论文2G精华版本 ICRA 2018 files 0380

Adaptive Oscillator based Control for Active Lower Limb Exoskeleton and Its Metabolic Impact Keehong SeoKyungrock KimYoung Jin ParkJoon Kee ChoJongwon Lee Byungjune ChoiBokman LimYounbaek LeeYoungbo Shim Abstract We developed a robotic lower limb exoskeleton for those who have weakened muscle due to aging and experience diffi culty in walking or getting up without help The exoskeleton covering both limbs from the feet to the waist has 6 electric actuators in the hip abduction adduction hip extension fl exion and knee extension fl exion joints For users with volitional motion delivering assistance power according to their intention is a challenging task We propose an adaptive oscillator based controller to assist users walk in the lower limb exoskeleton To adapt to changes in walking speed and environment motion command from the controller is modulated by estimate walking speed and walking environment recognized as one of the following categories level ground stairs up down and slope up down Experimental results demonstrate the feasibility of the proposed environment recognition method and the impact of assistance on the metabolic cost of walking on level and inclined treadmills I INTRODUCTION Increasing number of people suffer from loss of gait function due to various reasons including aging and neu ropathy In industry technologies to prevent injuries of workers handling heavy loads are demanded As a solution to these issues robotic technologies are actively transferred to the domain of medical or industrial exoskeletons In the course various types of exoskeletons have been developed by universities and industrial companies Their purposes control methods and mechanisms are all different which could however fall into several categories as seen in the extensive reviews 1 2 3 Some exoskeletons like ATLAS 4 Ekso 5 ReWalk 6 were developed for paraplegic or quadriplegic people and cover full lower limb for assistance In this application wearers would have no volitional motion in the legs and the exoskeletons can only replay pre recorded leg motion when triggered Other exoskeletons like HAL 7 and SMA have been developed for those with muscle weak ness or partially impaired gait Increased mobility of stroke patients using SMA was reported in 8 A soft wearable robot Exosuit 9 has shown that it can improve mobility of post stroke patients 10 In this type of exoskeleton or exosuit the users have volitional motion and thus human robot interaction becomes a main challenge At Samsung Electronics Co Ltd Suwon Korea we have developed a series of single joint exoskeletons several hip types 11 12 13 and an ankle type 14 to support the elderly with degraded gait performance and the moderate to Authors are with Samsung Advanced Institute of Technology Samsung Electronics Co Ltd Suwon South Korea Correspondance should be sent to Keehong Seokeehong seo at mild chronic stroke patients Given the challenge of human robot interaction problem we have developed several control approaches for tasks such as walking on level ground stairs and slopes The controllers based on adaptive oscillator AO 11 13 15 16 and fi nite state machine FSM 17 18 14 19 as well as environment recognition method 20 have been proposed and validated with healthy subjects 13 15 16 and the elderly 21 Lim et al in 18 have worked on simulation based optimization of control parameters for patients with neuropathy While we are witnessing the effi cacy of the single joint exoskeletons with the target users there are people who need more than single joint assistance for example one can manage to walk alone and can be short of strength to stand up without help He or she may also need help to climb up a single step In this case a lightweight multi joint exoskeleton that is capable of partial body weight support should be helpful In this paper we present a lower limb type exoskele ton Gait Enhancing Mechatronic System for Lower Limb GEMS L with actuated hip abduction adduction a a hip extension fl exion e f and knee e f joints and passive ankle joints to partially support the body weight and its control framework The GEMS L in Fig 1 features lightweight and fi tting silhouette to minimize confl ict with the environments of daily lives To control the device we propose to use AOs An AO learns a periodic signal in terms of its frequency compo nents amplitudes and phases online The way we apply AOs to GEMS L is less complicated than other AO based approaches for lower limb exoskeletons Yan and colleagues 22 gave different control objectives at different FSM phases for knee joints while hip joints were controlled by AOs and 23 implemented force fi eld toward predicted positions from periodic motion In our approach controller obtains continuous gait phase from a single AO and then feeds it to assistance torque generators to control heterogeneous joints such as hip a a hip f e and knee f e For adaptability in changing condition the torque generation is modulated by walking speed and environment The walking environment is recognized by using a support vector machine as one of 5 classes level slope up down and stairs up down Experimental test results on the recognition accuracy and assistance performance of GEMS L is presented 2018 IEEE International Conference on Robotics and Automation ICRA May 21 25 2018 Brisbane Australia 978 1 5386 3080 8 18 31 00 2018 IEEE6752 Fig 1 A lower limb exoskeleton Samsung GEMS L II THEEXOSKELETONDESIGN GEMS L in Fig 1 is a lower limb exoskeleton to support users with their daily activities including walking sit to stand and so on Joints at hip a a hip e f and knee e f are actuated by electrical motors with maximum torque of 30 Nm Without output torque sensors motor torque is esti mated and controlled by sensing motor electrical current No foot pressure sensor is equipped the foot contact information and overall pose are estimated from 5 IMUs placed on the pelvis shank and foot segments Featuring lightweight