On-board Training Strategy for IMU-Based Real-Time Locomotion Recognition of Transtibial Amputees With Robotic Prostheses

Abstract

The paper puts forward an on-board strategy for a training model and develops a real-time human locomotion mode recognition study based on a trained model utilizing two inertial measurement units (IMUs) of robotic transtibial prosthesis. Three transtibial amputees were recruited as subjects in this study to finish five locomotion modes (level ground walking, stair ascending, stair descending, ramp ascending, and ramp descending) with robotic prostheses. An interaction interface was designed to collect sensors' data and instruct to train model and recognition. In this study, the analysis of variance ratio (no more than 0.05) reflects the good repeatability of gait. The on-board training time for SVM (Support Vector Machines), QDA (Quadratic Discriminant Analysis), and LDA (Linear discriminant analysis) are 89, 25, and 10 s based on a 10,000 × 80 training data set, respectively. It costs about 13.4, 5.36, and 0.067 ms for SVM, QDA, and LDA for each recognition process. Taking the recognition accuracy of some previous studies and time consumption into consideration, we choose QDA for real-time recognition study. The real-time recognition accuracies are 97.19 ± 0.36% based on QDA, and we can achieve more than 95% recognition accuracy for each locomotion mode. The receiver operating characteristic also shows the good quality of QDA classifiers. This study provides a preliminary interaction design for human-machine prosthetics in future clinical application. This study just adopts two IMUs not multi-type sensors fusion to improve the integration and wearing convenience, and it maintains comparable recognition accuracy with multi-type sensors fusion at the same time.

Noninvasive Human-Prosthesis Interfaces for Locomotion Intent Recognition: A Review

“…They have conducted a preliminary study with an adaptive recognition system for novel users using a powered lower-limb prosthesis and achieved good effects: compared to a nonadaptive system, the adaptive system can reduce the number of errors by 32.9% [ 104 ]. Researchers have also developed the user-independent research by pooling data from a large subject group and got high accuracies of gait mode identification for a novel subject [ 24 ]. Spanias et al have compared the classification types (user-independent, partially dependent, and user-dependent) and get some results.…”

“…Figure1: The locomotion intent recognition research conducted in the structured environment based on a robotic prosthesis (adapted from[24]). …”

Task Offloading and Scheduling Strategy for Intelligent Prosthesis in Mobile Edge Computing Environment

“…Idowu et al [8] present an integrated deep learning model (deep neural networks, DNN) for motor intention recognition of multiclass signals. Deep learning inference and training require substantial computation resources to run quickly; a common approach is to leverage cloud computing [13,14]. However, sending data to the cloud for inference or training may incur additional queuing and propagation delays from the network and cannot satisfy strict end-to-end low-latency requirements.…”

A Review on Locomotion Mode Recognition and Prediction When Using Active Orthoses and Exoskeletons

“…Labarrière et al’s [ 1 ] review is centered on the sensors and machine learning classifiers used to recognize and predict LMs when using AOs, exoskeletons, and prostheses. Nonetheless, most of the studies presented in [ 1 ] were designed for assisted conditions when using prostheses [ 16 , 17 , 18 , 19 , 20 ]. According to [ 11 ], the LM recognition performed when using orthotic/exoskeleton systems is different from prostheses or conditions without a lower-limb assistive device (non-assisted conditions [ 21 , 22 , 23 , 24 , 25 , 26 , 27 ]) due to the distinct positions that the sensors can take in these situations.…”