Journal of Signal Processing, Vol.18, No.4, pp.225-228, July 2014

SELECTED PAPER AT NCSP'14

Direct Estimation of Hand Motion Speed from Surface Electromyograms
Using a Selective Desensitization Neural Network
Kazumasa Horie1 , Atsuo Suemitsu2 and Masahiko Morita1
University of Tsukuba
1–1–1 Tennodai, Tsukuba, Ibaraki 305-0006, Japan
Phone/FAX:+81-29-853-6554
E-mail: horie@bcl.esys.tsukuba.ac.jp
mor@bcl.esys.tsukuba.ac.jp
12Japan Advanced Institute of Science and Technology
1–1 Asahidai, Nomi, Ishikawa 923-1211, Japan
Phone/FAX:+81-761-51-1227
E-mail: sue@jaist.ac.jp

Abstract

ous training samples to learn relationships. Suitable function
approximators for EMG-based devices should have the folDuring the application of surface electromyograms (EMGs) lowing characteristics.
in human-machine interfaces, direct estimates of multiple• High learning ability
hand motion speeds are required to facilitate operations that• High robustness against redundant inputs
fully reflect the user’s intentions. However, no practical meth• High generalizability
ods are available for this purpose because conventional func• Low computational costs for learning
tion approximators cannot learn the complex relationships between the motion speeds and surface EMGs within a practical
Recent studies have shown that a selective desensitization
period of time. By contrast, it has been shown that a selec- neural network (SDNN) may fulfill these requirements. Fortive desensitization neural network (SDNN) can learn com- example, Nonaka et al. applied a SDNN to the approximation
plex input-output relationships with low computational costs. of a two-variable function and showed that the SDNN had aIn this study, we propose a method for the direct estimation of high learning ability and high generalizability [3]. Based on
hand motion speeds from surface EMGs using a SDNN. We previous studies, we consider that a SDNN may be highly
estimated the motion speed in practice to assess the efficacy suited for application to EMG-based devices.
of this proposed method. Our experimental results show thatThus, the aim of the present study was to develop a method
the proposed method can estimate the approximate speeds of for the direct estimation of the speed of multiple hand mosix basic motions in real time.
tions from surface EMGs using SDNNs. The following
sections describe the SDNN and our proposed estimation
method. We also experimentally verified the efficacy of the1. Introduction
proposed method.
Recently, methods have been proposed for classifying multiple types of hand motions from surface electromyograms 2. Selective Desensitization Neural Network(EMGs) [1, 2] to facilitate the development of EMG-based
human-machine interfaces. However, estimates of hand moSDNNs are known to provide good approximations of ation speed are also required to allow these interfaces to fully wide range of functions using a small set of training data samreflect the user’s intentions. Unfortunately, practical meth- ples. In this section, we explain how a function y = f (x) isods are not available for the direct estimation of hand motion approximated based on a SDNN given a continuous-valued
speeds from surface EMGs.
input vector x = (x1 , . . . , xm ) (m > 2).
The estimation of motion speeds is regarded as a function
The input layer comprises m neural groups (G1 , . . . , Gm ).
approximation problem, which describes the relationship be- Each group has n units that represent an input signal xi , i.e.,
tween surface EMGs and motion speeds. However, this func- the input signal is represented in a distributed manner by thetion is readily affected by individual differences in surface activity patterns of the units. If the range of possible values
EMGs. Thus, we consider that a function approximator is for input signals is divided into q ranges, each range correrequired to approximate individual functions.
sponds to different activity patterns, which are configured usHowever, most function approximators are not suitable for ing equal numbers, 1 and −1. In addition, similar patterns
application to EMG-based interfaces. For example, a mul- are encoded based on the proximity between the ranges oftilayer perceptron (MLP) cannot learn complex relationships the input signals.
within a practical period of time; radial basis function netThe middle layer comprises m(m − 1) neural groups
works are not suitable for practical applications since they Gµ,ν (µ, ν = 1, . . . , m; µ = ν). The units in Gµ,ν are conhave excessively high computational costs and require numer- nected with both of the units in Gµ and Gν , which are located

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225

in the input layer. This realizes a procedure called desensitization, which neutralizes the outputs of the units, regardlessof their inputs and inner potential. For example, if the i-thunit of Gµ is desensitized by the j-th unit of Gν , the outputof the i-th unit of Gµ,ν is given bygiµ (1 + gjν )µ,ν
gi =(1)2where giµ is the output of the i-th unit of Gµ and gjν is theoutput of the j-th unit of Gν .
The output layer of the SDNN comprises l (l > 2) units,each of which is connected to all of the units in the middlelayer. The output of the i-th unit in the output layer is calculated byyiH(u)==H{( ∑n∑µ,ν(µ=ν) j=1µ,ν µ,νwijgj − hi)

1 (u ≥ 0)
0 (otherwise)

Figure 2: Installed gyroscope

(2)
(3)

(a) Thumb sideµ,νwhere wij
is a synaptic weight from the j-th unit of Gµ,ν inthe middle layer and hi is a threshold. The final output y of
the SDNN is determined based on the number of units withoutputs of 1 in the output layer. Learning is achieved by error
correction training in this network.

