Identifying Electromyography Sensor Placement using Dense Neural

Networks

Paolo Cappellari

, Robert Gaunt

, Carl Beringer

, Misagh Mansouri

and Massimiliano Novelli

College of Staten Island, City University of New York, U.S.A.

RNEL, University of Pittsburgh, Pittsburgh, U.S.A.

Keywords:

Neural Network, Machine Learning, Sensor Data, Predictive Modeling.

Abstract:

Neural networks are increasingly being used in medical settings to support medical practitioners and resear-

chers in performing their work. In the ﬁeld of prosthetics for amputees, sensors can be used to monitor the

activity of remaining muscle and ultimately control prosthetic limbs. In this work, we present an approach to

identify the location of intramuscular electromyograph sensors percutaneously implanted in extrinsic muscles

of the forearm controlling the ﬁngers and wrist during single digit movements. A major challenge is to con-

ﬁrm whether each sensor is placed in the targeted muscle, as this information can be critical in developing and

implementing control systems for prosthetic limbs. We propose an automated approach, based on artiﬁcial

neural networks, to identify the correct placement of an individual sensor. Our approach can provide feed-

back on each placed sensor, so researchers can validate the source of each signal before performing their data

analysis.

1 INTRODUCTION

Artiﬁcial neural networks (ANN) (LeCun et al., 2015)

are being used in many ﬁelds, including computer vi-

sion, speech/audio recognition, bio-informatics, etc.

Practical evidence gathered from ANN powered ap-

plications shows results that are comparable and often

better of those achievable by humans. For instance,

in the computer vision ﬁeld, deep learning techniques

can accurately identify objects in pictures (Lin et al.,

2013; Szegedy et al., 2016); in natural language pro-

cessing, they are used for language modeling (Luong

et al., 2015; J

ozefowicz et al., 2016) to achieve speech

recognition, machine translation, part-of-speech tag-

ging, parsing, handwriting recognition, amongst other

applications.

Most of the work with ANNs focus on automatic

classiﬁcation of images and textual data. There are

areas, however, where data are not immediately usa-

ble and/or represent very different underlying signals,

like in the medical domain. Some studies using deep

learning already exist in the medical domain, e.g. (Li-

ang and Hu, 2015; Rubin et al., 2017), and a number

of challenges, e.g. (Kaggle, 2014; Kaggle, 2015) have

been published to see how deep learning can help with

speciﬁc problems.

In the area of prosthetic limbs control for ampu-

tees, intramuscular electrodes could be used to record

the bioelectric activity from muscles in the residual

limb and in turn used to control the movement of

a prosthesis. These sensors record the electromyo-

graphy (EMG) activity in the set of muscles that

collaboratively contribute to the execution of a mo-

vement. In current commercial myoelectric systems,

EMG signals are acquired from just a few sensors on

the surface of the residual limb. One potential chal-

lenge in the design of advanced prostheses based on

fully implanted systems and intramuscular EMG is to

assess, from the immediate sensor readings, the iden-

tity of the speciﬁc muscle that a given sensor is re-

cording from. Given that in the forearm, for example,

there are approximately 28 muscles that control the

wrist and ﬁngers and are often coactivated, it is not a

trivial task to simply identify the signature of a muscle

based on hand and wrist movements.

In this work, we present a neural network appro-

ach to identify which muscle a particular sensor is re-

cording from using noisy data where the validity of

the labelling is uncertain.

Paper Structure. The paper is structured as fol-

lows: in the remainder of this section, we discuss the

background and motivation for this research, the re-

search aims, and the contribution; in Sec. 2, we pre-

130

Cappellari, P., Gaunt, R., Beringer, C., Mansouri, M. and Novelli, M.

Identifying Electromyography Sensor Placement using Dense Neural Networks.

DOI: 10.5220/0006912501300141

In Proceedings of the 7th International Conference on Data Science, Technology and Applications (DATA 2018), pages 130-141

ISBN: 978-989-758-318-6

sent the related research; in Sec. 3, we provide de-

tails about the experimental setup, data collection and

the initial pre-processing of the acquired raw data; in

Sec. 4, we present a process to transform such raw

data into usable information for the neural network;

in Sec. 5, we describe the neural network architecture

and conﬁguration; in Sec. 6 we detail our evaluations

and we discuss the results; and ﬁnally, in Sec. 7, we

draw our conclusions.

