Wearable Embroidered Muscle Activity Sensing Device for the Human Upper Leg

Wearable Embroidered Muscle Activity Sensing Device for the Human Upper Leg

R. B. Ribas Manero, J. Grewal, B. Michael, A. Shafti, K. Althoefer, Member, IEEE,
J. Ll. Ribas Fernández and M. J. Howard, Member, IEEE
R. B. Ribas Manero, J. Grewal, B. Michael, A. Shafti, K. Althoefer and M. J. Howard are with the Centre for Robotics Research at King’s College London, WC2R 2LS, London, UK - roger_bernat.ribas_manero, brendan.michael, ali.shafti, kaspar.althoefer, matthew.j.howard@kcl.ac.uk, J. Ll. Ribas Fernández is with the Human Anatomy and Embryology Unit at Universitat de Barcelona, Carrer de Casanova, 143, 08036 Barcelona, Spain - jribasfe@ub.edu
Abstract

Within the last decade, running has become one of the most popular physical activities in the world. Although the benefits of running are numerous, there is a risk of Running Related Injuries (RRI) of the lower extremities. Electromyography (EMG) techniques have previously been used to study causes of RRIs, but the complexity of this technology limits its use to a laboratory setting. As running is primarily an outdoors activity, this lack of technology acts as a barrier to the study of RRIs in natural environments. This study presents a minimally invasive wearable muscle sensing device consisting of jogging leggings with embroidered surface EMG (sEMG) electrodes capable of recording muscle activity data of the quadriceps group. To test the use of the device, a proof of concept study consisting of runners performing a set of running trials is presented in which the effect of running surfaces on muscle fatigue, a potential cause of RRIs, is evaluated. Results show that muscle fatigue can be analysed from the sEMG data obtained through the wearable device, and that running on soft surfaces (such as sand) may increase the likelihood of suffering from RRIs.

Keywords - running related injuries, running surfaces, electromyography, wearables.

I Introduction

Today, running is one of the most popular physical activities, as of Spring 2014 there were million runners in the USA accounting for a of the American population. Moreover, during the last decade, the number of runners has grown considerably [1]. The benefits of running are numerous, ranging from increased life expectancy to preventing chronic diseases or conditions (e.g., type-2 diabetes, cardiovascular disease, and obesity) [2].

However, the practice of running also gives rise to Running Related Injuries (RRI) of the lower extremities (e.g., aquiles tendinopathy, plantar fascitis, and patellar tendinopathy) [3]. The causes of RRIs are heterogeneous, and changes in the biomechanical factors of the runner (e.g., contact time, stride length) have previously been investigated [4]. However, research is still lacking in determining what role running surfaces have of changing these biomechanical factors and hence causing RRIs. Particularly, how running surfaces influence the fatigue levels of the leg muscles as a result of a running activity is not clear.

The most common method for estimating muscle fatigue, by examining the electrical activity of the skeletal muscles (known as electromyography (EMG)) is however, difficult to perform in natural running settings. Examining EMG is often done in a laboratory setting, requiring not only delicate expensive equipment, but specialised anatomical knowledge for sensor placement, and interpretation of results preventing non-specialists from using EMG in other environments.

Fig. 1: (a) participant wearing the jogging leggings with embroidered sEMG electrodes made for this study. (b) asphalt track route for participant 1. (c) and (d) top and bottom view of the embroidered sEMG electrodes respectively.

The objective of this study is to introduce a new wearable muscle sensing device consisting of jogging leggings with embroidered surface EMG (sEMG) electrodes capable of recording muscle activity data of the quadriceps group (i.e., Vastus Medialis, Vastus Lateralis, and Rectus Femoris) in a non-laboratory setting. The wearable sEMG device allows collection of long term EMG data in a great variety of environments due to its compact design. Furthermore, use of the device does not require extensive prior anatomical knowledge, as the sensing electrodes are embroidered into the garment at the correct locations, following standards set by the SENIAM project [5]. Thus, the user need not be concerned about sensor placement, and can simply pull the leggings on just like ordinary clothing.

