Edge Computing for Environment-Based Locomotion Modes Prediction and Terrain Features Calculation in Lower Limb Prostheses

Wearable lower-limb prostheses benefit from environment-aware control, yet strict on-device constraints (size and compute) make real-time perception and decision-making challenging. We present an edge-computing framework that integrates two inertial measurement units with an event-triggered array LiDAR sensor to enable synchronous locomotion modes prediction and terrain features calculation. Joint kinematics from the IMUs dynamically trigger LiDAR scans at critical gait phases, reducing LiDAR active sensing time while preserving forward terrain information for decision-making. The complete pipeline is deployed on an embedded platform with deterministic real-time operation. In experiments with six healthy participants and two hip-disarticulation amputees across five terrains in indoor and outdoor routes, the system achieved an overall locomotion-mode prediction accuracy of 97.85% (98.80% indoor, 96.90% outdoor) and 4.87% mean relative error for terrain features calculation. The end-to-end decision latency was under 11.5 ms, supporting low-latency prosthetic control.