A pilot study evaluating use of a computer-assisted neurorehabilitation platform for upper-extremity stroke assessment
© Feng and Winters; licensee BioMed Central Ltd. 2009
Received: 12 November 2007
Accepted: 28 May 2009
Published: 28 May 2009
There is a need to develop cost-effective, sensitive stroke assessment instruments. One approach is examining kinematic measures derived from goal-directed tasks, which can potentially be sensitive to the subtle changes in the stroke rehabilitation process. This paper presents the findings from a pilot study that uses a computer-assisted neurorehabilitation platform, interfaced with a conventional force-reflecting joystick, to examine the assessment capability of the system by various types of goal-directed tasks.
Both stroke subjects with hemiparesis and able-bodied subjects used the force-reflecting joystick to complete a suite of goal-directed tasks under various task settings. Kinematic metrics, developed for specific types of goal-directed tasks, were used to assess various aspects of upper-extremity motor performance across subjects.
A number of metrics based on kinematic performance were able to differentiate subjects with different impairment levels, with metrics associated with accuracy, steadiness and speed consistency showing the best capability. Significant differences were also shown on these metrics between various force field settings.
The results support the potential of using UniTherapy software with a conventional joystick system as an upper-extremity assessment instrument. We demonstrated the ability of using various types of goal-directed tasks to distinguish between subjects with different impairment levels. In addition, we were able to show that different force fields have a significant effect on the performance across subjects with different impairment levels in the trajectory tracking task. These results provide motivation for studies with a larger sample size that can more completely span the impairment space, and can use insights presented here to refine considerations of various task settings so as to generalize and extend our conclusions.
In the United States, stroke is the leading cause of disability and affects about 5.6 million individuals today, resulting in an estimated direct and indirect cost of $62.7 billion . Up to 85% of the stroke survivors show initial upper extremity sensorimotor dysfunctions. Between 55% and 75% of patients continue to experience upper extremity functional limitations after 6 months of the stroke, which are associated with diminished health-related quality of life .
Quantification of upper-extremity movement features in patients with stroke is a critical component for supporting the optimization of intervention plans , so as for understanding the underlying mechanism of the upper extremity impairments induced by stroke. In today's rehabilitation practice, stroke assessment in clinical settings generally involves use of observer-based, ordinal scale instruments, such as the Functional Independence Measure (FIM) , Fugl-Meyer Assessment , Wolf Motor Function Test , Chedoke-McMaster Stroke Assessment  and so on. Although these ordinal instruments are well established and have proven to be reliable and sensitive for measuring gross changes in functional performance, they can be problematic because of poor consistency in the differences between scale increments . They also lack sensitivity to characterize small yet potentially important changes during the intervention process [9, 10]. The subjectivity of these tests is well recognized . Furthermore, due to the economic pressure on the healthcare system, patients with stroke, particularly the outpatient population, have a limited access to rehabilitation resources . Due to these reasons, there is a need to develop cost-effective, semi-autonomous/autonomous, yet sensitive assessment instruments for patients with stroke at home, which is characterized by low cost and under-supervision from rehabilitation practitioners.
Measures derived from kinematic trajectories associated with goal-directed tasks are continuous metrics which can potentially be sensitive to the subtle changes in the intervention process. They can also be more objective and repeatable across subjects than clinical ordinal scales . The results from previous studies which examined the assessment capability of kinematic measures for stroke-induced impairments are summarized below.
Reaching to a target object is one fundamental component in the activities of daily livings (ADLs) (e.g. eating, drinking, grooming) which involve arm movements. Many studies have examined point-to-point reaching in subjects with stroke-induced impairments and found that their movements are characterized by slowness [13, 14], spatial and temporal segmentation , abnormal patterns of muscle activation [13, 16], decreased movement range  and so on. Selected kinematic measures developed by these studies, such as movement time, elbow extension, shoulder flexion, displacement of the trunk and the active ranges of motion (ROM), have shown a significant correlation with the clinical motor function scales (e.g. upper extremity motor control portion of Fugl-Meyer assessment, Chedoke-McMaster Stroke Assessment). Some of these kinematic measures, such as movement time, significantly correlated with clinical spasticity scales (e.g. Ashworth scale , modified Ashworth scale ).
Trajectory-tracking tasks require common components involved in both perception-action coupling and functional motor tasks: perception of environmental constraints, motor planning and execution, and corrective monitoring of performance including explicit feedback ). Several studies have already evaluated the assessment capability of trajectory tracking task with subjects with stroke-induced impairments. It has been demonstrated that the motor functional level of subjects and their performance in trajectory-tracking tasks are closely related [20, 21]. Furthermore, certain kinematic metrics (e.g. root mean squared error (RMSE)) derived from trajectory tracking tasks have been demonstrated as a reliable, sensitive assessment tool of the upper-extremity motor function in subjects with stroke-induced hemiparesis .
