Bai, Shaoping

Rasmussen, John

English

    Aalborg UniversitetModelling of Physical Human-Robot Interaction for Exoskeleton DesignsBai, Shaoping; Rasmussen, JohnPublished in:Proc. of Multibody Dynamics 2011, ECCOMAS Thematic ConferencePublication date:2011Document VersionPublisher's PDF, also known as Version of recordLink to publication from Aalborg UniversityCitation for published version (APA):Bai, S., & Rasmussen, J. (2011). Modelling of Physical Human-Robot Interaction for Exoskeleton Designs. In J.C. Samin, & P. Fisette (Eds.), Proc. of Multibody Dynamics 2011, ECCOMAS Thematic ConferenceGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            ? 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Fisette (eds.)Brussels, Belgium, 4-7 July 2011MODELLING OF PHYSICAL HUMAN-ROBOT INTERACTION FOREXOSKELETON DESIGNSShaoping Bai and John RasmussenDepartment of Mechanical and Manufacturing Engineering, Aalborg UniversityFibigerstraede 16, 9220 Aalborg, Denmarke-mail: shb@m-tech.aau.dk; jr@m-tech.aau.dkKeywords: Exoskeleton, musculoskeletal modelling, physical human-robot interaction.biomechanically optimized robot design.Abstract. Light-weight exoskeletons can be used for motion assistance for the elderly or thedisabled in their daily lives and for training patients under rehabilitation, for which the physi-cal human-robot interaction is a major concern for a safe and comfortable usage. A model thatwill help better understanding, estimating and analyzing the physical human-robot interactionis desirable for the purpose of design and control. In this paper, a modelling method compris-ing the biomechanics of human body as well as the robot dynamics of the exoskeleton wasproposed for the modelling of physical human-robot interaction. A human-robot model wasdeveloped, which integrates the musculoskeletal model of the human body and an exoskeletonarm. The model is able to estimate the muscles activities in cooperative motions and enablesthe design analysis and optimization of robotic exoskeletons.1ISBN 978-2-8052-0116-5Shaoping Bai and John Rasmussen1 IntroductionAn exoskeleton is a wearable robot attached to the human body to influence or assist humanmotion. Light-weight exoskeletons can be used for motion assistance for the elderly or thedisabled in their daily lives and for training patients under rehabilitation [1]. While they havethe potential to allow users to regain an independent lifestyle, the safe and comfortable interac-tion with human limbs remains as a major design challenge. Different engineering approacheshave been proposed by introducing new actuators and control methods to improve the physi-cal interaction between human and robot [2], but they have not achieved satisfying results. Itappears that the interaction between the two systems depends on the exoskeleton’s mechani-cal properties, its controlled movement, and also the limb’s biological features and activity. Acomprehensive model which could evaluate the influences of these factors is desired. However,very few models are available for this purpose. The challenge of modelling lies in the com-plicated interaction in which the robotic motion is coupled with human bodies, for which theestimation of motion and physiological parameters is very difficult. Moreover, the response ofthe human body to robotic assistance is not well understood yet.Attempts have been made to understand the biomechanics of the physical human-robot inter-action. Biomechanics of human body in selected activities of daily life was reported in [3]. Amodel of musculoskeletal kinematics, dynamics and actuation of the human body was devel-oped for robotics applications by Khatib et al. [4]. Since previous studies are mainly focusedon the human body alone, biomechanics considered in exoskeleton design is limited to an an-thropometric (morphological) analogy [5]. A notable research in the the human-robot biome-chanical modelling was reported in [6], in which Lee et al. investigated the interaction biome-chanics at the musculoskeletal level for rehabilitation devices. The work was conducted with abiomechanical modelling system, namely, the AnyBody Modeling System [7]. The AnyBodymodelling system provides the possibility to investigate the interaction biomechanics at themusculoskeletal level.In this paper, a modelling method by comprising the biomechanics of human body as well asthe multibody dynamics of exoskeleton was developed for the modelling of physical human-robot interaction for exoskeleton designs. The model deals with the human body’s biomechan-ics at the musculoskeletal level, considering the influence of robotic assistance, which is ableto estimate the muscles activities in cooperated motions. Such a model enables the designanalysis of robotic exoskeletons and also for the optimization of a system.2 ModellingA robotic exoskeleton is a typical bio-robotic system which consists two essential subsystems,the human body represented by a musculoskeletal model and the exoskeleton modelled as arobotic multi-body system. The two systems are of different properties: the biomechanics ofhuman body studied mainly by means of experiments and simulation, comparing the analyticalrobot dynamics. In this work, the study of human-body biomechanics and robot dynamicsis integrated in the AnyBody modelling system, where