Robot-Enhanced Mobility Training of Children With ... - IEEE Xplore

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Robot-Enhanced Mobility Training of Children With Cerebral Palsy: Short-Term and Long-Term Pilot Studies Sunil K. Agrawal, Member, IEEE, Jiyeon Kang, Graduate Student Member, IEEE, Xi Chen, Member, IEEE, Mi Jung Kim, Youngmyung Lee, Sang Won Kong, Hyungpil Cho, and Gyung-Jin Park

Abstract—Mobility is a causal factor in infant development. Neural impaired infants, e.g., with cerebral palsy (CP), are at risk for further developmental delays due to lack of self-generated mobility. It is possible that these infants may benefit from robot-enhanced mobility, where the mobility comes from a robot that the child drives via a joystick. We believe that such a mobility experience will enrich the child and minimize further delays in attaining some of the childhood developmental and social milestones. In order to address the broad goal posed earlier, one can ask the following simpler questions: 1) Will children with CP learn to drive a robot using a joystick? 2) Will these children with CP sustain interest in doing so over multiple training sessions? 3) Will such a robot-enhanced mobility impact development scores in this group of children? 4) Will a larger number of robotic training sessions be more effective to enhance these developmental scores? This paper reports the first results from two pilot studies conducted by our research group on robot-assisted mobility training, where children with CP performed two tasks across multiple training sessions. We found that, after training with the robot, children who performed 30 training sessions, labeled as “long-term study,” advanced in their driving skills and showed significant improvements in clinical scores such as Gross Motor Function Measure, Quality of Upper Extremity Skills Test, and Pediatric Evaluation of Disability Inventory (mobility with/without caregiver and social function with/without caregiver). Index Terms—Children with cerebral palsy (CP), clinical scores, robot-enhanced mobility, training with mobile robots.

Manuscript received February 26, 2014; revised July 27, 2014 and September 5, 2014; accepted October 13, 2014. This work was supported by the World Class University Program through the Korea Science and Engineering Foundation funded by the Ministry of Education, Science and Technology under Grant R32-2009-000-10022-0. S. K. Agrawal and J. Kang are with the Rehabilitation Robotics Laboratory, Department of Mechanical Engineering, Columbia University, New York, NY 10027 USA. X. Chen is with Energid Technologies, Cambridge, MA 02138 USA. M. J. Kim, S. W. Kong, and H. Cho are with the Department of Rehabilitation Medicine, Hanyang University Medical Center, Seoul 133-792, Korea, and also with the Hanyang University, Seoul 133-791, Korea. Y. Lee is with the Department of Mechanical Engineering, Hanyang University, Seoul 133-791, Korea, and also with Hanyang University, Ansan 426-791, Korea. G.-J. Park is with the Department of Mechanical Engineering, Hanyang University, Ansan 426-791, Korea. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSYST.2014.2368455

I. I NTRODUCTION

C

EREBRAL palsy (CP) is a group of disorders that impairs movement and posture in children, posing serious limitations in their daily functional activities. This condition is nonprogressive and is attributed to impairments in the developing fetal or infant brain [1]. CP affects about 3.6 per 1000 schoolaged children and is the most prevalent motor disability for children in the United States [2]. There are at least 8000 new CP patients every year in the United States [3], and the prevalence of CP is increasing as more premature infants survive using advanced medical technology [4]. In developing countries, there is a higher incidence of CP than developed countries, due to increased incidence of neonatal asphyxia and low birth weight. The increased prevalence and longer life expectancy of children with CP, therefore, requires new strategies for their functional management and care. The etiology of CP in infants is not identifiable in a majority of cases [5]. Major contributing factors to brain injury and CP are premature birth, infection, inflammation, and coagulopathy [6]. Prematurity is the greatest single risk factor for CP [7], [8], and the prevalence of CP in premature and low-birth-weight babies varies from 40 to 150 per 1000 live births [9]. Although CP is defined as a movement disorder, lesions in a premature brain are not limited to only the motor area. The population with CP has other accompanying disorders that include visual, hearing, psychological, and/or social dysfunction. In this paper, we use the following tools to clinically assess the outcomes of children with CP: the Gross Motor Function Measure 88 (GMFM-88), Gross Motor Function Classification System (GMFCS), Quality of Upper Extremity Skills Test (QUEST), and Pediatric Evaluation of Disability Inventory (PEDI). GMFM-88 evaluates changes in gross motor function of children with CP [10]. GMFCS characterizes children with CP based on their gross motor functional skills [11]. QUEST evaluates the upper extremity in movement, grasp, extension, and weight bearing [12]. PEDI is a comprehensive tool to assess skills with or without caregiver assistance in three domains: self-care, mobility, and social function [13]. Mobility is a crucial aspect in development of young children [14], [15]. Lack of self-generated mobility of neural impaired infants, e.g., with CP, causes advanced developmental delays [16]–[18]. Interventions with powered wheel chair were tried out to help these children [19], [20]. Researchers observed positive developmental changes after using a motorized wheel