1.1 Background and Motivation

The loss of a limb can have signiﬁcant effects that

place limitations on a person’s activities of daily

living. In many instances of limb loss however,

muscles remain in the residual limb that can be vo-

luntarily activated by attempting to move the missing

limb. For example, in the case of a wrist disarticula-

tion (hand missing at the wrist) or a transradial am-

putation (hand missing mid-forearm), nearly all the

muscles that control the wrist and ﬁngers remain in-

tact.

This muscle activity can be monitored via elec-

tromyography (EMG). Brieﬂy, when a muscle con-

tracts, it generates an electrical potential associated

with the ﬂow of calcium ions in the muscle ﬁbers. As

more ﬁbers in a muscle are recruited, the amplitude

of this signal, after some simple signal processing, in-

creases. This electrical potential can be monitored by

a sensor (an electrode) either placed on the surface

of the skin over a muscle, or surgically placed into

the muscle itself. A single sensor, usually, monitors

a speciﬁc muscle, or part of it. Multiple sensors are

placed in multiple muscles to monitor the overall be-

havior of a limb, as limb movement is the result of the

activity of many muscles.

1.2 Research Aims

Positively identifying the muscle associated with a

speciﬁc sensor is not a trivial task. Consider the con-

trol of the hand: there are more than 20

muscles at

work in the forearm alone. The activity of many of

these muscles are highly correlated across many diffe-

rent coordinated movements, and producing isolated

activity of speciﬁc muscles is a very difﬁcult for sub-

jects. This is especially true in amputees that may not

have attempted to move certain muscles for many ye-

ars. As a result, identifying the placement of an EMG

sensor can be a challenging and time-consuming task.

Misplacing a sensor may lead to changes in the out-

come of speciﬁc data analysis processes and ultima-

tely prosthesis control. The focus of this work is to

https://en.wikipedia.org/wiki/Forearm#Muscles

use a dense neural network to identify the relationship

between a speciﬁc sensor and the underlying muscle.

In short, we seek to label each sensor with the correct

muscle name. To accomplish this, we will use a data

set consisting of EMG and kinematic data collected

from a number of able-bodied individuals performing

structured hand and ﬁnger movements. Here, each

EMG sensor has been targeted to a speciﬁc muscle

under ultrasound guidance, but in certain cases we

know that these labels are incorrect, either due to elec-

trode migration or slight errors in targeting. The chal-

lenge is to validate each EMG signal, to capture any

mislabeling within the data collected during each ex-

periment, and provide a method to continually vali-

date the correctness of the labelling throughout the

experiment, given that the sensors can shift position

with muscle contraction.

1.3 Contribution

ANNs are capable of discovering and associating me-

aning to complicated and/or noisy data. During the

past number of years, ANNs have been used to iden-

tify patterns and trends that humans cannot (easily)

ﬁnd, which make them a potentially ideal method to

identifying EMG sensor labels. Most of the existing

works that focus on recognizing movements by mo-

nitoring EMG signals assume that the monitoring de-

vice is properly conﬁgured and deployed. With this

work we make the following contributions:

• Automatic collection and enrichment of low level

sensor values with the experiment contextual in-

formation;

• A method for feature extraction of both EMG va-

lues of muscle activity patterns and kinematic me-

asurements of related movements, to succinctly

represent a rehabilitation session’s data;

• The automatic classiﬁcation of sensor

(mis-)placement using an artiﬁcial neural

network.

This approach to sensor identiﬁcation could have se-

veral beneﬁts. First, it could reduce the time that re-

searchers or prosthetists require to identify a muscle

when setting up or programming a prosthetic control

system, and second, it could be used to automatically

track changes in the sensor locations that could occur

over time.

2 RELATED RESEARCH

Over the years, EMG signals have been used in many

diagnostic, research and rehabilitation applications.