and fi tting design the overall weight including the batteries is 9 98 kg and the extruding height of the exoskeleton out of the body silhouette is maximal as 4 2 cm at the hip joint and thigh frame To minimize unwanted tension due to misalignment of the knee joint a novel joint mechanism is devised which adapts to changes in relative joint positions of the device and the human by applying under actuated multiple rolling contact joints as presented in 24 The exoskeleton is also capable of supporting body weight of 10 kg as experimentally validated in 24 by lifting 10 3 kg from a squatting posture III CONTROLARCHITECTURE Using IMU sensors attached on the back of the waist the shanks and the feet the estimation module obtains information on the exoskeleton pose foot state and foot motion The pose describes relative position and orientation of each segment and joint angles The foot state block consists of an FSM that determines the foot state as one of the following foot impact foot rest heel rise and foot swing by using IMU signals Transition rules in the FSM was hand tuned until the foot FSM was accurate enough to use with dead reckoning The foot motion block describes the velocity of a foot in the global coordinates obtained by integrating the acceleration signals from IMU on the foot The computed foot velocity is used for environment classifi cation The dead reckoning module then processes the information of current pose and foot state to obtain spatiotemporal information of user motion As a result we can monitor the pose and spatiotemporal information of the exoskeleton on line as in Fig 3 Walking speed is also available at this stage and is fed to torque generating module The environment classifi cation module by observing se lected features from the pose and the foot motion data predicts the current walking environments into 5 classes level slope up slope down stair up and stair down The prediction is then provided to the torque generation module The foot to foot distance projected onto the sagittal plane marked in Fig 3 is computed from the pose and then used as input for an AO module which estimates the gait phase The gait phase is used by the torque generation module All the actuated DOFs are torque controlled throughout the whole gait cycle as long as the user is walking The initia tion and termination of the walking assistance is determined by the states of the left and right foot FSMs Assistance torque command for each actuated joint is generated from a predefi ned torque function of gait phase The function parameters are modulated by walking speed and walking environment Recognized walking environment is rendered continuous via a low pass fi lter to guarantee the continuity of torque command Torque patterns for hip a a and knee e f were determined by referring to the biological data in 25 and hip e f pattern was adopted from the previous study 13 on the GEMS H Torque from each motor is controlled by an off the shelf motor controller sensing the electric current and then delivered to a wearer through gears with reduction ratio of about 36 1 for the knee actuators and 47 1 for the hip e f and a a actuators It is open loop torque control without sensors to measure output torque delivered to a wearer Preliminary experiments on the actuators had been performed to identify gear effi ciency and the high level torque command is scaled up accordingly to compensate for loss The overall control architecture is illustrated in Fig 2 The AO module and environment classifi cation are further explained below A Gait Phase Estimation with AO module Particularly shaped adaptive oscillator PSAO estimates gait phase as a continuous and periodic value from 0 to 2 Unlike other AOs its basis function for the lowest frequency is a mapping from gait phase to the nominal pattern of input making estimated gait phase interpretable The difference between the nominal pattern and the actual input is fi lled by harmonic oscillators that learns it For example input to PSAO in 11 was hip joint angles and the basis function was the nominal hip joint pattern with respect to conventional gait phase which starts with heel strike resulting in the synchronization of actual heel strike and zero gait phase The dynamics of PSAO is as follows 1 k eg 1 1 1 k ef 1 2 i i k ecos i 3 i k esin i 4 6753 Fig 2 Functional Block Diagram for Control of GEMS L k eg 1 5 0 koe 6 0 1f 1 n X i 2 isin i 7 where i 2 n is the index of oscillators is the input 1and iare the phases of the oscillators in 0 2 1 0 and i 0 are the amplitudes of the oscillators 0is an offset 0 is the frequency of the base oscillator k s with subscripts are gains is the estimation for e is an estimation error The function f is the basis for the base oscillator which is the pattern of interest with respect to the phase and g is its derivative In this paper foot to foot distance is chosen for input Although the basis function f should be the nominal pattern of foot to foot distance over a gait cycle it is simply set to a cosine due to its similarity as shown in the top plot of Fig 4 B Enhanced Convergence of AO As is often the case with error driven dynamic system PSAO or AOs converges to an equilibrium asymptotically When the input changes its periodic behavior the phase estimation can be delayed until it converges to a new equilib rium The convergence can be shortened by increasing gains Fig 3 3 d pose and foot contact state are computed in real time from IMU sensor data Using dead reckoning we can obtain the spatio temporoal locomotion data and visualize it The foot to foot distance for AO module is marked on the side view panel in general which comes with trade off in noise rejection making the system susceptible to noise Using PSAO gives an advantage over AOs in the conver gence because the basis function f already contains prior information on the input to learn making the system less dependent to initial values In addition to such advantage in order to enhance the