A PORTABLE REAL-TIME MONITORING SYSTEM
FOR KINESITHERAPIC HAND REHABILITATION EXERCISES
Danilo Pani
1
, Gianluca Barabino
1
, Alessia Dess`ı
1
, Alessandro Mathieu
2
and Luigi Raffo
1
1
DIEE - Dept. of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
2
Chair of Rheumatology and Rheumatology Unit, University and AOU of Cagliari, Cagliari, Italy
Keywords:
Kinesitherapy, Real-time, Rheumatic disease, Hand rehabilitation.
Abstract:
Rheumatic diseases, such as rheumatoid arthritis and systemic sclerosis, may seriously reduce the quality of
life of the patients. Nowadays, their progress can be controlled only through personalised pharmacological
treatments. Kinesitherapy can also help in faster movement recovery, also contrasting the disability worsening.
This paper presents a portable low-cost system for the real-time quantitative monitoring and evaluation of hand
rehabilitation exercises. The system, based on a MSP430 microcontroller central unit, provides a platform for
the analysis of fine characteristics hitherto unavailable of 4 exercises required for the hand rehabilitation in
rheumatic patients. The systemcan be controlled, through a Bluetooth connection, by a graphical user interface
running on the physician’s PC. The first prototypical systems have been developed for experimental outpatient
trials.
1 INTRODUCTION
Rheumatic diseases, such as rheumatoid arthritis and
systemic sclerosis, may severely reduce the quality
of life of the patients, which are required to undergo
an integrated therapy including kinesitherapy and a
personalised pharmacological protocol. If the lat-
ter presently represents the only way to control the
progress of the disease, the former both allows a faster
recovery after inactivity periods caused by active dis-
ease and contrasts the progressive disability. For in-
stance, patients with scleroderma suffer a skin thick-
ening, typically localized on the hands, whose con-
sequence is a limited mobility which in turn exac-
erbates the problem giving rise to a vicious circle.
When the lesions are localized on the hand, such in-
validating diseases hamper the execution of normal
daily life activities such as hair brushing, dressing or
cooking. Both hand strength and fine movements are
often compromised.
Specifically designed physical exercises associ-
ated to an appropriate pharmacological therapy can
help in restoring the motor function of the hand. In
order to achieve the best results, such exercises must
be properly performed, with the right number of se-
ries and repetitions. During outpatient examinations,
an expert physician can evaluate the quality of the
movements in order to effectively guide the patient
through the training, avoiding the onset of inflamma-
tory flares involving the hand. However, both a quan-
titative measurement and analysis of the patient’s ef-
fort, and the definition of the best suited protocol for
a specific patient, are hampered by the lack of instru-
ments expressly designed and packaged to this aim.
Beyond qualitative analyses including visual inspec-
tion and questionnaires administration, only for some
exercises (typically grip and pinch strength) some
digital devices are able to provide one-shot measure-
ments. Unfortunately they are quite expensive and
hardly integrable in a complete rehabilitation moni-
toring framework.
In this paper, a portable prototypical system for
the real-time quantitative evaluation of hand rehabili-
tation exercises is presented. Compared to the typical
procedures at the state of the art, the proposed system
has been designed in cooperation with expert rheuma-
tologists to monitor 4 agility/strength exercises, al-
lowing to analyse with a finer resolution character-
istics of the execution otherwise hitherto unavailable
(e.g. speed, frequency, execution precision). On-line
monitoring is provided by a MSP430 microcontroller
(MCU) based subsystem able to perform real-time
event detection and measurements on the incoming
signals from the 4 sensorized devices. The system,
battery-powered for patient’s safety and conveniently
accommodated in a metal briefcase, is controlled by
82
Pani D., Barabino G., Dessì A., Mathieu A. and Raffo L..
A PORTABLE REAL-TIME MONITORING SYSTEM FOR KINESITHERAPIC HAND REHABILITATION EXERCISES.
