- Open Access
Movement variability in stroke patients and controls performing two upper limb functional tasks: a new assessment methodology
© Thies et al; licensee BioMed Central Ltd. 2009
Received: 28 April 2008
Accepted: 23 January 2009
Published: 23 January 2009
In the evaluation of upper limb impairment post stroke there remains a gap between detailed kinematic analyses with expensive motion capturing systems and common clinical assessment tests. In particular, although many clinical tests evaluate the performance of functional tasks, metrics to characterise upper limb kinematics are generally not applicable to such tasks and very limited in scope. This paper reports on a novel, user-friendly methodology that allows for the assessment of both signal magnitude and timing variability in upper limb movement trajectories during functional task performance. In order to demonstrate the technique, we report on a study in which the variability in timing and signal magnitude of data collected during the performance of two functional tasks is compared between a group of subjects with stroke and a group of individually matched control subjects.
We employ dynamic time warping for curve registration to quantify two aspects of movement variability: 1) variability of the timing of the accelerometer signals' characteristics and 2) variability of the signals' magnitude. Six stroke patients and six matched controls performed several trials of a unilateral ('drinking') and a bilateral ('moving a plate') functional task on two different days, approximately 1 month apart. Group differences for the two variability metrics were investigated on both days.
For 'drinking from a glass' significant group differences were obtained on both days for the timing variability of the acceleration signals' characteristics (p = 0.002 and p = 0.008 for test and retest, respectively); all stroke patients showed increased signal timing variability as compared to their corresponding control subject. 'Moving a plate' provided less distinct group differences.
This initial application establishes that movement variability metrics, as determined by our methodology, appear different in stroke patients as compared to matched controls during unilateral task performance ('drinking'). Use of a user-friendly, inexpensive accelerometer makes this methodology feasible for routine clinical evaluations. We are encouraged to perform larger studies to further investigate the metrics' usefulness when quantifying levels of impairment.
Stroke affects approximately 2 in 1000 people in the UK per year  and impaired upper limb function is reported to be a major problem . At 3 months post stroke only 20% of patients have normal upper limb function  and less than 15% with initial paralysis may regain complete motor recovery . Although there exist a number of promising approaches to the promotion of upper limb recovery after stroke, quantifying the effectiveness of such interventions remains somewhat limited by the available outcome measures.
Previous research found that following stroke upper limb movement trajectories during point-point reaching are more spatially segmented and motions are performed at slower speeds and with greater trunk involvement as compared to healthy controls . Furthermore, upper limb movement smoothness during reaching, as characterized by jerk, has shown good correlation with stroke recovery . Although these studies provided valuable insights into how stroke affects upper limb kinematics, only the forward reach and retraction of the arm during pointing tasks were investigated with expensive equipment such as 3D camera motion analysis systems that cannot easily be moved within the clinic or to a patient's home.
At present there remains a gap between such objective kinematic measures of upper limb impairment which characterise non-functional tasks (e.g. pointing tasks) in great detail [5–8] and clinical measures that evaluate functional task performance. Clinical tests often measure the time to complete a certain task (e.g. box-and-blocks test) , or collect categorical measurements of performance (e.g. ARAT) . Others, for example the Motricity Index , evaluate impairment quantitatively, however, previous work has addressed limitations of such tests, for example, poor standardization and/or reliability [10–13].
We therefore developed a new methodology for the characterization of functional upper limb movements which could bridge the gap between clinical assessment tests and complex, objective kinematic description of non-functional pointing tasks. More specifically, we employed user-friendly, inexpensive accelerometers for which we see many advantages in routine clinical evaluations. A small number of studies [14–16] have recently made use of inertial sensor technology to describe upper limb kinematics in functional tasks but have yet to develop appropriate metrics to characterise the motions.