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chair. Extending these interventions, novel wheel chairs for children were designed with advanced navigating functionalities and new control methods [21]–[23]. Our group has used mobile robots equipped with a conventional or a force-feedback joystick, to train infants and toddlers sitting on the robot to drive around in an environment [24]–[27]. The subjects were typically developing infants and toddlers with only a few kids with CP and Down’s syndrome [24]– [26]. Several key results that emerged from these studies were the following: 1) The children learn to drive the robot using a conventional joystick when trained over multiple sessions [26]; 2) The children learn higher level driving skills, such as obstacle avoidance, turning, or line following, more effectively when using a force field joystick [27]; 3) a case study of a child with special needs showed that training with the joystick-driven power mobility advances in motor, cognitive, perceptual, and social subscores of the Bayley-III assessment [24], [25]. The goal of this paper is to characterize the effects of robotenhanced mobility training on children diagnosed with CP. In this paper, position data of these children are accurately obtained via the robot system. This paper reports results from two pilot studies on robot-assisted mobility training, where children performed translational and rotational tasks across multiple training sessions. In order to record the position data of the children’s driving path reliably, particle-filter-based localization was applied using a laser rangefinder (LRF) [28]. A short-term study, labeled as pilot study 1, involved five children with CP, ages between 12 and 28 months. This study was performed over ten training sessions with the robot, with each session lasting for 20 min. The children continued conventional rehabilitation during robot training. A longer study, labeled as pilot study 2, involved five children with CP, ages 13–17 months. This study was performed over 30 training sessions with the robot, with each session lasting for 20 min. The children were trained three times a week, and it took roughly 10–12 weeks to complete the 30 sessions of training. This group also had the robot training, in addition to the conventional rehabilitation. A control group was also included, consisting of five children with CP, ages 24–36 months old, who continued their recommended conventional rehabilitation without the use of robots. Data are presented on outcome measures recorded by the robot and clinical tests, such as GMFM-88, PEDI, and QUEST. Control and experimental groups are compared for the differences in these outcome measures as a result of robotic training.

TABLE I AGE OF S UBJECTS IN THE S HORT-T ERM S TUDY, L ONG -T ERM S TUDY, AND C ONTROL

Fig. 1. (Left) Pioneer-AT and (right) PowerBot. Both experimental setups consist of a mobile robot as a platform, a conventional joystick, an LRF, and a booster seat.

The exclusion criteria for this study were: 1) neurologic problems other than CP; 2) medical problems that can make the training difficult, such as orthopedic problem in the hands, feet, or pelvis; and 3) severe cognitive deficit or emotional instability that may interrupt the training. Pilot study 1 involved five children with CP in the training group, pilot study 2 had five children with CP in the training group, while the control group also had five children. After an initial screening for inclusion and exclusion criteria, all participants were evaluated for GMFCS, GMFM-88, QUEST, and PEDI. These measurements took about an hour, and the children were evaluated around the same time to maintain consistency. Subjects were chosen with similar age and GMFCS level to reduce deviation of baseline for their developmental stages. Age and GMFCS levels reported in Table I are from their first examination once enrolled in the study. All clinical scores were taken again at the end of the training (pilot study 1) and at the middle and end of the training (pilot study 2). Assessment was performed by a single physical therapist who was blinded to the study.

II. E XPERIMENT D ESCRIPTION A. Participating Subjects

B. Robot Hardware and Interface

The research was approved by the Institutional Review Board of Hanyang University Medical Center (HYUMC) in Seoul, South Korea. The legal representatives of all subjects gave their written informed consent. They were selected from the outpatient department of rehabilitation medicine in HYUMC. The inclusion criteria for kids to participate in this study were: 1) each child should have clinical diagnosis of CP, and 2) the child must be able to work with the joystick for at least 30 min.