DOI: 10.5220/0003793400820089
In Proceedings of the International Conference on Biomedical Electronics and Devices (BIODEVICES-2012), pages 82-89
ISBN: 978-989-8425-91-1
Copyright
c
2012 SCITEPRESS (Science and Technology Publications, Lda.)
a desktop PC via a Bluetooth connection which re-
ceives both the raw signals, the measurements col-
lected until then and on-line refined statistics on them.
A graphical user interface (GUI) developed in MAT-
LAB allows a real-time qualitative and quantitative
analysis of the exercise execution. The presence of
different sensorized devices into a single system im-
proves usability and it opens to the integration of ad-
ditional devices. The system is going to be evaluated
in a clinical trial in Italy from October 2011.
The remainder of this paper is structured as fol-
lows. In Section 2 a brief description of the state of
the art is provided. Section 3 provides an overview
of the proposed system, whose hardware structure is
explained in Section 4, whereas the physician GUI is
presented in Section 5. Section 6 concludes this work
with final remarks and perspectives for future devel-
opments.
2 STATE OF THE ART
Several diseases can affect the human hand, impair-
ing its functionality and then negatively influencing
the daily life. The largest part of studies in the field of
the hand rehabilitation with biomedical devices deals
with the post-stroke recovery (Dovat et al., 2008). In
this case, cable-driven units connected to each fin-
ger by means of soft rings are exploited, being able
to move the fingers with predefined patterns (passive
movements) and/or to provide a tunable resistance to
the hand movement. Other approaches make use of
complex mechanical infrastructures (Huang and Low,
2008) or exoskeletons in order to assist the movement
(Brokaw et al., 2011) or help in restoring the motor
function (Iqbal et al., 2010).
For functional assessment only, the most common
evaluation involves pinch and grip exercises. Both the
Jamar dynamometer (isometric) and the Vigorime-
ter (dynamic) represent well established instruments
for the clinical evaluation of the grip strength (Peters
et al., 2011). Commercial devices such as Pablo by
Tyromotion GmbH or the H500 Hand Kit by Biomet-
rics Ltd. allow monitoring also the single finger pinch
force. In principle, isometric wrist dynamometer can
be also used to estimate the torque applied with the
finger when the wrist is in a fixed position, in order
to evaluate the hand performance with respect to this
task. Usually the digital versions of these devices are
able to provide maximum, average and standard devi-
ation of the force, but without any temporal analysis
within a series without additional electromyographic
signals (Seo et al., 2009). In (Helliwell et al., 1987),
a grip measurement device is presented, able to per-
form also some time measurements but only on a sin-
gle 4.4s grip exercise for the performance assessment
in rheumatic patients. A similar work has been pre-
sented in (Andria et al., 2006) for the parkinsonian
patients. In both cases the aim is a one-shot func-
tional assessment rather than the monitoring of a se-
ries of exercises, since multiple repetitions are some-
times used only for statistical purposes. An interest-
ing device for rehabilitation mixing torque and grip
force has been presented in (Lambercy et al., 2007),
but is not intended for monitoring purposes.
The hand agility (severely affected by rheuma-
toid arthritis and scleroderma) can be in principle
evaluated by means of finger tapping tests, originally
conceived to assess both motor speed and control in
neuropsychology. From the first mechanical devices,
other approaches for the monitoring of this kind of ex-
ercise arose. Approaches including a passive marker-
based motion analyser (Jobb´agy et al., 2005) present
a very complex setup not suited for a fast evaluation.
Other approaches, based on sensorized gloves (Bus-
tamante et al., 2010), are uncomfortable for patients
with hand deformities caused by arthritis. In (Muir
et al., 1995), a touch system based on a 4-finger ac-
tive sensor (injecting on the hand a small sinusoidal
current at 1.5 kHz) has been presented along with its
support software. An App (Digital Finger Tapping
Test 1.0) with limited functionalities is also available
for IPhone users. An approach based on the detection
of the exertedforce in the tapping activity is presented
in (Macellari et al., 2006).
It is worth to note that, to the best of our knowl-
edge, the realization of a low cost device for the quan-
titative monitoring of both agility and strength ki-
nesitherapy exercises for rheumatic patients has not
been presented in literature until now.