Many daily activities, such as holding a cup of tea, driving a car, and replacing light bulbs, require one to cope with some level of instability in the manipulated object. It is important to evaluate the performance of subjects with stroke in a goal-directed task in an unpredictable mechanical environment to better understand the strategy that they used to cope with instability [22, 23]. Recent experimental evidence also suggests that patients with stroke-induced impairments may likely benefit from training of the paretic limb in unpredictable mechanical environments [24–27], and the improvement can potentially be transferred to ADLs.
These studies laid down a rationale stage for developing kinematic measures derived from goal-directed tasks as upper-extremity assessment instruments, but also leave several fundamental questions unanswered. First, to date the majority of biomechanical upper-extremity evaluations involve reaching and trajectory tracking performed at a limited number of task settings, most commonly at one speed in the horizontal plane with the arm supported . In order to personalize the intervention plan for a given client with stroke-induced impairments with the goal of optimizing the functional outcome, we need to better understand the performance of subjects with stroke under various goal-directed tasks. It is necessary to develop a suite of performance metrics to characterize various movement features in the goal-directed tasks, such as slowness, segmentation, and a decreased range of motion, movement speed, and coordination and so on.
Second, while interventions that are based on robotic therapy have proven to be effective for sub-acute and chronic stroke populations [29–31], the advantages of mechanical guidance by the robotic device over other stroke therapy technique still remain elusive . It is worthwhile to examine the performance of subjects with stroke under various mechanical environments in goal-directed tasks, so that we can better understand the role of force on the performance of subjects with stroke-induced impairments and potentially optimize the mechanical environment in the robotic-assisted therapy plan for a given client.
Third but probably more important for outpatient rehabilitation, most of these studies summarized here are using either large robotic systems or three dimensional (3D) marker-based motion analysis systems as their research platform. While these tools provide abundant sensor-based performance data, high costs and mechanical sophistication appear to limit the likelihood of their large-scale implementation, particularly for the home setting, which is more convenient and sometimes the only option for many persons who could benefit from therapeutic interventions.
In summary, there is a need to develop alternative, cost-effective yet still sensitive tools for upper-extremity stroke assessment, particularly for outpatient rehabilitation. This paper presents the findings from a pilot study using UniTherapy software [33, 34] interfaced with a conventional force-reflecting joystick. This software also has been used by adapted larger joysticks called TheraJoy [35–37] and for driving wheels called TheraDrive , but with different aims. Here the focus is on evaluating a suite of performance metrics that were derived from goal-directed tasks supported by UniTherapy technology. The sensitivity of these metrics as home-based assessment instruments were evaluated within the context of two hypotheses: hypothesis 1) Impairment level of human subjects influences performance on various goal-directed tasks using a conventional force-reflecting joystick, and hypothesis 2) Force field settings in continuous tracking tasks influence the performance of human subjects across impairment levels. The focus here is whether our performance metrics, developed using a low-cost computer-assisted platform, have as enough usability and sensitivity for use as assessment tools for a home rehabilitation as a component within a larger-scale biomechatronic system. A key question relates to which of the many viable metrics are most effective in terms of sensitivity, here addressed within the context of these hypotheses, and this is the focus of the discussion.
In this section, we describe the experiment setup and protocols used in this study, particularly focusing on the selected goal-directed tasks for evaluating the potential of UniTherapy software interfaced with the conventional force-reflecting joystick for upper-extremity stroke assessment.
We utilize UniTherapy software interfaced with a conventional force-reflecting joystick (Microsoft Sidewinder) along with TheraJoy (adapted joystick) [35–37] for the data collection component of in this study. UniTherapy software implements three toolboxes consisting of customizable goal-directed tasks to quantify the various aspects of upper-extremity movement features . These toolboxes are outlined below:
• The Range of Capacity (ROC) toolbox can be used to assess the user's initial and final capability ROM when using an input device and optionally used to map between the input device workspace range and the user's capability range by a two dimensional (2D) transformation algorithm .
• The Tracking toolbox implements discrete tracking and continuous tracking. Discrete tracking (target acquisition) requires the subjects to move a cursor into a target window with accuracy; once the subjects get into the target window, they receive a positive visual feedback and optionally a sound cue, and they are required to stay as stable as possible for a threshold of success time (defined as dwelling time); after successful completion of dwelling time, the target jumps to the next predefined position. Continuous tracking instructs subjects to follow the continuously moving target and try to stay within the target window as much as they can, for which they receive a positive visual feedback when they stayed within the target window. The size of the target window and dwelling time are customizable.
