environment interface

Journal of Electromyography and Kinesiology 14 (2004) 539–554 www.elsevier.com/locate/jelekin

A pattern recognition technique to characterize the differential modulation of co-activating muscles at the performer/environment interface Lucie Pelland a,
,1, Patricia McKinley b,c,1 a

b

Faculty of Health Sciences, School of Rehab Sciences, Physiotherapy Program, University of Ottawa, 451 Smyth Road, Ottawa, Ont., Canada K1H 8M5 School of Physical and Occupational Therapy, McGill University, 3654 Sir William Osler Court, Montreal, Que., Canada H3G 1Y5 c Constance Lethbridge Rehabilitation Centre, 7005 de Maisonneuve Boulevard Ouest, Montreal, Que., Canada H4B 1T3 Received 15 November 2000; received in revised form 10 February 2004; accepted 19 February 2004

Abstract The purpose of this research was to develop and test an analytical tool that would recognize and classify the surface electromyographic (EMG) signal of co-activating muscles of the leg into pre-defined patterns of muscle activity: burst, tonic, and tonic– burst. Developed to study the task of landing from a jump in children, the pattern recognition technique (PRT) quantifies the full-wave rectified surface EMG signal over a short-duration sampling window by a single linear regression value. Shifting the sampling window across the data string ultimately defines the signal by a set of regression values that produce the recognizable burst, tonic and tonic–burst patterns on a least-squares surface plot. Statistical comparison of the PRT to the classical combination of threshold detection (+2 S.D. of mean baseline activity) and visual inspection proves the PRT to be more reliable on repeated measures for event detection and classification, with a Kappa statistic of 0.83 compared to 0.54 for threshold detection. Application of the PRT to motor control studies is presented for the regulation of the mechanical response of the leg during impact. Responsiveness of the PRT is tested, issues of accuracy and validity are addressed, and limitations in spatial-temporal resolution are identified. # 2004 Elsevier Ltd. All rights reserved. Keywords: Surface electromyography; Co-activation; Mobility; Sensory-motor development; Mechanical impedance

1. Introduction The capacity of the nervous system to adapt the mechanical response of the limb in task-specific ways underlies the vast repertoire of movements that allow us to interact with the environment in meaningful ways. Co-activation, or the simultaneous activation of opposing muscles, either across one joint or a series of linked-segments, provides the nervous system with a parsimonious strategy to adjust the limb’s mechanical
Corresponding author. Tel.:+1-613-562-5800x8121; fax: +1-613562-5428. E-mail address: [email protected] (L. Pelland). 1 Member of the Center for Interdisciplinary Research in Rehabilitation (CRIR).

1050-6411/$ - see front matter # 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.jelekin.2004.02.003

response in a global fashion [17,29,31] that will preserve the stability of the limb over a wide range of perturbing effects from externally applied forces [8,20,21,29], as well as from internal forces that arise from limb dynamics during movement [14,44]. By preserving mechanical stability, co-activation plays an important role in the early stages of acquisition of skilled motor performances, in both adults and children, allowing the novice performer to explore the nature of internal and external forces and moments associated with the execution of a specific motor task. The important contribution of muscle co-activation to the emergence of motor skills in development has been well documented for various tasks, including reaching [48] and kicking [19,45], as well as for the emergence of independent gait [10,23] and of independent sitting [16].

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The co-activation patterns that are associated with early skill acquisition in children are largely determined by the nervous system to provide high mechanical impedance that resists perturbing effects of internal and external loads on movement trajectory. These early patterns of global co-activation are gradually modified by sensory-motor experience [5,30,33,46] to ultimately link the mechanical response of the limb to the demands of the environment in task-specific ways [39,40,47]. Relatively little is known, however, about how information about the conditions of external load application interacts with and adapts the regulated characteristics of co-activation during development. This issue is of relevance in the design and implementation of sensory-motor training approaches to promote safe mobility skills, such as alternating stair descent, in different pediatric populations [38]. The task of landing from a jump provides a robust experimental model to study the interactive adjustments made by the nervous system to adapt the mechanical response of the leg to the conditions of load application at impact (i.e., direction and magnitude of applied force) as it includes two very distinct phases: (1) feedforward phase, over the first 40 ms of impact, when the mechanical response of the leg is governed by the level and timing of muscle co-activation that have been specified by the nervous system in anticipation of the conditions of load application at the support surface, and (2) during the subsequent feedback phase of impact, short and long latency (50–120 ms) afferent segmental stretch reflex and other sensory reflexes related to the magnitude of load application influences the mechanics of co-activating muscles, to adjust the mechanical response of the leg to the loading conditions at the support surface [41]. The analysis of muscle activity recorded from the leg at impact therefore provides a unique insight into: (a) the changing pattern of muscle co-activation specified by the nervous system over the course of sensory-motor development, as well as arising from sensory-motor experience, and (b) the changing integration of feedback reflex activity to the co-activation that, together, will govern the overall response of the leg at impact. In order to effectively study these changing characteristics of co-activation, a reliable method of signal analysis is required that will suitably quantify the surface electromyographic (EMG) signal to identify modulation in both the feedforward and feedback components of the co-activation response. In the motor control literature, the contribution of muscle activity to the measured movement outcome is generally described in the time domain by latencies of onset and offset boundaries relative to a joint or movement parameter or to an external event, such as the time of impact in landing from a jump. Relevant activity is typically identified and described using a pre-

defined threshold criterion, such as a rise in level of activity above 2–5 S.D. of resting baseline activity, either with or without a minimum duration criteria. Threshold detection (TD), however, does not lend itself well to study the modulation of co-activation during impact, particularly in children, as activity levels tend to vary significantly across trials (see [35] as an example) producing wide variability in identified latencies across trials that often make it difficult to clearly establish inter-muscle timing strategies. Another limitation that is specific to the landing response is the inability to establish a clear threshold criterion for post-impact activity that would permit us to identify modulation of the ongoing co-activation by feedback sensory input related to the conditions of load application at the support surface. For example, while preimpact muscle activity can generally be defined by a clear onset that is distinguishable from resting baseline activity (see Fig. 1 and [25]), changes in the modulation of this activity post-impact cannot be reliably identified

Fig. 1. The full interference and corresponding full-wave rectified surface EMG signal is illustrated for a typical muscle activity pattern for (a) the lateral gastrocnemius (LG); and (b) the vastus lateralis (VL). Impact is indicated with an arrow, separating the signals into pre-impact and post-impact events. For both signals, a circumscribed region of activity is identified at take-off (Ptake-off). Activity around impact is difficult to classify based on visual inspection alone; while A1 and P2 events appear to be separate for the LG, for the VL, these same two events may or may not be classified as separate events depending of the threshold and time parameters set by the observer.