Identifying Electromyography Sensor Placement using Dense Neural Networks

131

In particular surface EMG signals, that is signals re-

corded from skin surface, have been used in prosthe-

sis control (Khezri and Jahed, 2011; Chu et al., 2006;

Soares et al., 2003), analysis of functional electri-

cal stimulation (FES) (Kocyigit et al., 1996), human-

machine interaction (HCI) (Zazula et al., 1998), pat-

hological tremor analysis (Dideriksen et al., 2011),

and muscle fatigue analysis (Steens et al., 2012). The

main characteristic of surface EMG sensors, as op-

posed to intramuscular ones, is that are non-invasive

and thus easier to use in a broad range of applications.

On the other hand, surface sensors cannot be used to

monitor the activity of deep muscles as the electrical

activity of more superﬁcial muscles always appears

larger in amplitude. Further, surface EMG sensors ty-

pically have low spatial resolution and cannot distin-

guish between the activity of even superﬁcial muscles

that are immediately adjacent. Intramuscular sensors

are used to study single motor unit activity which is

necessary for analyzing neuropathies, such as myo-

pathes and diseases of neuromuscular junctions (Mer-

letti and Farina, 2009).

To date, there has been limited attention to the

challenges surrounding the identiﬁcation of high-

density intramuscular sensors. In simple systems,

with just a few sensors, this issue is less problematic.

However, as intramuscular systems containing dozens

of electrodes are developed, addressing this issue be-

comes paramount. In (Kamavuako et al., 2011; Ka-

mavuako et al., 2012), authors build an ANN to es-

timate grasping force of a hand movement given the

EMG signals. In this work, the researchers break the

input EMG signals in contiguous chunks of 200 milli-

seconds to extract entropy values, which are then as-

sociated to the subject’s grasping force. Altogether,

these values are provided as input to train the ANN,

which can then estimate grasping force by just rea-

ding the EMG signals.

In (Matsumura et al., 2002), authors use an ANN

to recognize wrist movements/positions by analyzing

EMG signals. Signals are transposed into the fre-

quency domain by evaluating the Fast Fourier Trans-

form, which is then classiﬁed into a number of ca-

tegories representing possible positions of the wrist

(neutral, up and down, right and left, wrist to inside,

wrist to outside), achieving an accuracy ranging from

about 50% to 90%.

In (Gandolla et al., 2017), authors predict a set of

hand grasp movements by using a sequence of ANNs.

The approach processes 10 EMG signals simultane-

ously, with the goal of predicting the subject’s mo-

vement intention so to command a robotic hand. A

ﬁrst network performs a subject’s speciﬁc clustering

on the input EMG signals. When the clustering de-

tects that more than one hand grasp task, then the out-

put of the ﬁrst network is passed to a second ANN

that classiﬁes hand grasp tasks within the cluster. The

results show an overall accuracy around 76%.

In (Atzori et al., 2016a) authors use convolutional

neural networks to classify more than 50 hand mo-

vements from EMG signals. Input signals are bro-

ken down into intervals so to achieve real-time cont-

rol of the prosthesis. Overall, the network was able to

achieve an accuracy ranging between 60-70%. Rese-

archers observed that the accuracy improves when the

class of movements to classify is reduced, reaching

the 90% when only 11 movements are considered, as

also noted by (Atzori et al., 2016b).

Other works, e.g. (Yang et al., 2017), extend on

the conventional rehabilitation setting by leveraging

the use of virtual reality so to provide patients with

a more familiar life-like scene, or tackle the issue of

develop a EMG controlled exoskeletons (Mulas et al.,

2005; Moital et al., 2015).

Finally, in (Karlik, 2014) authors provide a sur-

vey of the different machine learning approaches for

EMG signal characterizations, including ANNs. Aut-

hors brieﬂy present the surveyed works, discussing

performance, pros and cons of each. While some of

these reports discuss the processing of EMG signals,

none of the approaches surveyed emphasize the cor-

rectness of the input EMG labels themselves, which

is the primary goal of this work, and one where com-

plexity scales as the number of sensors increases.

3 DATA ACQUISITION AND

ANALYSIS

This section describes the experimental setup used to

record raw data, the physical movements performed

by the subjects, and how the data are stored and ma-

nipulated to make it processable by an ANN.