• The users' stable motor performance is also evaluated using the System Identification toolbox. Predefined force perturbations are applied to the subject under a certain instruction (e.g. "hold," "relax"). The force data and experimenter's instruction are recorded as input while subject's movement data is recorded as output.
UniTherapy applied none or varying levels of force-feedback to physical therapeutic interfaces, depending on the settings and the task; these were derived from a series of force effects such as spring, damper, constant and so on in DirectX. Both sampling of position data and the input of force were at 33 Hz.
The joystick systems used in this study consisted of the conventional force-reflecting joysticks (Microsoft Sidewinder) and the larger "TheraJoy" in horizontal and vertical settings [35–37], and incorporate a larger range of motion that can be scaled and modified depending on the anthropometrics and abilities of the user. Here the focus is on a detailed analysis of selected data for the conventional joystick, related to the aim of identifying sensitive assessment metrics; most of the other data was used as part of the Master's Thesis by Johnson .
There are multiple reasons for this focus on the conventional force-reflecting joystick. First, unlike the larger custom-made TheraJoys, these joysticks are available without special alteration. Second, it has been shown through video analysis that significant movement of the torso was uncommon when using the conventional joystick versus the TheraJoy devices . Third, EMG analysis has shown convincingly that the shoulder muscles are quite involved with the conventional joystick because the high degree of humeral rotation that accompanies "horizontal" movements and the natural reach involved in "vertical" movements – movement-related EMG activity for the pectoralis major, anterior deltoid and lattissimus dorsi was consistently higher than for the wrist flexor and extensor groups, and indeed even the triceps and posterior deltoid were typically more active than the wrist flexors . Fourth, the forces applied by the joystick motor to the hand are much higher for the conventional joystick, with its smaller lever arm. Fifth, the system bandwidth due to applied force oscillations is higher with the lower mass of the Microsoft Sidewinder joystick, about 9 Hz, with there still being plenty of movement response up to the upper limit of 16 Hz, and with reliable linearity of the output torque . Finally, a systematic study of task performance with the conventional joystick placed at 6 different locations within the workspace showed only moderate performance variance within the primary ability space of the user , supporting the decision made for this study of letting the subject select a location that they found to be a comfortable range, given their abilities.
Summary of the performance metrics for the goal-directed tasks
Percentage Time in Target (PTT)
The percentage of the time the human subject staying within the target window.
Accuracy and Steadiness
Root Mean Square Error (RMSE)
The squared root of the mean-squared distance from the subject position to the target position.
The mean of the perpendicular distance from the subject position to the target line within the movement time.
The mean of the subject speed during the continuous tracking task
The standard deviation of the subject speed during the continuous tracking task
The time from the movement of the target to the first significant movement made by the subject.
The time window from the end of the reaction time to the time after which the user has stably reached the target.
The mean of the perpendicular distance from the subject position to the target path line.
The mean of subject speed.
Peak Speed Number
The number of local maximum speed within the movement time window.
Dwelling Percentage Time in Target
The percentage of the time that the subject stayed within the target window during the Dwelling Time.
The percentage of the targets that have been successfully reached by the subject.
Overall Reaching Capability
The mean of the displacement outside of the holding area in both x and y direction
Strength (under "hold" instruction)
The standard deviation of the displacement outside of the holding area in both x and y direction
Summary information of the subjects with stroke
The experimental protocol consisted of two sessions focusing first on training the individual on using each device (conventional joystick (CJS) and TheraJoy in horizontal (HJS) and vertical (VJS) settings), then on collecting performance data for a suite of goal-directed assessment tasks in the second session. In the first session, all joysticks were placed in the position of greatest comfort for the subject, including altered handle position and interface to allow for maximum comfort. Subjects first completed several goal-directed tasks from the Tracking and System Identification toolbox with the conventional joystick. A subset of tasks was then repeated with the HJS and VJS. The medial-lateral and proximal-distal direction of the joystick movements were mapped to the X and Y direction on the computer screen. All tasks were repeated with both arms. On the second day, these goal-directed tasks were repeated but this time both video and EMG data were also collected but not presented here.
Representative results from three classes of task are presented here: 1) continuous circle tracking tasks under three different force settings (e.g. white noise perturbation, no force, spring-assistance), 2) eight-point rectangle target acquisition, and 3) pseudo-random perturbation task under "hold" instruction. Both the control and stroke groups were asked to complete these tasks. For both continuous tracking and target acquisition tasks, the target window size were set at 5% of the width of the workspace; for the target acquisition task, the dwelling time for successful completion was set to one second.
