IROS2019国际学术会议论文集 1052

Upper Limb Motion Simulation Algorithm for Prosthesis Prescription and Training Dimitrios Menychtas1 Stephanie L Carey 2 Redwan Alqasemi2 and Rajiv V Dubey2 Abstract A simulation algorithm to predict expected upper limb movements of prosthesis users performing activities of daily living ADL was developed It is quite challenging to determine the right type and fi t of a prosthesis and provide appropriate training to properly utilize it The amputee care team typically uses prior experiences to provide prescription and training customized for each individual It is also very diffi cult to anticipate expected and undesired compensatory motions due to reduced degrees of freedom of a prosthesis user We have developed a tool to predict and visualize the expected upper limb movements resulting from using a prescribed prosthesis and its suitability to the needs of the amputee It is expected to help clinicians make decisions such as the type of the prosthesis and whether to include a wrist joint based on the impact it will have on the rest of the joints The main focus of this work is to use robotics based methods to simulate human use of prostheses and identify the expected posture of the limited joints on the upper limbs Unlike other works this paper does not discuss the control of the prosthesis but the posture of the body A weighted least norm inverse kinematics algorithm was used to develop a robotics based model of the upper limbs and torso Motion capture data from the subjects were used to determine the weighting matrix the algorithm required Results show that this approach provides human like simulation of joint motions and matches the motion capture data The algorithm uses the individual s anthropometrics and level of amputation to create a personalized kinematic model of the upper body and the joint motions during ADLs A graphic user interface GUI was created to allow the clinician to input the relevant data resulting in arm movements of the prospective prosthesis user A custom made visualization software was developed to display an animation performing the simulated motion It should be stressed that this work does not adjust and replay motion capture recordings but solves the inverse kinematics of the human body I INTRODUCTION The post operative rehabilitation process of an amputee is challenging and often expensive In the United States there is no national registry for people with an amputation and the exact number is unknown Despite that the number of amputees in the United States is projected to reach 3 6 million individuals by the year 2050 1 Considering that in 2012 the number of amputees was estimated to be 1 7 million individuals 2 the projection appears to be realistic Even though the clinical need for a robust rehabilitation process is undeniable the current practice is fragmented 3 A syner gistic effect of fi nancial social psychological aspects as well This work was supported by TATRC Contract W81XWH 10 1 0601 1Dimitrios Menychtas is a Ph D Graduate from the Department of Medical Engineering University of South Florida 4202 E Fowler Avenue Tampa 33612 FL USA E Mail menychtas mail usf edu 2Stephanie Carey Redwan Alqasemi and Rajiv Dubey are Faculty in the Department of Mechanical Engineering University of South Florida 4202 E Fowler Avenue Tampa 33612 FL USA as the general health status beyond the amputation creates a large variety of issues that the amputee care team needs to address Currently the rehabilitation effort is defi ned as the process of fi tting a prosthesis to a person without any ob jective measurement regarding the outcome 4 This causes the results of the prosthesis prescription and training to vary based on the experience of the prosthetist Lack of training and or sub optimal fi tting can reduce the functionality of a prosthesis user There is a perceived failure of the prosthesis to meet the performance that is expected 5 This in turn might lead to prosthesis abandonment Even in cases where the prosthetic device is adopted the user will adopt different motions than able bodied individuals when performing upper arm tasks to compensate for the reduced number of joints on the arm This different motion profi le is called compensatory motion and varies between different prosthesis users 6 Excessive compensatory motion is undesirable as it will lead to the accumulation of microtrauma on the healthy joints This may eventually lead to permanent damage making the motions of a prosthesis user even less functional Injuries due to compensatory motion are collectively called overuse syndrome 7 and it appears mostly on the shoulder neck and the intact limb 8 To avoid overuse syndrome and help the prosthesis user become as functional as possible certain criteria need to be addressed during prescription and training Those criteria of ten include 9 length of residual limb amount of soft tissue coverage presence of an adherent scar range of motion of the proximal joints muscle strength on the residual limb muscle strength in the opposite limb adequate ability to learn and retain new information adequate sensation in the residual limb desire for function desire for cosmesis patient attitude and motivation vocational interests avocational interests third party payer consideration and family preferences Based on the specifi c importance of the above criteria for each individual the rehabilitation team will choose the type of the prosthesis Certain aspects such as the length of the residual limb and the range of motion of the proximal joints are quantifi able and directly affect the motions of each person However there is limited clinical use of those quantifi able factors The work presented in this article uses the body mea surements anthropometrics of each individual to create a kinematic robotic model of the human body and simulate motions that the prospective prosthesis user should employ by the end of the training process The human upper arm is highly redundant