(b) Little finger side

3. Motion Speed Estimation Method

Figure 3: Arrangement of EMG sensors

Our proposed method has three components: surface EMG 3.1 Measurement
measurement, signal preprocessing, and function approximaA three-axis gyroscope (MP-G3-2000B, MicroStone Co.
tion. Figure 1 shows a flowchart that illustrates this method.
Ltd) is used to measure the motion speeds. The gyroscope ismounted on the middle finger (Figure 2) and it measures theangular velocity. V (t) is calculated bymeasurement√V (t) = Vx (t)2 + Vy (t)2 + Vz (t)2(4)where Vx (t), Vy (t), and Vz (t) are the angular velocities
around the x-axis, y-axis, and z-axis of the gyroscope at timet, respectively. Finally, V (t) is normalized against the maximum value during each time step and defined as the actual
motion speed at time t.
The surface EMGs are measured at 11 points on the forearms (Figure 3). A relatively large number of sensors are required but it is not necessary to position the sensors accurately
on specific muscles. The surface EMG is measured and sampled at 1 kHz using Personal-EMG (Oisaka Electronic DeviceLtd) equipment and a 12-bit A/D converter.

approximation

Figure 1: Flowchart of the proposed method

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3.2 Signal preprocessing
Surface EMGs are not suitable for use as input signals because of their fluctuations. Thus, it is necessary to extract features by signal preprocessing. However, we cannot employ

Journal of Signal Processing, Vol. 18, No. 4, July 2014

pairs of the neural groups (G1 , G12 ), . . . , (G11 , G22 ). In the
middle layer, half of the units are desensitized by the corresponding units in the input layer.
The output layer comprises 70 units. The final output of theSDNN is calculated by 0.02k − 0.2, where k is the number ofunits with outputs of 1 in the output layer. Thus, the motion
speed is represented in the range [−0.2, 1.2].
According to the error correction training algorithm, theSDNN repeats the training process until the mean squared
error is sufficiently low or a specific number of iterations havebeen completed.
4. Experiment
4.1 Methods
inputlayer

middlelayer

outputlayer

Figure 4: Structure of the SDNN
complex methods because they are not applicable to multiple
motions. Using simple preprocessing, we obtain the integralof the surface EMG (IEMG) and its mean over the previous
200 ms (average IEMG: AIEMG) as features.
The IEMG can be obtained with little time lag but it is related to the forces generated by muscles rather than the motion speed. In contrast, the AIEMG is related more directly tothe motion speed than the IEMG, but there is a large time lag
between the AIEMG and the actual motion itself. Therefore,
the combined use of both of these features can be expected toenhance the accuracy of motion speed estimation.
In the proposed method, a filter box (Oisaka Electronic Device Ltd) is used to convert the surface EMGs into IEMGs anda simple moving average (SMA) filter converts the IEMGs
into AIEMGs. The IEMGs and AIEMGs are normalizedagainst the maximum value during each time step before being used as inputs for the SDNN.
3.3 Function approximation
The SDNN is constructed as shown in Figure 4 and it isused as a function approximator. Note that the SDNN is prepared for each motion, but all SDNNs have the same structureand parameters.
The input layer comprises 11 neural groups (G1 , . . . , G11 )and 11 other neural groups (G12 , . . . , G22 ), which represent
the IEMG signals (IEMG1 , . . . , IEMG11 ) and AIEMG signals (AIEMG1 , . . . , AIEMG11 ), respectively. The activity
patterns of the units are determined from the input signals
using the parameters n = 32 and q = 96.
The middle layer has two parts (P1 and P2 ). P1 and P2
comprise 110 and 22 neural groups, respectively. The unitsof the neural groups in P1 are connected with the units of twoneural groups from G1 , . . . , G11 . In P2 , the units of the neural groups are connected with the units in the corresponding