Continuous Circle Tracking
where Fx, yrepresents the force at x and y directions, K represents the spring coefficient and was set as the highest stiffness which the conventional joystick can provide, Random [0,1]x, yis a random function on both x and y direction separately (with values of 0 as no force and ± 1 as maximum force magnitude), Subjectx, y represents the subject position, and Targetx, y represents the target position. The spring-assistance force vector was directed to help pull the subject toward to current target location.
Eight-Point-Rectangle Target Acquisition
Pseudo-Random Perturbation under "hold" instruction
Data and statistical analysis
Representative tasks were analyzed across subjects using the performance metrics defined in Table 1. Mean and standard deviation values were calculated and presented for control (n = 8), high function (n = 5), and low function (n = 4) groups. For the continuous circle tracking task, a mixed-design repeated measure ANOVA test was used to test between group (by functional level) and within group (by force settings) difference. For the eight-point rectangle target acquisition and pseudo-random perturbation tasks, a repeated measure ANOVA test was used to test between group (by functional level) differences. The Tukey test was used for post-hoc analysis. A significance threshold level of p < 0.05 was used for interpretation. Statistical analysis was performed on the data using XLSTAT 2006 (AddinSoft, http://www.xlstat.com).
Continuous circle tracking under various force field
The mean and standard deviation of the performance metrics in the continuous circle tracking tasks
By Functional level
By force settings
40.87 ± 23.00†
31.80 ± 17.70†
14.30 ± 16.19‡
50.10 ± 23.94‡
33.28 ± 12.39‡
13.04 ± 6.63‡
5.02 ± 2.47†
6.25 ± 2.16†
19.29 ± 20.96‡
6.58 ± 8.71
7.54 ± 2.44
12.99 ± 16.05
1.13 ± 1.14†
1.11 ± 0.83†
9.57 ± 15.18‡
1.94 ± 5.56
2.68 ± 0.78
5.64 ± 11.50
1.93 ± 1.19†
2.39 ± 1.69†
3.23 ± 2.37‡
1.17 ± 0.08†
1.31 ± 0.24†
4.53 ± 1.09‡
1.37 ± 0.87‡
1.94 ± 0.99‡
2.85 ± 1.68‡
0.89 ± 0.35‡
1.54 ± 0.75‡
3.14 ± 1.10‡
For within group difference, there are significant differences with PTT and SS metrics between spring-assistance, no force and white noise perturbation settings. This suggests that spring-assistance can significantly improve the performance on accuracy, steadiness and speed consistency in the trajectory tracking across subjects with different impairment levels, while perturbation significantly worsens these aspects of movement performance. There is also a significant difference with SM metric between perturbation and no force/assistance setting, which confirms that perturbation significantly diminishes the capability of keeping consistent with the target speed in the trajectory tracking tasks across subjects.
Eight-point rectangle target acquisition
Pseudo-random perturbation under "hold" instruction
This paper evaluates the assessment capabilities of the UniTherapy software interfaced with the conventional force-reflecting joystick, using selected goal-directed tasks which were designed to capture the basic movement components (e.g. reaching, tracking, and coping with instability) that may relate to ADLs. A suite of kinematic measures were developed to examine various movement features in each type of goal-directed tasks. The results support the potential of using UniTherapy software with joystick system as an upper-extremity assessment instrument. We demonstrated the ability of using various types of goal-directed tasks to distinguish between subjects on different impairment levels (hypothesis 1). In addition, we were able to show that different force fields have a significant effect on the performance across subjects with different impairment levels in the trajectory (continuous) tracking task (hypothesis 2).
For assessment measures associated with the continuous tracking tasks, for the continuous circle tracking task, we found that certain measures can differentiate between control/high functional group and low functional stroke group: performance of able-bodied/high functional stroke subjects in the trajectory tracking tasks were significantly more accurate (Percentage Time in Target (PTT), Root Mean Square Error (RMSE)), stable (PTT), with less path deviation (deviation) and better speed consistency (Speed_Mean (SM), Speed_StdDev (SS)) than subjects with low functional stroke. This was true even for the relatively small size of the sample population, as reflected in the levels of statistical significance between groups. One possible reason for this performance difference was that subjects with moderate to severe stroke may take more time for movement planning and for correction based on their visual feedback. In comparing between control and high functional stroke group, there is also a significant difference on SS metric as well as a trend of difference observed on PTT and SM metrics: able-bodied subjects can perform more accuracy (PTT), more steadily (PTT) and with better speed consistency (SM, SS) than subjects with high functional stroke. The capability to differentiate between able-bodied subjects and subjects with high functional stroke as well as between high and low functional stroke subjects by using SS metric and potentially the PTT and SM metrics suggests that these metrics could be sensitive to the impairment level of human subjects across assistance, no force and perturbation force settings with the trajectory tracking task. The result on the RMSE metric was consistent with other published data that RMSE were found to be sensitive the impairment level of human subjects [20, 21, 10]. However, as is reflected in the paragraphs that follow, overall this most convenient metric was not found to be one of the more robust metrics in terms of sensitivity.
