Repetitive task practice is an activity-based therapeutic approach designed to rehabilitate the upper extremities of persons with hemiplegia secondary to stroke.1,2 Important factors related to the effectiveness of this intervention include maintenance of high levels of intensity throughout practice sessions and performance of higher repetitions of activity than are typically performed in more traditional approaches to upper extremity rehabilitation.3 Multiple investigators describing the training of upper extremity reaching and functional activities in virtual environments have demonstrated that motor skills can be learned through repetitive practice within both immersive and nonimmersive and visually simple and complex virtual environments.4 For example, it has been shown that changing the visual gain of a movement can harness small degrees of active finger movement and use these to perform meaningful activities in virtual environments.5 Robotics and virtual environments have been utilized to facilitate the intensity and volume requirements of repetitive task practice.6,7 To date, the majority of the interventions using these robotic systems have been studied with standardized experimental protocols in groups of participants, involving a uniform set of impairment-based upper arm movements. Two studies describe attempts at customizing interventions: one utilized custom force fields to shape reaching trajectories of individuals with stroke to conform to a goal trajectory,8,9 and a second combined standardized, impairment-based robotic activities with a customized, traditional inpatient rehabilitation program.8 Several systems strive to individualize interventions via more adaptable physical interfaces,10 adapted tasks,11 adapted feedback, or physical components of the system.12 However, none of the studies utilize a multifaceted intervention that selects activities to match the specific impairments demonstrated by a patient or addresses their individual goals.
Two robotically facilitated virtual rehabilitation systems, the NJIT-RAVR system and the NJIT Track-Glove system, were used in this case. The NJIT-RAVR system uses a CyberGlove and a Haptic Master, a three-degrees-of-freedom, force-controlled robot. Three more degrees of freedom (yaw, pitch, and roll) are added to the arm by using a gimbal, with force feedback available for pronation/supination (roll). A three-dimensional force sensor measures the external force exerted by the user on the robot. In addition, velocity and position of the robot's endpoint are measured at 1000 samples per second, allowing the robotic arm to act as an interface between the participants and the virtual environments. This enables movement against gravity in a three-dimensional workspace. The Track/Glove uses a CyberGlove and the Flock of Birds motion sensors for finger and arm tracking. Simulations were programmed with use of C++/OpenGL, the Virtools software package, or the Haptic Master's Application Programming Interface. These allow one to program the robot to produce haptic effects such as gravity and haptic objects like blocks, cylinders, walls, tables, and shelves. In comparison with other robotic training devices, the novel aspects of the NJIT-RAVR and Track-Glove training systems are as follows: (1) they are the only robotic systems that train three-dimensional reaching and hand-opening movements in an integrated fashion, (2) they are fully integrated with a variety of activity-based virtual reality gaming simulations, and (3) they incrementally modify task constraints on the basis of a participant's performance of each repetition of an intervention. In previously published studies using the NJIT-RAVR and Track-Glove systems, the experimental protocols used a standardized approach to test these systems' capabilities to train participants on activities that integrate arm and finger movement and then to compare this approach to training the arm and hand in isolation.6 The virtual reality simulations used, feedback provided to the participants, progression of difficulty, and positioning of the participants in relation to the robot were applied uniformly to all participants who met inclusion criteria, without regard to individual therapeutic goals, specific impairments, or responses to the intervention.6,13
This case study describes a clinically relevant use of the system that was designed specifically for an individual patient. Therefore, this case study adds a new dimension to the body of work previously published on robotically assisted interventions. Simulations were chosen that addressed the motor impairments identified during the patient's examination that limited his ability to perform goal activities. The simulations were configured in terms of required movement patterns, speed, range of motion, and task parameters to maximize his ability to benefit from them. The simulations were further modified on the basis of his responses to the first few training sessions.
It is timely to provide clinically relevant individualized case studies, to inform clinicians about the potential use, methods of application, and challenges encountered in providing interventions that utilize robotic/virtual reality systems. This report will demonstrate the process and challenges of developing a therapeutic intervention utilizing robots and virtual environments as well as the process of examining the generalization of motor skills attained with these technologies into real-world activities.
