Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

Improving human motor performance via physical guidance by an assist robot device is a major field of interest of the society in many different contexts, such as rehabilitation and sports training. In this study, we propose a Bayesian estimation method to predict whether motor performance of a user can be improved or not by the robot guidance from the user's initial skill level. We designed a robot-guided motor training procedure in which subjects were asked to generate a desired circular hand movement. We then evaluated the tracking error between the desired and actual subject's hand movement. Results showed that we were able to predict whether a novel user can reduce the tracking error after the robot-guided training from the user's initial movement performance by checking whether the initial error was larger than a certain threshold, where the threshold was derived by using the proposed Bayesian estimation method. Our proposed approach can potentially help users to decide if they should try a robot-guided training or not without conducting the time-consuming robot-guided movement training.

DOI： 10.3389/fnins.2021.704402

PubMed

Publishing type：Research paper (scientific journal)  

Previous works in the literature have claimed that the characteristics of electromyography (EMG) signals depend on each person, and thus, EMG interfaces need to be carefully calibrated for each user in myoelectric control. In this study, we show that the EMG interface used to estimate the joint torques of a user can be constructed simply by incorporating other users' data without typical calibration process. To achieve this plug-and-play capability, we introduce the concept of collaborative filtering to estimate the joint torque of a novel user by exploiting the preidentified relationships between motion-body features, including EMG signals, and the joint torques of other users. To validate our proposed approach, we compare the performance of estimating joint torque by the proposed method with that by conventional linear regression models as a baseline. We considered the following two baseline methods. Linear-own: The parameters of the linear model are calibrated for each subject from his/her own training data. Linear-others: The parameters of the linear model are calibrated with the other users' data in which the novel user's data are not included. As a result, the estimated joint torques from our proposed approach reveal a better estimation performance than those from the baseline approaches. Furthermore, we also successfully demonstrate online myoelectric control of an upper limb exoskeleton robot with an attached mannequin arm.

DOI： 10.1109/THMS.2021.3098115

Publishing type：Research paper (scientific journal)  

Previous works in the literature have claimed that the characteristics of electromyography (EMG) signals depend on each person, and thus, EMG interfaces need to be carefully calibrated for each user in myoelectric control. In this study, we show that the EMG interface used to estimate the joint torques of a user can be constructed simply by incorporating other users' data without typical calibration process. To achieve this plug-and-play capability, we introduce the concept of collaborative filtering to estimate the joint torque of a novel user by exploiting the preidentified relationships between motion-body features, including EMG signals, and the joint torques of other users. To validate our proposed approach, we compare the performance of estimating joint torque by the proposed method with that by conventional linear regression models as a baseline. We considered the following two baseline methods. Linear-own: The parameters of the linear model are calibrated for each subject from his/her own training data. Linear-others: The parameters of the linear model are calibrated with the other users' data in which the novel user's data are not included. As a result, the estimated joint torques from our proposed approach reveal a better estimation performance than those from the baseline approaches. Furthermore, we also successfully demonstrate online myoelectric control of an upper limb exoskeleton robot with an attached mannequin arm.

DOI： 10.1109/THMS.2021.3098115

Publishing type：Research paper (scientific journal)  

DOI： 10.14900/cjpt.47S1.F-144_2

CiNii Article

J-GLOBAL

Publishing type：Research paper (scientific journal)  

DOI： 10.14900/cjpt.47s1.f-144_2

CiNii Article

J-GLOBAL

Publishing type：Research paper (international conference proceedings)  

© 2018 IEEE. Haptic guidance is a motor training procedure in which the subject is physically guided through an ideal motion by an haptic interface. It is especially valuable for repetitive training, and more effective when coupled with additional sensory feedback, e.g. the visual modality. The advantage of additional feedback modalities may stem from the learner's increased active participation and engagement. Here, we test this hypothesis by analyzing the learners' brain state during haptic training. Specifically, we focus on the sensorimotor rhythms (SMR), since they have been previously associated with the level of engagement directed towards a motor task. We conducted a circle-drawing haptic training, where subjects were asked to memorise the guided trajectory while their electroencephalogram (EEG) was recorded. During the experiment, only the haptic modality was maintained (i.e. unimodal) by keeping the task workspace visually hidden. Results show a clear trend: subjects who exhibited a performance improvement, were characterized by a stronger desynchronization of the beta rhythms over the contralateral hemisphere. This is in agreement with recent studies showing that contralateral beta rhythms changes are associated with motor skills retention. Moreover, under the assumption that SMR are indeed a marker of engagement, our results represent accumulating evidence that active participation is crucial for haptic training.

