Asynchronous Prediction of Human Gait Intention in a Pseudo Online Paradigm Using Wavelet Transform

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 28; no. 7; pp. 1623 - 1635
• Main Authors: Hasan, S. M. Shafiul, Siddiquee, Masudur R., Bai, Ou
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain computer interface; Computational neuroscience; discrete wavelet transform; EEG; Electroencephalography; empirical wavelet transform; Feature extraction; Frequency ranges; Gait; gait intention prediction; Human motion; Internet; Latency; Legged locomotion; Locomotion; majority voting; Predictions; Rehabilitation; Reliability; Sensitivity; support vector machine; Support vector machines; Testing; Walking; wavelet synchrosqueezed transform; Wavelet transforms
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2020.2998778

Prediction of human voluntary gait intention is a very significant task to ensure direct cortical control of real-life assistive technologies for locomotion rehabilitation. Neurophysiological studies provide that human voluntary gait intention is represented by slow DC potentials and power shifts in specific frequency ranges of brain wave, which can be detected 1.5- 2 seconds before the actual onset. The goal of this study was to determine whether it is possible to reliably detect the intention of voluntary gait `starting' and `stopping' intention before it takes place. A computational algorithm was designed to implement asynchronous prediction of gait intention in an offline and pseudo-online environment using support vector machine. Six healthy subjects participated in the study and performed self- paced voluntary gait cycles. A combination of advanced wavelet transform algorithms resulted in 88.23± 1.59% accuracy, 85.42± 4.03% sensitivity and 90.24± 2.78% specificity for intention of start detection and 87.04± 1.72% accuracy, 82.69± 4.13% sensitivity and 89.59± 3.04% specificity for intention to stop walking in offline testing. Additionally, the wavelet transform methods accompanied with threshold regulation and majority voting algorithm resulted in a True Positive Rate of 85.5± 5.0% and 81.2± 3.3% for `start' and `stop' prediction with 6.8± 0.7 and 9.4± 1.0 False Positives per Minute respectively in pseudo online testing. The average detection latencies were -1002 ± 603 ms and -943 ± 603 ms, respectively, for `start' and `stop' prediction. The study provides promising outcomes in terms of TPR, FP/min, and detection latency, which suggests that human voluntary gait intention can be predicted before the onset of movement.