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by an onset boundary that is distinct from ongoing muscle activity. Therefore, in order to characterize the contribution of post-impact muscle activity to the changing mechanical impedance of the leg during load application, it would be necessary to arbitrarily adjust the TD criterion independently for pre- and postimpact activity in order to analyze these epochs as two separate identities rather than as one continuous signal. One aim of this study was to attempt to avoid this methodological issue in signal analysis in order to provide an analytical tool that would allow us to quantify changes in co-activation patterns during sensory-motor development in children. One approach, offered by Spencer and Thelen [42], is to quantify the relative activity of opposing muscle groups by classifying each muscle into on and off states. The resultant index of activation identifies the distribution of reciprocal activation and co-activation of opposing muscles over the time history of the movement. However, this state profile does not include information on the underlying modulation of the activity of individual muscles over the analyzed epoch of movement, information that could improve our understanding of the emergence of adaptive muscle activity with development. As an alternative solution, we propose to quantify modulation in co-activating muscles from changes in the pattern of activity across the pre- and post-impact phases of the landing response. The rationale for our proposed approach comes from observations of Hirschfeld and Forssberg [16] in their developmental study of children mastering the skill of independent sitting. Examining the pattern of muscle co-activation of the flexor and extensor muscles of the trunk in sitting subjects exposed to multidirectional surface perturbations, Forssberg and Hirschfeld [11] identified two types of activity patterns across co-activating muscles of the trunk, each pattern being described in qualitative terms: the phasic (or burst) pattern was described as activity that is sharp and short, while the tonic pattern was described as having a smoother onset and/or prolonged multi-peaked activity. In a follow-up study on differences in the induced postural adjustments of sitting children 5–8 months of age, Hirschfeld and Forssberg [16] presented evidence that these two patterns of muscle activity could be blended to more appropriately subserve trunk stability. Specifically, the authors proposed that the neuromotor capacity to superimpose phasic (burst) activity onto the high tonic background activity of the extensor muscles may be an important determinant of the more effective organization of postural control in older children who had mastered independent sitting. We were struck by their descriptions of these patterns as we have observed similar differences in the patterns of muscle activity of the leg in young and

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older children landing from a jump (see Fig. 1 for examples). As these qualitative descriptors of the surface EMG signal depend to a great extent on subjective decision-making, this process cannot be rigorously implemented in the study of motor control. For example, while the evaluation of timing (i.e., the descriptor short) is linked to the threshold parameters that have been set a priori to the analysis, the description of the shape (i.e., multi-peaked) in this case is completely determined by the researcher’s experience with EMG signals. The inherent difficulty with a subjective evaluation of the signal is well illustrated in our own data on landing from a jump in children (Fig. 1). If one wishes to evaluate the activity of the vastus lateralis related to landing (1B), it would be difficult to determine if the short activity (P1) is distinct from the longer activity (P2), or if this activity is a functional combination of two different patterns of activity into one continuous event. Therefore, in order to examine more rigorously the role that different patterns of activity underlying co-activation might have in the progressive adaptation of the mechanical response of the leg to load application at impact during development, we sought a methodological approach that would facilitate a more objective categorization of these three patterns. The specific objectives of this study, therefore, were focused on providing a methodological approach that would allow us to suitably characterize the surface EMG signal related to the landing response in quantitative terms: (1) To develop an analytical technique that would permit a unique identification of burst and tonic patterns of muscle activity, either as single entities or in combination; (2) To test the reliability and responsiveness of the technique relative to the classical combination of TD and visual inspection; (3) To quantify the responsiveness of the technique to different signal characteristics; and (4) To determine, if different combinations of burst and tonic activity patterns in co-activating muscles of the leg can be associated to the different mechanical responses of the leg to the application of load at impact in children 6–12 years old.

2. Materials and methods 2.1. Development of the PRT measurement tool 2.1.1. Statistical differentiation of the muscle activity patterns Our classification of surface EMG signals is based on the patterns of muscle activity that were broadly described by Hirschfeld and Forssberg [16] in their study on the postural adjustments of sitting subjects exposed to multi-directional surface perturbations. The first step in developing the PRT was therefore to

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determine if the burst and tonic patterns of muscle activity could be quantitatively differentiated from the full-wave rectified surface EMG signal. One of us, McKinley, identified ideal burst and ideal tonic signals from our experimental data set based on previous classification criteria [26,27]. These ideal signals were full-wave rectified and zero-offset. We were able to achieve a clear differentiation of the burst and tonic signals using a linear forecast procedure [2] that is responsive to the distinct statistical organization (i.e., mean amplitude and variance of the signal) of each of these two patterns. The linear forecast function predicts the combined spatial mean amplitude and variance parameters of the full-wave rectified surface EMG signal at a specified point in time (xi) based on the statistical properties of the signal over its past history (xji1 0 ). For the burst pattern (Fig. 2a), the forecast function produced a predicted increase in activity to a maximum that was followed by a return to baseline. In contrast, for the tonic signal, the linear forecast function produced a relatively straight line over the region of activity (Fig. 2b). As the burst pattern could be objectively differentiated from the tonic pattern based on its statistical properties, the PRT was operationally designed to recognize the presence of either the burst or tonic patterns from the full-wave rectified surface EMG signal, either as single entities or in combination.