Potential uses of EMG data collected in outdoor environments by the proposed device, include understanding power demands for bipedal robots in outdoor operation, as well as control of exoskeletons and prostheses outside of the laboratory. In this paper, the device is demonstated as a platform for monitoring muscle fatigue during endurance-based long distance () running tests on different surfaces (asphalt, sand and an athletics track). Using data from the device, the dependency between running surface and muscle fatigue is identified, whereby running on compliant surfaces with greater damping (e.g., sand) leads to the highest levels of muscle fatigue, followed by very stiff surfaces with little damping features (e.g., asphalt). These results confirm expectations about likelihood of suffering from RRIs on these different surfaces, and highlight the use of the proposed device for long term observation of behaviour. Fig. 1 illustrates a participant wearing the leggings as well as a top and bottom up of the embroidered sEMG electrodes and the route followed during the asphalt trial by one of the participants.

Ii Background and Related Work

This section provides a brief overview of the functioning of electromyography, and its use in measuring muscle fatigue. It also reviews related literature from laboratory based biomechanical studies on risk factors for RRIs.

Ii-a Methods for Electromyography

Electromyography enables the force generated by the skeletal muscles to be estimated by measuring their electrical stimulation from the central nervous system (CNS) [6], via either invasive or non-invasive techniques. In regards to the former, intramuscular EMG records activity by inserting a fine wire into the muscle and measuring muscle signals. However, while this results in a high quality signal its invasiveness makes it unsuitable for many applications, especially in wearable sensing devices.

A more suitable method is surface EMG (sEMG) where electrodes are placed on the skin surface above the target muscle. When following a bipolar configuration, pairs of electrodes are placed on top of the belly of the muscle with a   to   separation between the electrodes, and a reference electrode is placed on top of electrically neutral tissue. The difference between the signals collected by each of the two electrodes is amplified. Current commercial systems such as BTS Surface EMG (Vandrico, North Vancouver, Canada), and Tringo Wireless EMG (DelSys, massachusetts, USA) offer wireless EMG solutions. However, these present several disadvantages: (i) they are expensive (ranging from €448.00 to €15,675.00), (ii) they are bulky (reducing their portability and affecting natural user motion), and (iii) they rely on gel-based sEMG electrodes requiring anatomical knowledge for their placement and limiting their re-usability.

Ii-B Measuring Muscle Fatigue with EMG

Due to the inherent issues with collecting sEMG data with standard sensors (§II-A) the study of estimating and monitoring muscle fatigue has been limited. Muscle fatigue is a consequence of muscle activity [7], to prevent cellular or organ failure [8].

Fatiguing of the muscles influences both the amplitude and the frequency properties of EMG signals [9], and during fatigue, further muscle fibres need to be recruited (activated) in order to sustain the desired performance. This is shown as an increment of the amplitude of the EMG signal. Regarding the spectral features of the EMG signal, changes in the the firing behaviour of the motor units and the shape of the Motor Unit Action Potential (MUAP) result in a left-shifting of the power spectrum of the EMG signal. A decrease of the instantaneous mean or mean frequency of the EMG signal indicates the presence of muscle fatigue [9].

A number of different approaches have been proposed in the literature in order to elucidate muscle fatigue. These include {enumerate*}[label=(),mode=unboxed]

amplitude based parameters analysis,

spectral analysis, and

time-frequency analysis (i.e., wavelets ratios). In amplitude based analysis, the presence of muscle fatigue increases the instantaneous value of the EMG signal with time [7]. Regarding the spectral analysis, muscle fatigue results in a left-shifting of the power spectrum of the EMG signal and hence a decrement of the instantaneous mean and median frequencies is seen. Finally, for time-frequency analysis, the wavelets ratios increase their value as the muscles get fatigued. Due to the isotonic nature of running, the collected sEMG signals cannot be considered as stationary and hence only amplitude based parameters and time-frequency techniques are suitable [7].