CASE DESCRIPTION: HISTORY AND SYSTEMS REVIEW
P.M. is an 85-year-old gentleman with left hemiparesis secondary to an intracerebral hemorrhage and subsequent craniotomy 5 years prior to this examination. Medical history includes paroxysmal atrial fibrillation, atrophic right kidney, recurrent urinary tract infections, and several bouts of pneumonia. Surgical history includes a permanent pacemaker placement 35 years prior to examination.
Rehabilitation history following the intracerebral hemorrhage 5 years prior to examination included 16 weeks of inpatient rehabilitation, followed by 9 months of outpatient physical therapy and occupational therapy. After a hiatus, P.M. received 2- to 3-hour biweekly sessions of occupational therapy in his home for approximately 1 year, followed by a combined physical therapy and occupational therapy program focusing on ambulation, balance, transfers, sensory integration, and upper extremity function for the next 3 years. This was his therapy routine prior to the robotic intervention. A summary of these interventions, specifying the tasks performed, is included in the Supplemental Digital Content 1 (available at: http://links.lww.com/JNPT/A19).
P.M. lives with his wife and a home health aide. P.M. performs bed mobility and transfers with contact guard/close supervision and requires maximum assistance for lower body dressing and moderate assistance for toileting and upper body dressing. He performs grooming and feeding with supervision. He uses a power wheelchair for mobility but is able to walk up to 150 ft with contact guard, using a cane.
P.M. reported no pain during the examination. Light touch and proprioception were within normal limits. P.M. demonstrated decreased visual attention to his left hemispace. Cognition was within functional limits, but assessment was confounded by P.M.'s visual perception and use of bilateral hearing aids. Passive range of motion was comparable in both upper extremities, but P.M. demonstrated 1+/4 spasticity of the shoulder extensors and elbow flexors, measured by the Modified Ashworth Scale.14 P.M. was able to flex his hemiparetic shoulder 108° against gravity and extend his hemiparetic elbow, wrist, and fingers to neutral against gravity. P.M. demonstrated partially isolated movement against gravity at all joints of the upper extremity and fine motor coordination impairments of the hand.
P.M. was identified as a suitable candidate for this intervention for several reasons. He demonstrated sufficient motor abilities to interact with the system and the endurance to perform 90 minutes of treatment. Gross mobility, postural control, and perceptual impairments had been addressed effectively with his home therapy program, but he had not performed a focused intervention to address his upper extremity function in 18 months. His goals—improved use of the impaired upper extremity during transfers and dressing and improved use of the upper extremity during eating, grooming, and computer activities—are typical for a person in the chronic stage of stroke recovery.15 Several motor skills that are critical components of his goal activities can be addressed by the NJIT-RAVR system5,16 and his sensory and motor impairments were comparable with those of persons who had interacted with this system productively in the past.
We hypothesized that interventions targeting motor control and function limitations would translate into improvements in the timed batteries of real-world activities, measures of 24-hour activity, and participation. This protocol was approved by the institutional review boards of the New Jersey Institute of Technology and the University of Medicine and Dentistry of New Jersey; informed consent was acquired from the subject prior to participation.