2.1.2. Implementation of the pattern recognition technique To recognize the burst pattern of activity from the full-wave rectified surface EMG signal, the PRT uses a least squares estimation procedure in which the predicted statistical organization of the burst pattern is captured by the slope of the linear regression line over a finite sampling window. The sampling window is shifted across the data string at a fixed interval that is related to the sampling frequency of the signal, such that the EMG is completely defined by the resulting set of regression slope values. The step-wise implementation of the PRT includes: 1. Establishing the window of analysis. The time region of interest for the task under investigation is identified a priori. This region is then extended by a 40 ms lead-in (Tstart) and lead-out (Tend) window to assure the detection of any signal modulation at the onset and offset of the time region under analysis. 2. Full-wave rectification of the surface EMG in the time region from Tstart to Tend. 3. Regression sampling. An algorithm was developed in which a 20-point least squares sampling window [36] is applied at Tstart and the slope of the linear regression line is calculated for this data string. The sampling window is subsequently shifted by increments of 1 ms (for the surface EMG signal sampled at 1 kHz) from Tstart to Tend, and the regression slope value is re-calculated for each sampled interval (Fig. 3a). 4. Plotting the PRT. The calculated set of regression slope values is plotted on a least-squares surface from which the specific pattern of muscle activity is visually recognized (Fig. 3b). The PRT classifies experimentally recorded EMG signals into one of three patterns (Fig. 3c–e): (i) The burst pattern (3c)) is recognized by a series of slope values that together form one discrete high amplitude sine pattern on the least-squares surface; (ii) The tonic pattern (3d) is recognized by a family of sine patterns that are symmetrically distributed about the zero axis, and (iii) The tonic–burst activity (3e), that is a discrete region of burst activity within a region of tonic activity, is recognized by one discrete high amplitude sine pattern (burst pattern) embedded within a family of symmetrical sine patterns of lower amplitude (tonic pattern). 2.2. Test paradigm: application of the PRT to motor control

Fig. 2. The output of the linear forecasting function (thick line) is superimposed on the full-wave rectified interference pattern of the surface EMG signal for representative (a) burst and (b) tonic pattern of muscle activity.

The experimental task of landing from a vertical drop-jump was used to provide real surface EMG signals to: (a) develop the PRT, and (b) test the usefulness of the PRT to study the mechanical behavior of the leg

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Fig. 3. (a) Typical full-wave rectified EMG signal for the burst pattern, the region of burst activity identified with arrows. The 20-point sampling window used to calculate the linear regression value is shown; (b) The output of the PRT analysis for the signal in (a) is plotted on the leastsquares surface. The burst activity is recognized by one discrete high amplitude sine pattern that is identified with arrows. (c–e) The output of the PRT analysis of representative signals for the burst; (c) tonic; (d) and tonic–burst; (e) patterns of muscle activity. The recognized output that is used to classify the signal for each of the three patterns (burst, tonic and tonic–burst) is shown with arrows. In (e), the discrete high amplitude sine pattern representative of burst activity (black arrows) is embedded within a symmetrical distribution of smaller amplitude sine patterns (gray arrows) representative of tonic activity.

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under conditions of load application at impact in children. The procedures for the jump task were approved by the ethics committee of the Faculty of Medicine, McGill University. The subjects were nine boys, 6–12 years of age. Subjects were screened to exclude a medical history of developmental delay, orthopaedic conditions of the lower limb and back, as well as neurological and visual-vestibular impairments. To complete the standardized landing task, subjects were asked to step-off a hydraulic platform, adjusted to the height of the lateral tibial plateau for each child, and to land with both feet simultaneously onto the ground. The child practiced until comfortable with the performance of the task. For the experimental session, the child completed three blocks of 10 jumps, with rest periods provided as needed. 2.2.1. Kinematic analysis The response of the leg at impact was described in mechanical terms by considering the leg to behave as a spring–dashpot–mass system that under compression provides a resistive force to the displacement of the body center of mass, CoM [28,32]. In this single degree-of-freedom model of CoM displacement, the mechanical response of the leg is characterized in a global fashion from the vertical linear kinematics of the CoM. This approach has previously been used in the literature to study the mechanical behavior of the leg during different weight-bearing tasks [1,9,12,15,32]. In our study, the vertical displacement of the CoM was calculated in the sagittal plane from the recorded positions of reflective markers (at 60 Hz, Peak Performance Motion Analysis System) placed on the head, lateral acromion, mid-thoracic cage, anterior superior iliac spine, greater trochanter, lateral tibial plateau, lateral malleolus, base of fifth metatarsal and the heel. Positions were low-pass filtered at 12 Hz using a digitally implemented second-order Butterworth filter, and displacement derivatives calculated from the filtered data. The linear kinematics of CoM displacement were then calculated from 220 ms prior to impact to zero velocity of vertical CoM displacement post-impact, which defines the point at which the leg has successfully countered the forces associated with the downward motion of the CoM at impact. 2.2.2. Muscle signal analysis Surface EMG activity was recorded using bipolar silver-silver chloride 1.2 cm2 surface electrodes (MediTrace: ECE 1801; 1.5 cm dipole distance). Signals were recorded from six muscles of the leg, three uni-articular muscles (tibialis anterior, soleus, and vastus lateralis) and three bi-articular muscles (lateral gastrocnemius, rectus femoris, and biceps femoris). Together, these six muscles contribute to co-activation across the ankle, knee and hip. Following standard skin preparation,

