Motion Analysis of Upper Limb and Hand in a Haptic Rotation Task

Alexandra Moringen; Helge Ritter; Kathrin Krieger; Yuri De Pra

REVIEW 3 major objections 5 minor 14 references

Motion Analysis of Upper Limb and Hand in a Haptic Rotation Task

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Blindfolded people systematically overshoot when rotating a knob to a target angle, and this paper traces that error to the wrist and to sideways and distal finger joint motions.

desk verdict Novel decomposition of haptic overshoot into joint excursions, but unsigned predictors and pooled trials make the mechanistic claims conditional. read the letter →

arxiv 2411.12765 v1 pith:QD2E4A7A submitted 2024-11-17 cs.HC cs.RO

Kathrin Krieger , Yuri De Pra , Helge Ritter , Alexandra Moringen This is my paper · ORCID

classification cs.HCcs.RO

keywords hapticrotationovershootjointangleanalysismotioncapturestepwiseregressionegocentricreferenceframefingertiprollingorientationbias

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks why people overshoot when they rotate a rotary knob blindfolded to a target angle, and it locates the cause in specific joints rather than in a whole-arm systematic bias. Motion capture of 18 participants performing a 90-degree counterclockwise rotation with thumb and index finger yields joint-angle ranges for 15 hand and upper-limb degrees of freedom. Stepwise regression with backward elimination shows that the wrist joint, sideways movement at the proximal finger joints, and the distal finger joints significantly predict the overshoot, while the elbow and several thumb movements have negative coefficients, meaning they compensate. The authors conclude that two mechanisms are at play: a hand-centered egocentric reference frame that undercounts the wrist's own rotation, and un-compensated rolling of the fingertips over the knob surface that adds rotation without the person's awareness.

What carries the argument

The machinery is a motion-capture-to-regression pipeline. Reflective markers on the hand and upper limb are converted, via a 16-joint, 26-DoF kinematic hand model and a 7-DoF shoulder-elbow-wrist model, into joint-angle trajectories, synchronized with a rotary-knob apparatus that records absolute orientation with 0.022-degree resolution. Each trial is compressed to one predictor per joint — the range (maximum minus minimum angle) over the rotation segment — and to the criterion, the signed error from the 90-degree target. Stepwise ordinary least squares regression with backward elimination, using min-max normalization per participant per joint, then selects the joints whose ranges significantly predict the error, which is the step that localizes the overshoot.

What would settle it

Refit the same model with a random intercept per participant (a linear mixed-effects model) and check whether the wrist and finger coefficients remain significant; if they do not, the claimed localization is an artifact of pooled trials. Alternatively, measure fingertip contact-point displacement during rotation directly: if overshoot occurs without any rolling of the fingertips over the knob surface, the paper's rolling mechanism is falsified.

Watch

Extended reading notes

Core claim

The central discovery is a statistical localization of the haptic-rotation overshoot. Regressing the signed error (actual rotation minus 90 degrees, normalized per participant and per joint to [0,1]) on each joint's range of motion yields a final model with $R^2 = 0.691$, in which the three wrist axes (coefficients 30.61, 21.45, 20.13), the thumb's TMC flexion/extension (24.05), the index MCP abduction/adduction (8.55), and the index DIP flexion/extension (34.67) all contribute positively and significantly to overshoot. Elbow flexion/extension (-13.62), thumb MCP flexion/extension (-25.96), and thumb TMC abduction/adduction (-17.46) contribute negatively, i.e., they limit the overshoot. The paper interprets the wrist effect as evidence for a hand-centered egocentric reference frame and the finger effects as evidence that the fingertips roll over the knob surface, changing the contact point, without the participant compensating.

Load-bearing premise

The result depends on treating every trial from every participant as an independent observation in a single regression; if within-participant correlations are large, the reported p-values and the list of significant joints could be spurious.