P.M. performed twelve 60- to 90-minute sessions of training over 4 weeks with two robotically facilitated virtual rehabilitation systems, the NJIT-RAVR system (Figure 1a) and the NJIT Track-Glove system (Figure 1b), which are described in detail elsewhere.5,16 This training duration was chosen with the goal of achieving the 16 hours of practice associated with improvements in upper extremity function3 and to exceed the 300 repetitions per session that is the smallest number of repetitions associated with neuroplasticity.2 P.M. performed no upper extremity interventions during this time period other than the robotic intervention described in this article, but he performed ambulation and lower extremity strengthening 2 to 3 times per week with his home physical therapy. P.M. performed the same six simulations at all of the sessions. These simulations were chosen by the physical therapist managing the intervention (the primary author) from a suite of 10 simulations to address the component skills of the activities P.M. identified as goals. Calibration of the workspaces and positioning of the patient in relation to the system were performed by the primary author with the assistance of P.M.'s home therapists, who attended each robotic training session. This team monitored training performance and made adjustments to the robotic training intervention. A biomedical engineer was available during training to facilitate major adjustments as necessary. Based on time signatures from the records of each simulation, each activity was performed for 9 to 12 minutes during the first week and between 12 and 15 minutes per session in the 3 subsequent weeks. The variation in these times was predominantly related to setup. Activity times were distributed evenly to allow for balanced practice of all of the targeted skills.
Brief descriptions of each simulation, the therapeutic goals designed to address, the performance measure designed to track progress, the feedback provided, the range of repetitions performed, and the approach to increasing the intensity of the activity are given in Table 1. The configuration and application of each simulation as they relate to the patient's goals are discussed later.
P.M. had difficulty fastening buttons, donning glasses, and keyboarding because of an inability to flex fingers individually. We utilized the virtual piano trainer simulation (Figure 1b and Table 1) to address this impairment. P.M. played scales and drills with his hand stationary and attempted simple songs with his hand moving across the entire keyboard. The simulation measures fractionation, the difference between the flexion angles of the finger designated to press a key and the average of the other fingers. When the appropriate finger is used and the target fractionation is achieved, the note sounds. P.M. made little progress during training with the virtual piano trainer simulation, as measured by his first three daily fractionation scores (Table 2). We added an exoskeleton robot, the CyberGrasp, to assist P.M. in maintaining extension of the nondesignated fingers (Figure 1c). For example, when the index finger is cued, the middle, ring, and pinky fingers are held in extension by the CyberGrasp, but there is no extension force on the index finger, allowing it to flex independently of the others. This switching of resistance based on which finger is cued provides a different stimulus than would be afforded by an unprogrammed, dynamic finger extension splint that holds all fingers in extension.
P.M. used the CyberGrasp during the entire second week of training. During week 3, P.M. used the CyberGrasp during the first 5 minutes of each session with the virtual piano trainer, finishing the 15-minute block without the robot. P.M. did not use the CyberGrasp at all during week 4. P.M. demonstrated steady improvements in daily fractionation score after week 1 (Table 2). The mean number of repetitions per session for this simulation was 172 (range, 118–225).
P.M. demonstrated difficulty controlling the aperture of his hand during grasping activities that impacted dressing, eating, and grooming. We utilized the Space Pong simulation (Table 1) to address this impairment. P.M. played the pong game against the computer, controlling the paddle with flexion and extension of his fingers. Game score served as feedback. During week 1, P.M. had difficulty performing the Space Pong simulation, as evidenced by very low accuracy scores, because of an all-or-nothing quality of his hand opening (Table 2). After week 1, we decreased the gain from participant movement to virtual movement by 30%. This increased the amount of finger movement required to produce paddle movement, allowing P.M. to control his paddle accurately. Gain was increased by 15% on day 6 and back to 100% on day 10. The mean number of repetitions per session for this simulation was 52 (range, 36–72).
P.M. had difficulty opening his hand with his shoulder elevated, making it difficult to return cups to a table without spilling. We utilized the Hammer simulation in dynamic mode to address this impairment (Table 1). P.M. reached to targets in a virtual space and hammered them, using a repetitive finger extension movement. Vibration from the NJIT-RAVR simulated physical contact with the pegs when P.M. hit them successfully. A hammering sound signaled when successful movements were performed. We utilized an algorithm that increased the target area, decreasing shoulder stability demands when P.M. completed targets slowly. We decreased the target area, which increased the need to stabilize the arm when he completed targets quickly. During the first 2 weeks of the trial, P.M. made steady improvements in endpoint deviation, a measurement of extraneous movement, while performing the Hammer finger extension simulation in dynamic mode (Table 2). He maintained these gains during the final 2 weeks of training. The mean number of repetitions per session for this simulation was 21 (range, 10–35).