electrodes were positioned along the main fiber direction of the muscle belly [3]. The placement of the electrode at each muscle was verified by observing the EMG during manually resisted movements of the corresponding joint. The analogue EMG signals were differentially pre-amplified (10; CMRR ¼ 80 dB; input impedance >15 MX), band-pass filtered (10–450 Hz), amplified (100) and sampled at 1 kHz for offline analysis (Datapac, Run Technologies; Matlab). Trial-by-trial CoM displacement and lower limb muscle activity signals were aligned to the time of impact using contact data from the ball of the foot and heel (Tapeswitch Systems of America). 2.2.3. Statistical analysis One hundred and eighty surface EMG signals of 2000 ms duration, experimentally recorded from six muscles of the leg during the vertical drop-jump task, were analyzed using: (a) the combination of PRT and visual inspection, and (b) the classical combination of TD and visual inspection. One expert evaluator trained in the use of both the PRT and the classical TD approach evaluated all signals. Expert decision-making for event detection and classification was based on the following set of instructions. I. For the PRT approach, an event of activity is identified by the formation of at least one complete sine pattern on the least-squares surface plot. Classification of the event to one of the three patterns of activity is subsequently based on the distribution of the identified sine patterns about the zero axis (as previously described in Fig. 3c)—(i) burst activity is defined by the formation of one discrete sine pattern, (ii) tonic activity by a symmetrical distribution of a family of sine patterns, and (iii) tonic–burst activity by one discrete high amplitude sine pattern embedded in a family of lower amplitude sine patterns. II. For the TD approach, an event of activity is identified by a rise above 2 S.D. of the mean baseline activity prior to the initiation of the jump for a duration 20 ms. Classification of the event is subsequently based on qualitative descriptors—(i) burst activity is defined as a sharp rise of activity from baseline, that reaches a peak and subsequently returns to baseline, (ii) tonic activity as prolonged multi-peaked activity, and (iii) tonic–burst activity as a blending of the burst and tonic patterns, with a sharp increase in activity for a duration 20 ms embedded in prolonged multi-peaked activity. The signals were first analyzed and classified using the TD approach. The signals were subsequently redistributed within the analysis matrix and re-analyzed using the PRT approach 1 week later. The signals were

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again re-distributed within the analysis matrix and the analysis process repeated 1 month later. Outcome measures were evaluated to obtain a quantitative comparison of the responsiveness and reliability of the PRT and TD approaches for event detection, the classification of these events of activity into burst, tonic, and tonic–burst patterns, and the Kappa statistic for the repeated measures reliability of each of the two analytical techniques on these two factors. Following the analysis, the classification records were reviewed to determine the characteristics of the signal that (a) were not identified by one of the techniques, and (b) were classified to different patterns by the two techniques. The relationship between the distribution of patterns of activity across co-activating muscles of the leg and the linear kinematics of the CoM post-impact was tested using an Analysis of Variance (ANOVA) for repeated measures. 3. Results 3.1. Responsiveness The responsiveness of the PRT to event detection and classification was evaluated against TD (Table 1). Across our data set, the PRT detected a higher number of events, with 567 events recorded on the first evaluation compared to 384 identified by TD. The classification by visual inspection of the detected events into one of the three muscle activity patterns (burst, tonic and tonic–burst) also differed for the PRT and TD. For the TD approach, where classification is based on visual inspection of the full-wave rectified surface

EMG signal, the burst pattern of activity was predominant with a proportional distribution of 0.49, as compared to 0.21 for the tonic pattern and 0.30 for the tonic–burst pattern. Comparatively, for the PRT, where classification is based on visual inspection of the distribution of the sine pattern around the x-axis, the burst and tonic patterns were identified in almost equal proportions (0.35 and 0.37, respectively), while the tonic–burst pattern accounted for 0.28 of the total distribution. Reflecting the differences in classification, the overall agreement between the PRT and TD for event classification was low (Kappa statistic ¼ 0:048), with only 26% of events being similarly classified. 3.2. Reliability Repeated measures reliability for event detection and classification was also evaluated for the PRT relative to TD (Table 1). Reliability for event detection showed a small improvement with the use of the PRT, with 561 of the initial 567 events detected on the second evaluation (i.e., 98.9% reliability) as compared to 367 of the initial 384 for the TD technique (i.e., 95.5% reliability). By contrast, the PRT markedly improved the reliability of classifying the detected events into one of the three pre-determined muscle activity patterns, with a mean overall agreement of 89% (Kappa statistic ¼ 0:829) compared to 51% for TD (Kappa statistic ¼ 0:543). The identified differences in reliability between the PRT and TD could largely be explained by two characteristics of the surface EMG signal: amplitude and inter-event interval. The influence of these signal characteristics on the differential reliability of the PRT

Table 1 Psychometric evaluation of the pattern recognition and TD techniques Responsiveness Method TD

No. of events dectected 384

PRT

567

Repeated measures reliability Method Evaluation 1/evaluation 2 TD 1/TD 2 384/367, 95.6% reliability

PRT 1/PRT 2

567/561, 98.9% reliability

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Pattern distribution (propotion of the total no. of events) Tonic ¼ 80 (proportion ¼ 0:21) Tonic burst ¼ 117 (proportion ¼ 0:30) Burst ¼ 187 (proportion ¼ 0:49) Tonic ¼ 212 (proportion ¼ 0:37) Tonic burst ¼ 157 (proportion ¼ 0:28) Burst ¼ 198 (proportion ¼ 0:35)

Pattern distribution and % agreement in the classification of events in evaluations 1 and 2 Tonic ¼ 80=85 (overall mean proportion: 0.22) Number of agreements ¼ 40 (50%) Tonic burst ¼ 117=119 (overall mean proportion: 0.31) Number of agreements ¼ 57 (43%) Burst ¼ 187=163 (overall mean proportion: 0.47) Number of agreements ¼ 114 (61%) Tonic ¼ 212=211 (overall mean proportion: 0.38) Number of agreements ¼ 192 (91%) Tonic burst ¼ 157=154 (overall mean proportion: 0.28) Number of agreements ¼ 134 (85%) Burst ¼ 198=196 (overall mean proportion: 0.35) Number of agreements ¼ 181 (91%)

