Force Control in Manipulation Tasks: Comparison of

Motor Control, 2014, 18, 18-28 http://dx.doi.org/10.1123/mc.2012-0121 © 2014 Human Kinetics, Inc.

Official Journal of ISMC www.MC-Journal.com ORIGINAL RESEARCH

Force Control in Manipulation Tasks: Comparison of Two Common Methods of Grip Force Calculation Mehmet Uygur, Goran Prebeg, and Slobodan Jaric We compared two standard methods routinely used to assess the grip force (GF; perpendicular force that hand exerts upon the hand-held object) in the studies of coordination of GF and load force (LF; tangential force) in manipulation tasks. A variety of static tasks were tested, and GF-LF coupling (i.e., the maximum cross-correlation between the forces) was assessed. GF was calculated either as the minimum value of the two opposing GF components acting upon the hand-held object (GFmin) or as their average value (GFavg). Although both methods revealed high GF-LF correlation coefficients, most of the data revealed the higher values for GFavg than for GFmin. Therefore, we conclude that the CNS is more likely to take into account GFavg than GFmin when controlling static manipulative actions as well as that GFavg should be the variable of choice in kinetic analyses of static manipulation tasks. Keywords: hand function, neural control, load, coupling, correlation

Using hands to manipulate objects could arguably be the most important motor function needed to maintain an independent and professionally active life. As a result, a number of studies have been performed to explore various aspects of hand function. From the kinetic aspect, the interaction between the hand and a hand-held object has often been decomposed into the load force (LF; the force that lifts and moves an object or, alternatively, provides a reaction force from an external support) and grip force (GF; the grasping force that enables a steady contact by providing friction between the hand and object; Jaric, Knight, Collins, & Marwaha, 2005; Johansson & Westling, 1984; Westling & Johansson, 1984). According to a simple and frequently used mechanical model of lifting a vertically oriented object (Figure 1A), two opposing components of GF are needed to produce the total friction force (2μGF; μ—coefficient of friction), which must be equal to or higher than the LF (originating from the weight and inertia of the object) to prevent slippage (Johansson & Westling, 1984; Westling & Johansson, 1984). The kinetic studies of manipulation activities have consistently revealed a high level of GF-LF coordination. The coordination has often been evaluated through the Uygur and Jaric are with the Dept. of Kinesiology and Applied Physiology, University of Delaware, Newark, DE. Prebeg is with the Research Center, Faculty of Sport and Physical Education, University of Belgrade, Belgrade, Serbia. 18

