a comparison of semg temporal and spatial information in the analysis of continuous movements

Abstract Much effort has recently been devoted to the analysis of continuous movements with the aim of promoting EMG signal acceptance in several fields of application. Moreover, several studies have been performed to optimize the temporal and spatial parameters in order to obtain a robust interpretation of EMG signals. Resulting from these perspectives, the investigation of the contribution of EMG temporal and spatial information has become a relevant aspect for signal interpretation. This paper aims to evaluate the effects of the two types of information on continuous motions analysis. In order to achieve this goal, the spatial and temporal information of EMG signals were separated and applied as input for an offline Template Making and Matching algorithm. Movement recognition was performed testing three different methods. In the first case (the Temporal approach) the RMS time series generated during movements was the only information employed. In the second case (the Spatial approach) the mean RMS amplitude measured on each channel was considered. Finally, in the third case (the Spatio-Temporal approach) a combination of the information from both the previous approaches was applied. The experimental protocol included 14 movements, which were different from each other in the muscular activation and the execution timing. Results show that the recognition of continuous movements cannot disregard the temporal information. Moreover, the temporal patterns seem to be relevant also for distinguishing movements which differ only in the muscular areas they activate

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

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

Example of modules and weights resulting from the NMF analysis for flexion/extension of the index (on the left) and middle (on the right) fingers for one representative subject.

<p>(A) sEMG mean envelopes estimated on all movement cycles for each channel (black line) and their approximation obtained with three NNMF modules (gray line). Two distinct areas of activity for index and middle fingers can be identified. (B) The three sets of modules identified for this subject varying the number of modules from one to three. (C) NNMF coefficient maps for the identified modules. It is possible to observe that increasing the number of modules from one to two, it is possible to separate and highlight the areas of activity during extension (map of coefficient for module 1) and flexion (map of coefficient for module 2). By increasing the number of modules from two to three, the maps corresponding to the first two modules remain mainly unchanged while module 3 highlights some detail of sEMG activity distribution.</p

Example of the sEMG envelopes during a wrist flexion/extension task.

<p>The envelopes are shown for wrist flexion/extension with the hand in prone position (top) and in neutral position (middle). For each condition, the envelopes estimated for each movement cycle are shown superimposed (gray lines). The columns correspond to the electrode matrix columns (medio-lateral direction) while the rows corresponds to the rows of the electrode matrix (proximal-distal direction). On the bottom, in correspondence of each sEMG column, the wrist flexion/extension angle time courses for all movement cycles are represented superimposed. The black line represents the mean envelope/joint angle. The number near to each sEMG envelope is the CMC value calculated on all cycles. The envelopes show a good repeatability with CMC higher than 0.8 except for the bad channels. The missing channels are bad channels.</p

Example of sEMG areas of activity detected during the wrist flexion/extension task with the hand in neutral and prone position.

<p>Top: areas of sEMG activity identified using the set with two NNMF modules. Bottom: areas of sEMG activities identified using the set with three NNMF modules. For each area the position of the barycenter and the weight of the NNMF modules in that position are reported.</p

Movements Included In The Protocol.

<p>The Subjects Were Asked To Perform 14 Tasks, Tasks 1–4 Involving The Wrist Joint With The Hand In Two Positions (Neutral And Prone), Tasks (5–14) Involving The Fingers. ¹MCP: Metacarpophalangeal, ²PIP: Proximal Interphalangeal, And ³DIP: Distal Interphalangeal.</p><p>Movements Included In The Protocol.</p

Percentage of overlapping and distance among the barycenters for the identified areas of sEMG activity.

<p>In A) the percentage of overlapping between the area of sEMG activity identified for each finger with respect to the areas of sEMG activity identified for each one of the other fingers are shown. The percentage of overlapping is calculated with respect to the number of electrodes in both the smallest (gray) and largest (black) activity area. In B) the distance between the barycenters of the areas of sEMG activity, for all possible pairs of fingers and all modules, are shown. The values (median (25%–75%)) are reported for MCP and PIP joints.</p

Experimental setup and protocol.

<p>(A) Wearable detection system consisting of a grid of 112 silver circular electrodes (14×8, diameter: 6 mm, inter-electrode distance: 15 mm) integrated into a stretchable textile sleeve with the 14 columns of electrodes placed around the forearm circumference. (B) Approximate position of the electrode matrix on the forearm. (C) Sensorized hand and forearm. The subjects had worn the sEMG textile detection system with the first column of electrodes in correspondence of the ulna and with the more proximal electrodes at approximately 2 cm from the elbow crease. A sensorized hand glove was used to record the kinematics of the hand and of the fingers. (D) The protocol consisted in 12 different cyclic dynamic tasks involving the wrist and index, middle, ring, and little fingers (see text for details). E) One example of wrist flexion/extension with the hand in prone position.</p

Ratio between the module weights Associated To the area barycenterS in prone versus neutral position.

<p>Ratio between the module weights Associated To the area barycenterS in prone versus neutral position.</p