From Touch to Movement: Reconstructing Human Motion from Interaction Data

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Author: Denis Avetisyan

Researchers have developed a new technique to synthesize realistic human movements directly from user interaction logs, offering a powerful tool for understanding and improving user experience.

The system formulates motion synthesis as a Partially Observable Markov Decision Process (POMDP) and integrates a physics simulator to capture biomechanical and display dynamics, enabling the learning of plausible movement policies for dexterous touch interactions through reinforcement learning from sequences of events-whether user-driven or expert demonstrations-thereby addressing the inevitable decay of predictable control in complex systems.
The system formulates motion synthesis as a Partially Observable Markov Decision Process (POMDP) and integrates a physics simulator to capture biomechanical and display dynamics, enabling the learning of plausible movement policies for dexterous touch interactions through reinforcement learning from sequences of events-whether user-driven or expert demonstrations-thereby addressing the inevitable decay of predictable control in complex systems.

Log2Motion utilizes biomechanical forward simulation and reinforcement learning to generate plausible movements from interaction data.

Despite the wealth of data captured from everyday touchscreen interactions, understanding the underlying biomechanics of user movement remains a significant challenge. This paper introduces Log2Motion: Biomechanical Motion Synthesis from Touch Logs, a novel approach that synthesizes plausible human motion directly from interaction logs using reinforcement learning-driven musculoskeletal forward simulation. By bridging the gap between digital input and biomechanical realism, Log2Motion generates detailed estimates of motion, speed, and effort, offering new insights into user ergonomics and motor control. Could this method unlock a deeper understanding of human-computer interaction and ultimately lead to more intuitive and efficient interfaces?

The Fragility of Simulated Motion

The synthesis of human-computer interaction frequently depends on overly simplified models that struggle to replicate the subtlety of natural human movement. Current techniques often prioritize computational efficiency over biomechanical accuracy, resulting in interactions that, while functional, lack the fluidity and responsiveness characteristic of genuine human behavior. This simplification manifests in several ways, from the limited range of motion permitted by digital avatars to the stilted transitions between actions. Consequently, synthesized movements can appear robotic or unnatural, diminishing the sense of presence and immersion in virtual environments and hindering the development of truly intuitive interfaces for robotics and teleoperation. Capturing the full spectrum of human motion – the micro-adjustments, anticipatory gestures, and subtle variations – remains a significant challenge in creating believable and engaging interactive experiences.

Current methods often fall short when attempting to transform recorded human interactions – the raw data of movement – into convincingly realistic animations or robotic actions. This translation process proves challenging because simply replicating logged positions and timings doesn’t account for the subtle biomechanical forces and adjustments inherent in natural motion. Consequently, digitally created avatars can appear stiff or unnatural, lacking the fluidity of human movement, and robots controlled by these systems struggle to perform tasks with the same grace and adaptability. This limitation significantly impacts fields reliant on seamless human-machine interaction, from creating immersive virtual reality experiences to developing assistive technologies and intuitive robotic companions that respond to human cues in a believable way.

The creation of truly natural human-computer interaction is significantly hampered by the difficulty in replicating the complex biomechanics underlying human movement. Current systems often treat the human body as a simplified kinematic chain, neglecting crucial factors like muscle co-activation, joint compliance, and the interplay between nervous system control and physical inertia. This simplification results in synthesized motions that appear robotic and unnatural, lacking the subtle variations and fluid transitions characteristic of real human behavior. Without accurately modeling these biomechanical factors – including the forces generated by muscles, the elasticity of tendons, and the impact of gravity – synthesized movements often fail to convey the intended emotion or action convincingly, limiting the effectiveness of applications reliant on believable avatar control or intuitive robotic assistance.

Log2Motion bridges interaction logs with corresponding human movements by combining a software emulator and musculoskeletal simulation, allowing for the evaluation of performance metrics like speed, accuracy, and effort.
Log2Motion bridges interaction logs with corresponding human movements by combining a software emulator and musculoskeletal simulation, allowing for the evaluation of performance metrics like speed, accuracy, and effort.

Biomechanical Synthesis: Reconstructing the Echo of Action

Log2Motion reconstructs human movement by employing biomechanical forward simulation, a process where touch logs – abstract records of user input – are translated into physically plausible motion. This is achieved by interpreting touch data as forces applied to a simulated musculoskeletal system, effectively reversing the typical motion-to-input pipeline. The system calculates the resulting dynamics – positions, velocities, and accelerations of the simulated body – based on these applied forces and the physical properties of the biomechanical model. This approach differs from traditional animation techniques by grounding movement in physics, resulting in more natural and reactive behaviors even with limited or noisy input data.

