Clinical Features to Predict the Use of a sEMG Wearable Device (REMO®) for Hand Motor Training of Stroke Patients: A Cross-Sectional Cohort Study

Giorgia Pregnolato, Daniele Rimini, Francesca Baldan, Lorenza Maistrello, Silvia Salvalaggio, Nicolò Celadon, Paolo Ariano, Candido Fabrizio Pirri, Andrea Turolla, Giorgia Pregnolato, Daniele Rimini, Francesca Baldan, Lorenza Maistrello, Silvia Salvalaggio, Nicolò Celadon, Paolo Ariano, Candido Fabrizio Pirri, Andrea Turolla

Abstract

After stroke, upper limb motor impairment is one of the most common consequences that compromises the level of the autonomy of patients. In a neurorehabilitation setting, the implementation of wearable sensors provides new possibilities for enhancing hand motor recovery. In our study, we tested an innovative wearable (REMO®) that detected the residual surface-electromyography of forearm muscles to control a rehabilitative PC interface. The aim of this study was to define the clinical features of stroke survivors able to perform ten, five, or no hand movements for rehabilitation training. 117 stroke patients were tested: 65% of patients were able to control ten movements, 19% of patients could control nine to one movement, and 16% could control no movements. Results indicated that mild upper limb motor impairment (Fugl-Meyer Upper Extremity ≥ 18 points) predicted the control of ten movements and no flexor carpi muscle spasticity predicted the control of five movements. Finally, severe impairment of upper limb motor function (Fugl-Meyer Upper Extremity > 10 points) combined with no pain and no restrictions of upper limb joints predicted the control of at least one movement. In conclusion, the residual motor function, pain and joints restriction, and spasticity at the upper limb are the most important clinical features to use for a wearable REMO® for hand rehabilitation training.

Keywords: hand gesture; myoelectric control; neurological rehabilitation; surface electromyography; upper extremity; wearable technology.

Conflict of interest statement

Author Mrs Francesca Baldan is employed by FisioSPORT Terraglio S.r.l. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationship that could lead to potential conflict of interest.

Figures

Figure 1
Figure 1
REMO® Test Setting. (a) The patient wears REMO® on the paretic forearm while seated at the height-adjustable ergonomic table. In this figure, the patient is performing an example of exercise for hand motor training. (b) Graphical user interface displaying real-time surface-electromyography (sEMG) amplitude on a radar graph with the list of movements to be tested and control buttons for sEMG-biofeedback training. The bar on the screen is the feedback provided to the patient referred to as the level of Contractio Ratio (CR).
Figure 2
Figure 2
The ten movements tested by REMO® In The Experimental Setting. Thumb abduction (a), pinch (b), fingers flexion (c), fingers extension (d), wrist flexion (e), wrist extension (f), pronation (g), supination (h), radial deviation (i), and ulnar wrist deviation (j).
Figure 3
Figure 3
Example of radar graph representation of ten movements’ muscle activations of two patients (Blue and Red line). Each graph is scaled according to the maximum channel output of the two patients.
Figure 4
Figure 4
Area under the Curve (AUC) graphs for classification accuracy. (a) 0 movements controlled (GLM0); (b) up to 5 movements controlled (GLM5); (c) up to 10 movements controlled (GLM10); (d) k-means classification (GLMk).
Figure 5
Figure 5
Subjects classification (K-Means) according to the number of movements tested. In the picture, X-signs correspond to k centroid of each cluster. Two clusters are shown with white diamonds and black filled points.

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