Congreso Nacional de Ingeniería Electrónica del Golfo CONAGOLFO 2009 Instituto Tecnológico de Orizaba

ISBN: 978 607 00 1861 9 -7 -

Abstract— This paper presents the development of a new Electromyography (EMG) Data Acquisition System capable to perform Real-Time Embedded Calculus for the estimation of Force, Frequency and Activation of the muscles. The system described in this paper is composed of two elements: the amplifiers sensors and the processing unit based on a DSPic Microcontroller which performs the analysis and calculus of the human muscular activity in an embedded processing. Moreover, different neuromechanical analyses using this novel device are presented in the experiments section of this paper.

Keywords-component; EMG data acquisition system, DSP embedded calculus, Multimodal systems, Skills transfer.

I. INTRODUCTION

Nowadays, the bio-signals have been adopted in the research field like a solution for the analysis of different problems. These signals can give information about the behavior of the human body when is performing a certain action. Unquestionably, the muscles give the most valuable data regarding to the human motion. Therefore, the acquisition of different parameters like the force level, fatigue, time activation and position of the muscles represent an interesting tool to carry out the control or analysis of different projects in the field of medicine, rehabilitation, haptics and sports. The electromyography signal EMG is a biomedical signal that measures electrical currents generated in muscles during their contraction. These signals are complex to acquire and process, because they are small (0 to 10mV) and highly susceptible to noise. Normally, a sampling frequency from 2 to 4 KHz should be performed in order to obtain good parameters in the behavior and spectrum of the signal. [1] Therefore, a big drawback is the considerable amount of data that is obtained during the acquisition process of the raw signal using many sensors. A new embedded system capable to perform diverse embedded calculus in real time in order to save computing-time and avoid saturation in the transmission with the raw signals was designed. The main objective of this device is to process, calculate and transmit three important parameters that synthesize the raw data from muscles: Muscular Time Activation, Force Intensity and Frequency Analysis.

The application of real-time EMG signals has been an important research field in the last years. One important application of real-time EMG is to control exoskeletons for human motion support, for example, for disabled people, including rehabilitation training, and for force enhancement in healthy subjects [2] [3]. Important researchers in the prosthesis field have developed robots capable to be controlled through the human muscles activation in real-time [4][5]. Machine Learning in another field that has developed algorithms to recognize and interpret the EMG signals in order to control different robots in real-time and analyze the data for the identification of diseases in the people [6] [7].

Figure 1. EMG sensor, b) Design phases of the EMG Data Acquisition System.

II. DESCRIPTION OF THE SYSTEM

A. Characteristics of the Signals The EMG signals in a fiber muscle are stochastic signals. Normally, they show the intensity of the muscle contraction and the time of activation.

• Frequency is between 0 to 5000Hz, but the dominant energy is concentrated in the range of 20 to 500Hz

• Amplitude is located from 0 to 10mV • Noise Affectation is a common problem.

Real-Time Embedded Calculus Processing Using a Low-Cost EMG Data Acquisition System

Oscar Sandoval-Gonzalez1, Otniel Portillo-Rodriguez2, Alejandro Cuellar-Cortes3, Emanuele Ruffaldi1, Carlo A. Avizzano1 and Massimo Bergamasco1

1P ERCRO Scuola Superiore Sant’Anna, Via Martiri 11, Pisa Italy, 56127 2UAEMex, Cerro de Coatepec s/n, Toluca, Mexico 50100

3ITO, Avenida Oriente 9, Orizaba, Mexico, 94320 Email: o.sandovalgonzalez@sssup.it1, oportillor@uaemex.mx2

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Figure 2. Single Threshold and Double Threshold

These signals are highly affected by different phenomena that produce noise in the signal and inaccuracy in the measurements. Therefore, it must be studied in detail these effects in order to reduce them in the best possible way [1]. These noisy phenomena are presented by the body fat that produces delays and decrements in amplitude on the measurements. Cross-Talking is another noisy phenomenon presented due to the motion of different muscles at the same time. The Fatigue in the muscles is another cause related to the variation of the measurements. Another problem is that the muscles have different behavior according to the age of the people. Moreover the size of the muscle and the level of muscular tone produce variations. The mechanical noise produced by the motion of the sensor cables generates mechanical vibrations, principally located in the range of first 20Hz.

