Eyes-Free Vision: 4D Human-Scene Understanding Using Wearable IMU Sensors

This article proposes the IMU-to-4D framework, which innovatively applies large language models to wearable IMU sensor data to achieve vision-free 4D human motion and scene structure reconstruction. This framework addresses the limitations of visual perception in privacy, energy consumption, environmental adaptability, etc., and shows great potential in privacy-sensitive scenarios (e.g., home health monitoring) and VR/AR fields.

Challenges of Visual Perception

Visual perception faces issues such as privacy leakage risks (banned in sensitive scenarios), high energy consumption and computing costs, and poor deployment scalability (affected by lighting/occlusion).

Advantages and Limitations of IMUs

IMUs (Inertial Measurement Units) are small, low-power, privacy-friendly (only capture motion), and not affected by the environment. However, traditional methods have weak generalization capabilities and are difficult to directly reconstruct poses and scenes.

Core Design

1. IMU Tokenization: Convert continuous IMU data into discrete tokens while preserving temporal features;
2. Spatio-Temporal Encoder: Transformer extracts motion features and fuses multi-source sensor information;
3. 4D Decoder: Autoregressively generates 3D human poses, temporally coherent sequences, and rough scene structures;
4. Physical Constraint Integration: Ensures physical plausibility of results through constraints like bone length and joint angles.

Datasets and Metrics

Using datasets like AMASS and HPS, evaluate pose accuracy (MPJPE), temporal consistency, scene understanding, and action recognition.

Key Results

• Pose reconstruction accuracy is comparable to state-of-the-art methods (using only 4-6 IMUs);
• Temporal stability is better than cascaded methods;
• Can infer rough scene structures (e.g., ground plane, obstacles);
• Good cross-dataset generalization ability.

Limitations of Traditional Methods

Cascaded architectures have issues like error accumulation, lag, and simplified patterns.

Advantages of IMU-to-4D

End-to-end generative framework, jointly optimizes pose and temporal sequence, uses LLM priors to solve underdetermined problems, resulting in more coherent and natural outcomes.

Conclusion

This framework achieves non-visual 4D perception, is privacy-friendly and has excellent performance, providing a new direction for intelligent perception.

Application Scenarios

Privacy-sensitive health monitoring, VR/AR pose tracking, sports rehabilitation analysis, smart home context awareness, industrial safety monitoring.

Future Directions

• Improve sensor configuration flexibility;
• Solve IMU long-term drift issues;
• Achieve fine-grained scene reconstruction;
• Personalize adaptation to user motion patterns;
• Optimize real-time inference performance;
• Explore multimodal fusion (IMU + audio + visual snapshots).