This paper is the first to demonstrate the use of our methodology in characterising impaired upper limb motion during functional tasks. More specifically, we chose to investigate upper limb movement variability in chronic (stable) stroke patients and matched controls for a unilateral ('drinking') and bilateral ('moving a plate') functional task. Previous analysis of kinematic data collected with a 3D motion capturing system  as well as recent computer modelling  suggest that upper limb movement variability increases following stroke. We therefore hypothesize that stroke patients will exhibit increased movement variability as compared to healthy control subjects in constrained functional tasks, i.e. when the start/end point of the hand, and the sequence of events within the task, are both fixed and the object is picked up from and returned to a marked target position in each trial. Furthermore, we hypothesise that group differences in movement variability would persevere in a retest session 1 month after the initial test.
Descriptive parameters of stroke patients.
Time since stroke
Light Touch Discrimination*: Wrist, Hand
Movement Detection*: Shoulder, Elbow, Wrist, Thumb
6/6, 6/6, 6/6, 6/6
6/6, 6/6, 6/6, 6/6
6/6, 6/6, 3/6, 6/6
6/6, 6/6, 4/6, 4/6
6/6, 6/6, 6/6, 6/6
5/6, 6/6, 6/6, 6/6
The experimental protocol was approved by the UK Central Office of Research Ethics Committee (Ref. # 06/Q1405/7) and the University of Salford Research Governance and Ethics Committee (Ref. # RGEC05/28 and RGEC06/92). Subjects were asked to sit close up against a table and the position of the torso and the start/end point of the hands were marked on the cover of the table to allow for reproduction of a similar posture on the second test day. The location of each object, at a self-reported comfortable distance to the individual, was likewise marked on the table's cover. Care was taken that the object was placed within a distance that did not require engagement of the torso during task performance. Both tasks ('drinking from a glass' and 'moving a plate') were performed at a self-selected comfortable speed and involved a forward reach followed by hand opening and object grasp, object manipulation, and finally object release and arm retraction. Manipulation of the glass was composed of lifting it towards the mouth, holding it briefly, and then replacing the glass onto the table. Manipulation of the plate contained a small upwards lift of the plate in front of the torso, followed by a sideways translation of the plate towards the side where the plate was then lowered onto the table. Stroke patients performed the glass task with their affected arm, and controls had to use the same arm as their corresponding match. Furthermore, the plate was moved towards the affected side of the patient and this was copied by each corresponding control subject. Eight trials per task were recorded, and this was done on two different days, approximately 1 month apart.
Instrumentation & data processing
Definition of variability metrics
Inspired by recent work that addresses limitations with traditional approaches [17, 20], we similarly employed dynamic programming  for curve-registration. The new approach presented here separately considers variability of any given signal in two parts, 1) variability in the timing of the signal, e.g. reoccurrence of a characteristic spike at a specific time instant in each trial, and 2) variability in the motion signal's magnitude, e.g. the maximum value of a characteristic spike reproduced from trial-to-trial. Our software algorithms, programmed in Matlab®, therefore uses a two stage process to quantify both aspects of movement variability separately.
The variability in signal magnitude is then reflected by the RMS error between the reference trial and the warped trial. For each trial-to-trial comparison, RMS errors are obtained for time-warped x, y, and z accelerations and the average across all three axes is calculated.
Finally, the mean value across all trial-to-trial comparisons for a particular task on a particular day is calculated for each of the two variability metrics. The mean value of each metric (warping cost, RMS error) corresponding to each task (glass & plate task) is thereby determined for each subject on each day.
Paired t-tests  were used to compare stroke patients to matched controls for each task with regard to 1) mean warping cost (day 1 and 2, separately), 2) mean RMS error (day 1 & 2, separately), and 3) mean time to complete task (day 1 & 2, separately) and corresponding confidence intervals were determined. Furthermore, differences between stroke patients and corresponding matched controls were graphically visualized.
Application of dynamic time warping
Only four trials per task per day were analyzed for the comparison of stroke patients to healthy controls. This was due to the stroke patients' insecure grasp of the object and onset of fatigue: trials during which the object was dropped were excluded and some patients fatigued so that no more than 4 good trials could be collected.