B. Muscle activity detection

The estimation of on–off timing of human skeletal muscles during movement has important clinical applications. There are different techniques referred to as “single-threshold methods” are based on the comparison of the rectified raw signals and an amplitude threshold whose value depends on the mean power of the background noise. And the “double threshold method” is based on the level of noise and the time of activation. Therefore, the second method was chosen by the accuracy and efficiency [8]. Figure 2. shows the difference between the single threshold and the double threshold. The clean signal obtained from the filtering process is rectified enveloped using a Butterworth lowpass filter of the fourth-order whose cutoff frequency was set at 10 Hz, and phase compensation was used. After the detection process, events identified and separated by a temporal distance smaller than 125 ms are considered as belonging to the same contraction and merged. This value (125 ms) corresponds to a global muscular firing rate of eight pulses per second (pps), which is arbitrarily assumed as the lowest effective muscle activity. The time activation detection process is performed. The events identified and separated by a temporal distance smaller than 125 ms are considered as belonging to the same contraction and merged.

C. Overview of the System

This EMG system consists of two modules: The signal amplifier sensors and the Digital Signal Processing System. On one hand, the sensors attached to the muscles of the human being, use in the first phase of amplification an instrumentation amplifier INA121 (specially designed for the use in biomedical signals) that amplifies the signal 500 times the difference in voltage of two EMG electrodes (Ag/AgCl) (Placed on the skin at 2.5cm of distance between them with a gel-skin contact area of 1cm2 for each electrode). An passive 1st order high-pass filter (50-500Hz) eliminates the noise at low and high frequencies and performs a second amplification that can be chosen by the user varying from 1 to 5 times. The signal passes to an ADC AD7683 16-bits resolution and finally the 2 bytes of information at 4 KHz sampling rate are sent to the Digital Signal Processing System via SPI protocol.

D. Electrodes and extensions

An EMG amplifier is designed to be used with a skin surface electrode. This type of electrode is defined as a bipolar electrode. The surface electrodes are not expensive. The problem with skins surface electrodes is that they create sometimes an unstable contact. An unstable contact causes potential motions artifacts. Until now, it has been performed several test using different Ag/AgCl EMG electrodes for Bio-medical company [9]. The sensors that have been used are the GS27.

Figure 3. a) Instrumentation Amplifier, b) band-pass filter, c) selectable amplifier, d) voltage divider, e) offset circuit ( add/substraction Opamp).

E. Instrumentation Amplifier

The amplitude of the EMG signal is between 0 to 10 millivolts (peak-to-peak), or 0 to 1.5 millivolts (rms). At this state, it is necessary to amplify the signal one hundred times to boost the EMG signal without changing phase or frequency. This preamplifier uses a typical differential amplifier circuit, which contains two inputs. The differential

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amplifier circuit subtracts two inputs and amplifies the difference. To get the right level of the input signal, we need a body reference circuit which works as a feedback from the inputs. Whenever the body temperature changes or signal changes due to noise introduced by the body, this body reference will help maintain the correct level of signal. It was performed a noise analysis of three different instrumentation amplifiers in order to identify the level of noise of each one of IC using the EMG electrodes. Table 2 shows the average noise performed in four tests.

Test 1 Test 2 Test 3 Test 4 INA114 14.2 mV 14.73 mV 16.32 mV 14.23 mV INA121 5.32 mV 6.12 mV 5.67 mV 4.89mV AMP04 20.3 mV 19.98 mV 18.78 mV 19.36 mV

According to the tests, the best result was obtained by the INA121, this IC achieved an average noise around 5.505 mV in a gain of 100. The INA121 is a FET-input, low power instrumentation amplifier offering excellent accuracy. In Figure 3. (A) is shown the Instrumentation amplifier circuit used in the sensor. Equation (1) shows the gain of the system.

Figure 4. Noise Level, Green line-INA121, Blue Line – INA114, Red Line – AMP04

F. Band-Pass Filter The second step in the signal conditioning is to filter the signal using simple RC high/low pass filters creating a bandpass filter in the range of 15 to 5000Hz. After this it is performed the last amplification process with a Gain Adjustment. Figure 3. B shows the Band-Pass filter.