Two mobile robots, i.e., Pioneer-AT and PowerBot, were used in our experiments as the platforms to train the children. Both robots are manufactured by Adept. Pioneer-AT (see Fig. 1, left) was used in the short-term pilot study, and PowerBot (see Fig. 1, right) in the long-term pilot study. An off-the-shelf booster seat with safety strips was attached to the top of both mobile robots. Identical conventional joysticks (ATTACK 3, Logitech Inc.) were mounted on each robot for

This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. AGRAWAL et al.: ROBOT-ENHANCED MOBILITY TRAINING OF CHILDREN WITH CP: PILOT STUDIES

Fig. 2. Schematic of the robot. The joystick position input was mapped to the translational/rotational velocity of the robot. Data of LRF and wheel encoders are fused to measure the accurate position and orientation of the robot.

the child–robot interface. Children sitting on these robots can control the motion of the robots by manipulating this joystick. A separate joystick (FREEDOM 2.4, Logitech Inc.) was provided to a caregiver to override the inputs of children’s joystick for safety. Caregiver joystick was also able to decide control mode, whether robot moves by the child joystick or caregiver joystick. In addition, an LRF (LMS-200, SICK Inc.) with an angular resolution of 1◦ was used in the experiment for accurate localization of the robot. For both mobile robots, interface software was written in C++ for each onboard computer (EBX12 Pentium M 1.8-GHz CPU, Versalogic Co.). All components of the two robots (joystick, bumper, seat, onboard computer, and LRF) were identical, except for the seating position of the child with respect to the rotational axis of the robot. The rotational axis of PowerBot was at the center of the child seat. But in Pioneer-AT, the child seat was attached to an extended frame and was 10 in off the rotational axis of the robot. As a result, the position data of the Pioneer-AT were converted to the location of the child seat. Fig. 2 shows the schematic diagram of both robot systems. When the experiment started, a child on the robot manipulated the baby joystick. This joystick sent position data (x, y) to the onboard computer. These joystick data were scaled and mapped to the translational velocity v and rotational velocity w of the robot, as follows: x vmax JOY _M AX y wmax w= JOY _M AX v=

(1) (2)

where JOY_MAX is the maximum joystick output, and vmax , wmax are the maximum translational and rotational velocity of the robot. In this experiment, we set vmax = 0.4 m/s and wmax = 26 degree/s, for both robots. To read and send joystick inputs to the robot, a DirectX library was used as an interface between the joystick and the CPU of the robot. Robot motions induced by the joysticks were measured by wheel encoders and an LRF with 180◦ scanning angle. Accurate position data were important to obtain in this experiment, since performance variables were based on the recorded data of the robot’s position. However, the wheels were prone to slip; hence, the position/orientation of the robot computed solely by wheel encoders were prone to errors. Therefore, a

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Fig. 3. Monitoring window for the experimental setup. Left figure shows LRF data denoted by blue dots. Right figure shows the path driven by the child within the rectangular task area.

particle-filter-based localization algorithm [28] was applied, which was provided by the robot software. The localization algorithm minimizes errors by fusing data from wheel encoders and the data from an LRF, along with a preloaded map of the room. Before the experiment, a map of the room with wall positions was created, and this blue print was saved within the robot. The accuracy of the position was within 5 cm with both robots, which was estimated by repeated measurements. The corrected position data of the robot were recorded in a log file. A monitoring window was created to check the experimental setup, as shown in Fig. 3. Here, the mobile robot is shown as a circle, surrounded by walls (solid line) in the room. Laser readings are expressed by blue dots, showing obstacles. When a child drives the robot, the path driven by the child is displayed as a blue line. As the robot position in this screen is based on fused data from the encoders and the LRF, the position of the robot is continuously updated and monitored by the caregiver. C. Task Description The experiment workspace area was chosen to be a square, as shown in Fig. 4. Six subregions were identified within the area, which served as the start, intermediate, or goal points for the two tasks that were defined. Task 1 consisted of straight lines, which are performed by simply pulling or pushing the joystick. Task 2 was more complex, as it required simultaneous translational and rotational control of the vehicle to complete the task. The two tasks are schematically sketched in Fig. 4(b). Each task includes two different paths, of which the subjects were trained alternately. In task 1, a child was initially located at position 5 and was asked to move to position 4. Once the child drove the robot successfully from position 5 to position 4, the caregiver relocated the robot to position 6 for the next path within task 1. The caregiver then encouraged the child to move from position 6 to position 2. After the child completed these two paths of task 1, the child moved on to task 2. For task 2, the first path was to go from position 6 to position 1. After the child succeeded, the robot was repositioned at 6, and the child was urged to move toward position 3. A caregiver relocated the child to the initial position, if she/he failed in the given task. In addition, the caregiver joystick was used to stop the robot if the child was moving unsafely in the room. The dimensions in the two pilot studies were selected based on the available floor space in the room: 1) pilot 1: a = 7 m,