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GF-LF coupling calculated as the high maximum values of GF-LF cross-correlation and the corresponding time lags close to zero (de Freitas, Markovic, Krishnan, & Jaric, 2008; Flanagan & Wing, 1995; Hermsdorfer & Blankenfeld, 2008; Uygur, de Freitas, & Jaric, 2010a; Zatsiorsky, Gao, & Latash, 2005). The consequences of high GF-LF coupling are a high GF modulation (amount of GF change relative to changes in LF) and a low GF scaling (i.e., coupling of GF with changes in LF enables relatively low and stable GF/LF ratio that still prevents slippage; de Freitas, Krishnan, & Jaric, 2007; Flanagan & Wing, 1995; Zatsiorsky, et al., 2005). In general, it has been suggested that the CNS coordinates the forces by coupling the actions of the muscles that produce GF and LF using feed-forward (i.e., the “predictive”) control mechanisms (Flanagan & Wing, 1995; Johansson & Westling, 1984). The importance of studying GF-LF coordination in contemporary research originates from at least two foremost sources. First, the changes in GF-LF coordination associated with various task related variables (e.g., mechanical conditions, instructions, feedback, task complexity) proved to be a promising window into the neural control mechanisms of voluntary movements (Krishnan & Jaric, 2010; Sarlegna, Baud-Bovy, & Danion, 2010; Uygur, et al., 2010a; Uygur, Jin, Knezevic, & Jaric, 2012). Second, it has been consistently shown that individuals with impaired hand function also exhibit deteriorated GF-LF coordination (for review, see Nowak & Hermsdorfer, 2005). As a result, it is believed that the methods for recording the GF-LF coordination in manipulative actions could be developed into a routine clinical test of hand function (Jaric, Knight, et al., 2005; Krishnan & Jaric, 2008; Nowak & Hermsdorfer, 2005). Taking into account the importance of GF-LF coordination in contemporary motor control research, it is surprising that the methods used to calculate GF still remain ambiguous. For example, the studies of either free or static manipulation tasks in both the field of robotics and voluntary human movements frequently define GF as an internal force that corresponds to the minimum of the opposing forces acting perpendicularly upon the object (Gao, Latash, & Zatsiorsky, 2005; Kerr & Roth, 1986; Martin, Latash, & Zatsiorsky, 2011; Winges, Soechting, & Flanders, 2007; Yoshikawa & Nagai, 1991). Specifically, if the opposing components of GF are unequal, the GF is considered to be equal to the weaker one, while the nonbalanced part of the stronger component is a “manipulative force” that laterally accelerates the object (Kerr & Roth, 1986; Yoshikawa & Nagai, 1991). In the further text we will refer to this method of GF calculation as GFmin (i.e., GFmin equals to the lower of the two opposing GF components). Conversely, in the studies of virtually all static and a number of free-movement manipulation tasks, GF has often been calculated as the average of the two opposing GF components (Brandauer, Timmann, Hausler, & Hermsdorfer, 2010; Crevecoeur, Giard, Thonnard, & Lefevre, 2011; Jaric, Collins, Marwaha, & Russell, 2006; Johansson, Riso, Hager, & Backstrom, 1992; Kwok & Wing, 2006; Uygur, de Freitas, & Jaric, 2010b; Uygur, et al., 2012). In the further text we will refer to this method of GF calculation as GFavg (i.e., GFavg equals average of two opposing GF components). Taking into account the general importance of the studies of hand function, as well as the important role that GF plays in those studies, we designed two experiments to compare the GF-LF coupling observed from a variety of static manipulation tasks, where GF was calculated as GFavg and GFmin. Specifically, taking into account the elaborate neural control mechanisms involved in the studied motor

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activity, higher indices of GF-LF coupling could reveal not only a more valid method of GF assessment, but also provide new insight into the involved neural control mechanisms.

Methods Subjects Seven female and seven male right-handed volunteers (27.6 ± 4.5 years of age) participated in Experiment 1, while seven female and seven male right-handed volunteers (26.9 ± 1.4 years of age) participated in the Experiment 2. All subjects were healthy and without recent injuries to upper extremity that could compromise the tested performance. The experiment was approved by the IRB of the University of Delaware and conducted in accordance with the declaration of Helsinki.

Device The custom designed experimental device was used in both experiments (see (Jaric, Knight, et al., 2005; Jaric, Russell, Collins, & Marwaha, 2005) for details). In short, it consists of two vertically oriented and externally fixed grasping surfaces connected by a single-axis force transducer (WMC-50, Interface Inc, USA) and a multiaxis force transducer (Mini40, ATI, USA) attached underneath (Figure 1A). The grasping surface of the device was covered with rubber to provide a high friction between the tips of the fingers and the grasping surface (Uygur, et al., 2010b). The single-axis force transducer recorded the compression force (FC) applied by the fingertips, while the multiaxis one recorded the net force applied against the handle in three dimensions. The horizontal forces acting normally against the handle contact areas (i.e., FC recorded by the single-axis force transducer and FY recorded by the multiaxis force transducer) were used to calculate both the GFavg and the GFmin. The remaining two components, FZ and FX, recorded by the multiaxis transducer provided the data for calculating the tangential force (i.e., the load force; LF) that tends to cause the slippage of the digits along the contact surface (Figure 1B). Note that the LF components were also used to provide both one- (i.e., FZ) and two-dimensional (i.e., FX and FZ) visual feedback during the Experiment 1 and 2, respectively (see further text for details).