G. Offset Circuit

According to the characteristics of the ADC (unipolar), it is necessary to send a signal with positive voltages, for this reason it is was implemented a add/substraction Opamp

configuration in order to generate an offset and move the signal to the positives values.

H. ADC 16 bit resolution

Because loss of data can produce significant changes for the pattern recognition and analysis process, it is important to have an efficient device of 16 bit resolution for the Analog to Digital conversion process. Another important consideration in order to avoid the noise in this part of process is to place the ADC as near as possible from the electrodes and send the signal in a serial way. It was selected the AD7683 is a 16-bit, charge redistribution, successive approximation, PulSAR® analog-to-digital converter (ADC) that operates from a single power supply, VDD, between 2.7 V and 5.5 V. It contains a low power, high speed, 16-bit sampling ADC with no missing codes, an internal conversion clock, and a serial, SPI-compatible interface port.

Figure 5. Electrodes-Instrumentation Amplifier-BandPass Filter & ADC configuration

I. DSP-PC Communication

It will be used a SPI protocol in order to transmit the information from the ADC to the DSP Microcontroller. The information can be managed in different ways. In this case is presented two examples using a Bluetooth wireless technology and a common USB connection.

Figure 6. Bluetooth & USB Comunication

J. Embedded Calculus The Digital Signal Processing System is controlled by a Microchip DSPic30F4012 device which performs three important routines: a) Voltage reference and noise level detection, b) Static Calibration (correlation EMG - Force), c)

ADCFilter

Amplifier

Rx/Tx

ADCFilter

Amplifier

Rx/Tx

ADCFilter

Amplifier

Rx/Tx

ADCFilter

Amplifier

Rx/Tx

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Double Threshold Methodology in Real-Time. The detection of discrete events in the EMG is an important parameter in the analysis of the motor system. Therefore, it is essential to apply special methodologies for the correct retrieval of information due to the stochastic and noisy characteristics of the EMG signals. The estimation of on-off timing of human skeletal muscles plays an important role in the EMG analysis. There are different techniques referred to as “single-threshold methods” which are based on the comparison of the rectified raw signals and an amplitude threshold whose value depends on the mean power of the background noise. However, this methodology is not efficient because it does not take into consideration involuntary contraction of the muscles that normally happens in short time lapses of 30msec. Therefore a “double-threshold method” is applied in order to verify the level of noise and the period of time of activation of the contraction.

Figure 7. Butterworth Digital Filter Scheme

The Digital Signal Processing System has the objective of acquiring the signals from the sensors at 4KHz. The signals are full-wave rectified and then enveloped using a Digital fourth-order Butterworth low pass filter with a cut off frequency of 10Hz. The time activation detection process is carried out using a sliding window of 50msec which performs the analysis of the whole signal in order to detect events of signals smaller than 30msec which are considered to be part of individual MUAP (Motor Unit Action Potential) contraction or a noise introduced in the signal.

Figure 8. Magnitude and Phase response of the 4th order Digital Butterworth filter.

This algorithm scans the noise levels of the sensors when the muscles are in repose. The maximum, minimum, average values and the noise band are obtained.

Figure 9. Computing the maximum, minimum and average values and the noise band

K. Calibration Process In this algorithm, the user applies a MVC (Muscular Voluntary Contraction). The algorithm transforms the signal to absolute values. It is performed a windows analysis of 50ms. It was applied a stage of integration. The activation time and the force levels are obtained.

Figure 10. Computing the ABS values and the Integration of the signal

Finally, the real-time analysis consists into proccess the information in a window sampling of 50ms. The time activation, the force estimation and frequency analysis of each window sampling are obtained in real time.