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and TD is illustrated for two representative signals (Fig. 1). Focusing on the activity pre- (A1) and post(P2) impact, both the PRT and TD similarly detected two separate events for the LG (1A), with A1 being classified as burst activity and P2 as tonic activity. The two techniques varied, however, in their reliability for parceling the activity around impact for the VL (1B). Over this portion of the signal, the PRT consistently classified the activity as two separate events, separated by a brief inter-event interval at impact. As amplitude across both signals was high, and inter-event interval was short (i.e., ffi20 ms), activity did not clearly fall below the TD of 2 S.D. and, therefore, TD inconsistently classified the activity as two separate events on the first evaluation and as one event of continuous activity on the second evaluation. These signal characteristics also influenced the reliability of the two techniques for classifying these events of activity into a specific pattern. The PRT classified the activity in the VL as a combination of pre-impact (A1) burst and post-impact (P2) tonic activity on both evaluations. In contrast, the TD classified this same activity as one burst (A1) and one event of tonic activity (P2) at the first evaluation, and as a single event of tonic–burst activity on the second evaluation. While the overall repeated measures reliability for event classification was high with the PRT, reliability was not as robust for the tonic–burst pattern, with an agreement of 85% as compared to 91% for the burst and tonic patterns. A simulation approach was used to test the limits of responsiveness of the PRT in recognizing the tonic–burst pattern. Two spatial-temporal conditions that directly influence the recognition of burst activity that is embedded in tonic activity were examined: (1) the mean amplitude of the surrounding tonic signal relative to the peak amplitude of the burst signal, and (2) the time interval between two successive events of burst activity (Fig. 4). To test the effect of the mean amplitude of surrounding tonic activity on PRT responsiveness, a 20-ms simulated burst was embedded in a simulated tonic signal of 500 ms duration (Fig. 4a). The spatial characteristics of the simulated tonic–burst signal were then varied by manipulating the mean amplitude of the tonic signal while keeping the amplitude characteristics of the burst signal invariant. For the baseline condition, the mean amplitude of the tonic signal was set to the mean amplitude of the reference burst signal. On subsequent simulations, the mean amplitude of the tonic signal was increased progressively to 10 baseline value and the PRT was generated for the convolution of the two signals at each incremental increase. The embedded burst signal was reliably recognized by the PRT until the mean amplitude of the tonic signal was increased to 60% of the peak amplitude of the burst signal. At this level, while the symmetry of the output sine pattern for the refer-

ence burst signal was maintained on the least-squares surface plot, its amplitude was reduced relative to the PRT output for the surrounding tonic activity. As a result, the PRT output for the signal convolution could be classified as either a burst or a tonic–burst pattern. To test the responsiveness of the PRT to the temporal characteristics of the signal, two identical reference bursts of 20 ms duration were simulated, and the inter-event interval was manipulated with simulations of decreasing intervals (Fig. 4b). For inter-event intervals ranging from 50 to 10 ms, the PRT reliably recognized the two reference signals as independent events of burst activity. At the temporal resolution limit of 10 ms, the PRT output generated two symmetrical sine patterns on the least-squares surface that were recognized as a single event of tonic activity. Further decreases in inter-event interval produced a relative decrease in the amplitude of the sine pattern for the second reference signal such that the two events were now recognized as a single event of tonic–burst activity. 3.3. Applicability to motor control A comparative analysis of the regulation of muscle co-activation at impact was completed (Fig. 5) using the PRT and a classical combination of low-pass filtering at 10 Hz (second-order Butterworth filter) and a TD of +2 S.D. of baseline resting activity [50]. The resultant linear envelope of muscle activity [49] shows co-activation at the hip, knee and ankle that is initiated prior to impact and peaks for all six recorded muscles post-impact (Fig. 5a), in the interval that would include stretch reflex activity to the control of leg displacement. The low-pass analysis, however, does not provide information on how this general co-activation may be differentially modulated across opposing muscles of a joint by the nervous system (i.e., in the interval prior to impact) to achieve a functional integration of the feedforward and feedback muscle mechanisms regulating joint impedance, additional information that is available with the PRT. Typical results for the ankle response in 6-year-old children are shown as an example in Fig. 5b. The global co-activation of the TA and the SOL/LG complex is associated with different activity patterns, ranging from a post-impact burst pattern for TA to a combination of pre-impact tonic and post-impact burst patterns for LG and tonic-burst activity for SOL. The PRT therefore provides evidence for a differential modulation of the global co-activation response at the ankle. 3.4. Validity The analysis of muscle activity patterns with respect to the kinematics of the CoM, over the interval from 220 ms prior to impact to zero vertical velocity post-

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Fig. 4. (a) Spatial resolution of the PRT. Left upper two traces illustrate the simulated reference burst and tonic signals; right upper two traces illustrate the PRT equivalents of the two reference signals. In the lower series, the left panel shows increasing levels of tonic activity with respect to the reference burst signal; while the right panel illustrates the PRT equivalents of the signal convolutions. The recognized PRT pattern is highlighted in gray, and the brackets in the last two plots indicate the region of tonic–burst and tonic activity produced at the limit of spatial resolution; (b) Temporal resolution of the PRT. Left panel illustrates the two simulated burst signals with decreasing inter-event intervals of 50, 10 and 1 ms. The PRT equivalents are illustrated in the right panel.

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Fig. 5. (a) The full-wave rectified surface EMG signal is low-pass filtered at 10 Hz from 220 ms prior to impact to the point of zero velocity of the downward displacement of the CoM post-impact. The rectangle shows the +2 S.D. level that was used as threshold detection to identify the onset of muscle activity; (b) The low-pass trace is shown on the left for the co-activating muscles at the ankle (TA, SOL and LG) with the PRT equivalent shown on the right.