Figure 1: A picture of the prototypical system.
A PORTABLE REAL-TIME MONITORING SYSTEM FOR KINESITHERAPIC HAND REHABILITATION
EXERCISES
83
3 THE SYSTEM AT A GLANCE
The system is conveniently packaged in a lightweight
metal briefcase, as shown in Figure 1. With the pro-
posed system, the patient can perform 4 exercises
with a single hand a time, with as many sensorized
devices.
There are 2 knobs on the vertical panel. The outer
one allows the evaluation of the patient manipulation
dexterity (exercise of dynamic rotation). The patient
must rotate as fast as possible the knob using his fin-
gers, shaped in a pinch grasp, without any wrist ro-
tation and maintaining the forearm on the horizontal
plane. The inner knob allows to evaluate the clock-
wise and anticlockwise rotation torque (isometric ro-
tation exercise) with the same grasp type and restric-
tions of the previous exercise.
On the horizontal panel it is possible to perform
the other two exercises. One is a revised version of the
finger tapping exercise, which must be performed on
the exposed printed circuit board (PCB). The patient
must touch key-shaped pads on the PCB following a
specific sequence (little finger, ring finger, middle fin-
ger, first finger and thumb) as playing the piano. It is
allowed to have multiple finger on the keys provided
that the sequence is correctly performed and closed
with a thumb tapping. The last exercise allows eval-
uating the hand extension ability. The patient must
rest the hand between the two L-shaped aluminium
profiles, touching them with the thumb and the little
finger. Then he must open and close the hand (always
on the horizontal plane) in rhythm, allowing the sys-
tem to appreciate opening and closing agility. A small
counter-resistance is applied.
The system considers the exercise completed af-
ter a number of repetitions, previously established by
the physicians and hard coded in the system firmware,
have been executed. By using a GUI installed on his
PC, the physician can choose which exercise to exe-
cute, evaluating in real-time how the patient executes
it not only in terms of correct position but also look-
ing at barely perceptible execution parameters that the
digital system is able to reveal. For instance, a real-
time updated plot discloses sensors wave shape while
numerical data such as peak and running-average val-
ues are displayed on the GUI, allowing a finer moni-
toring compared to a traditional visual inspection.
4 SYSTEM ARCHITECTURE
Figure 2 shows the most important parts of the system
and their interconnection. Beyond the MCU subsys-
tem controlling the whole system, we can see:
the analogue sensorized devices;
the digital sensorized device (for finger tapping);
the analogue interface circuitry;
a Bluetooth module, which provides a wireless
link to the host PC;
additional components to provide a visi-
ble/audible feedback to the user.
The system can be easily supplied by a single-
cell Li-Ion battery. For improved safety, the inter-
nal battery can be recharged only when the system
is switched off. Along with the DC power supply
adaptor to recharge the internal battery, the Bluetooth
module (Bluegiga WT-11) is the only device that is
not embedded in the metal briefcase. The module
implements the Bluetooth stack and communicates
with the MCU via a standard UART port. A 25-pole
female D connector has been included providing a
clean way to access the Bluetooth module pins and
the JTAG ports to program the 2 MCUs embedded in
the system.
MSP430FG4618
Bluetooth
module
Feedback
Touch board
Signal
conditioning
stage
Sensors
UART I2C
GPIO ADC_IN
Figure 2: System block diagram.
4.1 The Sensorized Devices
The simplest device is that for the dynamic rotation
exercise. In this case, a precision multi-turn poten-
tiometer, equipped with a 30mm aluminium knob, has
been used. The potentiometer (Vishay 534, 20k,
2W) is able to perform 10 turns opposing a torque
of 0.006 Nm. The resistance varies linearly with the
rotation of the knob so that it suffices to measure the
voltage on the wiper to detect the angular position at
any instant. Due to the low opposing torque, the exer-
cise can be considered without any load.
