A comparative analysis of three non-invasive human-machine interfaces for the disabled

Abstract

In the framework of rehabilitation robotics, a major role is played by the human-machine interface (HMI) used to gather the patient's intent from biological signals, and convert them into control signals for the robotic artifact. Surprisingly, decades of research have not yet declared what the optimal HMI is in this context; in particular, the traditional approach based upon surface electromyography (sEMG) still yields unreliable results due to the inherent variability of the signal. To overcome this problem, the scientific community has recently been advocating the discovery, analysis, and usage of novel HMIs to supersede or augment sEMG; a comparative analysis of such HMIs is therefore a very desirable investigation. In this paper, we compare three such HMIs employed in the detection of finger forces, namely sEMG, ultrasound imaging, and pressure sensing. The comparison is performed along four main lines: the accuracy in the prediction, the stability over time, the wearability, and the cost. A psychophysical experiment involving ten intact subjects engaged in a simple finger-flexion task was set up. Our results show that, at least in this experiment, pressure sensing and sEMG yield comparably good prediction accuracies as opposed to ultrasound imaging; and that pressure sensing enjoys a much better stability than sEMG. Given that pressure sensors are as wearable as sEMG electrodes but way cheaper, we claim that this HMI could represent a valid alternative/augmentation to sEMG to control a multi-fingered hand prosthesis.

Keywords: force control; human–machine interaction; incremental learning; machine learning; pressure sensing; rehabilitation robotics.

Figure 1

HMI devices used: (A) customized arrangement of FSR housed in a semi-rigid bracelet; (B) ten sEMG electrodes arranged on a strip of bio-compatible self-adhesive tape; (C) ultrasound transducer fixed to a custom-made cradle.

Figure 2

Experimental setup comprising HMIs on the forearm and the finger tips on the FFLS. (A) FSR bracelet and sEMG band; (B) FSR bracelet and US transducer applied to the forearm.

Figure 3

Prediction accuracy (nRMSE) obtained by each HMI for each degree of freedom considered, during the high-forces experiment (stimulus at 80% of the maximum voluntary contractions). (A) Error obtained when the FFLS data are used as ground truth; (B) error obtained when the stimulus is used as ground truth. The legend denotes, in turn, sEMG (EMG), the two FSR sessions (FSR1, FSR2) and ultrasound imaging (US). Bars and stems denote average nRMSE values across ten subjects, plus/minus one standard error of the mean.

Figure 4

Prediction accuracy (nRMSE) obtained by each HMI for each degree of freedom considered, during the low-forces experiment (stimulus at 15% of the maximum voluntary contractions). (A) Error obtained when the FFLS data are used as ground truth; (B) error obtained when the stimulus is used as ground truth. The legend denotes, in turn, sEMG (EMG), the two FSR sessions (FSR1, FSR2) and ultrasound imaging (US). Bars and stems denote average nRMSE values across ten subjects, plus/minus one standard error of the mean.

Figure 5

Prediction accuracy (nRMSE) obtained by FSR (A) and sEMG (B) for each degree of freedom considered, during the high-forces experiment (stimulus at 80% of the maximum voluntary contractions); the system was trained on the first repetition; the graph shows the error obtained while testing on repetitions #2, #3, #4, and #5. The FFLS data are used as ground truth.

Figure 6

Prediction accuracy (nRMSE) obtained by FSR (A) and sEMG (B) for each degree of freedom considered, during the low-forces experiment (stimulus at 15% of the maximum voluntary contractions); the system was trained on the first repetition; the graph shows the error obtained while testing on repetitions #2, #3, #4, and #5. FFLS data are used as ground truth.