Real-time walking gait terrain classification from foot-mounted Inertial Measurement Unit using Convolutional Long Short-Term Memory neural network

作者：

Highlights：

• Real-Time Gait terrain classification from foot IMU in <135ms after push-off.

• Capability to timely detect transitions between terrains in midswing.

• Subject-independent classifier.

• Invariant to sensor orientation.

• Shallow CNN-LSTM network suitable for implementation in low-cost hardware.

摘要

•Real-Time Gait terrain classification from foot IMU in <135ms after push-off.•Capability to timely detect transitions between terrains in midswing.•Subject-independent classifier.•Invariant to sensor orientation.•Shallow CNN-LSTM network suitable for implementation in low-cost hardware.

论文关键词：Convolutional Neural Network,Long Short-Term Memory,Gait terrain,Real-time classification,Inertial Measurement Unit

论文评审过程：Received 9 August 2021, Revised 16 February 2022, Accepted 23 April 2022, Available online 10 May 2022, Version of Record 14 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117306