Editorial extensions

If this is right

The overshoot in blindfolded haptic rotation is a local, joint-specific phenomenon: the wrist and particular finger joints carry the error, while the elbow and some thumb motions actively compensate.
The wrist's significant positive contribution supports a hand-centered egocentric reference frame, meaning the brain systematically under-weights the hand's own rotation.
Fingertip rolling over the knob surface, driven by sideways proximal-joint motion and distal flexion, adds rotary movement that the blindfolded participant does not compensate.
Grasping orientation changes the joint usage pattern: the shoulder contributes in all five orientations, the wrist drops out at the 90-degree grasp, and the index finger shifts from MCP abduction/adduction to PIP and DIP flexion as the grasp angle increases.
Future experiments can test the two mechanisms directly by restricting hand movement or preventing rolling, and the recorded data can be re-analyzed to explain why using more fingers reduces overshoot.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the egocentric reference-frame account is right, then providing participants with visual or haptic feedback about hand orientation during the movement should reduce the wrist's contribution to overshoot, a testable prediction the paper does not make.
The per-orientation regressions imply that the set of significant predictors is not universal: a joint can contribute positively in one grasp and negatively in another, so the overshoot mechanism likely shifts with task geometry rather than being a fixed motor error.
Compressing each joint trajectory to a single range discards timing and phase; using velocity or temporal-alignment features might reveal that the order of joint recruitment matters for the size of the overshoot.
A direct non-invasive check is feasible: track the fingertips with small markers or high-speed video during rotation to confirm the rolling motion and quantify its angular contribution.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Briefly: this paper does something genuinely new—it takes motion-capture data from a previously published haptic rotation experiment and asks which individual joints' excursions predict the well-known overshoot. That decomposition hasn't been done before, and it's a useful step for haptics and rehabilitation. The paper is also admirably transparent: it describes the preprocessing, includes detailed regression tables in the appendix, and flags the elbow joint's approximate tracking. The writing is direct and the reasoning is easy to follow.

The soft spot is the predictor construction. The regressions in Section 3 use unsigned max-min ranges δ_j as predictors for signed overshoot (δτ − 90°). A range cannot distinguish movement that adds rotation from movement that opposes it. So a positive coefficient on, say, the wrist range only tells you that larger total wrist excursion is associated with more overshoot—it does not tell you the wrist moved in the direction that produces overshoot. The two proposed mechanisms (egocentric wrist bias, fingertip rolling) are directional, so the evidence doesn't really test them. This is not a minor presentational issue; it is the difference between 'the wrist is involved' and 'the wrist drives overshoot.' The per-orientation regressions in Table 1 (right) make the problem concrete: the same joint appears with opposite signs across grasp angles, which is exactly what you'd expect if ranges are collapsing opposite-signed movements.

The other concerns are real but secondary. Trials from 18 participants are pooled as independent observations; no random intercepts, no correction for the stepwise selection. The R² values look fine, and the coefficients are large, but the p-values are optimistic. The paper reuses the authors' own earlier data, which is fine as long as the reader knows; it's stated clearly.

My take: the central claim about which joints contribute is plausible but not established. The paper deserves a serious referee, but I would send it back for major revision—add a mixed-effects model, and more importantly, replace or supplement the unsigned ranges with signed or phase-specific joint angles. As it stands, the mechanistic interpretation outruns the statistics.

Who reads this: haptics researchers, motor control people, and anyone designing knob interfaces. It's a good case study for a reading group on regression pitfalls. I'd engage with it, but treat the conclusion as a conditional hypothesis.

Referee Report

3 major / 5 minor

Summary. The paper analyzes motion capture data from a haptic rotation task (90° counterclockwise, blindfolded, two-finger grasp) to identify which upper limb and hand joint angle ranges predict the signed overshoot (δτ − 90°). Using stepwise ordinary least squares regression with backward elimination, the authors report that the wrist joint, the sideways movement in proximal finger joints, and the distal finger joints contribute significantly to overshooting. They also run per-grasping-orientation regressions to examine which joint ranges contribute to the achieved rotation. The results are interpreted as evidence for two mechanisms: a hand-centered egocentric reference frame bias (wrist contribution) and fingertip rolling over the knob surface (finger joint contributions).