Journal of Signal Processing, Vol. 18, No. 4, July 2014

To obtain measurements of motion speeds and surface
EMGs, five adult male subjects were asked to execute six basic motions (flexion, extension, grasp, open, pronation, andsupination) repeatedly at different speeds, each for 60 s. In
total, 25–50 data samples were collected for each motion typeand 25 of these data samples were selected randomly for use
in the experiment. Next, we performed the following crossvalidation (similar to a sixfold cross-validation): (1) for eachmotion type, 21 data samples including the fastest motion
were selected to train the corresponding SDNN, whereas theremaining four data samples were used as test data; (2) theprocess was repeated by changing the training data samples,
except the fastest motion.
4.2 Results
Figure 5 shows an example of the estimated motion speeds,
which demonstrates that our proposed method could estimate
the approximate motion speed. In addition, this method did
not show a time lag between the measured and estimated motion speeds. Thus, we consider that the proposed method canbe applied to EMG-based interfaces.
5. Discussion
To investigate the effect of the SDNN on the performanceof this method, we compared the estimation accuracy withconventional function approximators: MLP and logistic regression (LR).
The MLP had one hidden layer (20 units) and it was trainedby back-propagation learning. The training process was repeated until the mean squared error was sufficiently low ora specific number of iterations had been completed. The parameters of the LR model were determined using the steepest
descent method.
The experimental conditions were same as those used in
the previous experiment. To evaluate the estimation accuracy,
the average correlation coefficients (CCs) between the actual227

FlexionOpen0Extension

100

200

0

100

200

100

200

200

0

100

200

100

200

0

100

200

Pronation
0Grasp100

Supination
0

100

200

time (ms)
Estimated

10time (ms)Measured

Figure 5: Examples of estimated motion speeds
Table 1: Comparison of different function approximators(a) Test dataSDNNMLPLRFEMotion
GO0.900.84
0.87

0.88
0.82
0.85

0.85
0.69
0.70

0.89
0.81
0.85

PS

Ave.

0.90
0.83
0.85

0.91
0.83
0.86

0.89
0.80
0.83

(b) Training data

SDNNMLPLRFEMotion
GOPS

Ave.

0.98
0.90
0.91

0.97
0.89
0.90

0.96
0.80
0.80

0.97
0.91
0.90

0.97
0.92
0.91

0.97
0.89
0.89

F: Flexion
O: Open

0.97
0.91
0.92

E: Extension
P: Pronation

G: Grasp
S: Supinationand estimated motion speeds were calculated for each motion
type.
The experimental results are shown in Table 1(a). The average CCs with the MLP and LR models were 0.80 and 0.83,
respectively, which were 0.09 and 0.06 lower than that obtained with the SDNN (0.89). Furthermore, the average CCs
with both the MLP and LR models were much lower for thegrasp motion. These results indicate that the accuracy of theresults obtained using the MLP and LR models were not suitable for practical use.
To understand the sources of the estimation accuracy differences, the learning performance was compared using theSDNN, MLP, and LR models. Table 1(b) shows the average CCs for the actual and estimated motion speeds using thetraining data samples, where the CCs obtained with SDNN
were greater than 0.96 for all of the motions, whereas thoseobtained with the MLP and LR models were much lower, especially for the grasp motion. This suggests that the SDNN
has a greater learning capacity than the MLP and LR models, and this difference caused the variation in the estimation
accuracy.

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6. Conclusion
In this study, we proposed a method for directly estimating
the speed of multiple hand motions from multichannel surface EMGs using SDNNs. The experimental results showedthat the proposed method could estimate the approximate motion speed and there was no time lag between the actual andestimated motion speeds.
Given its practical advantages, such as the lack of a requirement for adjustment sensors and the low computational costs,
the proposed method may be suitable for application to EMGbased devices. Thus, combining this method with a practicalmotion classification method may facilitate the developmentof user-friendly EMG-based devices.
In future research, we aim to improve the estimation accuracy and to implement an EMG-based device using the proposed method.
Acknowledgment
This work was supported partly by JSPS KAKENHI Grant
Numbers 22300079 and 24700593.
References[1] M. Yoshikawa, M. Mikawa and K. Tanaka: Realtime hand motion classification using EMG signals withsupport vector machines, IEICE Trans. Inf. & Syst.
(Japanese Edition), Vol. J92-D, No. 1, pp. 93–103, 2009.
[2] H. Kawata, F. Takana, A. Suemitsu and M. Morita: Practical surface EMG pattern classification by using a selective desensitization neural network, Neural Information
Processing (Part II), Lecture Notes in Computer Science,Vol. 6444, pp. 42–49, 2010.
[3] K. Nonaka, F. Tanaka and M. Morita: Empirical comparison of feedforward neural networks on two-variable function approximation, IEICE Trans. Inf. & Syst. (JapaneseEdition), Vol. J94-D-II, No. 12, pp. 2114–2125, 2011.

Journal of Signal Processing, Vol. 18, No. 4, July 2014