capable to perform a variety of task in a very agile manner This makes the solution of the 2019 IEEE RSJ International Conference on Intelligent Robots and Systems IROS Macau China November 4 8 2019 978 1 7281 4003 2 19 31 00 2019 IEEE6495 inverse kinematics IK for the upper arm problematic To simulate human motion many different approaches have been tried Nakaoka et al 10 used human motion recordings to control a humanoid robot while it imitated dancing Park et al 11 used motion capture recordings to create a database of primitive motions In general the human motion is recorded altered and scaled down to fi t each model After the alteration the motions are replayed by an animation avatar or a humanoid robot However for the scope of this work the human motion needs to be reproduced by a person More importantly the simulated motions have to be refl ective of the individual s amputation level anthropometrics and prosthesis As such imitation or synthesis from previous recordings of different people will have limited clinical use Therefore a solution to the IK problem that will require only the hands trajectory and the kinematic model of the human upper body is needed Similar to previous works 12 13 a weighted generalized inverse of the Jacobian matrix is used to create a human like motion that can be displayed using an animated avatar This approach requires MoCap only to create the parameters that the IK solution will satisfy and for validation of the results It needs to be stressed that the motion is generated by solving the IK problem and not by replaying MoCap Different prostheses require a different control strategy but the user has to choose one However as Carey et al pointed out there is not enough evidence regarding the functionality of different prosthesis 14 which means a complex prosthesis will not be necessarily better The focus of this work is not the control of the prosthesis but how each prosthetic device will affect the rest of the intact joints The purpose is to create a method to evaluate the impact of the prosthesis on the compensatory motion without having to train a person for each different control method This paper describes a simulation tool for prosthesis prescription and training II METHODS AND EXPERIMENTAL SET UP A Motion Capture Joint Angle Evaluation For this study 14 able bodied individuals with an average age 30 1 12 6 mean standard deviation and eight pros thesis users amputated below the left elbow left transradial amputees using a body powered device with an average age 53 6 8 1 participated in motion capture MoCap recording sessions All subjects signed an informed consent form IRB number Pro00000991 All participants performed range of motion ROM and ADL tasks The ADL tasks included brushing hair drinking from a cup and eating using utensils knife and fork These tasks require only the upper body to be performed A refl ective marker based MoCap system Vicon Denver CO was used to record each task at 120 frames per second Post processing of the data using Nexus Vicon Denver CO and BodyBuilder C Motion Germantown MD ensured that noise was minimized and no markers were fl ickering throughout each trial The markers locations are shown in Fig 1 Fig 1 Refl ective markers positions on the human body during MoCap The green colored markers are on the right side the red colored markers are on the left side of the body The yellow colored markers belong to the torso The recorded MoCap data were used to generate the robotic model of the human body and to validate simulated results Joint angles were calculated for each joint using the helical angles method 15 16 Using this method a joint coordinate frame JCF is assigned to each joint A unitary axis of rotation is defi ned in space with respect to each JCF and the magnitude of rotation about the axis is used to describe each motion The helical angle convention is also known in the fi eld of robotics as equivalent axis angle representation 17 For this representation to have clinical meaning the axis is multiplied by the magnitude of rotation This creates a vector that has the projection of about each axis of the JCF By defi nition this vector has the joint angles about the XYZ axes as shown in Eq 1 x y z x y z 1 There are two very specifi c arguments that can be made for the use of helical angles in the biomechanics of the upper arm First the helical angles do not suffer from gimbal lock This also means that the angles are always well defi ned when complex human motion is analyzed The second benefi t is the independence of rotation order when calculating the helical angles While the rotation order is defi ned when the manipulator is designed in robotics applications it is very diffi cult to establish such rotation order in the biomechanic analysis of the upper arm The major drawback of the helical joint angles is the sensitivity to noisy data 18 especially when relatively small motions are under consideration The Euler angles are recommended for gait analysis however on the upper arm differences in the selected order can have a large impact on the calculated joint angles 18 B Creation of the Robotic Model of the Human Body The creation of the robotic model of the human body was based on Lie algebra 19 to defi ne each DoF as a screw 6496 Fig 2 Robot model of an able bodied participant using POE Out of the page axes are denoted as oop Into the page axes are denoted as itp The X axis is colored red the Y axis is colored green and the Z axis is colored blue axis 19 For the transformation matrix of the kinematic chain the product of exponentials POE is used 19 This is a departure from previous work that used DH parameters to defi ne the human body 12 13 It should be noted that the screw axis used here is the same mathematical entity as the helical axis 15 or axis of the equivalent axis angle representation that was briefl y discussed in the previous section This work will use the term helical axis to avoid confusion but the reader should