A. Robot Measurement The total path length driven by the children, the success ratio in completing the task, and the average velocity of each task were collected to evaluate the driving skills of children. Each outcome measure was derived from a recorded log file, as illustrated in Fig. 2. — Success ratio: if distance between the child and the given target subarea (see Fig. 4) was within 20 cm, it was considered as a success. That is Success ratio =

number of successes . number of trials

(3)

All the children who completed task 1 were given the chance to try task 2.

Fig. 4. (a) Floor plan of the experiment area: Target subareas are used to describe the different task levels. (b) Schematic description of tasks 1 and 2: A bold arrow indicates the initial starting direction.

b = 5 m, c = 2.5 m; 2) pilot 2: a = 5 m, b = 3 m, c = 1 m. In order to guarantee the safety of the participants, the surrounding area outside the experiment was covered by soft protective mats. Some specific rules in the experiments were as follows. 1) A task was considered complete only if it could be successfully repeated twice in a row by a child. This ruled out success by chance. 2) All sessions started from task 1. Once the child successfully completed task 1, the child moved to task 2. Task 1: move from areas 6 to 2 then 5 to 4 Task 2: move from areas 6 to 1 then 6 to 3 3) Hand gestures, sounds, and toys were used to encourage the child to go to the intermediate and/or goal regions. 4) If the child did not activate the joystick longer than a minute, the caregiver showed the child how to move the joystick toward the goal. 5) If a child got tired or cried, the caregiver stopped the robot temporarily. 6) If a child went outside the area or near to the wall, the task was considered to be a failure. 7) At the beginning of the session, the caregiver gave to the child a simple instruction about turning, pushing, and pulling the joystick. III. R ESULTS F ROM P ILOT S TUDY 1 (T EN T RAINING S ESSIONS ) Five children participated in this first study to evaluate the effect of the robotic training. Driving skills of children were evaluated based on a recorded log file. In addition, clinical assessments were made before and after training to relate the robotic mobility training with various developmental measures.

— Velocity: driving speed (v) was recorded by the robot to see how fast the child drove the robot. It was averaged with task time (tf ). That is tf v dt . (4) Velocity = 0 tf — Path length: the total driving path of task 1 and task 2 during one session (20 min) was observed. As each child spends different times to complete tasks 1 and 2, we used the overall path length for consistency. Fig. 5 shows averaged data of five subjects for success ratio, velocity, and path length. The plots of average values, particularly the success ratio of task 1 and path length, show a linear trend with training sessions. Statistical analysis is carried out based on the moving average to check the significance of the data. Before statistical analysis, moving average was computed with (5) to account for small subject number and fluctuation in the data. That is x ¯i =

xi + xi+1 + xi+2 3

(5)

where x ¯i is the ith moving average of each robotic measurement, and xi is the data for the ith session of each robotic measurement. x4 ), and eighth (¯ x8 ) moving We picked the first (¯ x1 ), fourth (¯ average values to compare and check for significant effects of training. Each of these moving averages represents the initial, middle, and final parts of the training. One-way repeated measures analysis of variance (RM-ANOVA) [29] was run for each robot-measured metric independently to find the significance level (α = 0.05). For pairwise comparison, dependent t-test with Bonferroni–Holm correction [30] was used to find the significance level of each measurement. All statistical analysis was performed in the Statistical Package for the Social Sciences (IBM statistics, Version 20). However, for short training, no significant level was found: success ratio of task 1 (p = 0.402), success ratio of task 2 (p = 0.201), velocity of task 1 (p = 0.189), velocity of task 2 (p = 0.360), path length (p = 0.218). We used the same statistical procedure for robotic/ clinical long-term measures.


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Fig. 6. Clinical measurements of five experimental children before and after robotic training for ten sessions. GMFM and QUEST scores show increment for short-term training session.

