Database Open Access

# Surface electromyographic signals collected during long-lasting ground walking of young able-bodied subjects

Published: March 31, 2022. Version: 1.0.0

Di Nardo, F., Morbidoni, C., & Fioretti, S. (2022). Surface electromyographic signals collected during long-lasting ground walking of young able-bodied subjects (version 1.0.0). PhysioNet. https://doi.org/10.13026/bwvb-ht51.

Di Nardo F, Morbidoni C, Cucchiarelli A, Fioretti S (2021) “Influence of EMG-Signal Processing and Experimental Set-up on Prediction of Gait Events by Neural Network,” Biomed Signal Process Control. 63: 102232. doi: 10.1016/j.bspc.2020.102232

Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

## Abstract

The present dataset is composed of long-lasting (around 5 minutes) surface electromyographic (sEMG) signals recorded from 2011 and 2018 during ground walking of 31 young (20 years < age < 30 years) able-bodied subjects in the Movement Analysis Lab, Università Politecnica delle Marche, Ancona, Italy. Underweight, overweight, and obese people (body mass index, BMI < 18.5 Kg/m2 and BMI > 25 Kg/m2) and subjects affected by any pathological condition, joint pain, or undergone orthopedic surgery are not included in the present dataset. sEMG signals are acquired from the following ten different leg muscles (five per leg): gastrocnemius lateralis (GL), tibialis anterior (TA), rectus femoris (RF), hamstrings (Ham), and vastus lateralis (VL). Synchronized footswitch and electrogoniometric data are provided in order to allow users to achieve a spatial/temporal characterization of the sEMG signals. Data have been acquired in subjects walking barefoot on level ground for around 5 min at their natural speed and pace, following an eight-shaped path which includes rectilinear segments and curves. The considerable length of the signals makes this dataset very suitable for those studies where the numerosity of the data is essential, such as machine/deep learning approaches, studies for analyzing and quantifying the variability of muscle recruitment during physiological walking, and creation of reference dataset in the characterization of pathological conditions.

## Background

Electromyography (EMG) is a widely accepted tool able to provide an essential and original contribution to the characterization of the neuromuscular system. Both fine-wire and surface EMG are adopted to quantify muscular activity during different motor tasks. However, the use of fine-wire EMG is not recommended for monitoring dynamic tasks such as walking, because it is an invasive and sometimes painful technique. On the contrary, adoption of surface electromyography (sEMG) is strongly suggested because it is non-intrusive, does not expose patients to pain or discomfort, is easily applied to the skin, and is able to capture muscle activity from a significant proportion of motor-units that are likely representative of whole muscle activity.

It is acknowledged that sEMG signals vary from subject to subject and even within the same person. In this scenario, it is important to analyze the natural variability associated with muscle activity during free walking in order to improve the interpretation of sEMG signals in both physiological and pathological conditions. It was emphasized, indeed, the significance of examining EMG variability to understand neural basis of muscle synergies, to quantify the flexibility of musculoskeletal system, and to deepen the comprehension of the processes that the neuro-muscular system uses to adjust to various mechanical conditions. This can be achieved by recording and analyzing the sEMG signal over numerous strides per subject. Although a few strides might be enough in some applications, the analysis of a larger number of gait cycles is highly recommended for analyzing the variability in muscle recruitment during walking. However, free databases which include long-duration sEMG signals during walking are very scarce.

The present project introduces a database composed of long-duration (around 5 minutes) surface EMG signals recorded from 2011 and 2018 during ground walking of 31 young able-bodied subjects in the Movement Analysis Lab, Università Politecnica delle Marche, Ancona, Italy. sEMG signals are acquired from ten different leg muscles (five per leg).

## Methods

Footswitch, electrogoniometric, and sEMG signals have been acquired in a population of 31 young able-bodied subjects at Movement Analysis Lab, Università Politecnica delle Marche, Ancona, Italy. Inclusion criteria: 20 years < age < 30 years; 18.5 Kg/m2 < body mass index, BMI < 25 Kg/m2. Exclusion criteria: pathological condition, joint pain, or having undergone orthopedic surgery.

All signals are recorded with a sampling rate of 2 kHz and a resolution of 12 bit by the multichannel recording system Step32, Medical Technology, Italy. Each subject has been instrumented with foot switches, knee electrogoniometers and sEMG probes on both lower limbs. Three footswitches (size: 11×11×0.5 mm; activation force: 3 N) have been attached beneath the heel, the first and the fifth metatarsal heads of each foot. An electrogoniometer (accuracy: 0.5°) has been attached to the lateral side of each lower limb for measuring knee joint angles in the sagittal plane.

sEMG signals have been detected with single differential probes with fixed geometry constituted by Ag/Ag-Cl disks (manufacturer: Medical Technology, size: 7×27×19 mm; electrode diameter: 4 mm; inter-electrode distance: 8 mm, gain: 1000, high-pass filter: 10 Hz, input impedance > 1.5 GΩ, CMRR > 126 dB, input referred noise ≤ 1 µVrms), and with variable geometry constituted by Ag/Ag-Cl disks (manufacturer: Medical Technology, minimum inter-electrode distance: 12 mm, gain: 1000, high-pass filter: 10 Hz, input impedance > 1.5 GΩ, CMRR > 126 dB, input referred noise ≤ 200 nVrms). sEMG signals were further amplified and low pass filtered (cut-off frequency 450 Hz) by the recording system.