Significance. The paper addresses a clearly relevant question in haptics and human motor control: where in the kinematic chain the systematic overshoot in blindfolded rotary knob rotations originates. The experimental setup (motion capture with 18 participants, five grasping orientations, repeated trials) and the joint-level analysis framework are potentially valuable for future work in rehabilitation, robotics, and human-robot interaction. The paper is also transparent in reporting coefficients and p-values. However, the current analysis has serious statistical and conceptual limitations that prevent the central claim from being established. The proposed framework may be salvageable with a different predictor definition and a mixed-model approach, but as presented the evidence does not support the conclusions.

major comments (3)

[Section 2.6 and Table 1 (left)] The predictors are unsigned max-min ranges δ_j of each joint angle over the rotation segment, while the criterion is the signed overshoot δτ − 90°. A positive regression coefficient for a range variable only indicates that larger total joint excursion is associated with more overshoot; it cannot distinguish movement in the direction that adds to the rotation from movement in the opposite (compensatory) direction. For example, a wrist rotation that opposes the knob movement and a wrist rotation that reinforces it can produce the same range value. The central claim that the wrist and finger joints 'contributed significantly to overshooting' (Abstract and Section 4) is therefore not supported by the regression as specified. The two proposed mechanisms are directional (egocentric reference frame bias and fingertip rolling), so the analysis needs a signed measure of joint displacement (e.g., net angle change over the rotation segment) or a decomposition into positive and negative contributions.
[Section 3, Tables 1 and G (Tables 2–7)] The overshoot regression pools all trials from 18 participants, with each trial treated as an independent observation. Since each participant contributes multiple trials under each grasping orientation, within-participant correlations are likely substantial, and the reported p-values and standard errors are accordingly unreliable. Additionally, the stepwise backward elimination procedure performs many significance tests without any correction for multiple comparisons, so the final model (reported R² = 0.691) is likely overfitted and the list of 'significant' joints may be spurious. The lack of random effects for participants is a load-bearing problem for the central claim because the identification of contributing joints depends on the validity of these p-values.
[Section 3, Table 1 (right) and Tables 3–7] The per-orientation regression analyses of the rotation angle on joint ranges suffer from the same unsigned-predictor problem. A negative coefficient is interpreted as the joint 'compensating' for the rotation (Section 4), but a negative association between a range variable and the rotation angle does not imply that the joint moved in the opposite direction to the knob rotation. Moreover, these regressions also pool trials without participant random effects, and the constant term (ranging from 28.36 to 70.50) is treated as a catch-all for 'additional body movements' without a clear mechanistic interpretation. The reported p-values are conditional on the stepwise selection and are not corrected for multiplicity.

minor comments (5)

[Section 1] The phrase 'the mean difference between the target and the encoded positions' is ambiguous; it should be clarified whether the signed error is computed as encoded minus target or vice versa.
[Section 3] There is a typo: 'shown in shown in Table 1' should be 'shown in Table 1'.
[Appendix G, Table 7] In the regression results for the free grasping condition, the predictor labeled 'δf/e i,MCP' appears twice with different coefficients (41.50 and 26.80). One of these is likely meant to be a different joint (e.g., PIP); please correct the labeling.
[Introduction/Appendix access] The appendix is hosted on a password-protected website with the German word 'Passwort'; for an international readership and for review, the access information should be in English and ideally the supplementary material should be included with the submission.
[Section 2.5] The description of the hand model states that the thumb has joints TMC, MCP, and IP, while the index finger has MCP, PIP, and DIP; this is clear, but the notation in Table 1 (e.g., δf/e_t,MCP) could be confusing because the subscript 't' is used for the thumb and 'i' for the index finger; a brief reminder in the caption of Table 1 would help.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the joint-contribution analysis is an independent regression on measured kinematics and does not reduce to its inputs or to prior conclusions.