DOI： 10.1109/SMC.2018.00098

Publishing type：Research paper (international conference proceedings)  

Haptic guidance is a motor training procedure in which the subject is physically guided through an ideal motion by an haptic interface. It is especially valuable for repetitive training, and more effective when coupled with additional sensory feedback, e.g. the visual modality. The advantage of additional feedback modalities may stem from the learner's increased active participation and engagement. Here, we test this hypothesis by analyzing the learners' brain state during haptic training. Specifically, we focus on the sensorimotor rhythms (SMR), since they have been previously associated with the level of engagement directed towards a motor task. We conducted a circle-drawing haptic training, where subjects were asked to memorise the guided trajectory while their electroencephalogram (EEG) was recorded. During the experiment, only the haptic modality was maintained (i.e. unimodal) by keeping the task workspace visually hidden. Results show a clear trend: subjects who exhibited a performance improvement, were characterized by a stronger desynchronization of the beta rhythms over the contralateral hemisphere. This is in agreement with recent studies showing that contralateral beta rhythms changes are associated with motor skills retention. Moreover, under the assumption that SMR are indeed a marker of engagement, our results represent accumulating evidence that active participation is crucial for haptic training.

DOI： 10.1109/SMC.2018.00098

Other URL： https://dblp.uni-trier.de/db/conf/smc/smc2018.html#TakaiRLNTIM18

Publishing type：Research paper (scientific journal)  

© 2018 Lisi, Rivela, Takai and Morimoto. Quick detection of motor intentions is critical in order to minimize the time required to activate a neuroprosthesis. We propose a Markov Switching Model (MSM) to achieve quick detection of an event related desynchronization (ERD) elicited by motor imagery (MI) and recorded by electroencephalography (EEG). Conventional brain computer interfaces (BCI) rely on sliding window classifiers in order to perform online continuous classification of the rest vs. MI classes. Based on this approach, the detection of abrupt changes in the sensorimotor power suffers from an intrinsic delay caused by the necessity of computing an estimate of variance across several tenths of a second. Here we propose to avoid explicitly computing the EEG signal variance, and estimate the ERD state directly from the voltage information, in order to reduce the detection latency. This is achieved by using a model suitable in situations characterized by abrupt changes of state, the MSM. In our implementation, the model takes the form of a Gaussian observation model whose variance is governed by two latent discrete states with Markovian dynamics. Its objective is to estimate the brain state (i.e., rest vs. ERD) given the EEG voltage, spatially filtered by common spatial pattern (CSP), as observation. The two variances associated with the two latent states are calibrated using the variance of the CSP projection during rest and MI, respectively. The transition matrix of the latent states is optimized by the "quickest detection" strategy that minimizes a cost function of detection latency and false positive rate. Data collected by a dry EEG system from 50 healthy subjects, was used to assess performance and compare the MSM with several logistic regression classifiers of different sliding window lengths. As a result, the MSM achieves a significantly better tradeoff between latency, false positive and true positive rates. The proposed model could be used to achieve a more reactive and stable control of a neuroprosthesis. This is a desirable property in BCI-based neurorehabilitation, where proprioceptive feedback is provided based on the patient's brain signal. Indeed, it is hypothesized that simultaneous contingent association between brain signals and proprioceptive feedback induces superior associative learning.

DOI： 10.3389/fnins.2018.00024

PubMed

Publishing type：Research paper (international conference proceedings)  

© 2018 IEEE. This paper introduces an approach that robotizes an ankle-foot orthosis (AFO). In particular, toward post-stroke gait rehabilitation, we robotize a double-bar AFO, which is widely used in rehabilitation facilities, by newly designing a modular joint, a pneumatic actuator, and a Bowden cable force-transmission system. Our modular joint system, called the Modular Exoskeletal Joint (MEJ), has a hollow shaft for simple attachment to an AFO's pivot. We designed MEJ to compactly house an encoder that is built in a bearing in a pulley. We adopted Bowden cables to transmit contraction forces from an actuator to the MEJ. As an actuation scheme, we developed the Nested-cylinder Pneumatic Artificial Muscle (NcPAM) system. Even though PAMs are mechanically compliant and lightweight, they can still generate a large force. Therefore, they can provide an ideal actuation system for exoskeletal robots. The nested-cylinder in NcPAM houses a cable-tensioning spring to properly maintain small cable tension for passive movements and a cable stopper to connect the PAM and the cable for properly transmitting the large force generated by PAM. We show the ankle-joint trajectory tracking performances of this integrated system using iterative learning control.

