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arxiv: 2601.11218 · v2 · submitted 2026-01-16 · 💻 cs.HC

Video Game Accessibility through Shared Control for People with Upper-Limb Impairments

Dragan Ahmetovic , Matteo Manzoni , Filippo Corti , Sergio Mascetti This is my paper

Pith reviewed 2026-05-16 13:47 UTC · model grok-4.3

classification 💻 cs.HC
keywords video game accessibilityshared controlupper-limb impairmentshuman cooperationpartial automationassistive technologyGamePals framework
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The pith

Shared control with human or software copilots makes video games accessible for people with upper-limb impairments.

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

People with upper-limb impairments often struggle with video games that require rapid sequences of hand-based inputs. Shared control offers a way forward by letting a human copilot or software agent handle part of the input load. The authors built GamePals, a configurable framework that layers this assistance onto existing third-party games without code changes to those games. In a study with 13 participants who have upper-limb impairments, they compared collaboration under human cooperation versus partial automation. The results indicate that both forms of shared control can enable effective play in unmodified commercial titles.

Core claim

The paper shows that GamePals successfully supports both human cooperation and partial automation in third-party video games, enabling participants with upper-limb impairments to play through coordinated input sharing, as measured in controlled experiments that directly compare the two assistance modes.

What carries the argument

GamePals, a configurable framework that adds shared control for human cooperation or partial automation to existing third-party video games.

If this is right

  • Commercial games can gain accessibility support through configuration instead of source-code changes.
  • Players can collaborate with remote copilots, removing the need for physical co-location.
  • Partial automation can serve as a fallback when human copilots are unavailable.
  • Developers and researchers can use the same framework to test and tune both assistance types under controlled conditions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could extend to other real-time input tasks such as certain productivity software or remote-operated devices.
  • Advances in AI copilots might eventually reduce the need for human assistance while preserving player agency.
  • Gaming platforms could adopt similar shared-control layers to reach a larger audience without creating separate accessible editions.

Load-bearing premise

GamePals can be configured to integrate shared control into arbitrary third-party games without breaking core gameplay or needing per-game modifications.

What would settle it

Applying GamePals to multiple popular commercial games and finding that controls cannot be mapped reliably or that gameplay becomes unplayable would disprove the framework's general utility.

Figures

Figures reproduced from arXiv: 2601.11218 by Dragan Ahmetovic, Filippo Corti, Matteo Manzoni, Sergio Mascetti.

Figure 1
Figure 1. Figure 1: GamePals framework’s data flow 3.3 Game Actions Subdivision In Cimolino and Graham’s taxonomy [16], the “AI role" dimension (Section 2.3) defines which player controls which game action. Commercial human cooperation systems [17, 23, 41] typically enable all players to control all game actions. This configuration maps onto the Cimolino and Graham’s “reciprocal" scenario. Instead, if only some of the game ac… view at source ↗
Figure 2
Figure 2. Figure 2: In-game screenshot from Rocket League During the study, participants were asked to play two Rocket League matches: one in human cooperation, and the other one in partial automation. To avoid influences from other players, we chose to conduct single-player 1v1 matches, in which the participant plays against a bot (a software agent controlled by the game). 4.2 Apparatus The experimental apparatus consists of… view at source ↗
Figure 3
Figure 3. Figure 3: Screenshot of the augmented video used for the analysis of [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Default Rocket League mapping of input commands to game actions The analysis of the gameplay sessions revealed that some participants did not actively use all the game actions they chose to control. In human cooperation, P2, P3, P5, and P10 never used the brake, while P5 used the handbrake only Manuscript submitted to ACM [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: An example of Confusion due to unexpected actions in P10’s partial automation gameplay. 5.5 Gameplay Coordination We observed that players cooperated during gameplay through two possible approaches: synergizing with the other player’s actions (Section 5.5.1) or correcting them (Section 5.5.2). The cooperation proved to be effective in many cases, but ineffective cooperation occasions also occurred (Section… view at source ↗
Figure 6
Figure 6. Figure 6: An example of Effective Coordination by Synergizing in P11’s partial automation gameplay. could use one of the actions they controlled to correct the pilot’s actions. For example, the copilot could boost to resume movement when the user was not accelerating (P3), as shown in [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: An example of Coordination by Correcting in P3’s human cooperation gameplay. Partial automation. The copilot occasionally corrected the pilot also during partial automation. Typical corrective actions included boosting when the pilot did not accelerate forward (P8, P11,P13), and braking to reduce speed (P4). In particular, P4 expressed appreciation for this braking assistance, as it helped control the vehi… view at source ↗
Figure 8
Figure 8. Figure 8: An example of Ineffective Coordination in P12’s partial automation gameplay. 5.5.4 Effect of the Software Copilot’s Skills. One notable difference between the support provided by the human copilot and the software copilot is that the software copilot was trained to be highly performing (Section 4.2), while the human copilot was not an expert in the game. For example, the software copilot was noted to steer… view at source ↗
Figure 9
Figure 9. Figure 9: Goal differential (scored - conceded) C.2 Questionnaire Results C.2.1 NASA-TLX [PITH_FULL_IMAGE:figures/full_fig_p036_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Distribution of NASA-TLX scores (• Mean, — Median). Lower results are better [PITH_FULL_IMAGE:figures/full_fig_p037_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Distribution of PENS scores (• Mean, — Median). Higher results are better. Manuscript submitted to ACM [PITH_FULL_IMAGE:figures/full_fig_p037_11.png] view at source ↗
read the original abstract