For assessment measures associated with discrete tracking tasks, for the eight-point rectangle task, the results for Reaction Time (RT), Movement Time (MT), Movement Speed (MS), Peak Speed Number (PSN), Dwelling Percentage Time in Target (DPTT) and Success Percentage (SP) metrics show that significant differences exist between control/high functional stroke group and low functional stroke group. This suggests that the able-bodied/high functional stroke subjects have higher movement capabilities in reaction quickness (RT), movement quickness (MT, MS), smoothness (PSN), stability (DPTT) and overall reaching (SP).
For assessment measures associated with perturbation with "hold" instruction tasks, under x-direction pseudo-random perturbation, the EM_X and ES_X metrics showed significant difference between low functional stroke group and controls/high functional stroke group, which suggest that it is challenging for patients with low functional stroke to compensate for the perturbation from the medial-lateral direction. This seems likely due to the directional differences in the spring-like impedance field within the horizontal arm workspace, with the stiffness value generally higher in the proximal-distal direction than the medial-lateral direction as documented by the previous studies [43–45]. Previous studies have also shown that shoulder stiffness is direction dependent and task dependent [46, 47]. Under y-direction pseudo-random perturbation, there is no significant between-group difference shown in EM_Y and ES_Y metrics. This suggests that subjects with different impairment level, including low functional stroke, either can compensate for the pseudo-random perturbation from the proximal-distal direction or there are intrinsic direction-sensitive mechanical impedance reasons that are helpful, or most likely a combination of both. Of note is that the control and high functional stroke group can compensate quite well. In terms of functional assessment strategies, these results suggest that when using the perturbation task under the "hold" instruction as a strength assessment test, perturbations in the medial-lateral direction provide a more sensitive task setting (and associated metrics) than perturbations in the proximal-distal direction, and can potentially be a challenging task for the stroke subjects with weak strength.
In summary, despite the relatively small sample size, kinematic metrics developed for various types of goal-directed tasks in UniTherapy were able to differentiate subjects with different impairment levels, supporting hypothesis 1. This is consistent with previous findings by other research studies [20, 21, 13, 17, 10]. Particularly, SS, and potentially PTT and SM metrics, derived from the continuous tracking task, showed the best capability for differentiating between subjects with low function stroke and high function stroke, as well as between subjects with high functional stroke and able-bodied subjects.
When we tested three different force setting (e.g. white-noise perturbation, no force, spring-assistance) across subjects in a continuous circle tracking task, significant differences were shown by the PTT and SS metrics between spring-assistance, no force and white noise perturbation settings, thus supporting hypothesis 2. This suggests that spring-assistance can significantly improve the performance on accuracy (PTT), steadiness (PTT) and speed consistency (SS) in the trajectory tracking across subjects with different impairment level, while perturbation significantly worsens these aspects of movement performance. These results also confirm that perturbations significantly worsen the capability of keeping consistent (SM) with the target speed in the trajectory tracking tasks across subjects. Also, these results suggest that PTT emerges as a potentially sensitive assessment metric for trajectory tracking tasks across various task settings, since PTT has the capability to characterize different phases in the trajectory tracking task, including motor planning, motor execution, and movement correction based on the visual feedback . While showing that perturbation force is challenging across subjects during the continuous tracking tasks, it has been suggested by other studies that persons with stroke-induced impairments may likely benefit from this type of "error augmentation" training of the paretic limb in unpredictable mechanical environments, and potentially that improvement can be transferred to ADLs [24, 25].
The main limitations of this pilot study relate to the relatively small subject sample size. and also that the motor impairment level of our stroke population, as measured by the upper-extremity Fugl-Meyer score, was polarized in that we did not fully span the impairment workspace. Despite these limitations, our results suggest that the UniTherapy system and force-reflecting joystick tracking tasks in general have great potential for being used as a sensitive upper-extremity assessment tool. For instance, we saw clear evidence of a ceiling effect in the UE-FM in that we identified assessment metrics that could strongly delineate between stroke subjects with near-normal UE-FM scores and the normal population. Also of note is that a narrower in scope follow-up pilot study that more fully spanned the Fugl-Meyer score space and systematically considered the effects of task settings for force and speed magnitudes, but only for a subset of the class of continuous tracking tasks described here, confirmed that the force input settings and the tracking speed used in this study were within the range of effective choices .
