TLDR: This research introduces “Talking Tennis,” a framework that combines 3D motion capture, CNN-LSTM models for action recognition, and Large Language Models (LLMs) to provide personalized, biomechanically grounded language feedback for tennis players. It extracts key features like joint angles and limb velocities, compares them to optimal ranges, and uses an LLM to generate interpretable diagnoses and three actionable corrections, validated by expert coaches.
In the world of tennis, improving performance often relies on expert coaching that can pinpoint subtle flaws in a player’s stroke and offer actionable advice. While technology has made strides in analyzing tennis strokes, a significant gap has remained: connecting complex biomechanical data with easy-to-understand, personalized language feedback for players and coaches. A new research project, “Talking Tennis: Language Feedback from 3D Biomechanical Action Recognition,” addresses this challenge by developing an innovative framework that bridges this gap.
The core idea behind this research is to move beyond simply identifying a tennis stroke (like classifying it as a “forehand” or “backhand”) to providing detailed, corrective feedback. Imagine a system that not only tells you what stroke you just hit but also explains why it might have been ineffective and precisely how you can improve it. This is what the researchers aimed to achieve.
The framework operates in several key stages. First, it uses advanced deep learning models, specifically a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, to analyze 3D motion data. This data, sourced from the THETIS dataset which includes detailed 3D skeleton representations of players, allows the system to understand human movement in intricate detail. The CNN-LSTM model is particularly effective at recognizing fine-grained differences in tennis strokes, achieving a high accuracy of 79.17% on the THETIS dataset, outperforming previous methods.
Once the stroke is recognized, the system extracts crucial biomechanical features. These aren’t just simple measurements; they include sophisticated details like joint angles (e.g., shoulder, elbow, wrist, hip, knee during different phases of a stroke), limb velocities (of the racket, hand, forearm, etc.), racket dynamics (speed, impact detection), kinetic chain patterns (how energy flows through the body to the racket), and body rotation. These features are carefully chosen because they are known to influence stroke effectiveness and potential injury risk, based on sports biomechanics literature and discussions with tennis coaches.
The truly novel aspect comes in the next stage: using Large Language Models (LLMs) to translate these complex numerical biomechanical features into human-interpretable coaching feedback. The LLM receives a structured input that includes the predicted stroke type and a comparison of the player’s biomechanical features against optimal ranges. These optimal ranges are pre-defined based on expert knowledge, ensuring the feedback is grounded in established coaching principles.
The LLM is carefully prompted to act as a “precise, evidence-based tennis coach.” It generates feedback that includes an overall skill score (out of 10), a concise diagnostic summary explaining why the stroke was ineffective, and exactly three actionable corrections. For example, it might identify that a player’s “Rotation range is HIGH – significantly excessive” and suggest, “Reduce the backswing rotation to a more compact range, focusing on generating power through the core and legs rather than relying on excessive arm swing.”
The effectiveness of this language feedback was rigorously evaluated by three varsity tennis coaches. They rated the LLM-generated feedback highly for its interpretability (easy to understand), actionability (concrete suggestions for practice), and alignment with expert coaching strategies. The coaches’ skill scores for strokes also closely matched those generated by the LLM, indicating the system’s ability to provide relevant and consistent guidance.
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This research represents a significant step forward in automated sports analysis. By combining advanced vision-based action recognition with detailed biomechanical feature extraction and intelligent language generation, “Talking Tennis” offers a scalable path toward personalized, evidence-based athlete support. It demonstrates how embodied AI can translate complex motion data into practical, coach-level advice, with potential applications extending beyond tennis to other sports, rehabilitation, and even human-robot skill learning. You can read the full paper for more details here.