B. Clinical Measures Clinical measures were made before the first session and after the tenth session to observe the effects of robotic training. Target measurements were as follows: — GMFM: hip/knee flexion, roll, sit, kick, walk; — MACS: manual ability to handle objects; — QUEST: shoulder/elbow/wrist flexion, grasp, weight bearing; — PEDI_C: self-care; — PEDI_M: mobility; — PEDI_F: social function; — PEDI_CC: self-care with caregiver assistance; — PEDI_MC: mobility with caregiver assistance; — PEDI_FC: social function with caregiver assistance. Fig. 6 shows small changes in GMFM score and QUEST. However, other scores did not change after robotic training. A paired t-test was run to check the significance level, but there were no significant changes between before and after training: GMFM (p = 0.180) and QUEST (p = 0.423). IV. R ESULTS FOR P ILOT S TUDY 2 (30 T RAINING S ESSIONS ) In the first pilot study (ten training sessions), we could observe some hints that children could benefit from our robotic rehabilitation training. However, presented data for ten training sessions did not show any significant enhancements in both driving skills and clinical scores. As a follow-up study, robotic training with larger training sessions, i.e., 30 sessions for 10– 12 weeks, was undertaken for five children. Each training session lasted for about 20 min, and the children participated in this training three times each week. A. Robot Measured Results

Fig. 5. Average value and standard deviation of success ratio, velocity, and path length for ten sessions of the robot-trained experimental group. A linear trend is seen in most of these metrics along ten training sessions.

All five children sustained interest in driving the robot and finished 30 sessions of training. The same measures were used to evaluate the driving skills of children in the second pilot study. In Fig. 7, success ratio, velocity, and path length are shown with a strong linear trend with session numbers. As the children participated in more sessions, they could accomplish



TABLE II x1 ), F OURTEENTH (¯ x14 ), T WENTY-E IGHTH (¯ x28 ) M OVING F IRST (¯ AVERAGE VALUE AND I TS PAIRWISE C OMPARISON R ESULTS OF x ¯1 ∗ ∗∗ V ERSUS x ¯14 AND x ¯1 V ERSUS x ¯28 ( p < 0.05, p < 0.01, ∗∗∗ p < 0.001). PAIRWISE C OMPARISON W ITH L ONGER T RAINING P ERIOD (¯ x1 VERSUS x ¯28 ) S HOWS H IGHER S IGNIFICANT L EVEL OF p-VALUE

the given task with better performance and drove the robot with higher average speed for both tasks. Statistical analysis was run to look for significant correlation between training sessions and robotic measurement. Instead of choosing the fourth and eight moving averages, the fourteenth (¯ x14 ) and twenty-eighth (¯ x28 ) moving averages were chosen to represent the middle and final evaluations. Longer training showed significant changes for RM-ANOVA. Children showed significant improvement in success ratio between initial, middle, and final sessions (task 1: F(2,8) = 5.490, p < 0.05; task 2: F(2,8) = 15.938, p < 0.01). In addition, children drove faster as they were trained with this mobile robot (task 1: F(2,8) = 30.866, p < 0.001; task 2: F(2,8) = 20.08132, p < 0.001). Furthermore, increased path length indicates that children drove further as they participated in more sessions (F(2,8) = 31.30402, p < 0.001). From these observations, we can conclude that children enhanced their driving skills with 30 sessions of training. Additionally, pairwise comparison between first, fourteenth, and twenty-eighth moving averages was performed with a dependent t-test. In Table II, moving average values and pairwise comparison results are expressed in p-value. This result shows that training with more sessions had greater improvement in all robotic measures. In other words, pairwise comparison be¯28 ) tween first and twenty-eighth moving averages (¯ x1 versus x shows a higher significant level than the comparison between ¯14 ). For first and fourteenth moving averages (¯ x1 versus x example, velocity of task 1 showed stronger significant change ¯28 : p < 0.01) than after after 30 training sessions (¯ x1 versus x ¯14 : p < 0.05). This fact 15 training sessions (¯ x1 ) versus x strongly supports that there is a definite correlation with the robotic training and driving skills of children. B. Clinical Measures

Fig. 7. Average value and standard deviation of success ratio, velocity, and path length for 30 sessions of the robot-trained experimental group. All five children sustained interest in driving the robot and completed 30 sessions of the robotic training.