The capability of examining basic upper-extremity movement assessment metrics periodically (and more frequently) can help optimize the intervention plan in order to yield functional benefits for a given client. While not emphasized here, the UniTherapy technology also supports telerehabilitation connections between a "telepractitioner" and "home client" which can extend the use of this platform into the scope of intervention [33, 34]. Finally, a larger longitudinal study is still needed to evaluate these systems so as to reach a more general conclusion related to the sensitivity of these metrics to measuring subtle, yet important changes during an ongoing intervention process.
This study, involving use of the UniTherapy software interfaced with a conventional force-reflecting joystick, validated the viability of using the combination of goal-directed tasks with associated kinematic metrics to obtain sensitive upper-extremity performance metrics. The results show that the UniTherapy platform can potentially be a sensitive upper-extremity assessment tool: it shows significant differences between low function and high function stroke subjects as well as high functional stroke and able-bodied subjects by using selected goal-directed tasks and kinematic metrics. This also helps inform other research groups on the most sensitive types of assessment metrics.
It is suggested that to get potentially more sensitive assessment results, the type of goal-directed task, the task settings and the kinematic metrics should be carefully selected, and based to some extent on a given client's impairment level and motor deficit. We also found that in the trajectory tracking task, mechanical assistance by our simple robotic device significantly improved the tracking performance of subjects across impairment levels, while perturbation significantly worsened it. Studies with a larger sample size with subjects in a spanned impairment space, and with considerations of various task settings, are necessary to generalize our conclusions and broaden the scope of application.
We thank Dr. Michelle Johnson, Dr. Tina Stoeckmann, Dr. Robert Scheidt and Dr. Mei Wang for giving their comments on the initial draft of the manuscript. The financial support from The Ralph and Marian Falk Medical Trust Foundation and the Rehabilitation Engineering Research Center on Accessible Medical Instrumentation (U.S. Department of Education, NIDRR #H133E020729) is gratefully acknowledged. The opinions are those of the authors
- Rosamond W, et al.: Heart Disease and Stroke Statistics–2007 Update: A Report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 2007, 115: 69-171. 10.1161/CIRCULATIONAHA.106.179918View ArticleGoogle Scholar
- Nichols-Larsen DS, Clark PC, Zeringue A, Greenspan A, Blanton S: Factors influencing stroke survivors' quality of life during subacute recovery. Stroke 2005, 36: 1480-84. 10.1161/01.STR.0000170706.13595.4fView ArticlePubMedGoogle Scholar
- Winters JM, Winters JM: A Telehomecare Model for Optimizing Rehabilitation Outcomes. J Telemed & E-Health 2004, 10: 200-212. 10.1089/tmj.2004.10.200View ArticleGoogle Scholar
- Hamilton BB, et al.: A uniform national data system for medical rehabilitation. In Rehabilitation outcomes: analysis and measurement. Edited by: Fuhrer JM. Baltimore: Brookes; 1987:137-147.Google Scholar
- Fugl-Meyer AR, Jaasko L, Leyman I, Olsson S, Steglind S: The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance. Scand J Rehabil Med 1975,7(1):13-31.PubMedGoogle Scholar
- Wolf SL, Catlin PA, Ellis M, Archer AL, Morgan B, Piacentino A: Assessing Wolf motor function test as outcome measure for research in patients after stroke. Stroke 2001,32(7):1635-1639.View ArticlePubMedGoogle Scholar
- Gowland C, et al.: Measuring physical impairment and disability with the Chedoke-McMaster Stroke Assessment. Stroke 1993,24(1):58-63.View ArticlePubMedGoogle Scholar