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impact, was used as a starting point to explore the possible association between the pattern of muscle coactivation and the changing mechanical response of the leg in children 6–12 years old. Three types of post-impact CoM vertical displacement curves were identified in our data set, each of these responses being associated with different distributions of patterns of activity for the co-activating proximal and distal muscles of the leg (Fig. 6, Table 2). Overall, the kinematics of the CoM were qualitatively indicative of a changing mechanical response of the leg from low stiffness and low viscous damping for young children to a combination of low stiffness and high viscous damping in older children. The under-damped response to impact (Fig. 6a), that is typical of 6-year-old children, includes

Fig. 6. The CoM response to impact is quantified by the linear acceleration profile of the body CoM to reflect the contribution of muscle co-activation of the active braking response of the leg that is responsible for resisting the downward acceleration of the CoM at impact. The mean (95% confidence interval) of the linear vertical and horizontal acceleration of the CoM plotted as a function of time from 50 ms prior to impact to the point of zero acceleration for the three mechanical responses (a) under-damped (typically for children 6–7 years old); (b) critically damped (typically for children 8–10 years old); and (c) over-damped (typically for children of 12 years). The three responses present an initial bell shaped curve in the vertical acceleration domain that includes the initial rapid descent of the body as the limb contacts the ground (e.g., zero to peak acceleration), followed by an actively controlled deceleration phase that is qualitatively described in terms of a spring–dashpot–mass system.

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Table 2 Proportional distribution of muscle activity patterns Muscles

Burst pattern

Tonic pattern

Tonic–burst pattern

Under-damped TA 1.0 LG 0.1 SOL 0.0 VL 1.0 RF 0.1 BF 0.6

0.0 0.4 0.5 0.0 0.8 0.2

0.0 0.5 0.5 0.0 0.1 0.2

Critically damped TA 1.0 LG 0.0 SOL 0.5 VL 1.0 RF 0.6 BF 0.6

0.0 0.6 0.4 0.0 0.3 0.4

0.0 0.4 0.1 0.0 0.1 0.3

Over-damped TA LG SOL VL RF BF

0.0 0.3 0.2 0.0 0.9 0.7

0.0 0.7 0.6 0.0 0.0 0.2

1.0 0.0 0.2 1.0 0.1 0.1

a large initial deflection in the vertical acceleration path of the CoM at impact, indicative of low stiffness that contributes to an instantaneous large change in leg position on load application. The initial deflection is subsequently corrected through a series of oscillations of decreasing amplitude. This progressive damping is characteristic of a spring–dashpot–mass system of low viscous damping that requires a prolonged settling time to correct for the initial disturbance to the path of the CoM induced by impact. In contrast, the critically damped response (Fig. 6b), which is a transition strategy, does not include significant deviation in the ongoing acceleration path of the CoM at impact. In fact, the post-impact vertical acceleration of the CoM is reduced to near to zero in a steep slope, which is characteristic of a system with high stiffness [6]. The critically damped response however includes, on some trials, low amplitude oscillations that are sustained throughout post-impact and which are indicative of a low viscous damping in combination with the high stiffness. The over-damped response (Fig. 6c) of older children does not include either important deviations in the acceleration path of the CoM at impact or postimpact oscillations. The vertical linear kinematics of the CoM are controlled through a single-surge response in which peak acceleration gradually declines over a prolonged settling time to steady-state zero, at which point upright position can be resumed. This response is characteristic of low stiffness and high viscous damping.

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The strength of the association between the distribution of activity patterns in co-activating proximal and distal muscles of the leg and the measured mechanical response at impact was tested using an ANOVA for repeated measures. For every trial, activity for the six recorded muscles of the leg over the interval from 220 ms prior to impact to zero velocity of vertical CoM displacement post-impact was classified into burst, tonic or tonic–burst patterns using the PRT. The overall proportional distribution of the different activity patterns for each of the three CoM response curves was subsequently calculated (Table 2). The three CoM response curves were associated with different distributions of activity patterns underlying co-activation (F122 ¼ 4:55; P ¼ 0:01). For the uni-articular muscles (TA and VL), the burst pattern was used in all three CoM response curves, and related to dorsiflexion and knee extension respectively. Post-hoc analysis, however, revealed significant differences (P < 0:01) in the reliance of the burst pattern of activity for the twojoint proximal muscles (RF and BF) and the ankle plantarflexor muscles (LG and SOL) for the three CoM response curves. While the burst pattern was predominant in the proximal two-joint muscles for the under-damped response and in both proximal two-joint and distal plantarflexor muscles for the critically damped response, this pattern was near absent in the over-damped response, with a predominance of the tonic–burst pattern in distal muscles and the tonic pattern in proximal muscles.

TD technique rely on visual inspection for classifying events of activity within specific pattern, the process of reducing the complexity of the EMG signal by linear regression sampling, as employed by the PRT, greatly enhanced the Kappa statistic for repeated measures reliability (0.82 for the PRT, as compared to 0.54 for TD). This standardized representation of the EMG signal on the least-squares surface plot also improved discrimination between the three types of activity patterns in co-activating muscles of the leg. While the classical combination of TD and visual inspection highly weighted the burst pattern of activity (with 47% of events detected classified as burst of muscle activity), the combination of PRT and visual inspection produced a more neutral distribution of activity patterns across the co-activating muscles of the leg, with 35% of events being classified as a burst, in comparison to 38% as tonic and 28% as tonic–burst. The PRT also improved repeated measures reliability for classification of events to specific patterns, including for the complex tonic–burst signal for which reliability was almost twice that of the TD technique (i.e., 43% repeated measures agreement for the tonic–burst pattern with TD versus 85% with the PRT). Importantly, the unique representation produced by each of the three muscle activation patterns on the least-squares surface not only improves reliability, but also offers the possibility of developing an algorithm that would automatically recognize and classify events of muscle activity. An automatic recognition would remove subjectivity produced by reliance on visual inspection and therefore increase the uniform application of the PRT.