Clinical assessment was made to observe if the robotic training could foster improvements in the child development. In previous sessions, we showed that children learned to drive the robot over multiple sessions. Clinical scores were examined if those mobility experiences, i.e., learning how to drive a robot, can help children with CP to enhance their development scores. Clinical evaluation was performed three times: before first session, after 15 sessions, and after 30 sessions. All clinical scores


Fig. 8. Clinical measurements of five experimental children before, middle, and after robotic training for 30 sessions (∗ p < 0.05, ∗∗ p < 0.01). Clinical scores for mobility (GMFM, PEDI_M, and PEDI_MC) and social function (PEDI_F and PEDI_FC) showed strong improvements after the robotic training.

showed significant differences with RM-ANOVA (α = 0.05). In particular, GMFM, PEDI_M, and PEDI_MC showed significant improvement, as shown in Fig. 8 (GMFM: F(2,8) = 22.90, p < 0.001; PEDI_M: F(2,8) = 9.65, p < 0.01; and PEDI_MC: F(2,8) = 11.26, p < 0.01). As those scores are highly related to the mobility, we can conclude that the robotic training device can help the children to train their mobility. Concurrently, social function with/without caregiver was increased (PEDI_F: F(2,8) = 29.48, p < 0.001 and PEDI_FC: F(2,8) = 11.23, p < 0.01). Social function showed large improvements, as children were interacting with caregivers who encouraged them to drive the robot. The QUEST score also increased, which measures upper limb and manual skills (F(2,8) = 15.51, p < 0.01). This improvement might be caused by the control scheme of the mobile robot, where continuous hand movement was needed to control the robot with a joystick. There was no significant enhancement in PEDI_C score, as this training was not related to self-care. Based on these results, we conclude that the robot training has positive clinical effect on mobility and social functions for the children with CP.

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Fig. 9. Clinical score for experimental and control children (Pre: before first session, Mid: after 15th session, Post: after 30th session). TABLE III R ESULT OF I NDEPENDENT t-T EST OF AVERAGE S CORE I NCREMENTS B ETWEEN L ONG -T ERM E XPERIMENTAL AND C ONTROL C HILDREN (∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001)

QUEST, PEDI_M, PEDI_F, PEDI_MC, and PEDI_FC. This supports the fact that the increased score was because of the robotic training, not the development of child.

V. D ISCUSSION : C OMPARISON OF R ESULTS B ETWEEN E XPERIMENTAL AND C ONTROL G ROUP

VI. C ONCLUSION

As all children in the study are developing, one needs to justify if the changes in the scores were caused by training and not by their normal development. For this reason, five control subjects were recruited to compare with experimental children. Subjects who participated had similar levels of impairment. They had the same GMFCS score, except for one child, as shown in Table I. GMFCS is a representative metric for the classification of impairment. The control group did not participate in the robotic training, but shared the same timeline of clinical measurement as the experimental group. Fig. 9 shows clinical scores of GMFM, QUEST, and PEDI for both groups. Independent t-test was used to identify the significance level between experimental and control group children. Table III shows increments of scores during different periods (post–pre, mid–pre, and post–mid) and results of independent t-test (α = 0.05). We found that there are significant changes for GMFM,

This novel study, where children with CP sit on a robot and drive using a joystick, has shown the following results: 1) Children with CP can learn to drive a robot using a joystick; 2) these children can sustain interest in driving over multiple training sessions and over many days; 3) the robot-enhanced mobility not only impacts their driving ability but also impacts their clinical scores positively. A short-term study involved five CP children, ages 18.6 ± 5.28 months. This study was performed over ten training sessions with the robot, with each session lasting for 20 min. A longer study involved five children with CP, ages 14.4 ± 2.33 months. This study was performed over 30 training sessions with the robot, with each session lasting for 20 min. A control group was also included, consisting of five children with CP, ages 28.8 ± 5.88 months, who continued their recommended conventional rehabilitation without the use of robots.