- Cramer SC, Nelles G, Schaechter JD, Kaplan JD, Finklestein SP: Computerized measurement of motor performance after stroke. Stroke 1997, 28: 2162-2168.View ArticlePubMedGoogle Scholar
- Dobkin BH: Neurologic Rehabilitation. Philadelphia, PA: FA Davis; 1996.Google Scholar
- Patten C, Kothari D, Whitney J, Lexell J, Lum PS: Reliability and Responsiveness of Elbow Trajectory-tracking in Chronic Post-stroke Hemiparesis. J Rehabil Res Dev 2003,40(6):487-500. 10.1682/JRRD.2003.11.0487View ArticlePubMedGoogle Scholar
- Salter K, Jutai JW, Teasell R, Foley NC, Bitensky J: Issues for selection of outcome measures in stroke rehabilitation: ICF Body Functions. Disabil Rehabil 2005,27(4):191-207. 10.1080/09638280400008537View ArticlePubMedGoogle Scholar
- Ottenbacher KJ, Smith PM, Illig SB, Linn RT, Ostir GV, Granger CV: Trends in length of stay, living setting, functional outcome, and mortality following medical rehabilitation. JAMA 2004,292(14):1687-95. 10.1001/jama.292.14.1687View ArticlePubMedGoogle Scholar
- Levin MF: Interjoint coordination during pointing movements is disrupted in spastic hemiparesis. Brain 1996, 119: 281-93. 10.1093/brain/119.1.281View ArticlePubMedGoogle Scholar
- Wu CY, Wong MK, Lin KC, Chen HC: Effects of Task Goal and Personal Preference on Seated Reaching Kinematics After Stroke. Stroke 2001, 32: 70.View ArticlePubMedGoogle Scholar
- Rohrer B, Fasoli SE, Krebs HI, Hughes R, Volpe BT, Frontera WR, Stein J, Hogan N: Movement smoothness changes during stroke recovery. J Neurosci 2002,22(18):8297-304.PubMedGoogle Scholar
- Cirstea MC, Levin MF: Compensatory strategies for reaching in stroke. Brain 2000,123(Pt 5):940-53. 10.1093/brain/123.5.940View ArticlePubMedGoogle Scholar
- Kamper DG, McKenna-Cole AN, Kahn LE, Reinkensmeyer DJ: Alterations in reaching after stroke and their relation to movement direction and impairment severity. Arch Phys Med Rehabil 2002,83(5):702-7. 10.1053/apmr.2002.32446View ArticlePubMedGoogle Scholar
- Ashworth B: Preliminary trial of carisoprodol in multiple sclerosis. Practitioner 1964, 192: 540-542.PubMedGoogle Scholar
- Bohannon RW, Smith MB: Inter rater reliability of a modified Ashworth Scale of muscle spasticity. Physical Therapy 1987, 67: 206-207.PubMedGoogle Scholar
- Jones RD, Donaldson IM: Measurement of integrated sensory-motor function following brain damage by a computerized preview tracking task. Int Rehabil Med 1981,3(2):71-83.View ArticlePubMedGoogle Scholar
- Halaney ME, Carey JR: Tracking ability of hemiparetic and healthy subjects. Phys Ther 1989,69(5):342-8.PubMedGoogle Scholar
- Won J, Hogan N: Stability properties of human reaching movements. Exp Brain Res 1995, 107: 125-136. 10.1007/BF00228024View ArticlePubMedGoogle Scholar
- Mussa-Ivaldi FA: Modular features of motor control and learning. Curr Opin Neurobiol 1999, 9: 713-717. 10.1016/S0959-4388(99)00029-XView ArticlePubMedGoogle Scholar
- Wei Y, Patton J, Baja P, Scheidt R: Visual Error Augmentation for Enhancing Motor Learning and Rehabilitative Relearning. Proceedings of the Intern Conf of Rehabilitation Robotics (ICORR), Chicago, IL 2005, 505-510.Google Scholar
- Patton J, Stoykov M, Kovic M, Mussa-Ivaldi F: Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors. Exp Brain Res 2006, 168: 368-383. 10.1007/s00221-005-0097-8View ArticlePubMedGoogle Scholar
- Scheidt RA, Dingwell J, Mussa-Ivaldi FA: Learning to Move Amid Uncertainty. J Neurophysiol 2001, 86: 971-985.PubMedGoogle Scholar
- Scheidt RA, Stoeckmann T: Reach Adaptation and Final Position Control Amid Environmental Uncertainty Following Stroke. J Neurophysiol 2007, 97: 2824-36. 10.1152/jn.00870.2006View ArticlePubMedGoogle Scholar
- McCrea PH, Eng JJ, Hodgson AJ: Biomechanics of reaching: clinical implications for individuals with acquired brain injury. Disabil Rehabil 2002, 24: 534-541. 10.1080/09638280110115393PubMed CentralView ArticlePubMedGoogle Scholar
- Lum PS, Burgar CG, Shor PC, Majmundar M, Loos HF: Robot-assisted movement training compared with conventional therapy techniqu es for the rehabilitation of upper limb motor function following stroke. Arch Phys Med Rehabil 2002, 83: 952-59. 10.1053/apmr.2001.33101View ArticlePubMedGoogle Scholar