Ii-C Muscle Fatigue as a Cause of RRIs

Among a number of other biomechanical factors, muscle fatigue has been shown to significantly increase the likelihood of injury in dynamic behaviour such as running [10]. To date, several studies have examined a number of factors in relation to this, including {enumerate*}[label=(),mode=unboxed]

training characteristics (e.g., running volume),

anthropometric factors (e.g., foot strike),

biomechanical factors (e.g., contact time),

physiological variables (e.g., lactose level), and

running-related medical history [11]. However, largely due to the technological barrier of taking measurement equipment out of the laboratory, few have examined these factors in ordinary environments, as may be used by the general running population [12]. This limits such studies to simulating the running conditions, ignoring possible contributing factors.

One such factor may be the effect of the running surface on fatigue, which is typically difficult to simulate in laboratory studies. Previous research in this area has mainly focused on how these might affect the biomechanical factors of the runner, using short () tracks [13, 4, 12]. As can be seen, despite the great amount of work in studying the biomechanics of running, and the causes of musculoskeletal injuries, there is an apparent a technological barrier in taking these studies into real world environments. In the next section, the implementation of a new textile-based EMG system is presented along with the signal processing techniques necessary for its use in estimating muscle fatigue.

Iii Implementation

To overcome the limitations of collecting EMG data outside the laboratory environment (as discussed in §II), this paper presents a new, textile-based acquisition system. Specifically, the system consists of a pair of jogging leggings with integrated sEMG electrodes, made from conductive yarns directly embroidered into the fabric.

Iii-a Design and Fabrication of the Jogging Leggings

The leggings overcome many of the technical and user-specific limitations of current sEMG monitoring devices. It includes built-in electrodes at appropriate locations such that anatomical knowledge of optimal sensor locations is not required when using the device. It is also designed in mind of portability for use in multiple environments, with built-in flexible low-weight printed circuit boards (PCBs) to acquire raw sEMG signals in conjunction with an Arduino micro-controller to digitise and store the collected data.

Iii-A1 Embroidered electrodes

The leggings make use of state-of-the-art textile sEMG sensors, recently developed in a parallel study at the Centre for Robotics Research (CORE) at King’s College London, for measuring muscle activity in the legs. As a precursor to this study, experiments were performed to elucidate the best fabrication process and design of these electrodes and characterise their behaviour for sEMG signal acquisition. Results show that similar sEMG measurements can be obtained by using conductive textile electrodes in place of gel-based disposable electrodes [14]. Comparisons between the developed stainless-steal thread-based sEMG electrodes (using conductive thread Sparkfun DEV-11791, ) and standard gel-based electrodes (Covidien Kendall Arbo H124SG) show that, while there is greater noise presence when using textile-based electrodes, muscle activity can still be observed and different levels of muscle engagement distinguished. For this paper, this technology is exploited to enable long term wearable measurement of muscle activity through an integrated textile device recording sEMG of the quadriceps muscles.

Iii-A2 Circuitry and sensor placement

In the presented system, three pairs of electrodes are embroidered onto a pair of jogging leggings using a Pfaff Creative 3.0 (Pfaff, Kaiserslautern, Germany) sewing machine based on design variables obtained in [14] and according to the indications of the SENIAM project following a bipolar configuration. The placement area is tightened using extra thick felt fabric to enhance the contact with the skin tissue. European medium (M) leggings size is selected and the in leg longitude of the participants’ leg measured to assure the location of the sensors to be the one outlined by the SENIAM project. To prevent voltage loss due to the high resistance of the conductive thread, the length of the connection lines is limited to for which voltage losses are negligible. A double stitching pattern along with a zig-zag pattern ensured robust and stretchable connection lines. The leggings are powered at using a single rechargeable battery of . Fig. 2 shows the final stages of the fabrication process. Silicone sealant (102 RTV, Hylomar) is used to isolate the connections of the power unit. Finally, a waterproof bag around the waist is used to carry the batteries whilst running.

Fig. 2: (a) the interior of the leggings. Notice how the connection lines were first sewn on top of stabilizer fabric. This type of fabric prevents the stretchy fabric of the leggings to stretch during the sewing process. (b) the exterior of the leggings. Note that the electric thread of the connection lines is sewn on top of the exterior face of the leggings in order to isolate them from the skin tissue.