full rationale

The paper's central claim is that wrist, proximal sideways finger, and distal finger joint ranges contribute significantly to overshoot in a blindfolded 90-degree haptic rotation. This claim is obtained by stepwise OLS regression in Section 3, where the criterion is the measured signed error (delta_tau - 90 degrees) and the predictors are measured joint-angle ranges delta_j. The criterion and predictors are distinct physical measurements: the knob rotation comes from the Twister encoder, and the joint ranges come from Vicon marker trajectories processed through an inverse-kinematics hand model. No equation defines one in terms of the other by construction, and no fitted parameter is renamed as a prediction. The paper does reuse data and experimental apparatus from the authors' prior work [4], but that reuse supplies the dataset and the known overshoot phenomenon, not the joint-level attribution; the regression analysis is new and its significant-joint results are not assumed in the preprocessing or model setup. Statistical concerns about pooling trials across participants, unsigned range predictors, and multiple testing are real threats to the inference but are not circularity: they concern whether the regression estimates are unbiased and interpretable, not whether the conclusion is equivalent to the input data or to a self-citation. The self-citations to [3], [4], [7], and [8] are used for apparatus, motion-tracking software, and prior experimental context, not as an unverified authority that forces the paper's central result. No step in the derivation chain reduces to its own inputs, so no circular step can be identified with a specific quotation.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical entities or theoretical constants. Its central claim rests on measurement and modeling assumptions: the joint angle estimates, the range-based predictor definition, and the independence of trials. The only hand-chosen analysis parameters are the normalization and the stepwise stopping rule.

free parameters (2)

Stepwise elimination significance threshold = p < 0.05
Hand-chosen stopping rule for backward elimination; determines which joints are retained in the final model and thus which joints are reported as significant.
Min-max normalization bounds = [0,1] per participant per joint
Predictors are rescaled to each participant's own minimum and maximum range; this transformation affects coefficient sizes and the joints selected.

assumptions (3)

domain assumption Vicon marker trajectories and the model-based inverse kinematics software produce accurate joint angle estimates without ground-truth validation in this dataset.
All hand and upper-limb predictors are computed from these estimates; the paper does not report validation against measured joint angles, and the elbow joint center is explicitly acknowledged as an approximation in Section 4.
domain assumption Each trial is an independent observation in the OLS regressions.
Section 3 pools trials from 18 participants with 8 repetitions per condition and no random effects for participant; correlated residuals would invalidate p-values and significance claims.
domain assumption The max-minus-min joint angle range over the rotation segment is a sufficient summary of a joint's contribution to the final rotation and overshoot.
Section 2.6 defines delta as max(Theta_start:end) minus min(Theta_start:end); this ignores timing, direction, velocity, and repeated reversals within a trial.

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Cite this review

Pith. "Pith review of Motion Analysis of Upper Limb and Hand in a Haptic Rotation Task." pith.science (2026). https://pith.science/paper/QD2E4A7A

@misc{pith2026241112765,
  author       = {Pith},
  title        = {Pith review of: Motion Analysis of Upper Limb and Hand in a Haptic Rotation Task},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QD2E4A7A}},
  note         = {Machine review of arXiv:2411.12765}
}

read the original abstract

Humans seem to have a bias to overshoot when rotating a rotary knob blindfolded around a specified target angle (i.e. during haptic rotation). Whereas some influence factors that strengthen or weaken such an effect are already known, the underlying reasons for the overshoot are still unknown. This work approaches the topic of haptic rotations by analyzing a detailed recording of the movement. We propose an experimental framework and an approach to investigate which upper limb and hand joint movements contribute significantly to a haptic rotation task and to the angle overshoot based on the acquired data. With stepwise regression with backward elimination, we analyze a rotation around 90 degrees counterclockwise with two fingers under different grasping orientations. Our results showed that the wrist joint, the sideways finger movement in the proximal joints, and the distal finger joints contributed significantly to overshooting. This suggests that two phenomena are behind the overshooting: 1) The significant contribution of the wrist joint indicates a bias of a hand-centered egocentric reference frame. 2) Significant contribution of the finger joints indicates a rolling of the fingertips over the rotary knob surface and, thus, a change of contact point for which probably the human does not compensate.

Figures

Figures reproduced from arXiv: 2411.12765 by the authors.