4. Discussion 4.2. Accuracy The PRT was developed to provide researchers with a more reliable, responsive and objective method than TD to identify three muscle activity patterns (burst, tonic and tonic–burst) in order that the contribution of these patterns in the regulation of the mechanical response of the leg to load application might be assessed. The paradigm of landing from a jump was used as an exemplar in order to determine if different combinations of the activity patterns could be associated with different mechanical responses of the leg at impact in children 6–12 years of age. We have shown that the PRT is more responsive and reliable than the TD, and that it is able to distinguish mechanical differences in the landing response of the leg. 4.1. Reliability and responsiveness The PRT is more responsive than TD for detecting events of activity from the full-wave rectified surface EMG signal; it provides a more reliable classification of these activities into one of three pre-defined patterns (see Table 1). Although, at present, both the PRT and

From a theoretical perspective, however, the favorable characteristics of reliability and responsiveness are not sufficient to support the use of the PRT in the study of motor control. Rather, the accuracy and validity of the classification obtained with the PRT must also be explored as a way of assessing its merit as an analytical tool. Accuracy addresses the question: Does muscle activity classified as the burst pattern truly represent activity that is different from the tonic pattern? This question is difficult to answer explicitly as the issue of changing muscle activity patterns with development has not been widely addressed in the motor control literature except in very qualitative terms such short and sharp for burst-like activity, smooth and prolonged for tonic activity [16] or more or less pronounced to describe levels of activity [24]. To avoid relying on qualitative descriptors to characterize muscle activity, we adopted the pragmatic approach of using a mathematical definition that is sensitive to the statistical organization of the activity (i.e., mean and variance) rather than to discrete amplitude and timing character-

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istics. We sought ideal signals in our data that fit the general criterion described by Hirschfeld and Forssberg [16] using the TD approach, and subsequently used these signals to define each pattern mathematically. This process resulted in an algorithm that uniquely recognized the burst, tonic and tonic–burst patterns reliably within reasonable limitations in the time domain of 10 ms inter-event interval (Fig. 4b) and in the spatial domain with tonic activity reaching 60% of the peak amplitude of an embedded burst pattern (Fig. 4a). The limit of resolution in the time domain may be important only if the PRT were to be used to examine the modulation of muscle activity during fine movements of very short duration, as for example rapid finger movements. For examination of sensorymotor muscle mechanisms governing the mechanical response of the leg during gross motor activities of mobility, however, this temporal resolution limit would not impinge greatly on the signal identification; due to the low-pass response of the muscle [43], the effect of an interburst interval of 10 ms between two bursts of muscle activity on the mechanical response of the leg to load application may not be different from a short period of tonic activity, as long as amplitudes are similar. Experimental research would be needed to determine the applicability of the PRT to other motor tasks. At the spatial resolution limit, the event of activity was classified either as a burst or tonic–burst pattern of activity, leading to an increased frequency in the falsenegative identification of the burst pattern as background levels of tonic activity increase and therefore to an overall decreased reliability for identifying the tonicburst signal. This leads to the important question of validity, that is: Do different patterns of muscle activity confer a changing mechanical response to the leg under conditions of load application? 4.3. Validity Within the context of the present study, this question of validity could only be implicitly evaluated from the strength of the association between the distribution of muscle activity patterns across co-activating muscles of the leg and whole leg mechanical behavior derived from the linear kinematics of the body CoM postimpact. The three mechanical responses identified in our data set (i.e., over-damped, critically damped and under-damped in Fig. 6) were statistically associated with different distributions of the muscle activity patterns, despite the small number of children (n ¼ 9), showing a shift from reliance on the burst pattern in younger children using the under-damped and critically damped responses to a dominant reliance on the tonic and tonic–burst patterns for the over-damped response adopted by older children. These data support the notion that different combination of tonic and tonic–

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burst activity patterns in co-activating muscles may be used to preferentially modulate the viscous damping characteristic (i.e., velocity-dependent term) of the mechanical response of the leg to compression at impact. By possibly absorbing energy from external load application and from intrinsic structures of the leg, the tonic pattern appears to favor the reduction or elimination of oscillatory behavior from the postimpact linear kinematics of the CoM. The burst pattern of activity, in contrast, could provide a timereferenced increase in stiffness (i.e., position-dependent term) that would enhance the control of force to resist displacement of the CoM at impact while reducing any available viscous damping to a minimum. Modulation of the pattern of activity underlying co-activation could therefore provide the nervous system with a way to adjust the viscous damping and stiffness response of the leg to internal and external task constraints, in the same fashion that stiffness is scaled to the level of muscle activation [13,18]. In this context, changing activity patterns across co-activating muscles, and more specifically the emergence of different combination of burst, tonic and tonic–burst patterns pre- and post-impact (see Fig. 5 as an example), could reflect the capacity of the developing nervous system to modulate anticipatory co-activation as a function of post-impact feedback activity in a way that promotes the best possible solution for stability. The PRT can be used to methodologically explore this issue in the context of landing from a jump. Indeed, by experimentally manipulating the conditions of load application at impact, in such a way as to alter the required properties of mechanical response, De Serres and Pelland [7] have shown that it is possible to alter the feedback regulation of the mechanical response of the leg at impact. This experimental paradigm allows us to quantify, with the PRT, how postimpact load information will gradually impinge on the descending regulation of co-activation pre-impact, and could provide further insight into the possible relationships between muscle activity patterns and the control of position-dependent and velocity-dependent control of forces [37]. At issue is the nature of the determinants of regulatory muscle mechanisms that could provide the developing system with a strategy to explore the sensory-motor landscape of adaptive regulation that reduces the complexity of CoM regulation for different tasks and different conditions at the environmental interface. An interesting feature of the PRT, that was identified through our simulation analysis and could be of relevance to the study of motor control, is the corollary finding that physiological tonic activity can be quantitatively distinguished from a random signal of similar bandwidth. While the spatial-temporal organization of physiological tonic activity, bandpass filtered at 10–450