Iii-A3 Data acquisition

Three custom PCBs were created to acquire the sEMG signal through each pair of electrodes. Each PCB includes an instrumentation amplifier with adjustable gain according to , a first order active high pass filter (HPF), a first order active low pass filter (LPF), and a full-wave rectifier connected in cascade. The instrumentation amplifier computes the difference between the two collected signals and amplifies it. The cut-off frequencies of the HPF and the LPF are and respectively. The final PCB is flexible, with a thickness of and weight of . An Arduino micro-controller is used to sample the signal from each PCB with a sampling frequency of and an SD shield (Adafruit, NY, USA) is used to store the collected data in a text file with a .csv format. Fig. 3 shows the circuit diagram of the PCB.

Fig. 3: Circuit diagram of the sEMG acquisition PCBs. In red the active high pass filter, in green the active low pass filter and in yellow the full wave rectifier.

Iii-B Signal Processing for Muscle Fatigue

To quantify muscle fatigue from the sEMG data collected using the sensor-embedded jogging leggings, the presented system includes a signal analysis script written in MATLAB which implements the fatigue analysis methods outlined in §II-B. The script includes computations of the instantaneous average rectified value (iARV), the instantaneous mean average value (iMAV) and the instantaneous root mean square (iRMS) value for amplitude based analysis. For the spectral analysis approach, the instantaneous mean and median frequencies, as well as the Dimitrov’s spectral fatigue index (FInsm5) [15] are implemented. For the time-frequency analysis, the instantaneous WIRM1551, WIRM1M51, and WIRM1522 wavelets are implemented. The wavelet forms to calculate the wavelet indices are chosen to be the Symlet5 (sym5) and the Daubechies5 (db5) as according to Gozalez-Izal et al.[16]. Due to the inherent characteristics of the running activity outlined in §II-B and the fact that it has been extensively used in the literature [7], the iARV signal was considered for assessing muscle fatigue. Such signal results of computing the average rectified value (ARV) value for a window, where the ARV is computed as shown in (1).

(1)

Here, are the values of the sEMG signal and the number of samples.

Iv Experiments

This section presents experimental evaluations of the proposed device. A case study in which the device is used to detect muscle fatigue in a long distance running experiment outdoors is introduced.

Iv-a Measuring Fatigue in Long Distance Running

In this section, two evaluations are described. Firstly, an assessment of the wearable sEMG monitoring jogging leggings outlined in §III is made, with an aim to investigate its suitability to natural environment motion experiments. Secondly, the sEMG data collected is analysed using the custom scripts in §III-B to evaluate how the fatigue of the quadriceps group varies depending on the running surface.

The experimental procedure is as follows. sEMG data is collected from participants (mean weight , and mean height ). For each participant, data is collected from three running trials (each of in length) on sand, asphalt, and an athletics track, where participants run at their normal training speed. Note, there is a minimum resting period between trials, to allow for muscles to recover. In accordance with the methodology outlined in §III, placement of the electrodes should not require anatomical knowledge. As such, runners were instructed to wear the leggings, and the electrodes were kept in the pre-built, standard position.

Before beginning the experiments, participant data is collected, including fitness level, age, height, weight, sex and running gear. A pre-screening test is performed to ascertain whether the participants were eligible for the experiments or not (PAR-Q&U questionnaire approved by the Canadian Society for Exercise Physiology). Ethical approval for the study was obtained prior to experiments from the King’s College London Research Ethics office 111Ethics reference number LRU-14/15-1681.

Fig. 4 shows the iARV for participant 1 whilst running on the three different surfaces. As explained in §II, the amplitude of the sEMG signal increases in presence of muscle fatigue as more muscle fibres are recruited. This phenomena is observed for the three surfaces with varying rates of increase, as the iARV value of the sEMG signal increases with time. Table 2 summarises the percentage increase of the iARV signal for each muscle whilst running on each surface between the beginning and end of the running trials. It can be seen that running on the sand surface leads to the maximum amplitude increment of the iARV signal for the three muscles whereas the athletics track led to the minimum increase after the trials. Similar results are observed for participant 2, indicating that running on sand increases the likelihood of suffering from RRIs when compared to asphalt or athletics track surfaces.

