2.4GHZ WIRELESS ELECTROMYOGRAPH SYSTEM

WITH STATISTICALLY OPTIMAL AUTOMATIC GAIN CONTROL

Design and Performance Analysis

Andrea Morici, Giorgio Biagetti and Claudio Turchetti

DIBET — Dipartimento di Ingegneria Biomedica, Elettronica e Telecomunicazioni

Universit`a Politecnica delle Marche, I-60131 Ancona, Italy

Keywords:

Wireless sensors, Surface EMG, IEEE 802.15.4, LR-WPAN, AGC, Laplacian distribution.

Abstract:

In this paper a wireless system for non-invasive surface electromyography (SEMG) is presented. The use of a

wireless technology, that substitutes cabled electrodes with a wireless link, allows the number of sensors on the

body to be increased without affecting the patient’s freedom of movement. Problems in this setup, that extend

from energy consumption minimization, to satisfaction of wireless link operational bandwidth and distance

requirements, and from the necessity of embedding hardware in an appreciably small device, to making it not

too expensive to ﬁnal customers, have been deeply analyzed and solved. In this context, low rate wireless

personal area networks (LR-WPANs) proved to be a good choice for the realization of low-cost embedded

wireless electrodes for electromyography. Following these considerations, a low-cost electromyographical

wireless device, based on an off-the-shelf IEEE 802.15.4-compatible RF transceiver, have been designed and

realized, and optimized signal processing algorithms developed to enhance the system accuracy. In particular,

due to the wide range of possible amplitudes for the SEMG signal, an optimal automatic gain control, based on

a detailed statistical signal analysis, have been developed to reduce the distorsion at the output of the quantizer.

1 INTRODUCTION

Electromyography (EMG) is a useful diagnosis tech-

nique in the ﬁeld of neurophysiology, used for evalu-

ating and recording physiologic properties of muscles

both at rest and while contracting. EMG can either

use needle electrodes (intramuscular EMG) or sur-

face electrodes (surface EMG) (Berzuini et al., 1985).

Naturally, only the latter is a non-invasive technique,

and is the one with which we deal in this paper.

The prediction of muscle force from EMG (Stau-

denmann et al., 2006) may aid with the diagnosis of

some medical conditions in which the electrical ac-

tivity of the muscles or nerves is not normal, such as

nerve compression or injury, nerve root injury, and

with other possibly muscle-related problems such as

deambulation difﬁculties. This technique is not only

useful in hospital environments, but also in rehabili-

tation and motion analysis laboratories, as well as in

ﬁtness centers, as it provides an assessment of the

electrical activity generated by contracting muscles

during movements (Jansen et al., 2003). Today the

non-invasive Surface Electromyography (SEMG) has

become very popular owing to the great variety of

applications it can be used in. Recently, to mention

just few examples, EMG has proven to be useful as a

sensor for measuring everyday playing behaviour of

children (Kawakami et al., 2007), as an interface for

inputting characters to a computer (Miyazawa et al.,

2006) and for studies of gait dynamics in free-running

insects (Lemmerhirt et al., 2006).

A typical SEMG analysis may exercise multi-

ple sensors positioned on the patient’s body, each of

which may require one or more data channels. A

wireless recorder system is thus demanded so that the

patient’s freedom of mobility does not decrease with

the number of sensors applied. Some issues that an

SEMG recorder system must face are related to its

input sensivity, as it is well established that the am-

plitude of the EMG signal ranges from 0 to 10mV

(peak-to-peak) or 0 to 1.5mV (rms) and that is con-

taminated by several sources of noise, thus a high-

gain differential ampliﬁer with a CMRR of at least

80dB is required. Other problems are related to the

number of channels to be acquired: successful anal-

ysis of motion activity involving a group of muscles

Morici A., Biagetti G. and Turchetti C. (2009).

2.4GHZ WIRELESS ELECTROMYOGRAPH SYSTEM WITH STATISTICALLY OPTIMAL AUTOMATIC GAIN CONTROL - Design and Performance

Analysis.

In Proceedings of the International Conference on Biomedical Electronics and Devices, pages 39-45

DOI: 10.5220/0001511300390045

 SciTePress

needs a multiple channel recording, able to evaluate

more than one EMG channel simultaneously. In this

paper a multi-channel SEMG recorder system, con-

sisting of a wireless wearable sensor interfaced to a

standard PC for back-end data analysis, has been de-

veloped to satisfy the requirements of portability, high

sensivity and low-cost.