Warping cost and RMS error, glass task
Glass task variability metrics.
p = 0.002
p = 0.063
p = 0.008
p = 0.086
RMS Error for the glass task had a p-value of less than 0.1 for both days but did not reach significance for the six stroke-control pairs (p = 0.063 and p = 0.086 for day 1 and day 2, respectively, see Table 2). Figure 7 shows the individual pairs (stroke and control): stroke patients showed higher variability in the accelerometer's signal magnitude (as reflected in a larger RMS error) in 5/6 cases on day 1 and day 2. The p-values for RMS error between groups became significant (p = 0.029 and p = 0.003) when the last pair was excluded from statistical analysis.
Warping cost and RMS error, plate task
Plate task variability metrics.
p = 0.050
p = 0.064
p = 0.180
p = 0.031
The RMS error was significant on day 2 (p = 0.031) and had a p-value of less than 0.1 on day 1 (Table 3). Figure 8 shows that 4/6 patients had a larger RMS error on day 1 than their corresponding control subject, and 5/6 did so on day 2.
Time to complete functional tasks
Time (in seconds) to complete task.
p = 0.001
p = 0.006
p = 0.049
p = 0.040
To our knowledge this is the first study that has applied dynamic time warping for curve registration to forearm acceleration signals from stroke patients and matched controls performing a unilateral and a bilateral functional task. It is noteworthy that two objective metrics of movement variability are obtained: 1) warping cost, representative of the variability in the timing of the acceleration signal, and 2) RMS error, representative of the variability in the signal's magnitude.
The warping cost for the glass task was significantly larger in stroke patients than controls on both days; since group differences persisted over the course of a month for this variability metric (as indicated by p-values < 0.05 on both days) it appears to be a promising clinical outcome measure if applied to unilateral functional tasks. It is noteworthy that we employed root mean square error calculation, a measure insensitive to trial length, to quantify warping cost. Moving generally at a slower speed therefore does not increase this metric, instead trial-to-trial variability of the timing of the acceleration signals' characteristics is captured by it. The RMS error for the glass task had a p-value < 0.1 when comparing stroke patients to controls and this became significant when the last stroke-control pair was removed from the analysis (p = 0.029 and p = 0.003 for day 1 and day 2, respectively). This parameter may therefore be useful in larger studies. The plate task provided less significant group differences when comparing variability measures for stroke patients to those of controls. This may be explained by the use of the healthy arm when moving the plate to the side: the affected arm may be guided and assisted by the healthy arm.
Curve registration was first applied to gait data by Sadeghi and colleagues . They recognised that characteristic features, such as peak values, vary between individuals in their precise location within the gait cycle. Averaging time-normalised curves across individuals therefore results in loss of information. Sadeghi and colleagues used the technique of curve registration to more appropriately align subjects' gait data prior to further analysis. Because upper limb motions during functional tasks are not cyclic yet have repetitive characteristics if constrained, we decided to apply such an approach to upper limb acceleration signals and report the warping cost as a valuable outcome measure. Our results support this approach in that significant group differences with regard to time-warping were obtained. The next step is to apply this new methodology to a large number of stroke patients with various degrees of upper limb impairment and at different stages of rehabilitation to evaluate the merit of these metrics in routine clinical evaluations.
Stroke patients were more variable in their movement and needed more time to complete each task. Recent research investigated gait variability in conjunction with walking speed in young and older adults  and the authors concluded that increased gait variability in older adults is better explained by loss of strength and flexibility rather than slower walking speed. Similarly, future research needs to address the driving factors for upper limb movement variability in stroke and controls. Moreover, as with present research investigating gait variability [24, 25], studies are needed to investigate the detailed interpretation of such data.
It is important to note that this work investigated group differences within a given day and showed if those differences persist when a retest is performed 1 month later. No direct trial-to-trial comparison between days was done, and it was therefore not necessary to exactly reproduce postural initial conditions and the sensor's orientation with respect to the forearm on the second test day.
In this initial study we acknowledge our small sample size and hence the wide confidence intervals. Nevertheless, graphical representation of stroke-control pairs for the glass task (Figure 7) supports application of variability metrics to unilateral functional tasks and encourages larger studies. In the long term, we envisage the design of a graphical user interface for the variability software that, together with an inexpensive and portable accelerometer, will allow researchers and clinicians to apply this software in routine clinical care.