Figure 11. Computing the Activation Time, Force Estimation and Frequency Analysis

L. Calibration and Relationship between EMG and Force Since the EMG signals have specific features for each person, the EMG activity requires to be calibrated in order to correlate it with a corresponding force level. A static calibration was carried out through MVC (Maximum Voluntary Contraction) of each user. The static calibration routines are normally performed making a person lift a determined weight in a determined position depending on the muscle to study. In this experiment the most representative muscles for boxing movements are the biceps and the triceps. Therefore the user must perform two gym exercises for calibration. For the bicep calibration the movement is called ”Curl” where the user holds a dumbbell (2 Kg) in a hand with arms hanging down and the palms of the hands facing the body: the user bends the elbow rotating the palm up before the forearm reaches an horizontal position. For the triceps

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brachii the user performs a movement called ”One-arm dumbbell triceps extension” when the user grips the dumbbell (2 Kg) in one hand with the arm vertical then bends the elbow to lower the dumbbell behind the head to the neck and return to the initial position. Digital Signal Processing System obtains the EMG intensity values during these exercises and performs the correlation between the force to lift a 2 Kg weight and EMG intensity.

M. PCB Layout The PCB layout of the EMG sensors was designed using two layers with a dimension of 2.5cm x 2.5cm. The frontal face contains all the analog circuits and the back face contains the ADC. The digital and analog grounds were separated to avoid noise.

Figure 12. PCB Layout of the EMG sensors, A) Frontal and Back Tracks, B) 3D representation.

III. EXPERIMENTS This section presents the experiments of 4 projects where were used the EMG data acquisition system to perform a neuromechanical analysis for the application and study in rehabilitation (Human Walking and Upper-Limb rehabilitation systems) and sports fields (Boxing and Rowing).

A. Walking Analysis Biped locomotion constitutes a complex interdisciplinary problem, which has attracted lot of attention over the last two decades in several research areas, such as robotics, mechatronics, biomechanics, neurosciences, bioelectronics, applied nonlinear control, virtual reality, who have contributed with basic understanding on this subject. [10]

Figure 13. EMG analysis of the Human Walking Gait

The neuromechanical analysis was performed using the VICON signals mapped properly from 3D space into 2D space sagittal plane. Preliminary EMG magnitude and its time activation analysis for estimating the applied force in each limb is also realized. Figure 13. Shows the EMG activity of four different muscles of the legs during the walking gait. These muscles are the Triceps Surae, Hamstring, Quadriceps and Tibialis Flexor.

B. Upper-Limb Rehabilitation using Exoskeletons Fixed–Base Exoskeleton applications have increased rapidly in the last few years, evidently as part of promisingrehabilitation robotic programs of the robotics worldwide community, where in particular Human–Robot– Interaction (HRI) plays an important role in its design and control because they are tightly coupled to human–limbs.

Figure 14. shows a preliminary experimental results that provide further insight of a haptic guidance scheme taking into account decisive factors into the HRI such as human pose, haptic guidance control, reaching and tracking tasks, the complexity of the virtual environment, and muscles activity. [11]

Figure 14. EMG analysis manipulating a upper limb exoskeleton

C. Boxing Analysis

A recent trend in virtual environments aims at making the user free of devices or limitations in its motion, at the benefit of immersiveness and interactivity. This work introduces an interaction paradigm that goes beyond motion based interaction, by making use of measured real forces exerted by the user in free space. The sensation of touch with virtual object is obtained by vibrotactile stimulation combining virtual contact information with measured forces. We improved this paradigm augmenting the vibrational feedback with EMG signals sensor assessing a precise estimation of the exerted force. EMG sensors can exactly detect the force applied during the interaction with the

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virtual world from the activation signals produced by the user’s muscles. [12]

Figure 15. Neuromechanical analysis of different boxing movements

D. Rowing Neuromechanical Analysis A brief experiment was performed to test the EMG data acquisition system. Four sensors were located in 4 muscles (Biceps, Triceps, Tibialis and Quadricep). The DSPIC performs a 2Khz sampling of each sensor and compute the estimation of force and the activation of the muscles. Figure 16. the four processes performed by the DSPic. The yellow line show the raw-signal, the pink line shows the absolute value of the signal. The blue line shows the low-pass filter to envelope the signal and finally the red line shows the activation time of the muscle [13][14].

Figure 16. Neuromechanical Analysis of different muscles obtained during a rowing session.