the estimation of the biomechanicalresponse of the human body is formulated as an optimization problem.2ISBN 978-2-8052-0116-5Shaoping Bai and John Rasmussen2.1 BiomechanicsIt is well known that a human body has more muscles than degrees of freedom. A muscu-loskeletal model describing the biomechanics of muscles and bones is thus statically indeter-minate. The muscle recruitment can be formulated as an optimization problem as:min obj(f (M))s.t. Cf = df (M) ≥ 0, i ∈ {1, . . . , n} (1)where f is composed of a n-dimensional vector of muscle forces, f (M), andjoint reactions f (R). The coefficient matrix C is built from the arm anatomyand muscle attachments, while d is the external force applied on the body.Figure 1: A human-exoskeleton modelThe choice of objective function depends muscle recruitment cri-terion. The possible criteria include the quadratic, polynomial andmin/max muscle recruitment, etc [8]. The polynomial criterion isadopted hereobj(f (M)) =f(M)iNip(2)where Ni is the strength of a muscle. The ratio f(M)i /Ni is referredas the muscle activity. The power p indicates the synergy of muscles.A value of p = 3 yields good results for most submaximal muscleefforts.2.2 Robot DynamicsThe equation of motion of a n-dof open-chain manipulators can beexpressed asM(q)q̈ + c(q, q̇) + e(q) = τ (3)where q, q̇ and q̈ are n-dimensional vectors of generalized joint positions, velocities, andaccelerations of the exoskeleton, respectively. M is the n × n general inertial matrix, c(q, q̇)is a n-dimensional vector representing the Coriolis and centrifugal forces. and e(q) is a n-dimensional vectors accounting for the force due to gravity. In the equation, τ is the vector ofgeneral external forces applied at joints.When there are forces applied to the end-effector, the EOM becomesM(q)q̈ + c(q, q̇) + e(q) = τ + JTF (4)where J is the Jacobian of the exoskeleton arm. For the human-robot system, Eq. (4) is addedto eq . (1) as an additional constraint.2.3 A Human-Robot ModelA human-robot model was developed with an exoskeleton and a musculoskeletal model, asshown in Fig. 1. The exoskeleton arm has two rotational degree-of-freedoms (dof), one at theshoulder and one at the elbow. The shaft of the shoulder joint is grounded, which in practice3ISBN 978-2-8052-0116-5Shaoping Bai and John RasmussenTable 1: Mass properties of the exoskeletonmass Moment of Inertia [Ixx, Iyy, Izz]The forearm 1.5kg [0.01, 0.01.0.15]kg ·m2The upper arm 2.5kg [0.02, 0.02.0.2]kg ·m2means that it is fixed to, for instance, a wheel chair. The musculoskeletal model is sized asa 50th percentile European male. Only the upper trunk and the right arm are included in thismodel.The model was developed with the AnyBody modelling system [7], by taking advantage ofa repository of musculoskeletal models for implementation. The musculoskeletal model iscomprised of 39 joints and 134 muscles. Hill-type muscle models are used, which consist ofthree elements, namely, the contractile element (CE) that generates force and represents themuscle fibers, the passive element (PE) in parallel with CE, and a serial elastic element (T)connected in series with the CE and PE.The exoskeleton was first built in SolidWorks and then was exported to AnyBody. All jointsare needed to be redefined in the AnyBody. The attachment of the exoskeleton to the humanwrist is modelled as a spherical joint.3 Results and DiscussionA simulation of the physical human-robot interaction was conducted for a cooperative motionof the exoskeleton and a human-arm in lifting a payload of F = 50N . In the simulated motion,the shoulder and the elbow joints rotate at angular velocities of 15◦/s and 5◦/s. The maximuminput torques applied to the shoulder and elbow joints, namely, Ms and Me, were defined,varying in the range of [5, 30]N.m, which implies a variable assistive input power to the humanbody. The maximum input torques were increased without overloading the muscles.In Fig. 2, activities of three selected muscles of the upper limb are displayed. These three mus-cles include the biceps, brachialis and br. radialis, which are considered as the major exten-sor/flexors of arm motion. Note that each of these muscles is composed of two to six muscles,which are modelled separately in the AnyBody. We evaluate the muscle activities by taking themean activity of muscles in each group. It is seen that these muscles exert different strengthsin the four cases of simulation with the payload of F = 50N . The variations of the maximumactivities of all muscles in the upper limb are displayed in Fig. 3a, showing together with theinterfacing forces in Fig. 3b. The influence of the assistive torques on the muscle activities issignificant, comparing the muscle activities in carrying the same payload without assistance asshown in Fig. 4.In another simulation, the physical human-robot interaction with a payload of F = 20N ap-plied at the hand was conducted. This is a light-duty manipulation like a daily activity. Theupper bounds of the input torques were defined in the range of [3, 10]N.m. The maximummuscle activities for all muscles and the interfacing forces are shown in Fig. 6, while the mus-cle activities of individual muscles are not included.The effect of the input power level on the muscle activities for both simulations are shown inFig. 