Co-Adaptive and Affective Human-Machine Interface for Improving Training Performances of Virtual Myoelectric Forearm Prosthesis

Abstract

Abstract-The real-time adaptation between human and assistive devices can improve the quality of life for amputees, which, however, may be difficult to achieve since physical and mental states vary over time. This paper presents a co-adaptive humanmachine interface (HMI) that is developed to control virtual forearm prosthesis over a long period of operation. Direct physical performance measures for the requested tasks are calculated. Bioelectric signals are recorded using one pair of electrodes placed on the frontal face region of a user to extract the mental (affective) measures (the entropy of the alpha band of the forehead electroencephalography signals) while performing the tasks. By developing an effective algorithm, the proposed HMI can adapt itself to the mental states of a user, thus improving its usability. The quantitative results from 16 users (including an amputee) show that the proposed HMI achieved better physical performance measures in comparison with the traditional (nonadaptive) interface (p-value < 0:001). Furthermore, there is a high correlation (correlation coefficient < 0:9; p-value < :01) between the physical performance measures and self-report feedbacks based on the NASA TLX questionnaire. As a result, the proposed adaptive HMI outperformed a traditional HMI.

Model and experiments to optimize co-adaptation in a simplified myoelectric control system

“…To overcome difficulties associated with establishing and maintaining an efficient decoder despite limited neural recordings and changes in signal quality, closed-loop adaptation of the decoder are increasingly studied and used to complement subjects' adaptation in brain machine interface (Iturrate et al, 2015;Dangi et al, 2013;Orsborn et al, 2012;Shenoy and Carmena, 2014). As similar issues occur for myoelectric controls, co-adaptation strategies are starting to be explored there as well (Hahne et al, 2015;Rezazadeh et al, 2012;Tuga et al, 2013). In both situations, however, the dynamics of the concurrent adaptation of the human and machine needs to be carefully considered since it is likely to be complex and to greatly impact the effectiveness of particular co-adaptation settings.…”

Concept of a Co-Adaptive Training Environment for Human-Machine Interfaces Based on EMG-Control

“…Note, that we only can vary the parameter sets p HMI and p TE . The parameter set p H represents the mental model of the HMI in the human's brain [9]. It cannot be affected directly by our adaptation.…”

“…Abundance of work has gone into the development of HMIs based on various kinds of biosignals such as hand prostheses [4,5,6]. However, comparatively little work has been done within the field of adaptive TEs and HMIs [7,8,9]. The characteristics can be adapted offline or online by a single-or multi-objective optimization.…”

Modelling and Control of a Robotic Arm Using Artificial Neural Network

“…Force plays an important role in these applications. Rezazadeh et al [1] proposed a co-adaptive Human-Machine Interface (HMI) that is developed to control virtual forearm prosthesis over a long period of operation. This paper has influenced us to make a robotic arm based on the hand EMG signal.…”