IV. CONCLUSIONS This paper presented a novel low-cost EMG data acquisition system which compute embedded calculus in real-time. This embedded calculus processing offers the possibility to create neuromechanical analysis and use the force of the muscles like an important variable to control process applied in

robotics, virtual environments and rehabilitation in real-time. Different applications in these fields mentioned before were presented and show the potentiality of this device.

V. ACKNOWLEDGEMENTS This work has been carried out within the context of the EU Integrated Project SKILLS that deals with the use of Multimodal system for the capture model and rendering of data signals for the transfer of skills in different application fields. The authors would like to acknowledge the European Commission for sponsoring this research inside the project SKILLS-IP. More information about the IP-SKILLS project may be found at http://www.skills-ip.eu or by contacting the authors.

VI. REFERENCES[1] Peter Konrad, “The ABC of EMG, A Practical Introduction to

Kinesiological Electromyography”, NORAXON INC. USA. [2] Christian Fleischer*, Andreas Wege, Konstantin Kondak and Gunter

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[3] Vijay Kumar, “Assistive Devices For People With Motor Disabilities”, Encyclopaedia of Electrical and Electronics Engineering Assistive Devices For People With Motor Disabilities - Kumar, Rahman & Krovi, 1997.

[4] J. L. Pons, R. Ceres, E. Rocon, S. Levin, I. Markovitz, B. Saro, D. Reynaerts, W. Van Moorleghem†† and L. Bueno. “Virtual reality training and EMG control of the MANUS hand prosthesis”, Robotica (2005) volume 23, pp. 311–317.

[5] Au, S.K. Bonato, P. Herr, H. “An EMG-position controlled system for an active ankle-foot prosthesis: an initial experimental study. Rehabilitation Robotics”, 2005. ICORR 2005, 375- 379

[6] Uchida, N. Hiraiwa, A. Sonehara, N. Shimohara, K. “EMG Pattern Recognition By Neural Networks For Multi Fingers Control”. Engineering in Medicine and Biology Society, 1992.

[7] Kumar, S. Kumar, D.K. Alemu, M. Burry, M. “EMG based voice recognition”, ntelligent Sensors, Sensor Networks and Information Processing Conference 2004”, 593- 597.

[8] Andrea Merlo and Dario Farina, “A fast and reliable technique for muscle activity detection from surface EMG signals”, IEEE transactions on biomedical engineering, Vol 50, No. 3, March 2003.

[9] http://bio-medical.com/[10] Ivan Lugo-Villeda, Antonio Frisoli, Vicente Parra, Oscar Sandoval-

Gonzalez, Carlo Avizzano and Massimo Bergamasco. : “A Mechatronics Analysis and Synthesis of Human Walking Gait”. International Conference of Mechatronics ICM 2009.

[11] Luis I. Lugo-Villeda, Antonio Frisoli, Oscar Sandoval–Gonzalez, Miguel A. Padilla, Vicente Parra-Vega and Massimo Bergamasco.: “Haptic Guidance of Light–Exoskeleton for Arm–Rehabilitation Tasks”, RO-MAN 2009.

[12] Paolo Tripicchio, Oscar Sandoval-Gonzalez, Alessandro Filippeschi, Emanuele Ruffaldi, Carlo Alberto Avizzano and Massimo Bergamasco.: “Human forces in hands free interaction: a new paradigmfor immersive virtual environments”, RO-MAN 2009.

[13] Emanuele Ruffaldi, Oscar Sandoval-Gonzalez, Alessandro Filippeschi, Antonio Frisoli, Carlo Avizzano and Massimo Bergamasco.:” Integration of Multimodal Technologies for a Rowing Platform”. International Conference of Mechatronics ICM 2009.

[14] Emanuele Ruffaldi, Alessandro Filippeschi, Oscar Sandoval-Gonzalez, Antonio Frisoli, Carlo Avizzano and Massimo Bergamasco.: “Vibrotactile Perception Assessment for a Rowing Training System”, World Haptics Conference 2009.

[15] W. M. Murray, S. L. Delp, and T. S. Buchanan, “Variation of muscle moment arm with elbow and forearm position,” J. Biomech., vol. 28, no. 5, pp. 513–525, 1995.