The research objectives of this article is to pro-

pose a new design of an embedded system made with

off-the-shelf components and suitable for the above

purposes. A statistical analysis of the EMG signal

is provided and exploited to optimize the algorithms,

used to enhance the performance of the low-cost com-

ponents employed in the system, to levels adequate

for the application. This is done by means of statisti-

cally optimized gain adjustment system that is able to

achieve a better SNR respect to ﬁxed-gain implemen-

tations.

2 ELECTROMYOGRAPHIC

SIGNALS

A brief introduction on the EMG signals is neces-

sary before presenting the hardware structure of the

system. The source of EMG signal is the electrical

potential generated by muscle cells when they con-

tract. Using a surface electrode, only the general pic-

ture of muscle activation is monitored, whereas the

activity of just a few ﬁbers can be observed using nee-

dle electrodes. The amplitude of the resulting sig-

nal can range from less than 50 uV to about 5 mV.

As the usable energy of the signal is limited to the

0 to 500 Hz frequency range, with the dominant en-

ergy being in the 50 to 150Hz range, the sampling

frequencies needed for its acquisition are in the or-

der of kilohertzs, generally from 1 kHz upwards. In

order to get EMG voltage signals, a differential pair

of Ag/AgCl electrodes is commonly used. These will

pick up the voltage difference and through two very

short shielded conductors it will be fed to an instru-

mentation ampliﬁer on the acquisition device, that ad-

justs the amplitude of the EMG signal to an appropri-

ate range for the analog to digital converter (ADC) as

will be shown next.

An example of a typical EMG signal is shown in

Figure 1, which reports its time progress as acquired

by our system at a sample rate of 2kHz.

The periods where the signal amplitude is low and

ﬂat correspond to the muscle being at rest, and the

recorded signal is dominated by electrical noise from

both the environment and the acquisition system. The

parts in which the amplitude is increased above such

noise ﬂoor correspond to muscle contractions. The

0 5 10 15

−4.5

−3

−1.5

1.5

4.5

time [s]

amplitude [mV]

Figure 1: EMG signal recorded on a biceps muscle with the

presented system.

subject of the acquisition is a biceps humerus muscle

of a young adult male who was asked to perform re-

peated liftings of his arm following a randomised se-

quence. Some contractions have been kept for several

seconds. The exercise was executed in a non stressed

condition.

2.1 Statistical Analysis for Optimal

Automatic Gain Control

A detailed knowledge of the statistical EMG signal

amplitude is necessary to devise optimum strategies

to properly amplify the EMG signals before feeding

them to the quantizer used for the analog to digital

conversion, since for proper evaluation of muscle ac-

tivity it is of utmost importance that the possibility of

saturation is kept to a minimum, while not degrading

the signal too much due to quantization.

To analyze the statistical properties of an EMG

signal it is necessary to separate the two main parts

previoulsy mentioned: rest condition and activation.

The former is primarily dominated by the noise gener-

ated by the electrical devices in the acquisition board,

and by their susceptibility to irradiated emissions. In-

deed, it can easily be veriﬁed that it is white noise

with a Gaussian probability density function (PDF).

The latter part consists in the recording of the over-

all effect of the activation potentials during the mus-

cle contraction. Figure 2 shows an estimate of the

PDF of the recorded signal cleared of the noise-only

portions. Conﬁrming already known ﬁndings (Clancy

and Hogan, 1999), the EMG signal, let us call it x(t),

displays a Laplacian PDF, deﬁned as

(x) =

exp



−

|x− µ|



(1)

where µ is the mean value, supposedly zero, and b is

the mean absolute value (MAV), that is, b = E{|x|},

and is usually one of the most important features to

be extracted from the EMG signal.

For reference, Figure 3 reports the same

experimentally-obtained distribution plotted against

BIODEVICES 2009 - International Conference on Biomedical Electronics and Devices

−3 −2 −1 0 1 2 3

0.2

0.4

0.6

0.8

1.2

amplitude [mV]

probability density [1/mV]

pdf estimate from data

interpolated Laplacian

Figure 2: Probability density function of the EMG signal of

Fig. 1 compared to its best ﬁtting Laplacian distribution.

−3 −2 −1 0 1 2 3

0.2

0.4

0.6

0.8

1.2

amplitude [mV]

probability density [1/mV]

pdf estimate from data

interpolated Gaussian

Figure 3: Probability density function of the EMG signal of

Fig. 1 compared to its best ﬁtting Gaussian distribution.

the best-ﬁtting Gaussian distribution. There is a clear

mismatch between the two, conﬁrming that the Lapla-

cian better describes the EMG signal statistics.