3.1 Experimental Setup

All procedures described here were approved by the

Institutional Review Boards at the University of Pitts-

burgh and the Army Research Lab. Informed con-

sent was obtained from the participants prior to any

study procedures being performed. The experiments

and data collection sessions typically lasted 8 hours

and are brieﬂy described here. At the beginning of the

experiment, a physician used ultrasound guidance to

place 16 ﬁne-wire intramuscular EMG sensors (Mo-

tion Lab Systems, Baton Rouge, USA) into a set of

wrist and ﬁnger extrinsic hand muscles of the fore-

arm. Each sensor was labeled with a code represen-

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132

Table 1: Muscle names and associated labels.

Label Muscle name

FPL ﬂexor pollicis longus

APL abductor pollicis longus

ED2 extensor digitorum index

ED3 extensor digitorum middle

ED4 extensor digitorum ring

ED5 extensor digitorum pinky

EIND extensor indicis

ED M extensor digit minimi

EPL extensor pollicis longus

ECR LO extensor carpi radialis longus

ECU extensor carpi ulnaris

FDS2 ﬂexor digitorium superﬁcialis index

FDP2 ﬂexor digitorum profundus index

FDS3 ﬂexor digitorium superﬁcialis middle

FDP3 ﬂexor digitorum profundus middle

FDS4 ﬂexor digitorium superﬁcialis ring

FDP4 ﬂexor digitorum profundus ring

FCR ﬂexor carpi radialis

FCU ﬂexor carpi ulnaris

SUP supinator

PTER pronetor teres

ting the muscle that was targeted for implantation. Ta-

ble 1 shows the muscle codes used in our experiments.

Subjects were asked to execute speciﬁc mo-

vements while researchers tested electrical connecti-

ons, signal quality, and attempted to conﬁrm sensors

location. Once the EMG sensor setup was valida-

ted, the forearm was wrapped with protective gauze

and the subject was asked to wear a glove that had

been instrumented with kinematics sensors (The Mo-

tion Monitor, Chicago, USA), as shown in Fig. 1(a).

The kinematic sensors enabled the tracking of all ﬁn-

ger, thumb and wrist joint kinematics in 3D space.

The subjects were seated on a comfortable chair

and were able to rest their forearms on the table bet-

ween recordings. A wide screen television was placed

in front of the subjects where videos explaining the

type of movements were shown. The complete setup

can be seen in Fig. 1(b)

Subjects were asked to perform a variety of repe-

titive hand movements. During each movement EMG

and kinematic signals were recorded. Each recording

where data was acquired is called trial. The trials of

interest for the purpose of this paper are single joint

movement trials where the subject was asked to main-

tain a speciﬁc wrist posture (neutral, ﬂexed, extended,

pronated, and supinated) and to move the selected ﬁn-

ger in ﬂexion and extension 10 times for the duration

of the trial as indicated by the video shown. Each sub-

ject was required to perform all the combinations be-

tween wrist positions and ﬁnger selected at the speed

of 1 movement per second, while only for some of

them a slower version (2 seconds per movement) were

Figure 1: Glove with embedded kinematics sensors (a).

Complete experimental setup (b).

collected due to time constrains. The duration of each

trial was set to allow the subject to complete 10 repe-

titions of ﬂexion and extension at the requested speed.

For each trial, 2 data ﬁles were generated and saved:

one containing EMG signals and one with kinema-

tics signals. These signals were acquired on different

computers with EMG ﬁles containing 32 single-ended

signals, sampled at 30 kHz. Kinematics ﬁles included

23 hand joints relative positions, which were sampled

at 100.2 Hz.

3.2 Raw Data Storage

Once the experiment is completed, researchers trans-

fer EMG and kinematic data in their raw proprietary

format to a central disk storage, in a directory struc-

ture dedicated to the subject with all ﬁle names pro-

perly assigned for easy management. Once there, they

are converted to a more accessible format and impor-

ted in our data framework which allows fast retrieval

and has query capabilities. At this time the full raw

data for each trial is accessible to any lab member ha-

ving access rights for processing and analysis.