We have found that the robot training promotes the children to enhance their driving skills and benefits their mobility development. Robot-trained children who participated in long-term studies showed significant improvements in GMFM, QUEST, and specific domains of PEDI: mobility with/without caregiver and social function with/without caregiver. R EFERENCES [1] M. Bax et al., “Proposed definition and classification of cerebral palsy,” Dev. Med. Child Neurol., vol. 47, no. 8, pp. 571–576, Aug. 2005. [2] M. Yeargin-Allsopp et al., “Prevalence of cerebral palsy in 8-year-old children in three areas of the United States in 2002: A multisite collaboration,” Pediatrics, vol. 121, no. 3, pp. 547–554, Mar. 2008. [3] S. Winter, A. Autry, C. Boyle, and M. Yeargin-Allsopp, “Trends in the prevalence of cerebral palsy in a population-based study,” Pediatrics, vol. 110, no. 6, pp. 1220–1225, Dec. 2002. [4] D. Wilson-Costello, H. Friedman, N. Minich, A. Fanaroff, and M. Hack, “Improved survival rates with increased neurodevelopmental disability for extremely low birth weight infants in the 1990s,” Pediatrics, vol. 115, no. 4, pp. 997–1003, Apr. 2005. [5] L. Taft, “Cerebral palsy,” Pediatr. Rev., vol. 16, no. 11, pp. 411–418, Nov. 1995. [6] T. O’Shea, “Cerebral palsy in very preterm infants: New epidemiological insights,” Ment. Retard Dev. Disabil. Res. Rev., vol. 8, no. 3, pp. 135–145, 2002. [7] A. Colver et al., “Increasing rates of cerebral palsy across the severity spectrum in north-east England 1964–1993,” North England Collaborative Cerebral Palsy Survey. Arch. Dis. Child Fetal. Neonatal. Ed., vol. 83, no. 1, pp. F7–F12, Jul. 2000. [8] B. Hagberg, G. Hagberg, and R. Zetterstrom, “Decreasing perinatal mortality—Increase in cerebral palsy morbidity,” ActaPaediatr Scand., vol. 78, no. 5, pp. 664–670, Sep. 1989. [9] C. Robertson, M. Watt, and Y. Yasui, “Changes in the prevalence of cerebral palsy for children born very prematurely within a populationbased program over 30 years,” JAMA, vol. 297, no. 24, pp. 2733–2740, Jun. 27, 2007. [10] D. Russell et al., “The gross motor function measure: A means to evaluate the effects of physical therapy,” Dev. Med. Child Neurol., vol. 31, no. 3, pp. 341–352, Jun. 1989. [11] R. Palisano et al., “Development and reliability of a system to classify gross motor function in children with cerebral palsy,” Dev. Med. Child Neurol., vol. 39, no. 4, pp. 214–223, Apr. 1997. [12] C. DeMatteo et al., “The reliability and validity of the quality of upper extremity skills test,” Phys. Occupat. Therapy Pediatrics, vol. 13, no. 2:18, 1993. [13] S. Haley, W. Coster, and R. Faas, “A content validity study of the pediatric evaluation of disability inventory,” Pediatr. Phys. Ther., vol. 3, no. 3, pp. 177–184, 1991. [14] D. I. Anderson et al., “The flip side of perception-action coupling: Locomotor experience and the ontogeny of visual-postural coupling,” Human Movement Sci., vol. 20, no. 4/5, pp. 461–487, 2001. [15] J. Wu, J. Looper, B. D. Ulrich, D. A. Ulrich, and R. M. Angulo-Barroso, “Exploring effects of different treadmill interventions on walking onset and gait patterns in infants with Down syndrome,” Dev. Med. Child Neurol., vol. 49, no. 11, pp. 839–845, 2007. [16] C. Butler, “Effects of powered mobility on self-initiated behaviors of very young children with locomotor disability,” Dev. Med. Child Neurol., vol. 28, no. 3, pp. 325–332, 1986. [17] D. Teft, P. Guerette, and J. Furumasu, “Cognitive predictors of young children’s readiness for powered mobility,” Dev. Med. Child Neurol., vol. 41, no. 10, pp. 665–670, 1999. [18] J. Campos et al., “Travel broadens the mind,” Infancy, vol. 1, pp. 149–219, 2000. [19] M. A. Jones, I. R. McEwen, and L. Hansen, “Use of power mobility for a young child with spinal muscular atrophy,” Phys. Therapy, vol. 83, no. 3, pp. 253–262, 2003. [20] L. M. Nilsson and P. J. Nyberg, “Driving to learn: A new concept for training children with profound cognitive disabilities in a powered wheelchair,” Amer. J. Occupat. Therapy, vol. 57, no. 2, pp. 229–233, 2003. [21] R. Ceres, J. L. Pons, L. Calderon, A. R. Jimenez, and L. Azevedo, “A robotic vehicle for disabled children,” IEEE Eng. Med. Biol. Mag., vol. 24, no. 6, pp. 55–63, 2005. [22] S. Stansfield, C. Dennis, and H. Larin, “WeeBot: A novel method for infant control of a robotic mobility device,” in Proc. IEEE ICRA, 2012, pp. 2451–2456.

