Abstract
Patients who have lost limb control ability, such as upper limb amputation and high paraplegia, are usually unable to take care of themselves. Establishing a natural, stable, and comfortable human-computer interface (HCI) for controlling rehabilitation assistance robots and other controllable equipments will solve a lot of their troubles. In this study, a complete limbs-free face-computer interface (FCI) framework based on facial electromyography (fEMG) including offline analysis and online control of mechanical equipments was proposed. Six facial movements related to eyebrows, eyes, and mouth were used in this FCI. In the offline stage, 12 models, eight types of features, and three different feature combination methods for model inputing were studied and compared in detail. In the online stage, four well-designed sessions were introduced to control a robotic arm to complete drinking water task in three ways (by touch screen, by fEMG with and without audio feedback) for verification and performance comparison of proposed FCI framework. Three features and one model with an average offline recognition accuracy of 95.3%, a maximum of 98.8%, and a minimum of 91.4% were selected for use in online scenarios. In contrast, the way with audio feedback performed better than that without audio feedback. All subjects completed the drinking task in a few minutes with FCI. The average and smallest time difference between touch screen and fEMG under audio feedback were only 1.24 and 0.37 min, respectively.
Highlights
Patients with paralysis and amputation are usually accompanied by loss of limb motor function
In order to solve these problems and optimize the human-computer interface (HCI) of rehabilitation and assistive machines, many researchers have begun to study HCI based on human physiological signals such as electroencephalogram (EEG), surface electromyography, electrooculography (EOG), and so on (Shin et al, 2017; Ding et al, 2019; Zhang et al, 2019; Gordleeva et al, 2020; Li et al, 2020)
Instead of limb EMG, a novel intention recognition method based on facial electromyography and the HCI based on fEMG have been paid attention and partly researched (Hamedi et al, 2011; Tamura et al, 2012; Bastos-Filho et al, 2014; Nam et al, 2014; Inzelberg et al, 2018; Kapur et al, 2018, 2020)
Summary
Patients with paralysis and amputation are usually accompanied by loss of limb motor function. In order to solve these problems and optimize the HCI of rehabilitation and assistive machines, many researchers have begun to study HCI based on human physiological signals such as electroencephalogram (EEG), surface electromyography (sEMG), electrooculography (EOG), and so on (Shin et al, 2017; Ding et al, 2019; Zhang et al, 2019; Gordleeva et al, 2020; Li et al, 2020). Instead of limb EMG, a novel intention recognition method based on facial electromyography (fEMG) and the HCI based on fEMG have been paid attention and partly researched (Hamedi et al, 2011; Tamura et al, 2012; Bastos-Filho et al, 2014; Nam et al, 2014; Inzelberg et al, 2018; Kapur et al, 2018, 2020)
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