FormCoach: AI-Powered Real-Time Coaching

• FormCoach is an interactive AI system that provides real-time, personalized exercise form corrections using synchronized vision and language analysis.
• It employs camera-based, side-by-side comparisons of user and expert performance to deliver actionable, rubric-driven feedback.
• Benchmarking shows high actionability rates yet exposes gaps with human expertise, highlighting ongoing research challenges and future innovations.

FormCoach is an interactive AI-based fitness feedback system that employs vision-LLMs (VLMs) to deliver tailored exercise form corrections in real time via camera input. Designed for at-home fitness enthusiasts, it enables side-by-side, context-aware analysis of user performance against expert references, coupled with a rubric-driven evaluation pipeline for benchmarking model progress. Through collaborative feedback mechanisms, FormCoach seeks to close the gap between AI and human-level coaching, highlighting opportunities and ongoing challenges for context-sensitive embodied AI.

1. Vision-LLM Framework

FormCoach’s core functionality is built upon state-of-the-art vision–LLMs, which jointly process visual and textual information to identify deviations in exercise performance. The operational pipeline synchronizes live user video frames with expert reference videos. The VLM receives as input $\{I_{\text{user}}(t)\}$ (user video frames), $\{I_{\text{ref}}(t)\}$ (reference video frames), and a text prompt $T$ specifying the user’s coaching goals or interests (e.g., “focus on my knee alignment”).

The system formalizes feedback generation as a conditional inference task: $$\text{Feedback} = F(\{I_{\text{user}}(t)\}, \{I_{\text{ref}}(t)\}, T)$$ or, in simplified probabilistic form: $$\text{argmax}_{\text{feedback}}\, P(\text{feedback}\ |\ I_{\text{user}}, I_{\text{ref}}, T)$$

This approach allows for nuanced comparison of a user’s biomechanics to canonical movement exemplars and context-sensitive detection of form errors, such as spinal flexion during squats or improper joint tracking during lunges.

2. Real-Time Interactive Feedback

FormCoach provides continuous, immediate feedback by processing video streams in real time. Utilizing standard consumer cameras (smartphone, webcam, or smart mirror), the system captures the user’s movement, synchronizes each frame to an expert reference, and analyzes the pair through the VLM.

Key elements of the feedback loop include:

• Display of synchronized user and expert reference video, allowing visual comparison.
• Text and optionally text-to-speech actionable corrections (e.g., "Keep your back straight") targeted to user-specified focus areas.
• Prompt-based customization of the feedback, influenced by user goals.
• Prompt engineering to elicit verification of error presence and concise, actionable correction.

This interactive regime emulates a responsive coaching session, prioritizing high-frequency, actionable communication.

3. Benchmark Dataset and Automated Evaluation

FormCoach’s evaluation protocol is anchored in a dataset of 1,700 expert-annotated pairs, sampled uniformly from approximately 10,000 options to represent 22 upper/lower/full body exercises. Source data are segmented from multiview Fit3D exercise recordings into single-repetition clips. Each user-reference video pair is annotated by experts (using Gradio as the interface) with short, actionable corrections written in under 15 words.

The automated, rubric-based evaluation pipeline leverages GPT-4.1 to assess feedback outputs according to three primary criteria:

• Accuracy (scored 1–5): Precision in identifying real form issues.
• Actionability: Clarity and practicality of prescribed corrections.
• Hallucination: Incidence of invented, unjustified feedback.

This system enables standardization and objectivity in model comparison across VLM variants and provides a uniform metric for progress tracking.

4. Benchmarking Outcomes and Human Gap

Empirical benchmarking demonstrates that the best-performing model (GPT-4.1) achieves 58.2% accuracy and 94.4% actionability, with remaining models displaying even lower accuracy and higher hallucination rates. For example, QwenVL2.5-7B and InternVL2.5 lag in both key metrics.

These results reflect substantial progress but underscore significant limitations, as expert humans recognize subtle biomechanical discrepancies, infer intent, and articulate precise, context-sensitive feedback beyond current AI capabilities. Video-only analysis faces intrinsic challenges from occlusions, viewpoint ambiguity, and phase segmentation; as a result, VLMs frequently overlook minor misalignments or misidentify non-existent errors.

5. Collaborative and Dialogic Correction Paradigm

FormCoach advances a collaborative model of exercise correction, targeting a dialogic paradigm rather than a purely instructive one. While the existing system focuses on delivering corrective prompts, the architectural vision includes bidirectional human-machine interaction, where users can query, clarify, or challenge feedback and the system adapts its analytical and instructional strategies accordingly. This suggests capacity for future dynamic, user-adaptive guidance distinct from static, prescriptive feedback.

The system’s collaborative process embodies an iterative, responsive framework whereby users’ goals and experiential feedback directly inform subsequent coaching adaptations.

6. Future Directions and Impact in Embodied AI

FormCoach identifies several pathways for enhancing embodied AI-driven coaching:

• Integration of additional sensing modalities—such as 3D joint estimation and inertial measurement units (IMUs)—to mitigate the shortcomings of video-only input.
• Augmented reality overlays via wearable devices or smart displays, enabling immediate, visually immersive feedback.
• Expansion to interactive guidance that encompasses user feedback and evolving training objectives.

These directions reflect an ambition to offer reliable, context-aware coaching accessible outside traditional gym environments. A plausible implication is increased democratization of expert feedback, potentially reducing injury risk and improving training efficacy for geographically distributed or resource-constrained users. The system’s rubric-based benchmarking pipeline and dataset serve as a foundation for ongoing research and incremental improvement in AI-driven form correction.

7. Significance and Open Challenges

FormCoach introduces a technical and methodological framework for scalable, automated form correction by unifying real-time vision-LLMing, rigorous benchmarking, and a collaborative interaction strategy. Its specificity and actionability in feedback, combined with a transparent evaluation protocol, distinguish it from prior approaches. The persistent gap relative to human-expert performance, however, identifies a fundamental challenge for embodied AI—one requiring continued advances in multimodal representation, error detection, and dialogic user adaptation. This positions FormCoach as both a practical system and a platform for further research into high-fidelity, interactive physical coaching.