Log2Motion employs the MuJoCo physics engine to simulate human musculoskeletal dynamics, utilizing a biomechanical model comprising 72 degrees of freedom. This model incorporates representations of muscles, tendons, and skeletal structures, allowing for the computation of forces and torques generated during movement. MuJoCo facilitates forward dynamics simulations, taking joint torques as input and calculating the resulting motion of the simulated human body. The biomechanical model is parameterized using anatomical data, ensuring realistic limb lengths, joint ranges of motion, and muscle attachment points, which are critical for accurately recreating human movement from interaction logs.

The ‘Screen Mirror’ technique addresses the challenge of synchronizing emulated visual feedback with the biomechanical simulation. This is achieved by rendering the emulator’s display state as a visual element within the MuJoCo simulation environment. Specifically, the emulator’s frame buffer is captured and presented as a texture applied to a virtual screen in the simulation. This allows the simulated agent to “see” what the emulated game character sees, establishing a direct perceptual link and ensuring that the simulated musculoskeletal responses are driven by consistent visual input. The technique relies on real-time capture and rendering, minimizing latency to maintain the illusion of simultaneous action and reaction between the virtual and simulated environments.

Log2Motion successfully generates human-like pointing trajectories, as demonstrated by normalized Dynamic Time Warping (DTW) distances between synthesized and participant trajectories falling within the range of natural human variability.
Log2Motion successfully generates human-like pointing trajectories, as demonstrated by normalized Dynamic Time Warping (DTW) distances between synthesized and participant trajectories falling within the range of natural human variability.

Validating Realism: The Metrics of Believable Motion

Log2Motion generates movements assessed as biomechanically plausible through the quantification of ‘Effort Cost’. This metric correlates movement speed with required muscular exertion; accurate movements synthesized by the method demonstrate an effort cost of 0.31, while faster movements require an effort cost of 0.65. This data indicates a measurable trade-off between movement velocity and the biomechanical effort required to execute the motion. The synthesized movements also exhibit a low error rate, achieving 0.5% accuracy for 4mm diameter buttons and 1.5% for 10mm diameter buttons, further supporting the biomechanical validity of the generated motion.

Log2Motion incorporates biomechanical constraints during motion synthesis, resulting in movements that reflect realistic human capabilities. The method models factors such as muscle activation and joint limits, preventing the generation of physically implausible trajectories. This is achieved by internally representing and respecting the boundaries of human motor control, meaning synthesized movements avoid unnatural accelerations, jerk, or positions. Consequently, the generated motions exhibit subtle variations consistent with the natural biomechanical noise present in human movement, rather than producing perfectly uniform or robotic actions.

Log2Motion improves upon traditional interaction models such as Fitts’ Law by integrating biomechanical considerations into the performance assessment. While Fitts’ Law predicts movement time based on target distance and size, Log2Motion accounts for factors like muscle effort and natural biomechanical constraints. This extension allows for a more nuanced understanding of human movement during interaction tasks, moving beyond purely spatial metrics to include the physiological cost of movement. By incorporating biomechanical factors, Log2Motion provides a more comprehensive model capable of predicting and interpreting interaction performance with greater accuracy and realism than prior methods.

To validate the fidelity of synthesized motions, Dynamic Time Warping (DTW) was employed to align generated trajectories with original user interaction logs. This alignment process yielded average DTW distances of 1.43 cm for movements prioritized for accuracy and 1.29 cm for movements emphasizing speed. These distances are demonstrably within the established range of natural variation observed in human-performed movements; discrepancies of this magnitude are typical when comparing interaction data from different users performing the same task. The use of DTW provides a quantifiable metric for assessing the similarity between synthesized and recorded motions, confirming the method’s ability to generate realistic and human-like movement patterns.

Analysis of synthesized movements revealed a quantifiable trade-off between movement accuracy and the associated muscle effort. Accurate movements, as generated by Log2Motion, consistently required a normalized muscle effort of 0.31. Conversely, movements prioritized for speed necessitated a higher muscle effort, averaging 0.65. This data demonstrates that increasing movement velocity directly correlates with increased physiological demand, reflecting a fundamental principle of biomechanical interaction and suggesting a necessary energetic cost associated with rapid, targeted actions.

Evaluation of synthesized movement accuracy, measured via interaction with virtual buttons, indicates an error rate of 0.5% when targeting a 4mm diameter button. Increasing the target size to 10mm results in a slightly higher error rate of 1.5%. These error rates reflect the precision of the generated movements and are established through quantitative testing of the synthesized motion data against intended target acquisitions.

Comparison of participant movement trajectories with those synthesized by Log2Motion reveals that participants P1 and P6 exhibit the closest alignment to the simulation, as determined by Dynamic Time Warping (DTW) distances.
Comparison of participant movement trajectories with those synthesized by Log2Motion reveals that participants P1 and P6 exhibit the closest alignment to the simulation, as determined by Dynamic Time Warping (DTW) distances.