[23] H. Soh and Y. Demiris, “Towards early mobility independence: An intelligent paediatric wheelchair with case studies,” in Proc. IEEE Int. Conf. Workshop Progress, Chall. Future Perspectives Navigat. Manipulation Assistance Robot. Wheelchairs IROS, 2012, pp. 1–7. [24] J. C. Galloway, J. C. Ryu, and S. K. Agrawal, “Babies driving robots: Self-generated mobility in very young infants,” Intel. Serv. Robot., vol. 1, no. 2, pp. 123–134, 2008. [25] C. Ragonesi, X. Chen, S. K. Agrawal, and J. C. Galloway, “Power mobility and socialization in preschool: A case report on a child with cerebral palsy,” Pediatric Phys. Therapy, vol. 22, no. 3, pp. 322–329, 2010. [26] S. K. Agrawal et al., “Feasibility study of robot enhanced mobility in children with cerebral palsy,” in Proc. IEEE ICORR, 2012, pp. 1541–1548. [27] X. Chen, C. Ragonesi, J. C. Galloway, and S. K. Agrawal, “Training toddlers seated on mobile robots to drive indoors amidst obstacles,” IEEE Trans. Neural Sysand Rehab. Eng., vol. 19, no. 3, pp. 271–279, 2011. [28] F. Dellaert, D. Fox, W. Burgard, and S. Thrun, “Monte Carlo localization for mobile robots,” in Proc. IEEE ICRA, 1999, pp. 1322–1328. [29] J. H. Zar, Biostatistical Analysis, 4th ed. Upper Saddle River, NJ, USA: Prentice Hall, 1999. [30] Y. Hochberg et al., Multiple Comparison Procedures, 1st ed. New York, NY, USA: Wiley, 2008.

Sunil K. Agrawal (M’92) received the Ph.D. degree in mechanical engineering from Stanford University, Stanford, CA, USA, in 1990. He is currently a Professor with the Department of Mechanical Engineering, Columbia University, New York, NY, USA. He has authored more than 350 journal and conference papers and two books in the areas of controlled mechanical systems, dynamic optimization, and robotics.

Jiyeon Kang (S’13) received the B.S. and M.S. degrees in mechanical engineering from Seoul National University, Seoul, Korea, in 2008 and 2010, respectively. She is currently working toward the Ph.D. degree in mechanical engineering with Columbia University, New York, NY, USA. Her research interests include mobility, balance, and gait training with robotic devices for children with special needs.

Xi Chen (M’08) received the B.E. degree in automation from Nankai University, Tianjin, China, in 2006 and the Ph.D. degree in mechanical engineering from the University of Delaware, Newark, DE, USA, in 2012. He is currently a Senior Robotic Systems Engineer at Energid Technologies, Cambridge, MA, USA. He is also with the Rehabilitation Robotics Laboratory, Department of Mechanical Engineering, Columbia University, New York, NY, USA. His research interests include mechatronics and robotics, simulation, dynamics and control, haptic feedback, and early mobility training of special needs children.

Mi Jung Kim received the M.D. and Ph.D. degrees from the University of Ulsan College of Medicine, Seoul, Korea, in 1989 and 1999, respectively. She is currently a Professor with the Department of Rehabilitation Medicine, Hanyang University Medical Center, Seoul, Korea, where she served as the head of the department. She has published considerable number of papers in various areas of rehabilitation medicine and is conducting several research works in robot-assisted training in pediatric rehabilitation and poststroke rehabilitation.


Youngmyung Lee received the B.S. degree in 2005 from Hanyang University, Seoul, Korea, where he is currently working toward the Ph.D. degree with the Department of Mechanical Engineering. His research interests include structural optimization and crashworthiness.

Sang Won Kong received the M.D. degree from the Hanyang University College of Medicine, Seoul, Korea, in 2010. He is currently a Resident with the Department of Rehabilitation Medicine, Hanyang University Medical Center, Seoul.

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Hyungpil Cho received the M.D. degree in 2006 and the M.M.Sc. degree in rehabilitation medicine in 2013 from the Hanyang University College of Medicine, Seoul, Korea, where he is currently working toward the Ph.D. degree. He is currently a Fellow with the Department of Rehabilitation Medicine, Hanyang University Medical Center, Seoul. His research interests are in robotassisted training for neurorehabilitation and virtualreality-based rehabilitation.

Gyung-Jin Park received the B.S. degree from Hanyang University, Seoul, Korea, in 1980, the M.S. degree from the Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, in 1982, and the Ph.D. degree from The University of Iowa, Iowa City, IA, USA, in 1986. In 1986–1988, he was an Assistant Professor with Purdue University, Indianapolis, IN, USA. He is currently a Professor with the Department of Mechanical Engineering, Hanyang University, Ansan, Korea. His research focuses on structural optimization, machine design, design theory, and multidisciplinary design optimization. His work has yielded over 4 books and 455 technical papers.