From a statistical point of view, as the non-

linearity affects the signal amplitude, an optimal au-

tomatic gain control (AGC) must be able to minimize

the distorsion at the output of the quantizer.

Let us then suppose that the quantizer has a satura-

tion level L, and that in between it performs the ideal

staircase quantization described by

y =



⌊x+ 1/2⌋ |x| < L

L sign(x) |x| ≥ L

(2)

then, the expected MAV at the quantizer’s output is

E{|y|} =

L+1/2

⌊x+ 1/2⌋e

−x/b

dx+

+∞

L+1/2

−x/b

(3)

where the second term at the right hand side is the

contribution due to saturation effects. The ﬁrst inter-

gral can easily be evaluated as follows

L+1/2

⌊x+ 1/2⌋ e

−x/b

dx =

∑

n=1

n+1/2

n−1/2

−x/b

dx (4)

0 0.1 0.2 0.3 0.4 0.5

−14

−12

−10

−8

−6

−4

−2

relative input MAV

MAV estimate relative error [%]

1024−levels quantizer

512−levels quantizer

256−levels quantizer

Figure 4: Error of MAV estimate after quantization and sat-

uration as a function of input MAV (relative to full scale)

for different quantizer resolutions.

which, after a few simple manipulations, yields

E{|y|} =

1− e

−L/b

1/(2b)

− e

−1/(2b)

(5)

Ideally, we would like to operate the quantizer

with an input signal whose amplitude b is such that its

post-quantization estimate E{|y|} is as close as pos-

sible to b. A measure of the error introduced by the

quantizer is the relative MAV error (E{|y|} − b)/b,

shown in Figure 4 for different quantizer resolutions

as a function of the relative input signal amplitude

b/L. As can be seen from the graphs, if the input

level is kept close to about 0.1L with a proper AGC

mechanism, which will be described in a following

section, even low resolution quantizers can give ex-

tremely good results.

3 WIRELESS SENSOR NODE

The wireless node we propose comprise all the elec-

tronics needed to measure the biological parameters

of interest, processing them both prior and after their

conversion to the digital domain, and to transfer them

through a wireless link to a nearby observation sta-

tion. All wireless board components were chosen

keeping in mind energy saving, low-cost, high inte-

gration and good electrical property, for the analog

parts, consistently to EMG signal requirements.

Figure 5 shows the block diagram of our wireless

node, while Figure 6 shows a photo of a prototype of

the wireless embedded board for electromyography.

The sensor, that was designed to be of peel-and-

stick type, detects at the skin surface a differential

voltage signal that is ampliﬁed by a low-noise dif-

ferential ampliﬁer and low-pass ﬁltered in the band

of interest. This is a solution known as “active elec-

trode,” for the differential ampliﬁer is placed as close

as possible to the detection surface of the electrodes,

2.4GHZ WIRELESS ELECTROMYOGRAPH SYSTEM WITH STATISTICALLY OPTIMAL AUTOMATIC GAIN

CONTROL - Design and Performance Analysis

VOLTAGE

REFERENCE

DIGITAL

POTENTIOMETER

LPF

IIC

ADC

PWM

ADC

GPIO

SPI

IEEE 802.15.4

PHY

ACCELEROMETER

BIASING

SEMG

Figure 5: Block scheme of the wireless electromyograph

node.

so as to improve the immunity to the noise induced by

external radiated interferences. The signal, so ampli-

ﬁed, is fed through an RC anti-imaging low-pass ﬁlter

that limits the upper bound of the frequency spectrum

to be acquired. It’s cut-off frequency was chosen to

be approximately 530Hz.

The active sensor prototype used a two chip so-

lution for the microcontroller part and for the wire-

less physical (PHY) transceiver, to facilitate labo-

ratory testing and debugging, but in future realiza-

tions it will more convenient to switch to Platform-in-

Package (PiP) solutions, where the above mentioned

two chips are integrated into one package, in order to

save cost, space, and PCB design difﬁculties.

The board also comprise the off-chip RF part, in-

cluding PCB traces designed for impedance match-

ing and discrete components chosen to achieve good

impedance matching between the PHY transceiver

and the chip antenna. This kind of antenna was cho-

sen because of its small size and characteristics opti-

mized for our operating wireless band.