Identifying Electromyography Sensor Placement using Dense Neural Networks

133

4 DATA PREPARATION

In this section, we describe how we transform the re-

corded EMG and kinematic data into a representation

that captures relevant features of the signals and that

can be processed by a neural network. We perform the

following transformation steps: signal ﬁltering, and

feature extraction.

4.1 Signal Filtering

EMG signals are typically transformed from their raw

state into a ﬁltered representation prior to use in ana-

lysis or prosthesis control. As described in (Kamavu-

ako et al., 2009), to extract an estimate of muscle acti-

vation, we performed the following 3 ﬁltering steps:

high pass ﬁlter at 100 Hz to remove motion artifacts,

rectify the signal (compute the absolute value of the

signal), and low pass ﬁlter at 10 Hz. Fig. 2 illustrates

the difference between EMG signals before (on left)

and after (on right) these ﬁltering steps.

Kinematic signals are low-pass ﬁltered at 10Hz.

These signals do not require additional frequency ﬁl-

tering, as their wavelets are not noisy, see Fig. 3. Ne-

vertheless, we apply a smoothing ﬁlter to soften sig-

nals peaks and asperities.

In ﬁgure, it can be observed a ﬂat kinematic acti-

vity at both the beginning and the end of the signals:

these are the intervals between the start of the signal

recording and the start of the actual articulation mo-

vement, and the between the end of the physical exer-

cise and the halt of the recording. We are interested at

the time window where the rehabilitation movement

occurs. Given the latencies of the system and the re-

sponse time of the subject, such window is not con-

stant across trials and subjects. To isolate the mo-

vement window, we have implemented an algorithm

to detect the beginning and the end of the movement

in the trials. This start/end times are calculated on

the kinematic signals, and applied to both EMG and

kinematic datasets to remove unnecessary data.

Brieﬂy, the algorithm ﬁrst ﬁnds the period of the

movement, then calculates the start and end times.

The period of each signal is calculated by transfor-

ming the signal in the frequency domain so to isolate

its harmonic with highest energy, which in this type of

trials is the frequency of the movement, from which

the signal period can be extracted. Periods from all

signals are aggregated and the median is computed to

calculate the value of the overall movement period.

The start/stop time of each input signal is calculated

as follows: (i) we ﬁnd the peaks of the signal (in the

time domain), (ii) we extract the time at which the ﬁrst

and last peaks occur, and (iii) we off-set the latter by

a quarter period in advance and in delay, respectively.

The start of the movement is considered to be the mi-

nimum value across all the start computed from the

kinematics signal, while the end is the max value be-

tween all the ends. During the process, some signals

do not report peaks so they are not included in this

computation.

4.2 Feature Extraction

In this section we describe how we encode EMG and

kinematic signals in a compact format that is both des-

criptive of the trial and processable by an ANN.

To prepare the continuous time EMG and kine-

matic data for the ANN, we extracted representative

and salient features from the input signals. The pri-

mary features chosen for this analysis were the cross-

correlations between the EMG signals and between

EMG and kinematic signals. All subjects performed

the same set of movements and we assume that the

time-varying patterns of muscular activity required to

produce these movements are similar. As a result,

we therefore assume that the cross-correlations bet-

ween muscle activations and kinematics contains uni-

que features that can be discovered by a neural net-

work when properly trained.

For each subject and trial, we calculated two dif-

ferent sets of cross-correlations. First, we calculated

the cross-correlation between each ﬁltered EMG sig-

nal and all other ﬁltered EMG signals, and second, we

calculated the cross-correlations between each ﬁlte-

red EMG signal and each ﬁltered kinematic variable.

This simple feature extraction approach reduced the

multi-channel continuous time series data to a 16 by

38 matrix, consisting of 16 EMG signals and 16 EMG

signals plus 22 kinematic. Fig. 4 shows a heat map

representation of the subset of the correlation matrix

obtained from the EMG signals from a single trial,

where the red and white indicate high and low corre-

lation values, respectively.

5 SIGNAL CLASSIFICATION

MODELING

In the following section we detail the architecture of

the ANN; the rationale for the selection of the trai-

ning, validation and test data; and, the training of the

artiﬁcial neural network for our classiﬁcation task.