In the future tasks could be subdivided into component features (e.g. reach forward, object grasp etc.) to provide further insight into particular aspects of upper limb function, for example hand opening, and their contribution to the variability scores. Moreover, given that task time is a crucial outcome measure in many clinical tests (e.g. ARAT), integration of variability measures with such tests merits further study. Furthermore, for evaluation of patients with no or poor hand opening, variability of other functional tasks could also be investigated. For example 'opening a door' and 'moving a box with both hands' are less challenging tasks and yet of real-life relevance and could accommodate patients with more severe impairments of the hand. Finally, there is recent evidence to show that, focusing on the characteristics of the movement itself rather than the outcome of the movement (e.g. end point accuracy in a pointing task), is of most benefit in promoting the recovery of normal motor patterns following stroke . Currently, there is a limited range of tools for quantifying upper limb motion in the clinical environment , particularly with respect to performance of functional tasks, practice of which are viewed as key to the rehabilitation process. In our study we have introduced a new tool that allows for a detailed analysis of upper limb motion variability, measured with low cost sensors during performance of functional tasks. Further, the results presented here suggest that higher variability is associated with stroke. It is possible to speculate that such metrics could be used as part of a biofeedback tool to encourage a return to more normal levels of variability in performance of functional tasks. Further clinical trial work is required to explore whether a move towards more normal levels of variability is associated with a reduction in disability measures.
The results of this study suggest that accelerometry, in conjunction with suitable variability metrics, has the potential to support clinicians and therapists in their assessment of upper limb impairment during functional task performance. Accelerometers are user-friendly and inexpensive and therefore of advantage in routine clinical evaluations.
The authors wish to acknowledge funding for this work from the European Commission (IST FP6 programme Healthy AIMS) and from the Nuffield Foundation (Undergraduate Research Bursaries Programme).
- Mant D, Wade D, Winner S: Stroke. In Health care needs assessment: the epidemiologically based needs assessment reviews. 2nd edition. Edited by: Stevens A, Raftery J, Mant J, Simpson S. Oxford: Radcliffe Medical Press; 2004:141-243.Google Scholar
- Broeks JG, Lankhorst GJ, Rumping K, Prevo AJ: The long-term outcome of arm function after stroke: results of a follow-up study. Disabil Rehabil 1999, 21: 357-364. 10.1080/096382899297459View ArticlePubMedGoogle Scholar
- Parker VM, Wade DT, Langton Hewer R: Loss of arm function after stroke: measurement, frequency, and recovery. Int Rehabil Med 1986, 8: 69-73.View ArticlePubMedGoogle Scholar
- Hendricks HT, van Limbeek J, Geurts AC, Zwarts MJ: Motor recovery after stroke: a systematic review of the literature. Arch Phys Med Rehabil 2002, 83: 1629-1637. 10.1053/apmr.2002.35473View ArticlePubMedGoogle Scholar
- Cirstea MC, Levin MF: Compensatory strategies for reaching in stroke. Brain 2000, 123: 940-953. 10.1093/brain/123.5.940View ArticlePubMedGoogle Scholar
- Rohrer B, Fasoli S, Krebs HI, Hughes R, Volpe B, Frontera WR, Stein J, Hogan N: Movement smoothness changes during stroke recovery. J Neurosci 2002, 22: 8297-8304.PubMedGoogle Scholar
- Flash T, Hogan N: The coordination of arm movements: an experimentally confirmed mathematical model. J Neurosci 1985, 5: 1688-1703.PubMedGoogle Scholar