Experimental Protocol The protocol consisted of two unrelated experiments (i.e., Experiment 1 and Experiment 2), in which each experiment consisted of two identical sessions performed on separate days. The first sessions served as practice, while the second ones served for data collection. Subjects completed at least three trials of each task within a particular session. The sequence of individual tasks was randomized within the sessions. All tasks were explained and demonstrated before the first session. Participants were asked to wash and dry their hands before the sessions to prevent any contamination that may alter the acting friction. They were also asked to dry their hands frequently with a paper towel during testing session

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Figure 1 — (A) A simple model of holding and lifting a vertically oriented object. Load force (LF) originates from the object’s weight and inertia, while the grip force (GF) originates from two opposing components exerted by the tips of the digits (circles) that can be either equal or unequal. (B) Schematic representation of the experimental device. The load sensor within the handle records one component of GF, while the sensor positioned beneath records both the lateral (FY; serves for calculating GF) and vertical force (FZ; corresponds to LF). The methods for calculating GFavg and GFmin are also depicted. (C) A typical LF profile observed from Experiment 1 is shown. The dashed line shows the prescribed maxima and minima of the oscillatory LF paced by metronome. (D) Illustration of the prescribed LF templates of Experiment 2. The dashed lines show the linear tasks (UX and UZ) and circular tasks (CX, CZ, and CXZ).

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to remove potential residuals from sweating and keep the acting friction constant. Thereafter, the standing participants oriented their upper arm vertically and their forearm horizontally, and exerted the prescribed force patterns against the device. Either 1-dimensional (Experiment 1) or 2-dimensional on-line force feedback (Experiment 2) was provided on a computer screen positioned in front of them (Uygur, et al., 2010a; Uygur, et al., 2012). A rest period of 20 s was allowed between the tasks and a 10 s one between the consecutive trials within the same task. In Experiment 1, the participants grasped the vertically oriented device while their fingers were pointing in anterior-posterior direction, and exerted an oscillatory pattern of isometric LF by pulling the device upward in the vertical direction that resulted in a quasi-sinusoidal pattern of the vertical component of LF (FZ; see solid line at Figure 1C). The FZ ranged between 11 N and 21 N as prescribed by two horizontal lines displayed on a computer monitor (see dashed lines in Figure 1C). The participants were paced by a metronome while completing the task under low (0.67 Hz), moderate (2 Hz), and high frequency (3.33 Hz; Jaric, et al., 2006; Uygur, et al., 2010a). The task performance was assessed through the absolute errors calculated from the average difference between the actual peaks and valleys and the prescribed levels of FZ. The trial with the lowest absolute error was taken for further analysis. All subjects provided at least one trial with average absolute error below 1.5 N. In Experiment 2, the participants were asked to grasp the vertically oriented device while their fingers were pointing in medio-lateral direction, as well as to exert various circular and linear LF patterns in X-Z plane by tracing the prescribed force patterns shown in Figure 1D. While the circular tasks were based on various LF ranges in two orthogonal directions, the linear tasks required exerting LF only along either X- or Z-axis. All tasks were paced by a metronome set at 1.5 Hz that resulted in exertion of 1.5 cycles of either circular or linear LF profiles per second. This frequency was selected since it was in the middle of a frequency range that allows a comfortable execution of these types of tasks (Jaric, et al., 2006). The task performance was calculated through absolute errors, as well as the ratio of the standard deviations (SD) of the recorded FX and FZ. Absolute errors were calculated for the minima and maxima of the exerted forces with respect to the prescribed ones (i.e., either for FX or FZ in the linear tasks, and for both FX and FZ in circular tasks) and were required to be below 1.5 N. Moreover, to ensure that the circular tasks provided similar ranges of FX and FZ, only the trials that provided the ratio of standard deviations (SD) of the recorded FX and FZ between 0.75 and 1.25 were accepted as successful (note that SDFx/SDFz ratio for a perfect circle would be 1). Regarding the one-dimensional tasks, only the trials that provided the same ratio, either above 4 or below 0.25 (when exerting LF in X and Z direction, respectively), were accepted. Note that SDFx/SDFz ratios for perfect one-dimensional tasks should be either infinite or zero, respectively. Three successful trials per task were recorded and the trials with the highest SDFx/SDFz ratio (for the linear tasks performed in X direction), the lowest SDFx/SDFz ratio (for linear tasks performed in Z direction), as well as the trials with SDFx/SDFz ratios closest to one (for the circular tasks) were taken for further analysis. On average, 41% of the trials performed in the experimental session had to be repeated due to the fact that those trials did not satisfy either the absolute error or SD ratio criteria.