On the contrary, the isometric rotation device is
composed of a 5-lobe 50mm plastic knob able to
slightly turn on its own axis pulling along with it a T
bar nut able to press one of two thin-film force sensors
(the low-cost Tekscan FlexiForce A201, max 110N),
for clockwise and anticlockwise rotations. These sen-
sors linearly vary their conductance in response to the
applied force. Being an isometric exercise, thanks to
the aforementioned design, the knob cannot spin.
BIODEVICES 2012 - International Conference on Biomedical Electronics and Devices
84
The hand extension exercise is dynamic but it
introduces a counter-resistance. It is evaluated by
means of an analogue draw wire position sensor (LX-
PA-15 by TME) mounted on a roller (CES30-88-ZZ
by Rollon) free to move on a 40cm linear zinc plated
guide (TES30-1040 by Rollon): the wire coming out
from the sensor is attached to a second roller mounted
on the same guide. The two rollers are attached to
as many L-shaped aluminium profiles actuated by the
patient opening and closing his hand. The sensor is
characterized by a nominal wire rope tension of 3.9N,
which must be overcome by the patient in order to
extend his hand.
Lastly, for the finger tapping exercise it has been
necessary to develop a capacitive touch board. Com-
pared to the one presented in (Muir et al., 1995), the
capacitive approach is still able to provide a detec-
tion of the touch without any counter-resistance from
the measuring device but also avoids any direct cur-
rent injection in the patient’s hand. The touch board
is based on the MSP430F2013 MCU, managing the
reading of the capacitance associated to 8 key-shaped
sensible areas on a PCB. The keys, which form a ca-
pacitor with the ground plane surrounding them, are
sequentially charged by the MCU, which is able to
measure the discharge time. Since the effect of touch-
ing a pad is the increase of the capacitance value, it
is easy to detect whether a sensor is touched or not,
comparing the measured discharge time with the base
value obtained when the pad is untouched. The de-
sign of this device followed the guidelines given in
(Albus, 2007) with some further consideration: the
layout of the board must accommodate both left and
right-handed exercises and the sensor shape should
lead to an ergonomic device (it should accommodate
different hand sizes and postures). Therefore the keys
were made slightly larger than the suggested value,
mesh-filled to keep the capacitance base value under
an acceptable level. The device provides over an I2C
bus, whenever required, the current status of the keys
in a single byte: the interpretation of the data in the
light of the exercise to execute is up to the main pro-
cessor firmware.
4.2 The Main Board
The main board, highlighted with a grey-shaded area
in Figure 2, hosts the MSP430FG4618 MCU, which
takes care of the actual processing and manages the
operation of the rest of the system. For the sake of
simplicity, a single power supply at 3.3V is available
on board.
The chosen processor embeds a 16-bit RISC CPU,
an 8kB SRAM, a 116kB flash memory for program
storage, different I/O ports, a 12-bit multi-channel
ADC, three timers and other unused peripherals. It
is clocked at 1MHz by means of an external quartz
oscillator.
4.2.1 The Signal Conditioning Stage
Given the nature of the involved signals, which are
slowly time-varying, it is possible to operate at rather
low sampling frequencies, with consequent benefits
in terms of real-time bounds for the signal processing
algorithms. On the other hand, the event detection
algorithms which underlie the system operations re-
quire an adequate time resolution: a fair trade-off be-
tween these two aspects led us to choose a sampling
frequency of 150Hz. The signal conditioning stage
must then implement a properly tuned anti-aliasing
filter.
The analogue interface block is essentially com-
posed of four non-inverting, active low-pass fil-
ters, implemented with an operational amplifier
(TLV2375) and a single pole RC net. The value of its
cut-off frequency has been set to about 48Hz to ex-
ploit the filter as anti-alias with guard band of about
25Hz under the Nyquist frequency, also limiting the
50Hz mains noise. The outputs of the four filters are
connected to as many different channels of the MCU
ADC.
R
1
R
R
s
2
IN
V = 0.5V
out
V
C
Figure 3: FlexiForce sensor conditioning stage.