5.1 Neural Network Architecture

After feature extraction, each trial can be represented

with a relatively small amount of data, representable

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134

Figure 2: A sample of EMG signals for a few sensors before (on left) and after (on right) frequncy ﬁltering.

Figure 3: A sample of kinematics signals for a few sensors.

Figure 4: Sample Heat map of correlation matrix between

data from all EMG sensors.

by the correlation matrix. In this matrix (or table), a

single trial consists of 16 rows (one for each EMG sig-

nal) and 38 columns describing the correlations of the

other EMG sensors and kinematic variables to each

EMG signals. Multiple trials are combined together

in the table by concatenating the matrices vertically to

form a table with 38 columns and a number of rows

equal to 16 multiplied by the number of trials. This

kind of input can be managed by a neural network

with a relatively simple neural network architecture,

in contrast to other approaches that use images thus

requiring deep neural networks, e.g. (Rubin et al.,

2017). Our neural network is a dense neural network

composed of the following layers:

• An input layer with 100 inputs, which correspond

the number of our features; activation is ReLU;

• 3 hidden layers completely interconnected of size

200, 300 and 200 neurons, with ReLU activation

function;

• The output layer with 21 neurons matching the la-

bels of the targeted muscle present in the raw data

with a softmax activation.

The output of our neural network is a set of 21

numbers with value between 0 and 1 indicating the

probability that the input is the muscle indicated by

the label assigned to the speciﬁc output. To assign

the correct label, we threshold the probabilities at 0.5,

anything above is considered 1, while below is 0.

5.2 Training, Validation, and Test Sets

In supervised learning, input data has to be separated

into three sets: training, validation, and test. The ﬁrst

set is used to train the network; the second to vali-

date the quality of the trained network during the trai-

ning process, on data for which classiﬁcation result is

known; and, the third represent unseen data to the net-

work and it is used to evaluate the prediction accuracy

when the training is done. With our goal being the

correct labeling (classiﬁcation) of the muscle associ-

ated with each EMG sensor represented by the input

Identifying Electromyography Sensor Placement using Dense Neural Networks

135

features across multiple type of movements, we devi-

sed the following input sets:

• Full-Random: at each training cycles observati-

ons belonging to each set are selected randomly

from the input data sets according to the following

proportions: 70% for training, 15% for validation,

and 15% test.

• Subject-specific: data from one of the subject

is completely removed from the data set and used

as test set. The remaining data is organized accor-

dingly: 85% for training; and, 15% for validation

Datasets built using the Full-Random are com-

mon to the majority of the approaches using ANNs.

This approach is the simplest and basically rely on

the amount of input observations in order to be able

to discover patterns. With Subject-specific, we

remove all data belonging to one subject from the

training and validation sets. In doing so, we are able

to evaluate how the network behaves when presented

with data that, in its entirety, has not been seen be-

fore, thus the network could not have assimilated any

pattern from it.

Results from training the network with these three

different types of sets will be described in Sec. 6.

5.3 Neural Network Training

The input to our neural network is the tabular repre-

sentation of the cross-correlation matrices represen-

ting the various trials. In preparing the input for the

neural network, we had to convert the categorical va-

riables to so-called one-hot encodings. Speciﬁcally,

each observation in our tabular representation is com-

posed of the following data:

• a one-hot encoding describing the type of mo-

vement (56 boolean inputs);

• the cross-correlation values between one EMG

channel and all the other EMG channels, that is

21 inputs with values between -1 and 1;

• the cross-correlation between one EMG signal

(the same as above) and all the kinematics signals,

that is 23 inputs with value between -1 and 1;

• a one-hot encoding describing which EMG signal

is the source of all correltions, that is what muscle

the EMG sensor is monitoring.

We created one model for each of our input sets

deﬁned in Sec. 5.2. To create each model, we run the

training cycle process for 100 epochs, with batch size

of 16 data samples. For a proper evaluation of the

model, we repeated the training cycle 1000 times for

each type of input sets. The next section presents the

results of our experiments.

6 EVALUATION

This section presents the evaluation of our approach

to automatic classiﬁcation of EMG sensor placement.

