unit are directly fused together to estimate the vehicle state. Two alternative feature-based observation models are considered within the proposed fusion architecture. The first model uses the trifocal tensor to propagate feature points by homography, so as to express geometric constraints among three consecutive scenes. The second one is derived by using a rigid body motion model applied to three-dimensional (3D) reconstructed feature points. A kinematic model accounts for the vehicle motion, and a Sigma-Point Kalman filter is used to achieve a robust state estimation in the presence of non-linearities. The proposed formulation is derived for a general platform-independent 3D problem, and it is tested and dem...

In human-robot interaction, it is important for the robots to adapt to our ways of communication. As humans, rules of non-verbal communication, including greetings, change depending on our culture. Social robots should adapt to these specific differences in order to communicate effectively, as a correct way of approaching often results into better acceptance of the robot. In this study, a novel greeting gesture selection system is presented and an experiment is run using the robot ARMAR-IIIb. The robot performs greeting gestures appropriate to Japanese culture; after interacting with German participants, the selection should become appropriate to German culture. Results show that the mapping of gesture selection evolves successfully.

Humanoid robots progress everyday closer and closer to a more stable walking suitable for a human environment as the researchers in the Humanoid robotics field focus their effort on the understanding of the human locomotion. Nonetheless for Social Robotics researchers, humanoid robots might have another use, such as being our companions from birth to nursing home. Designing social humanoid robots is one critical step if we want the robots to be active in our society. However, to our knowledge, only a few studies in the area of humanoid robotics have addressed emotion expression with robot gaits. In this paper we propose to assess different emotional gait patterns and the perception of the emotion intensity in those patterns. Actors' emotional movement were captured and then normalized for our robot platform. Several robot simulations were shown to human observers who completed a survey questionnaire in which they indicated their assessment of the portrayed emotion by the robot simulation. The surveyed emotions consist of Sadness, Happiness, Anger, Fear with different intensities (Intermediate, High and Exaggerated). We achieved a high recognition rate of emotions (72.32%). Even if the intensities were less well recognized (33.63%), our study indicates that the intensity might help the recognition of emotional walking.

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.

In this paper, we describe a human gesture recognition system developed to make a humanoid robot understand non-verbal human social behaviors, and we present the results of preliminary experiments to demonstrate the feasibility of the proposed method. In particular, we have focused on the detection and recognition of laughter, a very peculiar human social signal. In fact, although it is a direct form of social interaction, laughter is classified as semi voluntary action, can be elicited by several different stimuli, and it is strongly associated with positive emotion and physical well-being. The possibility of recognize, and further elicit laughter, will help the humanoid robot to interact in a more natural way with humans, to build positive relationships and thus be more socially integrated in the human society.

Endotracheal Intubation (ETI) is a common airway procedure used to connect the larynx and the lungs through the windpipe of patients under emergency situations. The process is carried out by a laryngoscope inserted into the mouth, and helps doctors in viewing the glottis and inserting the tube. In 2012, we developed a humanoid robot for ETI training, called WKA-5R, which was able to simulate various patients and scenarios and to evaluate the doctor's medical skills according to applied force, position of laryngoscopes and tubes, and procedure time. In 2013, we have focused our study on the development of a system for the biomechanical evaluation during simulated ETI. We conducted an experiment with 5 expert and 6 novice anesthesiologists and measured 3 biomechanical parameters: angular velocity of wrist joint and elbow joint, surface electromyography (sEMG) of the upper limb, and some angles on the upper part of the body, in order to evaluate joint stiffness in the arm and posture during ETI. In this paper, the authors show the experimental results on comparison between experts and novices, followed by discussion.

Endotracheal Intubation (ETI) is a common airway procedure used to connect the larynx and the lungs through a windpipe in patients under emergency situations. The process is carried out by a laryngoscope inserted into the mouth, used to help doctors in visualizing the glottis and inserting the tube. Currently, very few studies on objective evaluation of the biomechanics of the doctors during the procedure have been done. Additionally, these studies have been concentrated only on the overall performance analysis, without any segmentation, with a consequent loss of important information. In this paper, the authors present a preliminary study on a methodology to objectively evaluate and segment the biomechanical performance of doctors during the ETI, using surface electromyography and inertial measurement units. In particular, the validation has been performed by comparing three kinds of laryngoscopes involving an expert doctor. Finally, results are presented and commented.

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.

EMG sensors for biomechanics research and portable applications need to be small, wireless and power efficient. This imposes several constraints on the design methodology of such sensors. There are many commercially available devices which are expensive and have proprietary communication protocols. It is difficult to synchronize them with other devices. Furthermore, using gel-based electrodes for signal acquisition increases setup time and running costs. Hence we present the design of a miniaturized, wireless, low-cost and non-invasive dry-electrode EMG sensor that was prototyped in our lab and dubbed the `WB-EMG' Sensor. We demonstrate its working being fitted with a gold plated copper core electrode. By mounting the sensor on the extensor carpiulnaris and recording data during some simple exercises, we were able to show that its envelope in the frequency domain was similar to the expected EMG Pattern.

The understanding of emotions in humans is really important in the Human-Robot Interaction field. The affective state of a person can be expressed in several ways, one of them being through the way we walk and our gait can convey emotional clues in social context. Those clues can be used to improve the personal interactions with our peers or add meaning to any message we want to express. However, only a few studies in humanoid robotics were done on the effects of the emotions on the walking. In this paper, we propose to assess the emotional walking patterns created from motion capture data with a survey. Those patterns represent different emotions (sadness, happiness) with different intensities (middle, high and exaggerated). Those emotional walking patterns achieved a high recognition rate of the emotions and the subjects (N=13) could recognize whole body emotions without facial expression on our humanoid robot. Additionally, we found out that at first people might perform poorly at recognizing emotions and their intensities but can get better, even without correction or feedback on their performances.