We utilized the hammer simulation in static mode (with the arm fixed in space) to train a repetitive pronation movement in varying degrees of elbow extension and shoulder flexion (Table 1). Pronation is a critical component of eating and grooming skills that P.M. had identified as targets for improvement. P.M. did not make significant improvements in his ability to pronate his upper extremity with his shoulder flexed and elbow extended during the use of the hammer simulation in fixed mode (Table 2). We increased the gain between pronation and hammer movement, which decreased the amount of motion the participant had to perform in order to hammer the peg, but this manipulation did not result in a consistent increase in pronation excursion. The mean number of repetitions per session for this simulation was 28 (range, 20–40).
P.M. had difficulty making large excursion forward reaches, secondary to synergistic extension of his trunk during shoulder flexion that limited his ability to utilize grab bars during transfers. We utilized two simulations to improve this action. The Reach-Touch simulation trained shoulder active range of motion without trunk movement (Table 1). During this simulation, P.M. moved a cursor to touch 10 targets in a three-dimensional workspace presented in stereo. An exploding sound was heard when the appropriate target was touched. Time to touch each set of 10 targets was shown at the end of each set, along with P.M.'s best time for the day. We increased the workspace in which P.M. performed this simulation daily. P.M. maintained his movement times despite the increases in workspace (Table 2). The mean number of repetitions per session for this simulation was 44 (range, 20–51).
We utilized the Cup Reaching simulation to address shoulder elevation combined with trunk movement (Table 1). During training with this simulation, P.M. attached his hand to a virtual cup and placed it on one of nine spaces on a virtual shelf. We set the height, width, and distance of the shelves on the basis of P.M.'s maximum reaching excursions, measured weekly, with P.M.'s trunk moving freely, and encouraged him to flex at the trunk to increase his forward reaching distance. Force feedback was provided to prevent P.M. from moving the virtual cup through the shelves or virtual table during performance of this simulation, to encourage more natural behavior. The weight of the virtual cups was lowered if fatigue caused a visually obvious flexion synergy at the shoulder and elbow. In addition, the time to place each cup was provided immediately after each repetition. The excursion of his forward reach improved during performance of the Cup Reaching simulation, while movement time decreased (Table 2). Before calibrating the workspace on day 10, the physical therapist managing the trial encouraged P.M. to challenge himself. This resulted in a much larger increase in reach excursion, which P.M. maintained well during the last week of training. The mean number of repetitions per session for this simulation was 39 (range, 17–63).
Responses to Intervention
P.M. tolerated this intensive intervention very well, was focused, and required no encouragement to maintain attention during the training. P.M. described himself as “tired” after training sessions but did not need to modify his daily activities to accommodate the exertion. On training day 3, during the hammer simulation, he complained of shoulder pain while pronating his forearm with his shoulder flexed to approximately 80°. These complaints subsided immediately after we lowered his arm position during this simulation and did not reoccur.
The first author, who was not blinded to the intervention, performed all outcome measures. Data were collected 1 day before and 1 day after 12 sessions of robotically facilitated training. To examine at the body structure/function level, we used the Upper Extremity Fugl-Meyer Assessment (UEFMA) and the Reach to Grasp (RTG) Test.17 For the UEFMA, both intrarater reliability (intraclass correlation coefficient [ICC] = 0.87) and interrater reliability (ICC = 0.99) are high, and the correlation between UEFMA score and FIM score is statistically significant (r = 0.76) in persons with stroke.18
The RTG test is a kinematic analysis of a reach, grasp, transport, release sequence that was not specifically trained during our intervention.19 Time to peak velocity of the first reaching movement was used to analyze P.M.'s ability to coordinate his shoulder and elbow, and time after peak velocity to initiation of the object transport movement was used to evaluate P.M.'s ability to fine-tune grasp and initiate object transport. Hand trajectories of P.M. performing the entire three-movement sequence are presented to demonstrate changes in his ability to coordinate the shoulder and elbow joints (see Figure 2c).