Before positioning the probes, the skin was shaved, cleaned with abrasive paste, and then wet with a soaked cloth. To assure proper electrode-skin contact, electrodes were dressed in highly conductive gel. Probes with fixed geometry were applied over the gastrocnemius lateralis (GL), tibialis anterior (TA), and Hamstrings (Ham) and probes with variable geometry were applied over the rectus femoris (RF) and vastus lateralis (VL). Subjects were asked to walk barefoot on level ground for around 5 min at their natural speed and pace, following an eight-shaped path which included rectilinear segments and curves.

## Data Description

The dataset is composed of walking signals measured in 31 subjects. All signals are physical, according to definition of “physical” as adopted by PhysioNet. For each subject, two WFDB files are provided, with ".dat" and ".hea" extensions. A csv file including summary statistics of each subject is also included. All the files are in the same folder.  sEMG signals are expressed in μV; Foot-switch signal are expressed in V; and electrogoniometric signals are expressed in degrees. Footswitch and electrogoniometric data are provided in order to allow users to achieve a spatial/temporal characterization of muscular recruitment during walking. Footswitch, electrogoniometric, and sEMG signals are synchronized.

Please note when viewing the files in this project in Lightwave, by clicking the Visualize waveforms link below, the waveforms might not display cleanly. This is due to an issue in Lightwave with scaling highly variable waveforms.

## Usage Notes

The present database has already been adopted in many studies to identify the role of rectus femoris in able-bodied walking [1], to provide a statistical assessment of activation interval of ankle muscles [2], and as control group to study the variability of muscular recruitment in hemiplegic walking [3]. More recently, it has been used to feed a neural network in order to classify and predict gait events from sEMG signals [4,5] and to test the reliability of a new algorithm for muscular activation detection [6]. The considerable length of the signals makes this dataset very suitable for machine/deep learning approaches where the numerosity of the data is essential. Moreover, the dataset could be adopted to analyze and quantify the variability of muscle recruitment during physiological walking, and as reference dataset in the characterization of pathological conditions.

## Ethics

Data was collected by following the ethical principles of the Helsinki Declaration and this study was approved by the local ethics committee. All subjects provided written informed consent.

## Acknowledgements

The authors would like to thank Prof. Susanna Spinsante and Dr. Grazia Iadarola for their valuable support.

## Conflicts of Interest

The authors have no conflicts of interest to declare.

## References

1. Di Nardo F, Fioretti S (2013). “Statistical analysis of surface electromyographic signal for the assessment of rectus femoris modalities of activation during gait”. J Electromyogr Kinesiol. 23(1): 56-61. doi: 10.1016/j.jelekin.2012.06.011.
2. Di Nardo F, Ghetti G, Fioretti S (2013). Assessment of the activation modalities of gastrocnemius lateralis and tibialis anterior during gait: a statistical analysis. J Electromyogr Kinesiol. 23(6): 1428-1433. doi: 10.1016/j.jelekin.2013.05.011.
3. Di Nardo F, Spinsante S, Pagliuca C, Poli A, Strazza A, Agostini V, Knaflitz M, Fioretti S (2020). “Variability of muscular recruitment in hemiplegic walking assessed by EMG analysis”. Electronics (Switzerland). 9(10): 1572. doi: 10.3390/electronics9101572.
4. Di Nardo F, Morbidoni C, Cucchiarelli A, Fioretti S (2021) “Influence of EMG-Signal Processing and Experimental Set-up on Prediction of Gait Events by Neural Network,” Biomed Signal Process Control. 63: 102232. doi: 10.1016/j.bspc.2020.102232
5. Morbidoni C, Cucchiarelli A, Fioretti S, Di Nardo F (2019). “A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking”. Electronics. 8(8): 894. doi: 10.3390/electronics8080894.
6. Di Nardo F, Basili T, Meletani S, Scaradozzi D (2022). "Wavelet-Based Assessment of the Muscle-Activation Frequency Range by EMG Analysis." IEEE Access. 10: 9793-9805. doi: 10.1109/ACCESS.2022.3141162.

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## Files

Total uncompressed size: 427.0 MB.

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wget -r -N -c -np https://physionet.org/files/semg/1.0.0/

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