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Hz, produces a recognizable pattern on the least-square surface (i.e., symmetrical distribution of a family of sine patterns in Fig. 3c), the simulated tonic signal used to test the spatial resolution of the PRT (Fig. 4a) did not produce such a recognizable pattern. Similarly, the PRT differentiated the spatial-temporal organization of anticipatory tonic activity in the ankle plantarflexors (LG and SOL) from the non-specific activity of the TA prior to impact (Fig. 5). The mechanical consequence of each of these two types of anticipatory activation should be further explored to test the possibility that, while non-specific activity could be used to globally set a default stiffness profile at a joint, organized tonic activity could better represent the dynamic response of the nervous system that takes into consideration the reflex and mechanical components of the system, as well as the conditions of load application at the environmental interface. We therefore postulate that the PRT could provide a measurement tool to differentiate between disorganized patterns of muscle activity that are pathological [22] from tonic activity that is a normal feature of development but yet has often been described solely as being ‘non-specific’. This characteristic of the PRT may be useful in exploring the sensory-motor mechanisms that underlie motor coordination difficulties in different pediatric populations. The main disadvantage of the PRT is that temporal information (e.g., onset latencies, time to peak activity) must be derived from the characteristics of the sampling algorithm, rather than being directly available from the sampling frequency of the signal as possible with the TD technique. For the PRT, the timing of events of interest (Lms) is calculated as  
WLR  1=Freqsignal þ A 0 i  1=FreqLR ¼ LðmsÞ ð1Þ where WLR is the width of the sampling window (i.e., number of points used to calculate the regression coefficient), Freqsignal is the sampling frequency of the signal (Hz), A is the number of sampling windows applied from the Tstart (0) to the event of interest (i), and FreqLR is the sampling frequency of the linear regression analysis (Hz). There is a possibility, as presented in this paper, that the PRT be combined with a low-pass analysis to completely describe the spatialtemporal profile of the muscle activity, and thereby eliminating the need for transposition into the timedomain (Fig. 5). A specific challenge to the continuing development of the PRT for implementation in motor control studies will be to establish explicitly through experimental work, the effect of the different patterns of muscle activity on the mechanical response of the leg. We have begun to verify this relationship by manipulating the conditions of load application at landing [37] and/or

the inertial properties of the leg to determine (a) if the type of muscle activity pattern is a mutable parameter that can be scaled to the magnitude of the impact force, and (b) if the type of muscle activity pattern is adapted to the inertial characteristics of the limb. This information will improve our understanding of the global strategies that are used by the nervous system to manage the limb/environment interface. Also, as the PRT was designed to explore the sensory-motor mechanisms of co-activation at different stages of growth and development in children, it will be important to determine the effect of subcutaneous adipose tissue on the responsiveness of the linear regression that underlies event detection in the PRT. Adipose tissue produces a selective attenuation of high frequency components of the surface EMG signal [4] that could reduce the responsiveness of the PRT in reliably identifying the tonic–burst pattern of activity. This limitation in responsiveness would be specifically important when using the PRT with young children for whom there is typically a higher index of subcutaneous adipose tissue. Lastly, the effect of different sampling window sizes on the spatial-temporal resolution of the PRT should be explored if the technique is to be applied to fine motor behaviors (ex. grasping). In our experimental work on landing from a jump, an iteration process was used to determine that a 20-point window size produced the most reliable detection of the burst pattern of activity in a signal sampled at 1 kHz [36]. The combination of a small sampling window (n ¼ 20) and a 1 ms sampling frequency for the PRT eliminated the need for the surface EMG signal to be wide-sense stationary [34] for the burst pattern to be detected, provided that any low frequency baseline fluctuations, induced by movement artifact at impact, were removed. In summary, the PRT provides an analytical tool to study the functional interaction between feedforward and feedback mechanisms of changing muscle coactivation patterns in children during sensory-motor development. Based on our analysis of the landing response using the PRT, we propose that the functional integration of different activity patterns in co-activating muscles could be one of the determinant parameters of the multi-joint coordination that regulates the mechanical response of the leg to the conditions of load application at the environmental interface. Having defined the responsiveness, reliability and applicability of the PRT to study developing sensory-motor mechanisms of co-activation related to mobility, we anticipate the development of a computer algorithm that will provide an automatic identification and classification of events of muscle activity into the three pre-defined patterns. It is hoped that the preliminary identification of the type of muscle activity provided by the PRT will serve as a platform from which the interaction between TONIC

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and BURST activity can be further explored and, the sensitivity of the PRT concomitantly increased.

Acknowledgements The authors would like to thank B. Dulong for statistical consultation, Carlo deLuca for signal processing consultation, Bertrand Arsenault, Sophie De Serres and David Ostry for their advice on various aspects of this project. This study was supported, in part, by an NSERC grant from the Canadian Government to P. McKinley, and a FRSQ studentship from the Que´bec Government, the Eileen Peters Major Fellowship from McGill University, and a University of Ottawa Research Fund Grant to L. Pelland.

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Dr McKinley grew up in Los Angeles and was educated at UCLA where she took a Bachelor’s degree in Zoology, a Master’s degree in Developmental Biology and a PhD in Kinesiology, under the direction of Judith L. Smith. She also studied in the faculty of Letters at the University of Padova. Subsequently, she taught desert and island ecology/biology at the University of the Virgin Islands before returning to Chicago with a Hearst foundation post-doctoral fellowship in rehabilitative medicine under the direction of Barry Peterson. She returned to UCLA as an adjunct assistant professor in the department of Kinesiology and post-doctoral fellow focusing on neuroplasticity of the sensory-motor cortex under the direction of Drs Smith and Kruger. She then moved to Montreal where she has been employed as a professor in the School of Physical and Occupational Therapy at McGill University since 1986. Her current interests include the development of motor control during childhood, development and evaluation of efficacy and adherence of leisure-based physical activities for vulnerable populations, and the impact of performance anxiety on rehabilitation evaluation tools.