Design and Voluntary Motion Intention Estimation of a Novel Wearable Full-Body Flexible Exoskeleton Robot

Abstract EN

The wearable full-body exoskeleton robot developed in this study is one application of mobile cyberphysical system (CPS), which is a complex mobile system integrating mechanics, electronics, computer science, and artificial intelligence.

Steel wire was used as the flexible transmission medium and a group of special wire-locking structures was designed.

Additionally, we designed passive joints for partial joints of the exoskeleton.

Finally, we proposed a novel gait phase recognition method for full-body exoskeletons using only joint angular sensors, plantar pressure sensors, and inclination sensors.

The method consists of four procedures.

Firstly, we classified the three types of main motion patterns: normal walking on the ground, stair-climbing and stair-descending, and sit-to-stand movement.

Secondly, we segregated the experimental data into one gait cycle.

Thirdly, we divided one gait cycle into eight gait phases.

Finally, we built a gait phase recognition model based on k-Nearest Neighbor perception and trained it with the phase-labeled gait data.

The experimental result shows that the model has a 98.52% average correct rate of classification of the main motion patterns on the testing set and a 95.32% average correct rate of phase recognition on the testing set.

So the exoskeleton robot can achieve human motion intention in real time and coordinate its movement with the wearer.