- Cozens JA, Bhakta BB: Measuring movement irregularity in the upper motor neurone syndrome using normalised average rectified jerk. J Electromyogr Kinesiol 2003, 13: 73-81. 10.1016/S1050-6411(02)00070-6View ArticlePubMedGoogle Scholar
- Wade DT: Measurement in neurological rehabilitation. Oxford: Oxford University Press; 1994.Google Scholar
- Light CM, Chappell PH, Kyberd PJ, Ellis BS: A critical review of functionality assessment in natural and prosthetic hands. Br J Occup Ther 1999, 62: 7-12.View ArticleGoogle Scholar
- Light CM, Chappell PH, Kyberd PJ: Establishing a standardized clinical assessment tool of pathologic and prosthetic hand function: normative data, reliability, and validity. Arch Phys Med Rehabil 2002, 83: 776-783. 10.1053/apmr.2002.32737View ArticlePubMedGoogle Scholar
- Krakauer JW: Arm function after stroke: from physiology to recovery. Semin Neurol 2005, 25: 384-395. 10.1055/s-2005-923533View ArticlePubMedGoogle Scholar
- Krakauer JW: Motor learning: its relevance to stroke recovery and neurorehabilitation. Curr Opin Neurol 2006, 19: 84-90. 10.1097/01.wco.0000200544.29915.ccView ArticlePubMedGoogle Scholar
- Maitra KK, Junkins M: Upper extremity movement pattern of a common drinking task in well elderly women: a pilot study. Occup Ther Int 2004, 11: 67-81. 10.1002/oti.198View ArticlePubMedGoogle Scholar
- Zhou H, Stone T, Hu H, Harris N: Use of multiple wearable inertial sensors in upper limb motion tracking. Med Eng Phys 2008, 30: 123-133. 10.1016/j.medengphy.2006.11.010View ArticlePubMedGoogle Scholar
- Cutti AG, Giovanardi A, Rocchi L, Davalli A, Sacchetti R: Ambulatory measurement of shoulder and elbow kinematics through inertial and magnetic sensors. Med Biol Eng Comput 2008, 46: 169-178. 10.1007/s11517-007-0296-5View ArticlePubMedGoogle Scholar
- Chau T, Young S, Redekop S: Managing variability in the summary and comparison of gait data. J Neuroeng Rehabil 2005, 29: 2-22.Google Scholar
- Forner-Cordero A, Koopman HJFM, Helm FCT: Describing gait as a sequence of states. J Biomech 2007, 39: 948-957. 10.1016/j.jbiomech.2005.01.019View ArticleGoogle Scholar
- Reinkensmeyer DJ, Iobbi MG, Kahn LE, Kamper DG, Takahashi CD: Modeling reaching impairment after stroke using a population vector model of movement control that incorporates neural-firing-rate variability. Neural Computation 2003, 15: 2619-2642. 10.1162/089976603322385090View ArticlePubMedGoogle Scholar
- Sadeghi H, Allard P, Shafie K, Mathieu PA, Sadeghi S, Prince F, Ramsay J: Reduction of gait data variability using curve registration. Gait Posture 2000, 12: 257-264. 10.1016/S0966-6362(00)00085-0View ArticlePubMedGoogle Scholar
- Cormen TH, Leiserson CE, Rivest RL, Stein C: Introduction to algorithms. 2nd edition. Boston: MIT Press; 2001.Google Scholar
- Bland JM, Altman DG: Matching. BMJ 1994, 309: 1128.PubMed CentralView ArticlePubMedGoogle Scholar
- Kang HG, Dingwell JB: Separating the effects of age and walking speed on gait variability. Gait Posture 2008, 27: 572-577. 10.1016/j.gaitpost.2007.07.009View ArticlePubMedGoogle Scholar
- England SA, Granata KP: The influence of gait speed on local dynamic stability of walking. Gait Posture 2006, 25: 172-178. 10.1016/j.gaitpost.2006.03.003PubMed CentralView ArticlePubMedGoogle Scholar
- Beauchet O, Allali G, Berrut G, Dubost V: Is low lower-limb kinematic variability always an index of stability? Gait Posture 2007, 26: 327-328. 10.1016/j.gaitpost.2007.02.001View ArticlePubMedGoogle Scholar
- Cirstea MC, Levin MF: Improvement in arm movement patterns and endpoint control depends on type of feedback during practice in stroke survivors. Neurorehabil Neural Repair 2007, 21: 1-14. 10.1177/15459683071112010206View ArticleGoogle Scholar
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