Pedestrian State Prediction System and Method Using Domain Adaptation Techniques and Flexible Time Windows

Domain Adaptation-Based Gait State Prediction Technology with Enhanced Accuracy

This technology relates to a gait state prediction system and method using domain adaptation techniques and flexible time windows, enabling highly accurate estimation of gait state variables despite individual differences in walking patterns.

Existing gait state prediction models suffer from a sharp decline in accuracy when users or environments change, and their fixed time windows limit their ability to adapt flexibly to variations in walking speed.

By integrating domain adaptation algorithms with flexible time window techniques, this technology ensures robust predictive performance against individual differences and speed variations. It can be applied to gait rehabilitation, wearable robot control, and healthcare monitoring.

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Key Features:

• A database that stores motion signal values and ground truth gait state variables from previous test subjects.
• A motion signal measurement unit composed of inertial sensors that measures motion signal values, such as the thigh angle and angular velocity, of new test subjects.
• A feature extraction unit that extracts the characteristic factors used for gait prediction from the motion signal values of new test subjects.
• A gait state variable prediction unit that uses stored data as source data and extracted characteristic factors as target data to perform predictions via domain adaptation techniques.

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This invention was developed with support from the Korea Forest Service’s project for developing deep learning-integrated smart wearable suits to assist muscle strength, prevent injuries, and improve work efficiency for forestry workers; the Ministry of Science and ICT’s Zero-Power Human Augmentation Basic Research Laboratory; and the development of deep learning-based tactile/texture analysis and tactile-feedback augmented prosthetic hands using flexible artificial neural patches.