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Data Processing All force components were recorded at 200 Hz and stored for later analyses. The raw force signals were low-pass filtered with a 4th-order Butterworth filter with a cut-off frequency of 10 Hz. LF, GFavg and GFmin time-series were calculated as shown in Figure 1B. The data between the 5th and 9th s of each trial was analyzed [6,24]. The main dependent variable calculated was GF-LF coupling as assessed through the maximum cross-correlations between the GF and LF time series. We also assessed the root mean square error (RMSE) of FY, which indicates the deviation from the equality of the two opposing components of GF. Note that RMSE equaling zero would imply that the two opposing component of GF were equal over the entire trial, which would inevitably lead to equality between GFavg and GFmin. Over the course of data analysis we often found FY to switch direction as a result of the ongoing relative changes in the two opposing GF components (see Figure 2A for illustration). Due to the methods used to calculate GFmin, this phenomenon not only affected the magnitude of both GFmin and GFavg, but also produced ‘spikes’ in GFmin, but not in GFavg time series. As illustrated in Figure 2B, the above-mentioned spikes could severely reduce the GF-LF coupling calculated from GFmin (i.e., rGFminLF), but not those calculated from GFavg (rGFavg-LF). Therefore, in addition to the analyses of all trials of individual tasks, we also performed a separate analysis on the trials that did not have GFmin spikes. To compare the coupling obtained from GFavg and GFmin, we applied paired t tests of the Z-transformed correlation coefficients.

Figure 2 — (A) Typical force profiles directly recorded from a representative subject while performing a circular task (i.e., CZ) in Experiment 2. (B) The calculated dependent variables (see Fig. 1B for the calculation methods). Note that while LF and GFavg show relatively smooth profiles, GFmin reveals sudden changes (i.e., “spikes”) at the instants when FY changes direction.

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We did not find deviations in the normality of distribution in any of the dependent variables (Kolmogorov-Smirnov test). P-value was set at 0.05.

Results Figure 2A illustrates typical force profiles observed from the circular task (i.e., CX). This particular trial also shows consecutive switching of direction of FY that occurred in about 47% of all trials obtained from both experiments. The consequence is the appearance of “spikes” in GFmin (but not GFavg) that could affect the calculated force coupling. When averaged across the subject, RMSE of FY revealed values between 2.71 N and 3.36 N in Experiment 1 and 1.75 N and 4.09 N in Experiment 2. These values suggest prominent deviation from equality of two opposing GF components (see Figure 1A) that inevitably resulted in prominent differences in force profiles between GFavg and GFmin, as illustrated in Figure 2B. The main finding of the current study represents the difference in force coupling observed from GF calculated by two standard methods used in the literature. Experiment 1 revealed significantly higher coupling for GFavg in 2 out of 3 linear tasks (Table 1). Note that even when the trials with spikes are excluded, the pooled data still suggest a higher GF-LF coupling for GFavg than for GFmin. Regarding the data observed from Experiment 2, only one out of five tasks revealed a higher force coupling for GFavg than for GFmin (Table 2). However, similar to Experiment 1, the advantage of GFavg over GFmin remains both when all trials are pulled together and when the trials with the spikes are excluded.

Discussion Regarding the main finding of the study, note that force coupling as assessed by the correlation between GF and LF has arguably been the most often assessed index of GF-LF coordination (Flanagan & Wing, 1995). In addition, the indices of GF-LF coordination deteriorate either in tasks of high complexity or in individuals with impaired hand function, such as in neurological patients (Krishnan & Jaric, 2010; Nowak & Hermsdorfer, 2005; Uygur, et al., 2010a). As a result, a number of authors have argued that among the main aims of the CNS could be to couple GF with the ongoing changes in LF to provide a relatively highly modulated but overall low GF that remains sufficient to avoid slippage of a hand-held object (Blakemore, Goodbody, & Wolpert, 1998; Flanagan & Wing, 1995; Hermsdorfer & Blankenfeld, 2008). From that perspective, the observed higher force coupling obtained from GFavg than from GFmin could have a potentially important implication regarding the neural mechanisms involved in the control of static manipulative tasks. Namely, the CNS is more likely to take into account GFavg than GFmin when coupling forces during manipulative actions. From the methodological perspective, the same finding also implies that GF should be calculated as GFavg, rather than as GFmin, when assessing the GF-LF coordination, such as through the force coupling, scaling, or modulation (Flanagan & Wing, 1995; Johansson & Westling, 1984; Zatsiorsky, et al., 2005); see also Introduction). Regarding the neural mechanisms involved in the observed behaviors, however, it should be kept in mind that only a very few