DOI： 10.1109/ICRA.2018.8462911

Publishing type：Research paper (international conference proceedings)  

© 2017 IEEE. In this study, we propose a databasedriven torque estimation approach for EMG-based robot control. For conventional EMG-based controllers, torque estimation models need to be carefully calibrated to control robots that have multiple degrees of freedom. However, such a calibration procedure requires significant effort and restricts the applications of EMG-based methods to practical situations. To cope with this issue, we use large-scale data acquired from other users to avoid the calibration process and propose collaborative filtering to estimate the joint torque of a new user by exploiting the previously derived relationships between the EMG signals and the joint torque of other users. To validate our proposed method, we compared the joint torque estimation performance with a standard linear conversion model. In our experiments, we controlled an upper-limb exoskeleton robot with the estimated joint torque where we used 16-ch electrodes to measure the EMG signals of subjects. In a comparison, our proposed method showed comparable control performance with the standard approach that requires a careful calibration process.

DOI： 10.1109/HUMANOIDS.2017.8246934

Publishing type：Research paper (scientific journal)  

Sit-to-stand (STS) rehabilitation equipment traditionally simulates natural movements of unspecified healthy people. This type of movement may not place the lowest load on a patient's body. These factors let us develop a novel and widely applicable system for rehabilitation purposes that suggests a motion that places low body loads at the lower limb joints. This paper describes the core computation in the system that calculates a STS movement that places a minimum body load by using Continuous Genetic Algorithm (CGA). The minimization process starts from measurement of kinematic and kinetic data of a human subject by using gyroscopic sensors and reaction force plates, and then estimation and evaluation of the body load are followed. The body load which is minimized during STS movement was quantified by an index value. The index value strongly correlates with the chair height. Decreasing this index value by changing the movement during STS transfer could reduce the impact on the body, which is the same amount of load placed on the body while standing up from a higher chair height. The angular displacements of the averaged natural STS movement of the subject were considered as one of the several ways to stand up and the movements were assessed by means of the index value in CGA. A modified STS movement that can minimize the body load was computed after the optimization and it could be a personalized optimum movement allowing users to conduct rehabilitation with lesser unnecessary body load.

DOI： 10.1299/transjsme.2014trans0064

CiNii Article

J-GLOBAL

Publishing type：Research paper (bulletin of university, research institution)  

Sit-to-stand (STS) rehabilitation equipment traditionally simulates natural movements of unspecified healthy people. This type of movement may not place the lowest load on a patient's body. These factors let us to develop a novel and widely applicable system for rehabilitation purposes that suggests a motion that places low body loads at the lower limb joints. This paper describes the core computation in the system that calculates a STS movement that places a minimum body load by using continuous genetic algorithm. The minimization process starts from measurement of kinematic and kinetic data, and then estimation and evaluation of the body load are followed. The body load which is minimized during STS movement was quantified by an index value. The index value strongly correlates with the chair height. Decreasing the value of this index by changing the movement during STS transfer could reduce the impact on the body, which is the same amount of load placed on the body while standing up from a higher chair height. A modified STS movement that can minimize the body load could be a personalized optimum movement allowing users to conduct rehabilitation with lesser unnecessary body load.

CiNii Article

Publishing type：Research paper (international conference proceedings)  

We present a navigational interface for users of assistance robots. The interface suggests a motion that places low body load at the lower joints, carries a low risk of falls, and requires high voluntary activation of muscles. We describe an application that uses animations to provide feedback on a user's sit-to-stand motion in order to demonstrate the potential benefits of such an interface. © 2013 IEEE.

DOI： 10.1109/HRI.2013.6483589

Publishing type：Research paper (international conference proceedings)  

The natural standing-up movements of unimpaired people are often thought to be models to follow in rehabilitation, although it is not known whether these movements place a minimum body load. We aim to determine the angular trajectories of the ankle, knee, and hip joints when standing up from a chair without unnecessary body load. The minimized body load was quantified by an index value that has a strong negative correlation with the chair height. Decreasing the value of this index by changing the movement during sit-to-stand transfer could reduce the impact on the body, which is the same amount of load placed on the body while standing up from a higher chair height. We have also developed a novel integrated system that visualizes the differences between a patient's current movement and a load minimized movement with the associated kinematic and kinetic data. The visual supplement system could be used to help users see how much force and moment are placed on lower limb joints, and provide personalized optimum movements for rehabilitation with reduced body load on patients. An example minimization of an index value, accumulated moment of force at knee and hip, when standing up from a 45-cm-height chair is described here. If the system is properly developed, people in need could be able to repetitively exercise at any time without requiring human instructors to preserve their remaining function and improve their performance by quantitatively evaluated suggestions. © 2013 IEEE.