convergence we propose to couple the oscillator with an FSM that detects the frequency of transitions from the input In fact FSMs have been widely used in the literature to estimate gait phase as a discrete value such as stance and swing For example Lim et al 17 have demonstrated discrete gait phase detection where FSM transited at zero crossings in hip joint angles and hip joint angular velocities In our controller the gait frequency FSMmeasured by an FSM updated at every transition is then coupled as follows k eg 1 k FSM 8 The effect of a coupling from FSM frequency was tested in simulation as in Fig 4 where the gait phase is estimated correctly in shorter time when the coupling is stronger C Environment Classifi cation We categorized walking environment into 5 classes level ground stair up stair down slope up slope down because the joint torque patterns should be generated differently for each environment The following features have been selected as descriptors to distinguish gaits in the 5 environments 1 1 maximum foot swing speed on vertical axis before crossing with other foot 2 2 maximum foot swing speed on vertical axis after crossing with other foot 3 3 1 2 4 4 vertical foot speed when crossing with other foot 5 5 difference in the hip angles at foot impact 6 6 difference in the knee angles at foot impact 7 7 ankle angle at foot impact 8 8 ankle height difference from the other ankle at foot impact 6754 0 1 2 Input foot to foot distance 0 1 Gait Phase k 0 1 leftright 0 1 Gait Phase k 0 4 02468101214 Time sec 0 1 Gait Phase k 12 Fig 4 Adaptive oscillator coupled to discrete phase estima tor with different values for coupling gain k shows different convergence behaviors 9 9 ankle advance from the other ankle at foot impact 10 10 knee height difference from the other knee at foot impact We used support vector machine SVM to classify the environments To train an SVM model we collected data from 4 subjects walking on level ground a slope and a stair case installed in the lab Fig 5 An instance of observation was obtained from each step and the total numbers of the instances collected were 362 71 82 59 and 58 respectively for level slope up slope down stair up and stair down We then used a software library for SVM model LIBSVM 26 with the following setting C SVC mode RBF kernel and 0 16 The trained model was tested for two new subjects wearing the full exoskeleton without actuation The number of test instances for each environment ranged from 20 to 43 and all the 142 instances were classifi ed correctly implying 100 accuracy as in Table I Both for the training and the test of the SVM model we excluded steps in transition such as start or stop of walking which should addressed in our future work TABLE I Environment Classifi cation Test Subject12 Level11 119 9 Slope Up11 1116 16 Slope Down23 2320 20 Stair Up14 1414 14 Stair Down12 1212 12 Total71 7171 71 Fig 5 A subject is walking over stairs and ramps installed indoors to test an environment recognition model IV IMPACT ON THEMETABOLICCOST OFWALKING To evaluate the impact of the exoskeleton and its control framework on wearers we conducted an experiment with 2 healthy male subjects Full understanding on the impact of assistance at different joints and the synergy or interference between them would require testing all the possible combi nations of actuated joints In this study however we had to limit ourselves to the following cases no actuation actuate hip e f only actuate hip e f and hip a a and actuate all 6 joints For all these cases joints were bilaterally actuated for the left and right sides To compare the impact of exoskeleton for walking on level and inclined treadmill the subjects put on K5 Cosmed Italy wearable metabolic system and went through the protocol in Fig 6 The fi rst subject stood still for 5 minutes and then walked on the level treadmill of 3 6 km h speed for 6 minutes without the exoskeleton After wearing the exoskeleton he walked again under the following assistance conditions for 6 minutes each i Exo Off zero torque command ii Exo HP hip e f on iii Exo HP HR hip e f and a a on iv Exo HP HR K hip e f a a and knee on He then took off the exoskeleton to walk for 6 minutes again and then stood still for 5 minutes The treadmill was then inclined to 12 gradient and set to 2 4 km h speed The subject then proceeded to the following set of conditions He walked for 6 minutes and then put on the exoskeleton to go through the following 4 conditions for 6 minutes each i Exo Off ii Exo HP iii Exo HP HR iv Exo HP HR K The subject then took off the exoskeleton and walked on the inclined treadmill for 6 minutes and fi nally stood still for 5 minutes on level ground For the second subject we reversed the order to rule out time effect For the brevity of presentation the standing condition and walking without exoskeleton will be referred to as Stand and No Exo respectively Stand 1 No Exo 1 and Stand 2 No Exo 2 for level incline walking refer to the standing walking without exoskeleton condition before and after walking on the level inclined treadmill in the exoskeleton respectively Two successive Stand trials between the level walking and the incline walking were supposed redundant and therefore replaced by a single Stand trial as illustrated in Fig 6 The time series data of GEMS L in Fig 7 shows how AO estimated gait phase in walking as well as how joint kinematics changed as the hip e f joint starts actuation during 6755 Fig 6 Protocol for the experiment subject 1 and subject 2 went through in opposite orders the experiment For example one can see the knee joint angle trajectory change as the hip joint actuation starts Ensemble average of the GEMS L time series data over gait cycle for a subject at different conditions is shown in Fig 8 for level and incline walking Other than the data collected by the exoskeleton human motion itself was not measured in this experiment For level and incline walking we applied different assis tance torque patterns