Cerebrovascular anastomosis (for example in the management of Moyamoya disease or complex aneurysms) is a rarely performed but essential procedure in neurosurgery. Because of the complexity of this technique and the infrequent clinical opportunities to maintain skills relevant to this surgery, laboratory training is important to develop a consistent and competent performance of cerebrovascular anastomosis. We reviewed the literature pertaining to the training practices surrounding cerebrovascular anastomosis in order to understand the ways in which trainees should best develop these skills. A wide variety of training methods have been described. These may be classified into five general categories, according to training materials used, being synthetic material, living animal, animal carcass, human cadaver, and computer simulation. Ideally, a novice begins training with non-biological material. After gaining sufficient dexterity, the trainee will be able to practice using biological materials followed by high fidelity models prior to actual surgery. Unfortunately, the effectiveness of each model has generally, to our knowledge, only been judged subjectively. Objective quantification methods are necessary to accelerate the acquisition of competence.

As witnessed in several behavioural studies, a complex relationship exists between people’s cultural background and their general acceptance towards robots. However, very few studies have investigated whether a robot’s original language and gesture based on certain culture have an impact on the people of the different cultures. The purpose of this work is to provide experimental evidence which supports the idea that humans may accept more easily a robot that can adapt to their specific culture. Indeed, improving acceptance and reducing discomfort is fundamental for future deployment of robots as assistive, health-care or companion devices into a society. We conducted a Human- Robot Interaction experiment both in Egypt and in Japan. Human subjects were engaged in a simulated video conference with robots that were greeting and speaking either in Arabic or in Japanese. The subjects completed a questionnaire assessing their preferences and their emotional state, while their spontaneous reactions were recorded in different ways. The results suggest that Egyptians prefer the Arabic robot, while they feel a sense of discomfort when interacting with the Japanese robot; the opposite is also true for the Japanese. These findings confirm the importance of the localisation of a robot in order to improve human acceptance during social human-robot interaction.

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 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 that objectively evaluates surgical movement performance 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 ultraminiaturized wearable motion capture system (Waseda Bioinstrumentation system WB-3), therefore, has been developed to measure and analyze these movements. The data processing model was trained by using the subjects' motion features acquired from the WB-3 system and further validated to classify the expertise levels of the subjects with different laparoscopic experience. Experimental results show that the proposed methodology can be efficiently used both for quantitative assessment of surgical movement performance, and for the discrimination between expert surgeons and novices.

In this paper, we describe a human gesture recognition system developed to enable a robot instrument player to recognize the variations in tempo and in articulation dictated by a conductor's movements and accordingly adapt its performance. The enhanced interaction ability would allow the partner musicians, as well as the conductor, to better appreciate a joint musical performance, because of the complete naturalness of the interaction. In addition, the possibility for the robot to change its performance parameters according to the conductor directions, thus being synchronized with all the other human musicians, would lead to an improvement in the overall musical performance.

This paper presents the performance evaluation of our wireless miniature Inertial Measurement Unit (IMU) WB-4 by compared with the Vicon motion capture system. In particular, a magnetic field calibration method is introduced to improve the sensor orientation estimate accuracy. The WB-4 IMU primarily contains a motherboard for motion sensing, a Bluetooth module for wireless data transmission with PC, and a Li-Polymer battery for power supply. The motherboard is provided with a 32-bit microcontroller and 3-axis miniaturized MEMS accelerometer, 3-axis gyroscope and 3-axis magnetometer to estimate the sensor orientation based on an extended Kalman filter algorithm. In our previous research of WB-4 IMU performance evaluation, the factory calibration parameters of the magnetometer were used for the sensor fusion, which resulted in a higher error on the yaw angle in respect to roll and pitch. This study presents a magnetic calibration method for overcoming that limitation. The experimental results showed that the wireless WB-4 IMU could achieve better orientation performance in all the directions after the implementation of the magnetic calibration method. The yaw angle accuracy was significantly improved from previous error 5.46 degree to 1.77 degree.

In this paper, we describe the mechanical design system implemented to enable a robot flute player to enhance the expressiveness of its performance, reproducing the different musical articulations with the available features of the instrument. In addition, the robot has been equipped with a Human Gesture Recognition system to recognize the real-time variations in tempo, dynamics, and articulation dictated by the conductor's movements. The possibility for the robot to change its performance parameters according to the conductor directions, thus adding expression to its performance while being synchronized with all the other human musicians, would lead to an improvement in the overall joint musical performance.

Laparoscopy is a very popular surgical technique for abdominal operations. Due to the difficulties in using the laparoscopes, the surgeons need an extensive training to acquire the right locomotive skill and perform operations. Usually, the training is performed with dry boxes with simple and repetitive exercises, like the peg-board, in which a trainee uses the instruments to move sequentially a set of rubber O rings positioned inside pegs. However, even if the dry box is a good method to train the dexterity of a trainee, it does not include the mental stress, that could be significant during the operation in the actual Operating Room (OR). In this paper we present the comparison results during a peg-board training without and with induced mental stress. In particular, the stress has been introduced by a proposed protocol adaptively setting a countdown with noisy alarm for the last 60s of the exercise. We compared the results by a biomechanical analysis using surface Electromyography, showing that the induced stress situation revealed a significant larger activation of the arms and shoulders muscles.