Wearable system estimates walking phase speed and terrain type
Tread: A Visuo-Inertial Framework for Terrain and Locomotion Context-Aware Gait-Phase and Speed Estimation in Wearable Robotics
Robotics
Summary
Walking-assist robots need to know what kind of ground someone is walking on, how they walk, and how fast they move. The authors created a system that uses a small camera on the chest and sensors on the legs to guess this information accurately and quickly. Their system can tell if a person is on flat ground or stairs, predict upcoming changes in terrain, and measure walking speed and walking phases. This helps make robots that assist walking more responsive and safer.
What this means in practice
- •For wearable robotics engineers: Implement terrain-aware and gait-phase responsive control for assistive walking robots using combined camera and leg sensor data.$Commercial implications: Enables development of commercial wearable assistive robots that adapt their behavior to terrain and user gait in real time.
- •For physical rehabilitation teams: Use sensor-based gait and terrain estimation to monitor patient walking patterns and progress in therapy on various surfaces.
Authors
Debadrata Sarkar, Akash Bachhar, Aman Arora
Abstract
A wearable assistive robot must continuously estimate terrain, locomotion mode, gait phase, and walking speed. These quantities are commonly addressed by separate, terrain-specific models, with datasets rarely collected under a common protocol. This paper presents a visuo-inertial framework that connects these estimates through a causal pipeline using a chest-mounted camera and four lower-limb inertial measurement units (IMUs). Anticipatory Terrain Perception (ATP) classifies incoming terrain with 96.3% accuracy and anticipates terrain transitions by 1.46 s on average. Terrain-Aware Locomotion Mode Identification (TALMI) achieves 93.7% and 98.8% accuracy for its level-ground and stairs experts respectively, with mean transition latencies being below 200 ms. The relevant TALMI posterior is retained as a Probabilistic Locomotion Context (PLC) vector and provided to a shared Gait Kinematic Encoder (GKE), which supports 50-Hz bilateral gait-phase estimation and 2-Hz walking-speed estimation. Leave-one-subject-out evaluation yields gait-phase RMSEs of 1.75%, 2.97%, and 4.55% for level walking, stair ascent, and stair descent respectively while estimating speed over 3.5-5.5 km/h with a RMSE of 0.218 km/h. All inertial sensing based models use sensor-fused Euler angles as input rather than raw accelerometer and gyroscope signals, reducing dependence on sensor mounting variation.