The goal is to correctly associate each EMG input

channel with the correct muscle label, conﬁrming the

information provided during the experimental setup

where electrodes were placed under ultrasound gui-

dance, or, in case of different prediction, marking the

speciﬁc channel for more scrutiny and study.

For evaluating our approach, we built a prototype

system using R, python, and keras. The input data

was composed of the signals collected from 6 sub-

jects; each subject undergo between 24 and 55 trials,

for a total of 204 trials in total. In the reminder of this

section we present: the results obtained when using

the Full-Random input set, the results obtained when

using the Subject-Specific input set, and a discus-

sion of the discoveries.

6.1 Full-Random Model

Fig. 5 shows the progression of the model accu-

racy over 1000 training cycles when trained on the

Full-Random input set. The model reached an

average accuracy of 99.27% with a standard deviation

of 0.04 on the training set, and an accuracy of 90.10%

with a standard deviation of 0.01 on the validation set.

The test set results over the thousands iterations

computes to an average accuracy of 90.02% with a

standard deviation of 0.0148. Fig. 6 show the box

plot of model accuracy and loss on the test set. The

vast majority of training cycles perform well, while a

minority exhibit lower performance.

We proceeded by analyzing the accuracy on each

individual muscle label to investigate whether the

classiﬁcation model was performing better for some

muscles than for others. Fig. 7 shows that the muscle

label classiﬁcation performs well, except for ED3,

ED5, and ED M, to some extent.

We then analyzed the prediction accuracy for

every muscle of each subjects. Fig. 8 shows the neural

network predictions for the ﬁrst 8 channels, enumera-

ted from left to right by row, of subject 1. As we can

see, channels 1, 2, and 3, with labels ECR LO, ECU,

and FDP4, are predicted correctly almost all the ti-

mes. For channels 4, and 8, the prediction is not as

good as for the previous group. For channels 5, 6, and

7, the neural network performed poorly.

Lastly, we checked the accuracy over the same

muscle across different subjects. Fig. 9 shows pre-

dictions for channel FDP4 across all subjects. We can

observe that FDP4 is not present for subject 2, which

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136

Figure 5: Progression of accuracy during 100 epochs on training (blue) and validation (red) sets. Solid line is the average

value with plus and minus one std. The cloud contains each single training cycles.

Figure 6: Box plot of accuracy and loss for test set over the thousand training cycles.

likely means that for the trial no sensor was placed in

the associated muscle. For all the remaining 5 sub-

jects, we can say that the prediction is fairly accurate.

6.2 Subject-speciﬁc Model

With regards to the Subject-specific input sets,

we relegated data from a single subject to the test set,

while data from all other subjects composed the trai-

ning and validations set. The model accuracy was si-

milar to the Full-Random conﬁguration, where the

network achieved 99.03% and 90.19% accuracy for

the training and validation sets, respectively. With

data from the test subject, that had never been seen

by the network during training 88.17% of the EMG

sensors were identiﬁed as recording signals from the

muscle that was assigned under ultrasound guidance.

Fig. 10 shows the accuracy for ﬁrst 8 channels for

subject 1. It can be observed that the prediction accu-

racy was: very high for channels 1, 2, 3, 5 and 8, high

for channels 4 and 6, and low/poor for channel 7.

6.3 Discussion

Validation of placement of ﬁne-wire electrodes in the

extrinsic hand muscles and other locations has typi-

cally been accomplished by qualitative comparison of

EMG activity generated by the movement being per-

formed (Birdwell et al., 2011; Burgar et al., 1997;

Rudroff, 2008). The applications of small amounts

of current through EMG electrodes to stimulate a

muscle contraction has alternatively been used (Bird-

Identifying Electromyography Sensor Placement using Dense Neural Networks

137

Figure 7: Box plot for accuracy for each muscle.

well et al., 2013; Park and Harris, 1996), but this met-

hod may not be applicable for prosthetic limb users.

To the best of our knowledge, our approach is the ﬁrst

of its kind to use a machine-learning algorithm to va-

lidate electrode location.

The neural network model we developed delivers

very good performance in labeling EMG channels

with the identity of the muscle that was targeted du-

ring implant, especially when considering the relati-

vely small amount of subjects and trials composing

the training data. In analyzing the results from the

evaluation, we noted several interesting ﬁndings.

