Gait analysis device for lower-limb exoskeleton robots and gait analysis method using the same

Lower-limb exoskeleton analysis device that predicts gait state and terrain using CNN analysis of IMU signals

This technology is an AI-based analysis system that preprocesses IMU sensor data from a lower-limb exoskeleton robot into n-channel images. It analyzes gait states using a CNN-based feature network while simultaneously transmitting feature values from intermediate convolutional blocks to a head network to predict the terrain environment (uphill/downhill/flat).

Conventional technologies require separate training for gait state determination and terrain recognition algorithms, which is time-consuming and inefficient. Furthermore, they face limitations in integrated analysis due to the difficulty of securing large-scale data samples.

This technology constructs input data by converting and stacking IMU measurements into 2D channel images and utilizes a multi-output structure based on a common feature network (convolutional blocks) to perform gait state analysis and terrain classification in parallel within a single model. Applicable to rehabilitation training, gait assistance, and medical/welfare services, it integrates gait state and terrain recognition into one model to improve analysis accuracy.

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Key Features:

• A gait analysis device for a lower-limb exoskeleton robot, comprising a head network connected to one of the convolutional blocks, which calculates the terrain environment the user is walking on based on data processed by the connected convolutional block.
• A data receiver that receives measurement data from an IMU (Inertial Measurement Unit) sensor while a user wearing a lower-limb exoskeleton robot equipped with the sensor is walking.
• A data preprocessor that generates input data by converting n features included in the measurement data into an n-channel image.
• A feature network with convolutional blocks for calculating the gait state of the exoskeleton robot from the input data.

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This invention was developed with the support of the Ministry of Science and ICT's project for developing AI/big data-based integrated gait control solutions for personalized gait support and evaluation for lower-limb exoskeleton robots.