The node also includes a three-axis accelerometer

that can provide useful data for motion analysis when

combined to EMG measurements. Moreover, it pro-

vides three additional data channels to demonstrate

the bandwidth capacity in streaming four simultane-

ous real-time channels from the sensor node.

Acceleration data can also be an efﬁcient way to

give commands to the board, for instance to enable

low power operation modes by putting the electronic

devices of the board in standby during inactivity peri-

ods.

Figure 6: The wireless electromyograph embedded board.

Actual size is 50 mm × 30 mm.

Figure 7: Details of the instrumentation ampliﬁer conﬁgu-

ration used.

3.1 Signal Acquisition and Conditioning

The analog signal chain for the EMG signal requires

special attention. Because of the wide range in which

EMG signals can vary, a cascade of two variable

gain intrumentation ampliﬁers was used. A detailed

schematic diagram of their connection is shown in

Figure 7.

These ampliﬁers are also capable of convertingthe

differential EMG signal to single ended, with a high

CMRR, and although the circuit was optimized for

operation with a gain close to 1000 (60 dB), by vary-

ing both gains it is possible to change the overall gain

by ±40dB to accomodate very different and possibly

extreme application conditions.

Indeed, the algorithm in the sensor can choose

gains appropriate to different condition of use: for ex-

ample, if you are walking, sensor board will have to

set sensivity parameters to adequate values to sense

low level excitation potentials. On the contrary,

during sport trainings muscles are probably more

stressed, and so the sensor node can adjust gain pa-

rameters to achive best measuring performance.

A dual digital potentiometer serves to make the

gain variable, changing between a set of 256 different

values of resistance for each stage. This level of pre-

cision is very useful when the node is moved between

different muscle masses that need different gain lev-

els, and allows for the ﬁne-grained AGC that ensures

optimal quantization.

The analog signal so ampliﬁed is subsequently

converted to a digital signal by the ADC included

in the microcontroller, operating at a 2 kHz sampling

rate and with a maximum resolution of 10bits.

To provide a voltage bias to the output of the am-

pliﬁers, an integrated low-cost, band-gap reference

was used. The resistance seen between the output ter-

minal of the band-gap reference and ground is only

a fraction of an ohm, ensuring that the ampliﬁer in-

trinsic insensibility to common-mode voltages is not

reduced because of it. With a high-CMRR input stage

and a small battery-powered sensor node mounted

close to the patient’s skin, so as to minimize stray ca-

pacitance to ground, the use of a reference electrode

BIODEVICES 2009 - International Conference on Biomedical Electronics and Devices

to set the patient’s body potential was deemed unnec-

essary. Proper biasing of the input stage is provided

directly to the main electrodes by means of a resistive

network connected to the internal reference potential.

Despite all these precautions, it is still possible

that the detected signal presents a DC component, or

an input bias, high enough to cause saturation of some

ampliﬁer. To avert this possibility the circuit employs

an active bias control, fed between the two amplifying

stages by means of a PWM signal, that is able to can-

cel both ampliﬁer bias and (small) DC components

that can arise when the patient moves other parts of

their body.

This active offset compensation technique was

deemed to be superior to simple AC coupling, for it

can use more complex ﬁlters. Moreover, since the

communication channel between the node and the

base station is bidirectional, these ﬁlters can be run

on the PC side and thus there are potentially no lim-

its to their complexity. For instance, we made use of

fourth-order elliptical digital ﬁlters running on the PC

side, with their group delay properly compensated in

the reported plots. Such a solution proved to be very

good at removing the spikes, usually due to patient’s

motion, commonly found in EMG traces, but would

have been much more costly to implement in hard-

ware.

3.2 AGC Algorithm

AGC poses a different problem than offset and bias

compensation, since the latter are usually slowly

time-varying phenomena for which the round-trip de-

lay, due to the communication with the base station

system, does not cause harmful degradation of the

control loop stability and performance. On the con-

trary, the EMG signal can have quite abrupt transis-

tions. As a consequence, a simple but yet effective

AGC algorithm was devised, so that it could be run

on the wireless node to offer the quickest possible re-

sponse time.

As previously stated, the purpose of the AGC is

to keep the input MAV level to the quantizer as close

as possible to 0.1L, where L is the ADC saturation

level. To this end, an estimation

b(t) of the MAV is

computed with a ﬁrst-order recursive digital ﬁlter,

b(t) = (1− α)

b(t − 1) + α|z(t)/g(t)| (6)

with z(t) being the ADC output, g(t) the ampliﬁer

gain, and α controls the ﬁlter bandwidth. Good re-

sults have been obtained with α ≃ 1/64, which corre-

sponds to a cut-off frequency of about 5Hz.