DOI： 10.1109/HealthCom.2013.6720680

Publishing type：Research paper (international conference proceedings)   Kind of work：Single Work  

Design approaches to exoskeletal robots traditionally aimed to achieve mechanical forces many times larger than the level achievable by the corresponding natural musculoskeletal system. This has often obstructed the user's muscles during physical rehabilitation. Moreover, mechanisms designed for excessive force generation are heavy and rigid structures with little compliance and thus are not suitable for many mobile applications. Here, we present a novel biomimetic flexible solenoid-coil artificial muscle (FSAM) fibre with inherent mechanical compliance, a high pulling stroke, and structural flexibility that suit a lightweight wearable exoskeletal robot for rehabilitation use. FSAM consists of a chain of specially designed bobbins of solenoid coils that can slide against each other. The chain allows FSAM to form an arch shape when the coils are energized; this is reminiscent of a natural muscle that bends a joint by a combined contraction of the muscle length and bending in an arch shape. This strategy of bending a joint not only reduces the need for linear contraction but also allows the exoskeletal to be compliant with the contraction morphology of natural muscle. © 2013 IEEE.

DOI： 10.1109/ICCAS.2013.6704071

Publishing type：Article, review, commentary, editorial, etc. (scientific journal)  

BMI技術は、装着型のロボットを駆動するインタフェースとしては、ロボットが身体の一部のように制御できるようになるという意味において究極の形態である。現在のBMI技術は多自由度のロボットを意のままに動かすほどの情報を脳から抽出する方法をまだ持たないが、一方で、リハビリテーションなど医療応用には大きな期待が持たれている。また、ここに来て著名なハイテク企業がBMIの技術革新に向けた意欲を示しており今後がますます注目すべき研究領域となってきた。本稿では、BMI技術を用いて装着型のロボットを駆動する際に、そのシステムに含まれる要素技術について概説する。(著者抄録)

Publishing type：Article, review, commentary, editorial, etc. (scientific journal)  

BMI技術は、装着型のロボットを駆動するインタフェースとしては、ロボットが身体の一部のように制御できるようになるという意味において究極の形態である。現在のBMI技術は多自由度のロボットを意のままに動かすほどの情報を脳から抽出する方法をまだ持たないが、一方で、リハビリテーションなど医療応用には大きな期待が持たれている。また、ここに来て著名なハイテク企業がBMIの技術革新に向けた意欲を示しており今後がますます注目すべき研究領域となってきた。本稿では、BMI技術を用いて装着型のロボットを駆動する際に、そのシステムに含まれる要素技術について概説する。(著者抄録)

J-GLOBAL

Publishing type：Article, review, commentary, editorial, etc. (scientific journal)  

脳卒中後の麻痺の回復訓練において、実用化されているアシストロボットは主に股関節・膝関節をアシストの対象としており、末端効果器となる足関節の底背屈運動は出力重量比に制限があるため、アプローチが難しかった。空気圧人工筋を用いて短下肢装具を駆動するアプローチは広く研究されているが、簡便性や装着性に課題が残っていた。そこで我々は、既に広く普及している歩行用の短下肢装具にモジュール関節を取り付けてロボット化することを開発コンセプトとし、出力重量比で優れる空気圧人工筋の力をワイヤで伝達し、簡便に駆動することを特徴とした足関節用のロボット装具を開発した。本稿では、ワイヤ引き空気圧人工筋およびモジュール関節の開発と、歩行機能のリハビリテーションに向けた取り組みについて紹介する。(著者抄録)

Publishing type：Article, review, commentary, editorial, etc. (scientific journal)  

脳卒中後の麻痺の回復訓練において、実用化されているアシストロボットは主に股関節・膝関節をアシストの対象としており、末端効果器となる足関節の底背屈運動は出力重量比に制限があるため、アプローチが難しかった。空気圧人工筋を用いて短下肢装具を駆動するアプローチは広く研究されているが、簡便性や装着性に課題が残っていた。そこで我々は、既に広く普及している歩行用の短下肢装具にモジュール関節を取り付けてロボット化することを開発コンセプトとし、出力重量比で優れる空気圧人工筋の力をワイヤで伝達し、簡便に駆動することを特徴とした足関節用のロボット装具を開発した。本稿では、ワイヤ引き空気圧人工筋およびモジュール関節の開発と、歩行機能のリハビリテーションに向けた取り組みについて紹介する。(著者抄録)