In the individual muscle labelling experiment for

the Full-Random input set, see Fig. 7, we discovered

that one reason why channels were under-performing

is that the input signals for those channels do not ap-

pear for all monitored subjects. As a consequence, the

model had a smaller number of samples to use to infer

signal patterns, thus impacting the overall accuracy.

In the per subject muscle labelling experiment for

the Full-Random input set, see Fig. 8, the poor per-

formance on channels 5, 6, and 7 could be caused by

the presence of inconsistencies in the initial labelling

provided by the physician for these channels. To vali-

date this hypothesis, domain expert researchers have

to review the experimental data and analyze the dyn-

amics of the signal in relation to the movement.

Finally, we looked at the incorrect predictions to

investigate whether there is a correlation between the

actual (correct) muscle and the incorrect predicted

value. We built a directed graph of the wrong pre-

dictions, see Fig. 11 which show an excerpt of this

graph for a single example. The graph exhibits some

interesting patterns including that the ANN confu-

sed muscles within two separate groups: extensors

and ﬂexors. This ﬁgure also shows that some of the

muscle are not predicted at all, and they lead to the

use of the “none” label.

7 CONCLUSIONS

Artiﬁcial neural networks show great application po-

tential across a range of different domains. In this

paper, we focused on how to support physician and

medical researchers in assessing whether a set of in-

tramuscular electromyography sensors are placed in

the targeted muscle or not. We presented a number

of data processing steps which take raw sensor data

and transforms it in usable information that enables

domain specialists to perform their tasks more efﬁ-

ciently and effectively, improving both the rehabilita-

tion sessions as well as the quality of the data analysis

resulting from the multiple recording trials.

We have presented a novel approach that combi-

nes a dense neural network architecture with a com-

pact cross-correlation matrix describing the rehabili-

tation trial sensor readings. We have developed a pro-

totype system and evaluated it on real data. The expe-

riments demonstrated that our approach achieves an

accuracy around 90% in classifying the muscle from

which sensor readings are coming from. This is a very

promising result, especially when considering that: in

our experiments the classiﬁcation imprecision on spe-

ciﬁc muscles was caused by lack of data regarding

such muscles, possibly muscle mis-labelling; and that

the amount of observations available to train the net-

DATA 2018 - 7th International Conference on Data Science, Technology and Applications

138

Figure 8: Excerpt of the ﬁrst 8 channels from Subject 1 individual channel predictions. Each pie chart represent the predictions

for one channel which is indicated right below with the label that was assigned during experimental setup

Figure 9: Muscle FDP4 predictions across subjects.

Figure 10: Excerpt with the ﬁrst 8 channels from the predictions on all channels with subject 1 data as a test set.

Identifying Electromyography Sensor Placement using Dense Neural Networks

139

Figure 11: Directed graph of the wrong predictions: edge

thickness indicates the number of occurrences of the mista-

ken label.

work was limited in general, when compared to other

works using neural networks.

The results of this study can help with valida-

ting the implanted muscle identity using the pattern

of EMG activity. We used ultrasound techniques as

an initial tool to validate the predicted results. Future

work will focus on validating the predicted muscles

identities using the EMG pattern with the probability

of migration of each EMG sensor in the neighboring

muscle compartment based on the geometrical vici-

nity. Also, we are planning to extend this work to im-

prove classiﬁcation accuracy by obtaining access to a

larger amount of observations, further enriching the

trials’ metadata, and trying different neural network

architectures and parameters.

ACKNOWLEDGEMENTS

Research was sponsored by the U.S. Army Rese-

arch Ofﬁce and the Defense Advanced Research Pro-

jects Agency (DARPA) and was accomplished un-

der Cooperative Agreement Number W911NF-15-2-

0016. The views and conclusions contained in this

document are those of the authors and should not be

interpreted as representing the ofﬁcial policies, either

expressed or implied, of the Army Research Ofﬁce,

Army Research Laboratory, DARPA, or the U.S. Go-

vernment. The U.S. Government is authorized to re-

produce and distribute reprints for Government pur-

poses notwithstanding any copyright notation hereon.

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