be aware of the different names that are used across the literature The benefi t of using helical axes is the unifi ed framework it provides for biomechanics and robotics The total number of DoFs of the upper body model for able bodied individuals is 23 The model was adapted for the left transradial prosthesis users to refl ect the reduced number of DoFs The last three DoFs L11 L13 on the left extremity of the model were removed because of the lack of the wrist on the prosthesis Therefore the prosthesis user s model has a total of 20 DoFs The DoFs were selected as revolute to refl ect the func tionality of the joints Translation between the joints is too small for the scope of this work to be important This was done to reduce the complexity of the kinematic model to it s simplest yet still representative form Fig 2 shows the created kinematic model for an able bodied person with the respective motions for each DoF C Algorithms for Inverse Kinematics of the Human Body and Analysis of Error For a task that requires three position and three orientation coordinates one human arm has 13 DoFs meaning there are seven degrees of redundancy DoRs In order to resolve for the inverse kinematics the weighted least norm WLN solution 20 is used This method relies on a weighted generalized inverse of the Jacobian matrix When a joint velocity vector is known the Cartesian velocity vector x can be found using Eq 2 x J 2 To fi nd the joint velocity vector from the Cartesian velocity vector x the weighted generalized inverse of the non square manipulator Jacobian is used as shown in Eq 3 W 1JT JW 1JT 1 x 3 where JTdenotes the transpose of the manipulator Jacobian and W is a diagonal matrix as shown in Eq 4 that has weighting factors wito prioritize each joint When W 1 is absent or identity the Jacobian becomes the Moore Penrose inverse or pseudoinverse and the method is the least norm LN solution The LN solution distributes the motion evenly across all joints Each wican be considered inverted and it is directly implemented to the weighting matrix W 1 as shown in Eq 4 W 1 w100 0 0w20 0 00w3 0 000 wi 4 To calculate each wiEq 5 is used wi avg i avg max 5 6497 where avg iis the average joint velocity magnitude and avg imaxis the maximum average velocity that was recorded on one joint In essence all joints are normalized with respect to the most mobile joint of the task To fi nd the avg ithe absolute instantaneous joint velocity is averaged for each trial The average joint velocity shows how mobile each joint i is during the task as shown in Eq 6 avg i n P j 1 ij n 6 where ijis the velocity of the joint angle i at time j and n is the number of total data points for the trial The joint velocities are normalized based on the highest joint velocity for the trial and that creates the wi If there is only one trial then the results of Eq 5 can be used without any further manipulation However to make the algorithm more robust weights from different subjects for the same task are averaged to fi nd a single value for each joint This will result in a single factor withat is now the average of similar trials This new wiwill allow the joint to move based on the average velocity that it has during the task across subjects The resulting elements have weight values from 0 1 A value of zero will stop the joint from moving while a value of one allows for unconstrained motion A small number of subjects is required for suffi cient accuracy as long as they exhibit the same motion profi le From the 14 able bodied participants only seven were used since the average joint velocity wasn t particularly affected when more subjects were added For consistency seven pros thesis users were used to calculate their respective weighting matrices In essence as long as the joint velocity profi le is the same for a task the W 1will give reasonable simulated results for different individuals The tasks were separated to produce sets of weights specifi cally for each task WLNTask Each task has its own W 1to avoid interference with the joint priority across tasks For reference the results of the least norm LN solution where no weighting matrix is used are also presented It should be noted that previous work examined the possibility of using task specifi c range of motion ROM for each task The anatomical joint limits do not contribute to a more accurate simulation 21 because they are too large for everyday tasks This is why a task specifi c ROM was considered The concept was to bound each joint inside a specifi c range in order to avoid large motions 22 Though initial results were promising it proved challenging to estab lish a task specifi c ROM that generalizes well across multiple subjects As a result the task joint limits were abandoned in favor of the WLNTask The root mean square RMS error is used to examine the simulated motions Each calculated joint motion trajectory is compared with its respective MoCap recording After the RMS error for each joint is found the average RMS error is calculated for the whole body This is done by using the RMS error of each joint to get an average for the task This means that only the joints belonging to the arm that performs the Brush and the Drink tasks is used for their respective RMS and the Eat task is using both arms and therefore only the relevant DoFs are compared with the MoCap data To evaluate the system the weight values and the RMS error of each joint are examined The weight values have information regarding which joints are used more during each task while the average RMS error will show how accurately the motion is recreated from the MoCap D Graphical User Interface The simulation algorithm was developed in Matlab and integrated with a graphical user interface GUI developed in C that can display an animation of the simulated mo tions 23 The GUI can accept the anthropometrics of each person and their level of amputation and create the motions Additionally joint angles extracted from other sources e g MoCap can