Movement variability in stroke patients and controls performing two upper limb functional tasks: a new assessment methodology

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Figure 1

Application of time normalization to upper limb kinematics. Presentation of kinematic data from two repeats of a functional upper limb task (top) and illustration of the effect that uniform normalization of the time axis has on the data (bottom).

Figure 2

Experimental set up. The inertial sensor is shown on the proximal forearm as the subject reaches forward to grasp the glass.

Figure 3

Application of acceleration threshold algorithm. Movement onset and termination indices are denoted by '*' and are superimposed onto the corresponding x acceleration trajectory. Sample plots are shown for a control subject (left) and stroke patient (right) for the glass task (top) and plate task (bottom).

Figure 4

Time warping of acceleration signals. Linear acceleration signals of two trials that are to be investigated for trial-to-trial variability (left) and signals after having time-warped one signal to the declared reference (right).

Figure 5

Error surface and path of least error. Error surface and path of least error produced when warping each frame of one trial to each frame of the reference trial. The axes represent time in trials 1 and 2 (i.e. t and t') respectively; light areas indicate a high error between points while dark areas indicate low error between points. Each frame represents 0.01 seconds.

Figure 6

X accelerations of two trials after time warping. X acceleration signals of the glass task (top) and plate task (bottom) are shown for a control subject (left) and for a stroke patient (right).

Figure 7

Control-stroke-pairs, glass task. Warping cost ('WC', left) and RMS error ('RMS', right) for day 1 and day 2 (top & bottom, respectively). Controls are denoted by '*' and stroke patients by 'o'.

Figure 8

Control-stroke-pairs, plate task. Warping cost ('WC', left) and RMS error ('RMS', right) for day 1 and day 2 (top & bottom, respectively). Controls are denoted by '*' and stroke patients by 'o'.