5, where the input power index is defined asPI =ÈP 21 + P22 /F4ISBN 978-2-8052-0116-5Shaoping Bai and John Rasmussen0 1 2 3 4 50.20.250.30.350.40.450.5t[s]Muscle Activities  bicepsbrachialisbra. radialis0 1 2 3 4 500.050.10.150.20.250.30.35t[s]Muscle Activities  bicepsbrachialisbra. raidalis(1) Ms = 5,Me = 5[N.m] (2) Ms = 10,Me = 10[N.m]0 1 2 3 4 500.050.10.150.20.250.30.35t[s]Muscle Activities  bicepsbrachialisbra. radialis0 1 2 3 4 500.050.10.150.20.25t[s]Muscle Activities  bicepsbrachialisbra. radialis(3) Ms = 20,Me = 20[N.m] (4) Ms = 30,Me = 30[N.m]Figure 2: Comparisons of muscle activities with different level of assistive torques. The muscle activity is ex-pressed in percentage of maximum voluntary contraction0 1 2 3 4 50.20.250.30.350.40.45t[s]Maximum Muscle Activities  case 1case 2case 3case 40 1 2 3 4 520304050607080t[s]Interfacing Force [N]  case 1case 2case 3case 4(a) (b)Figure 3: Variations of (a) maximum muscle activities and (b) interfacing forces for the four cases of Fig. 25ISBN 978-2-8052-0116-5Shaoping Bai and John Rasmussen0 1 2 3 4 50.20.250.30.350.40.450.50.550.60.65t[s]Muscle Activities  bicepsbrachialisbra. radialismax. muscle activitiyFigure 4: Muscle activity with carrying a payload of 50N without assistance            	                     	         Figure 5: Influences of input power level on the muscle activitieswith P1, P2 are the input powers at the shoulder and elbow joints, which are obtained from theproducts of the input torque and the joint angular velocity. It is observed that the maximumactivities decrease with an increased input-power level in the two cases of simulation.The results of the above simulation provide useful information for the motor sizing of assistiverobot designs. In a lightweight robot, the weight of motors and transmissions accounts forabout half of the total weight. By reducing the size of the motors, the robot weight can beeffectively reduced. The developed model can be used to evaluate the muscle activities formotors of given size. By combining with some available robot design optimization method [9],the new model can lead to biomechanically optimized robot design, which will be safe andcomfortable to use. In addition, the model can be considered for the simulation of rehabilitationtraining with varying input powers.While the simulations can estimate the changes of maximum muscle activities for a given levelof input power, the results show that the changes do not follow exactly the same pattern, ascan be observed in Fig. 5. This indicates the complexity of the body’s response to the roboticassistance. More simulation and analysis, preferably with experimental validation, are desired.4 ConclusionsA preliminary model was developed with a 2-dof exoskeleton and a musculoskeletal humanmodel. The simulation results show that the model is able to estimate the muscle the activitiesand reactions forces at interface. The simulation results reveal a different response of body tovaried input powers and payloads. Further investigations are required to identify the body’sresponse to the robotic assistance for a better understanding of the physical interaction and for6ISBN 978-2-8052-0116-5Shaoping Bai and John Rasmussen0 1 2 3 4 50.120.140.160.180.20.220.240.26t[s]Maximum Muscle Activities  case Acase Bcase Ccase DFigure 6: Simulation with a payload of 20N for the maximum muscle activities. The input torque limits (unit:N.m) for four simulated cases are: (A) Ms = 3,Me = 3, (B) Ms = 5, Me = 5,(C) Ms = 10, Me = 5, and (D)Ms = 10,Me = 10the design optimization.REFERENCES[1] E. Guizzo and H. Goldstein. The rise of the body bots [robotic exoskeletons]. IEEE Spectrum,42(10):50–56, 2005.[2] A. Bicchi and G. Tonietti. Fast and soft arm tactics: Dealing with the safety-performance trade-offin robot arms design and control. IEEE Robotics and Automation Magazine, 11(2):22–33, 2004.[3] J. Rosen, J. C. Perry, N. Manning, S. Burns, and B. Hannaford. The human arm kinematics anddynamics during daily activities - toward a 7 dof upper limb powered exoskeleton. In Proc. 12thInt. Conf. on Advanced Robotics, pages 532– 539, 2005.[4] O. Khatib, E. Demircan, V. DeSapio, L. Sentis, T. Besier, and S. Delp. Robotics-based synthesis ofhuman motion. Journal of Physiology, 103:211–219, 2009.[5] S. Moubarak, M. T. Pham, T. Pajdla, and T. Redarce. Design and modeling of an upper extremityexoskeleton. In Proc. 11th Int. Congress on Medical Physics and Biomedical Engineering, Munich,Germany, 2009.[6] Leng-Feng Lee, M.S. Narayanan, S. Kannan, F. Mendel, and V.N. Krovi. Case studies ofmusculoskeletal-simulation-based rehabilitation program evaluation. IEEE Trans. Robotics,25(3):634–638, 2009.[7] AnyBody Technology A/S. http://www.anybodytech.com/.[8] J. Rasmussen, M. Damsgaard, and M. Voigt. Muscle recruitment by the min/max criterion: Acomparative numerical study. Journal of Biomechanics, 34(3):409–415, 2001.[9] L. Zhou, S. Bai, and M. R. Hansen. Design optimization on the drive train of a light-weight roboticarm. Mechatronics, 2011. doi:10.1016/j.mechatronics.2011.02.004.7ISBN 978-2-8052-0116-5

Modelling of Physical Human-Robot Interaction for Exoskeleton Designs

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Modelling of Physical Human-Robot Interaction for Exoskeleton Designs

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