Fig. 4: iARV of the quadriceps muscles whilst running on asphalt (blue), sand (red), and the athletics track (black). Note that the iARV signal increases with time due to the effect of muscle fatigue.
Muscle Asphalt Sand Athletics Track
Vastus Medialis 100.04% 127.71% 54.9%
Rectus Femoris 100.02% 126.75% 121.22%
Vastus Lateralis 99.14% 100.07% 35.9%
TABLE I: Percentage increase of the iARV signal for each muscle whilst running on asphalt, sand and the athletics track surfaces between the beginning and end of the running trials.

Iv-B Further analysis of iARV

As shown in §IV-A, wearable jogging leggings with in-built embroidered electrodes are a practical and effective method for the collection of sEMG signals in natural environments. However, due to the uncontrolled nature of these environments, there are a number of factors that could confound the results of the proceding section. This section discusses these issues, and their likely effect on the results presented so far.

Iv-B1 Second wind effect

Looking at the signals in Fig. 4, it can be observed that the iARV is greater at the beginning of all the trials and then decreases progressively before increasing in presence of muscle fatigue. This can be explained as a result of the second wind effect, which increases the serotonin levels of the participant after the first minutes of running [17]. This results in a decrement of the fatigue levels of the muscles at the beginning of an exercise.

Iv-B2 Effect of Perspiration

The second issue relates to the effect of perspiration on the data recorded. During the course of experiments involving physical activity, the conductivity the user’s skin tissue varies linearly with the number of active sweating glands [18]. In other words, the higher the sweating levels of the participant are, the more conductive their skin will be. In the device created for this study, the conductive thread wiring is not insulated and hence the conductance of the wiring changes, altering the behaviour of the circuit by increasing the inherent offset at the output of the PCBs. This phenomena is observed for all the trials and is particularly noticeable for the Vastus group muscles. Note that, the outdoor temperature for the first two trials (asphalt and sand) on the day of collecting data was and respectively whereas was registered during the athletics track trial suggesting higher sweating levels during the last trial and, therefore, a greater percentage increase of the iARV signal. This suggests that the athletics track might result in lower fatigue levels than the ones measured. In spite of this phenomena, the classification of the surfaces remains the same as the athletics track still led to the lowest levels of muscle fatigue independently of the effect of the sweating.

V Conclusion

In this study, the design of a new minimally invasive muscle sensing device capable of recording muscle activity data in natural environments is created. The device offers wearable capabilities that allow long-term collection of data by inexperienced users. As explained in §I, changes in biomechanical factors such as muscle fatigue increase the likelihood of suffering from RRIs. A common way to study those changes is through EMG technology. However, current technology limits studies of muscle EMG to laboratory setting and requires operation by users with anatomical knowledge.

The experiments presented here show that wearable sEMG technology is feasible and that it has practical research applications. The wearable sEMG device is cheap, robust, easy to build and suitable for the every day user. As a proof of concept, runners took part in three running trials on three different surfaces (asphalt, sand and athletics track) and with sEMG data recorded using reusable fabric-based electrodes. This equipment is then used in an outdoors setting to elucidate muscle fatigue. The results showed that different levels of muscle fatigue could be detected when running on different surfaces. They matched the expectations that running on the athletics track leads to the lowest levels of muscle fatigue, followed by the asphalt track and finally by the sand track as a direct consequence of muscle activity as outlined in [4]. This, in turn, suggests that running on sand increases the likelihood of suffering from RRIs when compared to the asphalt and athletics track surfaces.

There are many possible additions to the design which might improve performance. More robust PCBs without full wave rectification would make the overall design more resistant and allow time-frequency fatigue analysis techniques. Statex (PA66) and Loctite Hysol GR 9800 (Loctite, Düsseldorf, Germany) could be used for encapsulation of the PCBs [19]. Isolation of the conductive thread using Aleen’s flexible stretch fabric glue (Duncan Enterprises, California, USA) would make the whole design robust against sweating.

Acknowledgment

The authors would like to thank Karina Thompson (www.karinathompson.com) for her help and support in digital embroidery. This work was supported by the UK Crafts Council Parallel Practices project.

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