The optimum gain is then calculated as

eg(t + 1) =

0.1L

b(t)

(7)

from which the actual gain g(t + 1) to be used next

is chosen among the available gains, in steps of ap-

proximately 2 dB, with the help of a 22-entry look-up

table.

3.3 Wireless Data Transmission

LR-WPAN are emerging technologies for medium

distance low data rate communications. A protocol

to manage this kind of networks has been deﬁned by

IEEE 802.15.4, which describes both a MAC layer

and a PHY layer. The operating frequencies of the

wireless link can be 868MHz, 915MHz or 2.4 GHz,

for an available data rate respectively of 20 kbps,

40 kbps and 250kbps. Our active sensor operates

at 2.45 GHz in the ISM band to achieve maximum

throughput. In this band there are 16 channels, each

5MHz wide. Typical distances covered by this tech-

nology ranges from 30m to 70 m in open spaces. It

can be easily extended by the use of an RF power am-

pliﬁer joined to an LNA. Typically they are the same

as for other ISM wireless technologies such as Blue-

tooth and Wi-Fi. In customized applications IEEE

802.15.4 could imply difﬁculties in respecting timing

constraints posed by real-time streaming of data, such

as the one we need to perform in this context. We

hence decided to only use the capabilities of the PHY

layer of IEEE 802.15.4, customizing the MAC layer

to our purposes. A number of active SEMG sensors,

depending on how many data channels each uses, can

comunicate to the base station (BS) in a star topology

on the same RF channel, using a custom beaconed

time-division multiple access (TDMA) MAC scheme.

The BS is itself composed by an IEEE 802.15.4 com-

pliant transceiver and its task is to make data available

to the PC by the use of an USB link. For stream-

ing data from multiple sensors and for achieving full-

duplex operation it is necessary to assign time slots

to each sensor and to transmit/receive transactions.

Transmission and reception has to be scheduled by

devising an adequate timing of the active sensor con-

sidering the strict requirements of the ADC sampling

time. The adopted transceivers have particular tim-

ings regarding the transmission over the air of a data

packet. There is a warmup period t

warmup

= 144µs be-

fore the effective bitstream can be relayed, followed

by a t

cooldown

= 10 µs cooldown period. Timings are

then coherent with those reported in Figures 8 and 10:

pkt

(B) = t

warmup

+ t

header

+ B·t

byte

+ t

trailer

+ t

cooldown

(8)

≈ t

pkt

(B)



B=B

(9)

with t

byte

= 32 µs as per IEEE 802.15.4 speciﬁcations,

and where the payload length B

is comprised of the

EMG data bytes B

EMG

and of the acceleration data

2.4GHZ WIRELESS ELECTROMYOGRAPH SYSTEM WITH STATISTICALLY OPTIMAL AUTOMATIC GAIN

CONTROL - Design and Performance Analysis

Preamble SFD FLI CRC

144 us 192 us 64 us

4 bytes

byte

2 bytes

Payload

byte

Figure 8: PHY packet transmission timings.

Header

3.968 ms

4 bytes 96 bytes 2 bytes

EMG

Z / BATT

18 bytes

ACCGains

4 bytes

Figure 9: Packet payload content.

bytes B

ACC

, apart from side channels (e.g. gain lev-

els) and node status information, as shown in Fig. 9.

Packet length must be chosen as a compromise be-

tween latency and channel bandwitdth utilisation, as

shown below.

Let us call t

adc

the time needed by the ADC to ﬁll

bytes of the payload, for the EMG and each of the

three accelerometer channels:

adc

·n

(i)

(10)

where N

is the number of bytes per sample, n

the

number of channels per sensor type in each node,

sampled at a sample rate F

(i)

(usually n

EMG

=1 for the

single EMG channel sampled at F

(EMG)

= 2 kHz, and

ACC

=3 for the three accelerometer channels sam-

pled at a much lower sample rate F

(ACC)

=125 Hz).

Then we have the constraint

> t

pkt

(B)



B=B

, t

= t

adc

− m·t

(11)

where B

is the number of bytes needed for the con-

trol channel, and m is the maximum number of wire-

less nodes simultaneously active.

