Skill-informed Data-driven Haptic Nudges for High-dimensional Human Motor Learning

Abstract

In this work, we propose a data-driven skill-informed framework to design optimal haptic nudge feedback for high-dimensional novel motor learning tasks. We first model the stochastic dynamics of human motor learning using an Input-Output Hidden Markov Model (IOHMM), which explicitly decouples latent skill evolution from observable kinematic emissions. Leveraging this predictive model, we formulate the haptic nudge feedback design problem as a Partially Observable Markov Decision Process (POMDP). This allows us to derive an optimal nudging policy that minimizes long-term performance cost, implicitly guiding the learner toward robust regions of the skill space. We validated our approach through a human-subject study ($N=30$) using a high-dimensional hand-exoskeleton task. Results demonstrate that participants trained with the POMDP-derived policy exhibited significantly accelerated task performance compared to groups receiving heuristic-based feedback or no feedback. Furthermore, synergy analysis revealed that the POMDP group discovered efficient low-dimensional motor representations more rapidly.

Figures (7)

• Figure 1: Target Capture Game: (a) shows a participant playing our target capture game with the SenseGlove DK1 exoskeleton strapped to their right hand. (b) and (c) show the cursor trajectories during the first and eighth blocks of the game with the red dots representing the target locations. The trajectories get straighter as the participant learns to control the cursor by the end of the experiment session. (d) shows how the performance metrics, Reaching Error and Straightness of Trajectory, are computed.
• Figure 2: Simplified IOHMM: Human motor learning behavior modeled as an IOHMM shows how the hidden motor skill state $h_k$ transitions to $h_{k+1}$ under the effect of the input $\bf{x}_k = \{u_k: \texttt{slope}_k, a_k: \texttt{nudge}_k\}$, leading to the emissions $o_k = \{\texttt{RE}_k, \texttt{SoT}_k\}$ under the influence of the same input $\bf{x}_k$.
• Figure 3: Estimated Behavioral Model: (a) Predicted RE and SoT means out of the emission model for the ordered skill states, and (b) State Transition Matrices extracted from the estimated human motor learning behavioral model show how participants tend to transition from low latent skill states to high latent skill states under the effect of the inputs $u_k = -1.57, -1.11$ and $a_k = {0,\cdots,5}$. The latent skill states are ordered best-to-worst $(0-6)$ based on their mean output $\texttt{RE}$ emission values. The $\texttt{nudge} = 0$ value represents no nudging.
• Figure 4: Task Performance: (a) Mean SoT curves (with $95\%$ confidence intervals) of the three group participants, and (b) the mean number of trials (along with the scatter plot taken across participants) to converge to a specific SoT threshold under the three nudging policies shows fastest convergence for participants trained on POMDP-based optimal nudging policy, followed by the heuristic policy. The low p-values from two-tailed tests from the linear mixed model fits show a decrease in SoT across trials and in the number of trials required by the POMDP group for various SoT thresholds. Each scatter plot point represents the number of trials taken by a participant to achieve the SoT threshold.
• Figure 5: Motor Output Performance: (a) Mean RE curves (with $95\%$ confidence intervals) of the three group participants, and (b) the mean number of trials (along with the scatter plot taken across participants) to converge to different RE thresholds under the three nudging policies shows fastest convergence for participants trained on POMDP-based optimal nudging policy. The low two-tailed test p-values from the linear mixed model fits show a decrease in RE across trials and in the number of trials required by the POMDP group for all RE thresholds. Each scatter plot point represents the number of trials taken by a participant to achieve that RE threshold.