To test for changes at the activity level, we utilized a combination of the Wolf Motor Function Test20 (WMFT), the Jebsen Test of Hand Function (JTHF),21 and the Nine Hole Peg Test22 (NHPT). The WMFT has high levels of interrater reliability (ICC ≥0.97) and test-retest reliability (ICC = 0.90).23 Two studies by Wolf et al20 showed that the test can differentiate the more affected extremity and the less affected extremity in persons with chronic stroke. The modified JTHF has been demonstrated to have good levels of intrarater reliability (r = 0.72) and good concurrent validity with the Action Research Arm Test (r = 0.87) and NHPT (r = 0.84) in persons with stroke.24 The NHPT has demonstrated high levels of intrarater reliability (ICC ≥0.86)25 and strong correlations with the JTHF (r = 0.87) and Action Research Arm Test (r = 0.79)24 in persons with stroke. During performance of the UEFMA, the therapist noticed that P.M. had difficulty dissociating shoulder flexion and trunk extension and added the Modified Functional Reach (MFR) test26 to measure this construct objectively. The MFR test has been demonstrated to have high intrarater reliability (ICC = 0.95) and a statistically significant correlation with postural sway during forward reaching (r = 0.48)26 in persons with stroke.
We tested for changes at the activity level in his home environment by using an Actigraph triaxial accelerometer. We collected upper extremity acceleration data for 24 hours immediately after each testing session. Metrics included total vertical plane activity27 and total roll plane activity. Roll plane motion was added because pronation and supination movements tend to be a smaller component of nonpurposeful movement than the vertical flexion activity measured in other studies.28 Total activity measurements in each plane were measured in 1-second epochs. Any epoch in which the peak acceleration exceeded 0.16 m/s2 was identified as an active epoch in that plane. The number of active epochs was totaled for the day and divided by 60 to produce a total activity time in minutes for each 24-hour period.27 In addition, the ratio between the total vertical plane activity of the impaired arm and the total vertical plane activity of the unimpaired arm was calculated.29 To test for changes at the participation level, P.M. completed the hand, mobility, activities of daily living, and social participation subscales of the Stroke Impact Scale. This is the only self-report measure in the outcome battery. Each scale has been established as reliable and valid measurements in persons with strokes of moderate severity.30
P.M. demonstrated a 4-point improvement in the UEFMA after the month of robotically facilitated training (Table 3). Moreover, during the RTG posttest, P.M. demonstrated large improvements (146 milliseconds) in time to peak velocity, a measure of shoulder and elbow coordination (Figure 2b). Finally, time after peak velocity, a measure of the ability to fine-tune grasp, improved by 101 milliseconds (Figure 2b). Visual inspection of the movement trajectories of the entire three-phase activity demonstrates considerable improvements in smoothness of the movement and consistency of the trajectory (Figures 2c and 2d).
P.M.'s improvements in upper extremity activity measures were impressive as well (Table 3). He showed a 35-second improvement in the JTHF after the robotic intervention, and a robust 44-second improvement in the WMFT, which exceeds the published minimum clinically important difference of 2.7 seconds31 (Table 3). P.M. showed a 17-second improvement in function-level items of the WMFT and a 27-second improvement in activity-level tasks (can to mouth, card turning, towel folding, key turning, and checker stacking). The NHPT improved 14 seconds, which was smaller than the minimum clinically important difference of 30 seconds published for this test,32 and MFR distance increased 10 inches following robotic training (Table 3).
Twenty-four-hour activity measurements demonstrated across–the-board improvement subsequent to robotic training as well (Table 3). Active vertical plane movement increased by 26 minutes. Roll plane movement increased by 13 minutes. The ratio of impaired arm to unimpaired arm movement increased from 41% to 51%. P.M. demonstrated an improvement of 6 points in the social participation scale, 8 points in the ADL scale, and 7 points on the hand scale of the Stroke Impact Scale.
