Machine Learning for Human Motion Intention Detection

Abstract

The gait pattern of exoskeleton control conflicting with the human operator’s (the pilot) intention may cause awkward maneuvering or even injury. Therefore, it has been the focus of many studies to help decide the proper gait operation. However, the timing for the recognization plays a crucial role in the operation. The delayed detection of the pilot’s intent can be equally undesirable to the exoskeleton operation. Instead of recognizing the motion, this study examines the possibility of identifying the transition between gaits to achieve in-time detection. This study used the data from IMU sensors for future mobile applications. Furthermore, we tested using two machine learning networks: a linearfFeedforward neural network and a long short-term memory network. The gait data are from five subjects for training and testing. The study results show that: 1. The network can successfully separate the transition period from the motion periods. 2. The detection of gait change from walking to sitting can be as fast as 0.17 s, which is adequate for future control applications. However, detecting the transition from standing to walking can take as long as 1.2 s. 3. This study also find that the network trained for one person can also detect movement changes for different persons without deteriorating the performance.

A Novel Framework Based on Deep Learning Architecture for Continuous Human Activity Recognition with Inertial Sensors

“…In addition, the dataset of activities to be recognized can be enlarged so as to include other clinically relevant transitional motor actions (e.g., stand-to-walk, stand-to-sit, walkto-sit, or lying-to-sit). In so doing the proposed framework could be even addressed for evaluations of either motor or cognitive impact, such as motion intent recognition [68][69][70]. Furthermore, acquiring inertial signals during stair ambulation would offer the possibility to investigate another motor pattern in which some patients would exhibit an abnormal execution of the task due to the fear of falling [15].…”