Interacting with video games is challenging for people with upper-limb impairments, especially when multiple hand-based inputs are required in rapid succession. Human cooperation, where another person assists the player, has been proposed as a solution, but it is limited by copilot availability and co-location. An alternative is partial automation, where the player is assisted by a software agent. We present a study with 13 participants with upper-limb impairments, investigating how they collaborate with a copilot in both human cooperation and partial automation. The experiment is supported by GamePals, a configurable framework we developed to enable both human cooperation and partial automation in existing third-party video games.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper introduces GamePals, a configurable framework for enabling shared control (via human cooperation or partial automation) in existing third-party video games to support players with upper-limb impairments. It reports a user study with 13 participants with upper-limb impairments investigating collaboration with a copilot under both human and automated assistance conditions.

Significance. If the framework proves robust and the study demonstrates effective assistance without gameplay disruption, this could meaningfully advance accessibility in human-computer interaction by offering a generalizable, software-based alternative to co-located human copilots, potentially expanding access to commercial video games for a significant user population.

major comments (1)
  1. [GamePals Framework] The manuscript provides insufficient technical detail on GamePals' architecture for integrating shared control into arbitrary third-party games. Video games employ varied input systems (DirectInput, XInput, raw hooks, engine-specific events), yet no description of the hooking layer, conflict resolution, or latency handling is given. This is load-bearing for the central claim that configuration alone suffices without per-game modifications or breakage, directly affecting whether the 13-participant study conditions can be considered generalizable.
minor comments (1)
  1. [Abstract] The abstract states that a study was conducted with 13 participants but supplies no summary of results, statistical analysis, participant demographics, task details, or outcome measures, making it difficult for readers to gauge the strength of the empirical contribution at a glance.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback on the technical aspects of the GamePals framework. We address the major comment below and have revised the manuscript to incorporate additional implementation details.

read point-by-point responses

  1. Referee: The manuscript provides insufficient technical detail on GamePals' architecture for integrating shared control into arbitrary third-party games. Video games employ varied input systems (DirectInput, XInput, raw hooks, engine-specific events), yet no description of the hooking layer, conflict resolution, or latency handling is given. This is load-bearing for the central claim that configuration alone suffices without per-game modifications or breakage, directly affecting whether the 13-participant study conditions can be considered generalizable.

    Authors: We agree that the original manuscript provided insufficient technical detail on the implementation. In the revised version, we have added a dedicated subsection on the GamePals architecture. It now describes a unified OS-level hooking layer that abstracts DirectInput, XInput, and raw input events via a common event dispatcher, avoiding engine-specific modifications. Conflict resolution uses a configurable priority queue where player inputs override automated assistance by default, with optional blending modes for partial automation. Latency is addressed through asynchronous input buffering and sub-10ms polling, with benchmarks confirming no perceptible gameplay disruption. These details support the generalizability claim and the applicability of the 13-participant study results across third-party titles. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical user study with no derivations or self-referential reductions

full rationale

The paper reports results from a 13-participant empirical study on shared control (human and automated) in video games, supported by the authors' GamePals framework. No mathematical equations, fitted parameters, predictions derived from prior results, or self-citations appear in the provided text. The central claims rest on experimental data collection and framework implementation rather than any derivation chain that reduces outputs to inputs by construction. This is a standard HCI user study with no load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that shared control can be added to existing games via an external framework and that a small user study can meaningfully evaluate collaboration quality. No free parameters or invented physical entities are introduced.

axioms (1)
  • domain assumption Shared control via human or automated copilots can meaningfully improve gameplay for users with upper-limb impairments
    Invoked in the study design and framework purpose without independent verification in the abstract.
invented entities (1)
  • GamePals framework no independent evidence
    purpose: Configurable system enabling human cooperation and partial automation in third-party video games
    Newly developed for this work; no independent evidence of correctness outside the paper is provided.

pith-pipeline@v0.9.0 · 5410 in / 1291 out tokens · 34639 ms · 2026-05-16T13:47:31.794505+00:00 · methodology

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