- Lum PS, Burgar CG, Loos M, Shor PC, Majmundar M, Yap R: MIME robotic device for upper-limb neurorehabilitation in subacute stroke subjects: A follow-up study. J Rehabil Res Dev 2006,43(5):631-642. 10.1682/JRRD.2005.02.0044View ArticlePubMedGoogle Scholar
- Fasoli SE, Krebs HI, Stein J, Frontera WR, Hogan N: Effects of robotic therapy on motor impairment and recovery in chronic stroke. Archives of Phys Med Rehabil 2003,84(4):477-82. 10.1053/apmr.2003.50110View ArticleGoogle Scholar
- Kahn LE, Zygman ML, Rymer WZ, Reinkensmeyer DJ: Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: A randomized controlled pilot study. J Neuroengineering Rehabil 2006, 3: 12. 10.1186/1743-0003-3-12PubMed CentralView ArticleGoogle Scholar
- Feng X, Winters JM: An Interactive Framework for Personalized Computer Assisted Neurorehabilitation. IEEE Trans Inf Tech in Biomedicine 2007,11(5):518-526. 10.1109/TITB.2006.889700View ArticleGoogle Scholar
- Feng X: Upper-extremity Performance Assessment Using An Interactive, Personalized Computer-Assisted Neurorehabilitation Motivating Framework. PhD thesis. Marquette University, Biomed. Eng. Dept; 2007.Google Scholar
- Ellsworth C, Winters JM: An innovative system to enhance upper-extremity stroke rehabilitation. Proceedings of the IEEE/EMBS, Cancun 2003, 2367-68.Google Scholar
- Johnson LM, Winters JM: Evaluation of Tracking Performance Using Joystick Manipulators that Engage Different Arm W orkspaces. Proceedings of the Intern. Conf. of Rehabilitation Robotics (ICORR), Chicago, IL 86-90.
- Johnson LM: Evaluation of Movement Strategies when Using Joystick Manipulators Throughout the Arm Works pace: Prescriptive Projections for Rehabilitation of Stroke-Induced Arm Impairment. In Master Thesis. Marquette University, Biomed. Eng. Dept; 2006.Google Scholar
- Johnson MJ, Feng X, Johnson LM, Winters JM: Potential of a Suite of Robot/Computer-Assisted Motivating Systems for Personalized, Home-Based, Stroke Rehabilitation. J Neuroengineering Rehabil 2007, 4: 6. 10.1186/1743-0003-4-6PubMed CentralView ArticleGoogle Scholar
- Winstein CJ, Merians AS, Sullivan KJ: Motor learning after unilateral brain damage. Neuropsych 1999, 37: 975-987. 10.1016/S0028-3932(98)00145-6View ArticleGoogle Scholar
- Schendel KL, Robertson LC: Using reaction time to assess patients with unilateral neglect and extinction. Journal of Clinical and Experimental Neuropsychology 2002, 24: 941-950. 10.1076/jcen.24.7.941.8390View ArticlePubMedGoogle Scholar
- Johnson MJ, Loos HF, Burgar CG, Shor P, Leifer LJ: Experimental results using force-feedback cueing in robot-assisted stroke therapy. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2005, 13: 335-348. 10.1109/TNSRE.2005.850428View ArticlePubMedGoogle Scholar
- Ryan RM: Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory. J Personality and Social Psychology 1982, 43: 450-461. 10.1037/0022-3522.214.171.1240View ArticleGoogle Scholar
- Mussa-Ivaldi FA, Hogan N, Bizzi E: Neural, mechanical and geometric factors subserving arm posture in humans. J Neurosci 1985, 5: 2732-2743.PubMedGoogle Scholar
- Flash T: The control of Hand Equilibrium trajectories in multi-joint arm movements. Biol Cybern 1987, 57: 257-274. 10.1007/BF00338819View ArticlePubMedGoogle Scholar
- Flash T, Mussa-Ivaldi FA: Human arm stiffness characteristics during the maintenance of posture. Exp Brain Res 1990,82(2):315-26. 10.1007/BF00231251View ArticlePubMedGoogle Scholar
- Shadmehr R, Moussavi ZM: Spatial generalization from learning dynamics of reaching movements. J Neurosci 2000,20(20):7807-15.PubMedGoogle Scholar
- Casadio M, Morasso PG, Sanguineti V: Direct measurement of ankle stiffness during quiet standing: implications for control modelling and clinical application. Gait Posture 2005,21(4):410-24. 10.1016/j.gaitpost.2004.05.005View ArticlePubMedGoogle Scholar
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