J-GLOBAL

Publishing type：Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)  

CiNii Article

Publishing type：Research paper, summary (national, other academic conference)  

J-GLOBAL

Publishing type：Article, review, commentary, editorial, etc. (scientific journal)  

J-GLOBAL

Publishing type：Research paper, summary (national, other academic conference)  

J-GLOBAL

Publishing type：Research paper, summary (national, other academic conference)  

DOI： 10.1299/jsmekansai.2014.89._7-17_

CiNii Article

Publishing type：Article, review, commentary, editorial, etc. (scientific journal)  

Sit-to-stand (STS) rehabilitation equipment traditionally simulates natural movements of unspecified healthy people. This type of movement may not place the lowest load on a patient's body. These factors let us to develop a novel and widely applicable system for rehabilitation purposes that suggests a motion that places low body loads at the lower limb joints. This paper describes the core computation in the system that calculates a STS movement that places a minimum body load by using continuous genetic algorithm. The minimization process starts from measurement of kinematic and kinetic data, and then estimation and evaluation of the body load are followed. The body load which is minimized during STS movement was quantified by an index value. The index value strongly correlates with the chair height. Decreasing the value of this index by changing the movement during STS transfer could reduce the impact on the body, which is the same amount of load placed on the body while standing up from a higher chair height. A modified STS movement that can minimize the body load could be a personalized optimum movement allowing users to conduct rehabilitation with lesser unnecessary body load.

DOI： 10.1299/jsdd.7.488

CiNii Article

Publishing type：Research paper, summary (national, other academic conference)  

J-GLOBAL

Publishing type：Research paper, summary (national, other academic conference)  

DOI： 10.1299/jsmekansai.2013.88._8-3_

CiNii Article

Publishing type：Research paper, summary (national, other academic conference)  

J-GLOBAL

Publishing type：Research paper, summary (national, other academic conference)  

J-GLOBAL

Publishing type：Research paper, summary (national, other academic conference)  

DOI： 10.1299/jsmekansai.2011.86._10-6_

CiNii Article

Publishing type：Research paper, summary (national, other academic conference)  

J-GLOBAL

Publishing type：Research paper, summary (national, other academic conference)  

J-GLOBAL

Publishing type：Research paper, summary (national, other academic conference)  

In recent years, the number of senior citizens is increasing and the number of elderlies who complain of backache and arthralgia also tends to increase. This study attempts to comprehend the dynamic behaviors of a human rising from a chair through stand-up experiments using gyro sensors and force plate, and individual differences in rising motion are considered. In addition, authors develop the 2D analytical model and the equation of motion of a rising human and evaluate joint torque during rising performances by inverse analyses. The experimental results showed that standing-up motion changes depending on the chair height in general, but the motion for a particular subject does not depend on the chair height. Moreover, it is found that individual differences in rising motion are large for taller chairs, but not for lower chairs.

DOI： 10.1299/jsmemecjo.2010.7.0_419

CiNii Article

Presentation type：Oral presentation (general)  

DOI： 10.23919/ICCAS59377.2023.10317053

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Venue：大阪  

Presentation type：Oral presentation (invited, special)  

Venue：Tokyo, Japan  

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Presentation type：Poster presentation  

Venue：大阪公立大学  

Presentation type：Poster presentation  

Venue：九州大学  

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Presentation type：Oral presentation (general)  

Presentation type：Oral presentation (general)  

Presentation type：Oral presentation (general)  

Presentation type：Oral presentation (general)  

Presentation type：Oral presentation (general)  

Presentation type：Oral presentation (general)  

Presentation type：Oral presentation (general)  

Presentation type：Oral presentation (general)  

Presentation type：Oral presentation (general)  

Presentation type：Oral presentation (general)  

Presentation type：Oral presentation (general)  

property_type：Patent 

Application no：特願2018-521765 

Patent/Registration no：特許第6850026号 

J-GLOBAL

property_type：Patent 

Application no：特願2015-030778 

Announcement no：特開2016-150206 

Publication no：特開2016-150206 

Patent/Registration no：特許第6579507号 

J-GLOBAL