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Table 1 Data Obtained From the Experiment 1: Median Correlation Coefficients Calculated between the LF and GF Assessed by Two Different Methods 0.67Hz

2.00Hz

3.33Hz

All trials

No spikes

15

15

15

45

31

No. of trials rGFavg&LF

median

0.97

0.95

0.9

0.94

0.95

rGFmin&LF

median

0.96

0.91

0.85

0.91

0.91

t-test

t-value

2.83

4.98

1.51

4.66

4.72

p

< .01

< .001

.15

< .001

< .001

Note. rGFavg-LF = maximum correlation coefficient obtained from the GFavg method; rGFmin-LF = maximum correlation coefficient obtained from GFmin method; no spikes = only the trials without GFmin spikes included.

Table 2 Data Obtained From the Experiment 2: Median Correlation Coefficients Calculated Between GF Assessed by Two Different Methods and LF No. of trials

UX

UZ

CX

CZ

CXZ

All trials

No spikes

14

14

14

14

14

70

29

rGFavg&LF

median

0.92

0.94

0.79

0.70

0.84

0.85

0.91

rGFmin&LF

median

0.90

0.85

0.75

0.66

0.75

0.72

0.79

t-test

t-value

1.66

2.72

1.65

0.55

1.85

3.77

3.06

p

.12

< .05

.12

.59

.09

< .001

< .005

Note. See Figure 1D for illustration of the tasks. rGFavg-LF = maximum correlation coefficient obtained from the GFavg method; rGFmin-LF = maximum correlation coefficient obtained from GFmin method; no spikes = only the trials without GFmin spikes included.

studies of manipulation have also included a direct recording of the associated neural and myoelectric activity (Johansson & Westling, 1988; Winges, et al., 2007). Therefore, the following paragraph will only focus on the mechanical aspects of manipulation tasks that could discern the possible role of GFavg and GFmin in the control of manipulation tasks. A simple kinetic model of grasping a free-moving object shows that if one of the opposing GF components exceeds the other one (see Figure 1A), the uncompensated force results in the object’s lateral acceleration. Such an intended or unintended lateral acceleration inevitably leads to changes in the movement velocity and direction and, consequently, in the object’s final position. As a result, one could speculate that when controlling free-object manipulations the CNS needs to take into account both the compensated (that corresponds to GFmin and prevents slippage), and uncompensated GF component (that produces lateral acceleration which affects the object’s kinematics). Therefore, we evaluated only static manipulations since they do not allow for lateral acceleration. Note that the deviation of LF from

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the prescribed direction that inevitably occurs over the course of the manipulation lead transient differences between the opposing GF components. Since each of the opposing GF components independently produce friction, the total friction force depends on the sum or, alternatively, the average of the two opposing GF components (i.e., on GFavg). As a result, one could speculate that to produce a relatively low, but sufficient GF that prevents slipping of the hand along the object, the CNS only needs to control GFavg, while covariations of the opposing GF components do not affect the task. To conclude, a variety of static manipulation tasks that differed regarding their force range, frequency, direction, dimensionality, feedback, and wrist position revealed higher GF-LF coupling when GF was calculated as GFavg than as GFmin. This finding suggests that the CNS may be taking into account GFavg when controlling manipulation forces, as well as that GFavg, rather than GFmin, should be calculated as a typical dependent variable in studies of static manipulation. However, although a considerable body of evidence suggests that GF-LF coordination represents an important component of control in manipulation tasks (for review, see de Freitas et al., 2008a), it still remains possible that the assessed force coupling is only a by-product of neural mechanisms aimed to control other variables relevant for task execution. Therefore, future studies should extend this line of research to a larger diversity of both the static and free-movement manipulation tasks, as well as endeavor to relate the task kinetics with the neuromuscular activity to shed a light on the neural mechanisms involved in control of GF. Acknowledgment The study was supported in part by grants from the Brain Gain Program (World University Service) and the Serbian Research Fund (#145 082) to S. Jaric.

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