Sensor

Boards

Base

Station

Timeline

Wireless

Link

Personal

Computer

~4.4 ms

x m

~1 ms

~4.4 ms

x m

~1 ms

Cabled

Link

Figure 10: Communication ﬂow scheduling of the various

system components.

Table 1: Relative mean square error of the post-quantizer

MAV estimate, for different quantizer resolutions, and for

either a ﬁxed gain of 60dB and for AGC-determined gains.

gain policy 8 bit 9 bit 10 bit

ﬁxed gain −36.3dB −47.5dB −55.1dB

AGC −57.1dB −63.8dB −67.1dB

The samples of the myo-electric and acceleromet-

ric signals are packed for a better efﬁciency of trans-

mission. Each packet has a header deﬁned by means

of a protocol that has been developed for this speciﬁc

application, in order to perform the temporal synchro-

nization and the recovering of data ﬂow in a sensor

network. With the payload structure shown in Fig. 9,

deﬁned for ADC resolutions of up to 12 bits, and with

the previously mentioned sample rates, the theoret-

ical maximum number of nodes m operating on the

same RF channel is 6, with an extra time slot available

for retransmissions in case of errors. The actual limit

depends on the particular RF environment and back-

ground noise, which affects the need and frequency of

retransmissions, and is currently being investigated.

4 EXPERIMENTAL RESULTS

The prototype board was tested and used to acquire

a sample EMG signal, reported in Fig. 1. Although

proper shielding of the prototype was not employed

due to the presence of auxiliary debugging and de-

velopment connections, a quite good signal-to-noise

ratio was achieved. Power consumption resulted in

about 10mA, with many power-saving optimizations

that can still be implemented in the control software,

thus making it possible to achive a battery life of 5

to 10 hours of continuous operation out of a standard

coin-size rechargeable Li-ion cell.

In order to show the effectiveness of the proposed

AGC for EMG signal acquisition, some other experi-

ments were also made. Figure 11 shows some of the

results. It displays an EMG signal used as a refer-

ence, and the ampliﬁer gains that were chosen by the

AGC algorithm for each portion of the signal. The re-

sult was then quantized, and the quantized signal used

to compute an estimate of the MAV. The MAV was

smoothed with a fourth-order elliptical ﬁlter and the

result shown in the same ﬁgure, and compared with

that obtained from the unquantized version.

The results of the comparisons are shown in Ta-

ble 1, which compares the errors made with different

gain policies and different quantizer resolutions. As

can easily be seen, the adoption of AGC permitted

us to obtain an increase in the accuracy of the MAV

BIODEVICES 2009 - International Conference on Biomedical Electronics and Devices

0 10 20 30 40 50 60 70 80

−1.0

−0.5

0.0

0.5

1.0

time [s]

amplitude [mV]

0 10 20 30 40 50 60 70 80

100

time [s]

gain [dB]

0 10 20 30 40 50 60 70 80

0.0

0.1

0.2

0.3

time [s]

MAV [mV]

Figure 11: Operation of the AGC and resulting MAV es-

timate. From top to bottom: original EMG signal, optimal

gain as selected by the AGC algorithm, post-quantizer MAV

estimate.

estimation equivalent to more than two extra bits of

resolution in the ADC, thus making it possible to use

cheaper components without any serious degradation

of the performance.

5 CONCLUSIONS

A complete wireless electromyographic system was

developed, comprising a wearable sensor board, in-

teracting with software running on a common PC for

elaboration of the acquired data, and remote control

of the acquisition circuitry for optimal system perfor-

mance.

Moreover, the employed technology offers the ca-

pability of conﬁguring reasonably large sensor net-

works for the analysis of several concurrent muscle

ﬁber activities, also with base stations distributed in

large area laboratories, and for example makes the

analysis of running patients easier. The wireless EMG

sensor makes the patients free of cumbersome wires

and heavy transmitters and, as their movement are

more natural, the resulting analysis is more adherent

to reality. In comparison with other wireless technolo-

gies, these devices have a lower powerconsumption, a

longer battery life, and the networks they realize can

have a greater number of nodes and cover a longer

distance.

In future works the microcontroller will be substi-

tuted by a low-cost DSP, embedding all the peripher-

als on it and augmenting the on-board signal process-

ing capabilities.

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2.4GHZ WIRELESS ELECTROMYOGRAPH SYSTEM WITH STATISTICALLY OPTIMAL AUTOMATIC GAIN

CONTROL - Design and Performance Analysis