Beyond Mimicry: Envisioning the Future of Embodied Interaction

The creation of truly immersive virtual and augmented reality experiences hinges on the realism of the avatars inhabiting these digital spaces. Log2Motion addresses this challenge by offering a pathway to synthesize remarkably lifelike movements for these avatars. Unlike traditional methods reliant on pre-programmed animations or motion capture from specialized equipment, Log2Motion leverages data generated from everyday interactions with an ‘Android Emulator’. This innovative approach allows for the creation of avatars capable of nuanced, human-like behavior – subtle shifts in weight, variations in gait, and responsive reactions to virtual stimuli. Consequently, users can expect a more engaging and believable presence within digital environments, fostering stronger connections and more intuitive interactions with virtual worlds and the characters within them.

Log2Motion presents a novel approach to robot training, circumventing the need for physically guided demonstrations by leveraging human performance data captured within a readily accessible ‘Android Emulator’. This allows researchers to amass substantial datasets of complex task execution – everything from intricate assembly procedures to nuanced manipulation of objects – without the constraints of real-world robotics or the costs associated with extensive physical setups. The system effectively translates these emulated human actions into a format understandable by robotic systems, enabling them to learn and replicate behaviors through observation rather than direct programming. This methodology holds particular promise for automating tasks requiring dexterity and adaptability, potentially accelerating the development of robots capable of functioning effectively in dynamic and unpredictable environments.

Log2Motion distinguishes itself by moving beyond simple motion capture and instead modeling the underlying decision-making process of human movement through a Partially Observable Markov Decision Process (POMDP) framework. This allows the system to represent a ‘Motor Operator’ – the internal mechanism that selects actions based on perceived states and desired goals – and, crucially, to infer user intent even with imperfect information. By framing movement as a series of probabilistic choices within this POMDP, the system doesn’t just reproduce motion; it understands the goals behind it. This understanding is pivotal, as it enables the creation of intelligent interfaces capable of adapting to individual user preferences, correcting for errors in real-time, and even anticipating future actions, ultimately leading to a more fluid and intuitive human-computer interaction.

Researchers anticipate that integrating reinforcement learning (RL) will significantly refine the movement strategies generated by Log2Motion. This advancement aims to move beyond simply replicating human motion to actively optimizing it for various contexts. By allowing the system to learn through trial and error, RL can fine-tune synthesized motions, enhancing their efficiency, smoothness, and adaptability to unforeseen circumstances. This process will enable the creation of virtual avatars and robotic systems capable of not only mimicking human actions but also improving upon them, ultimately leading to more realistic and effective interactions in both virtual and real-world environments. The incorporation of RL promises a future where synthesized movements are not merely imitative, but intelligently adapted and demonstrably superior.

Simulating user interactions with the [latex] ext{Log2Motion}[/latex] tool on sequences from the Android-in-the-Wild dataset predicts task performance, including error rates, duration, and required effort.
Simulating user interactions with the [latex] ext{Log2Motion}[/latex] tool on sequences from the Android-in-the-Wild dataset predicts task performance, including error rates, duration, and required effort.

Log2Motion’s approach to synthesizing movement from interaction data acknowledges an inherent temporality within any system-a principle echoed by Henri PoincarĂ©, who stated, “Mathematical creation is not a laborious process of deduction, but a spontaneous act of the imagination.” The study’s reliance on biomechanical forward simulation isn’t about achieving a perfect, static model, but rather accepting that any representation of motion is a temporary approximation. Each iteration of the simulation carries the weight of prior data-the ‘past’ interaction logs-and must adapt to the unfolding dynamics. This aligns with the understanding that even the most robust systems inevitably decay, and longevity is measured not by stasis, but by graceful adaptation to changing conditions. The core idea of reconstructing plausible movements from logs inherently accepts this temporal reality.

The Echo of Movement

Log2Motion offers a compelling, if provisional, bridge between the ephemerality of interaction and the enduring constraints of biomechanics. Every failure of the synthesized motion-each jerk, each implausibility-is a signal from time, revealing the gaps in current user models and the inherent difficulty of retrofitting intention onto recorded action. The method’s strength lies in its forward simulation; yet, the true challenge resides not in generating movement, but in discerning the latent reasons for it. A complete accounting of human motion will forever remain asymptotic, a horizon receding with each refinement of the model.

Future work will inevitably address the limitations of relying solely on interaction logs. These records, however detailed, are merely shadows cast by a dynamic system. Incorporating physiological data – muscle activation, skeletal loading – will be crucial, but even this represents an incomplete picture. The system, in essence, will need to learn not just what was done, but why it was done, and, more importantly, what the user intended to accomplish beyond the immediate action.

Refactoring the system, therefore, is not merely a technical exercise, but a dialogue with the past. Each iteration should acknowledge the inherent decay of all models, recognizing that the goal is not perfection, but graceful aging. The enduring value of this work will lie in its ability to reveal, with increasing subtlety, the delicate balance between human intention and the physical realities of movement.

Original article: https://arxiv.org/pdf/2601.21043.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-30 21:33