**Figure 1.** Grasping orientations from the top view. T denotes the thumb’s position and IF the index finger’s position on the rotary knob. Participants were standing, in relation to the figure’s displayed top view, below the figure. This publication evaluates the results of five experimental conditions. Since this experiment had a within-subjects design, all participants executed all experimental conditions. In all five experi… view at source ↗

**Figure 2.** Schematic representation of the upper limb and hand model used in this work to estimate joint angles based on motion data. The upper limb model was created based on the example in [14]. The hand model is part of the automatic motion tracking software [7] and was created by [9]. Hand To accurately estimate the joint angles of the hand from typically incomplete motion data provided by VICON, we used the motion tracki… view at source ↗

**Figure 3.** Standardized body posture from the side and top view [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: Schematic representation of finger joint movements. Top: Movements of the thumb. Bottom: Movements of the index finger [PITH_FULL_IMAGE:figures/full_fig_p015_4.png]

**Figure 5.** Figure 5: Illustration of the rolling mechanism. T denotes the thumb and IF the index finger. The column top view, row start position shows with the blue and red dot the initial contact point between the fingers and the rotary knob. The row end position shows the fingers after t…

**Figure 6.** Figure 6: Example trajectories for one trial executed by participant 10. The x-axis interval between the vertical black lines corresponds to the rotation phase. The interval between two vertical black lines shows the estimated time span of the rotation movement [PITH_FULL_IMAGE…

**Figure 7.** Figure 7: Vicon marker placement on upper limb and hand. Markers grouped together to models and used by motion tracking software are marked [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]

**Figure 8.** Figure 8: Schematic description of upper limb joint angle estimation. The geometrical constructions using the measured participants’ shoulder width permit to recover the shoulder to shoulder axis (s4s0) from the 3 markers in the shoulder model, implicitly creating the shoulder f…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

motionless knobs

De Pra, Y., Fontana, F., Järveläinen, H., Papetti, S., Bianchi, M., Sonego, M.: Evaluation of rotation gestures in rotary vs. motionless knobs. In: 2022 IEEE Haptics Symposium (HAPTICS). pp. 1–6. IEEE (2022)

work page 2022

[2] [2]

Gurari,N.,Okamura,A.M.:Humanperformanceinaknob-turningtask.In:Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (WHC’07). pp. 96–101. IEEE (2007)

work page 2007

[3] [3]

Krieger, K., Moringen, A., Kappers, A.M., Ritter, H.: Influence of shape elements onperformanceduringhapticrotation.In:Haptics:Science,Technology,andAppli- cations: 11th International Conference, EuroHaptics 2018, Pisa, Italy, June 13-16, 2018, Proceedings, Part I 11. pp. 125–137. Springer (2018)

work page 2018

[4] [4]

In: 2019 IEEE World Haptics Conference (WHC)

Krieger, K., Moringen, A., Ritter, H.: Number of fingers and grasping orientation influence human performance during haptic rotation. In: 2019 IEEE World Haptics Conference (WHC). pp. 79–84. IEEE (2019)

work page 2019

[5] [5]

IEEE Transactions on Neural Systems and Rehabilitation Engineering15(3), 356–366 (2007)

Lambercy, O., Dovat, L., Gassert, R., Burdet, E., Teo, C.L., Milner, T.: A haptic knob for rehabilitation of hand function. IEEE Transactions on Neural Systems and Rehabilitation Engineering15(3), 356–366 (2007)

work page 2007

[6] [6]

KI-Künstliche Intelligenz24, 287–294 (2010)

Maycock, J., Dornbusch, D., Elbrechter, C., Haschke, R., Schack, T., Ritter, H.: Approaching manual intelligence. KI-Künstliche Intelligenz24, 287–294 (2010)

work page 2010

[7] [7]

In: 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids)

Maycock, J., Rohlig, T., Schroder, M., Botsch, M., Ritter, H.: Fully automatic optical motion tracking using an inverse kinematics approach. In: 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids). pp. 461–466. IEEE (2015)

work page 2015

[8] [8]

Moringen, A., Krieger, K., Kõiva, R., Ritter, H.: Haptic interface twister.https: //ni.www.techfak.uni-bielefeld.de/node/3573 (2017), [Accessed 21-October- 2022]

work page 2017