This case study addresses the developing need to explore how to effectively use robotic/virtual reality technology for individualized treatment programs. It describes a novel contribution to the growing robotic rehabilitation for stroke by presenting the outcomes of a personalized robotic intervention based on the findings of a physical therapy examination. We demonstrated the decision-making process and application of a haptic robot system that is integrated with activity-based virtual reality simulations for the management of a gentleman with hemiparesis poststroke. As in customary physical therapy care, the intervention was tailored for the individual and modified over time according to the his responses. The control systems governing the participant's robotic interactions and the feedback presented by the virtual reality simulations were adapted to address his impairments, functional limitations, and goals.
P.M. had a consistent volume of physical and occupational therapy, greater than the usual amount received by patients poststroke, yet he made substantial changes after the intervention described in this report. A critical dosage or intensity of training necessary for the improvement of hand function may not have been achieved during his home therapy sessions, despite the long treatment times. The challenge of providing sufficient repetition in traditionally implemented rehabilitation has been documented.2,3 Higher levels of efficiency in terms of the number of repetitions of task-based activity performed in a given period of time are a significant advantage afforded by technology-based rehabilitation. The novelty of the activities, the intensity of the cognitive experience, the progressive adaptation of the activities, and the focus on hand movements may also have influenced the improvement seen in this individual following this intervention. This report demonstrates the flexibility of the NJIT-RAVR and Track-Glove systems and the ability to individualize a robotic intervention. Following a search of more than 200 articles, it appears that this approach has not been adequately examined in the existing literature.7,10,11
The changes in movement kinematics exhibited by the participant were accompanied by robust changes in the real-world WMFT and JTHF outcome measures. It has been hypothesized that transfer of learning is more successful when there is similarity between practice characteristics and desired outcome;33 therefore, specificity of practice is important. Although the majority of tasks used during this training paradigm were activity based, one cannot argue that the practice characteristics of a virtual environment and the real world are similar. Therefore, it is interesting to conjecture what aspects of the robotic/virtual reality training may have influenced the changes in the outcome measures. Variation in practice is also considered an important component necessary for retention and generalizability of skills.34,33 Not only was this participant trained with a variety of tasks, but also the kinematic and task requirements of each task were modified and adapted at a level that would be difficult (and in some cases impossible) without the use of an integrated robotic/virtual reality system. Thus, the participant had to consistently select an appropriate movement pattern within the changing context of the task. This may have facilitated the transfer of learning seen in the execution of the real-world outcome measures.34 Variation in the activities performed during constraint-induced movement therapy has been proposed as the reason participants improve on the WMFT.1 Although there are substantial benefits to therapy that includes a robotic/virtual reality intervention, there are also substantial challenges. The system is costly, and patient-centered systems require engineering skills to individualize the robotic and simulation parameters.
Two limitations to this study were the nonblinded assessment of outcomes and the lack of follow-up data due to P.M.'s repeated urinary tract infections postintervention. The fact that the final outcome measures were performed immediately following the intervention limits our ability to make claims related to the durability of the changes P.M. made during the robotic intervention. Finally, there was extensive interdisciplinary collaboration among three therapists during the implementation of this intervention, which is not typical in current health care settings in the United States.
This detailed description of an individualized robotic/virtual reality intervention demonstrates how it can be successfully used as an intervention incorporating an individualized patient-client management model and that a person participating in such an intervention can generalize the motor adaptations he or she makes during practice into real-world function, at least in the short term. Future directions should include rigorous investigations of the effects of adaptive modifications of task parameters and manipulation of the scaling of the relationship between visual feedback and participant movement. Further study of the implementation of robotic and virtually simulated rehabilitation activities within the framework of the patient-client management model will also be necessary to establish the usability and effectiveness of these technologies in clinical settings. In addition, investigation of neurological mechanisms underlying the generalization of motor adaptations made during technology-based interventions into real-world motor skills may be necessary to fully justify their continued development and use.
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