Two different configurations have been employed:
the one used for the FlexiForce sensors is depicted
in Figure 3. The sensor has been connected between
ground and the operational amplifier inverting input,
making the stage a variable gain amplifier. Using a
fixed input, provided by a voltage reference at 0.5V,
the output varies linearly with the force applied to the
sensor (between 0.5 and 3.3V), according to:
V
out
=
1+
R
1
R
s
V
IN
= V
IN
+V
IN
R
1
G
s
= K
1
+K
2
G
s
(1)
where K
1
and K
2
are constants. Equation (1) shows
the linear dependency between output voltage and the
sensor conductance G
s
. The optimal value of R
1
has
A PORTABLE REAL-TIME MONITORING SYSTEM FOR KINESITHERAPIC HAND REHABILITATION
EXERCISES
85
been chosen in order to provide an adequate response
when the isometric rotation exercise is performed by
a rheumatic patient, even if this limits the operating
range of the sensor.
R
1
R
R
2
3
CC
V
out
V
C
R
s
R
4
Figure 4: Potentiometer-based sensors conditioning stage.
The second configuration, used for the potentio-
metric sensors and depicted in Figure 4, has a fixed
gain and a variable input voltage. The sensors are in-
serted in a voltage divider, with the wiper connected
to the stage input, so that the output value is propor-
tional to the voltage present at the wiper. A series
resistor limits the current sunk but the side effect is
that the voltage at the wiper cannot reach V
cc
, so a
gain greater than one must be used, which can be cal-
culated as
V
outmax
V
inmax
.
4.2.2 Patient Interface
The system includes low-level user interface elements
and some patient feedbacks, motivating him and aid-
ing a correct execution of the exercises. Two leds in-
dicate which hand must be used to execute the exer-
cise and another led gives a time reference blinking at
1Hz, which is useful for sustained position tests. They
are placed on the front panel for improved visibility.
Moreover, a buzzer chimes whenever the system de-
tects a successful event, letting the user know that the
system has effectively captured his action. The sys-
tem has been also provided with a double digit 7 seg-
ments display, which has different functions depend-
ing on the exercise. It displays:
the percentage of the effort with respect to the
maximum bound (extension and torque),
the number of correct sequences performed (fin-
ger tapping),
the percentage of rotation over 10 turns (dynamic
rotation).
Two buttons, white and red coloured, placed on
the horizontal plane and connected to two different
external interrupt pins of the MCU, provide a way for
the patient to interact with the device. The rst one
starts the exercise when the patient is ready, allowing
to correctly position the hand, whereas the second one
can be used to skip a single repetition of an exercise
(the whole exercise can be aborted from the GUI).
init_MSP430
check_BTooth
LPM0
init_resources
LPM0
exercise code
recieved from host
processing
LPM0
filter
detection
algorithm
send samples
to host
stop?
start signal
recieved
Figure 5: Firmware flow diagram.
4.3 Firmware
The operation of the MCU is controlled by the
firmware loaded onto its flash memory. This piece of
software is written in C and has been developed under
the CCS v4.0 IDE by Texas Instruments.
The firmware flow, depicted in Figure 5 is quite
simple: as soon as all the initializations have been
carried out, the MCU enters the low power mode
(LPM). The rest of the processing is then managed
asynchronously by interrupt service routines (ISR). In
the first phase:
the USCI A port, devoted to communicate with
the Bluetooth module, is configured in serial
mode;
the USCI B port, assigned to the communication
with the touch board, is configured in I2C master
mode;
the timer A and timer B, respectively used to beat
the sampling time and generate the reference time
for the timing led, are configured;
the ADC is set to perform a single conversion on
a single channel and initially left disabled;
the general purpose pins are set.
All the resources present on the board, as opera-
tional amplifiers, finger tapping MCU and Bluetooth
module, are initially held in reset. Then the Bluetooth
module is set up and configured by setting the operat-
ing mode, device name and password. The firmware
enters an endless loop, where each iteration corre-
sponds to the execution of an entire exercise. Inside
the loop the MCU goes immediately in LPM (both
CPU and MCLK disabled), waiting for the host to
receive the execution code of the exercise to launch.
Receiving the exercise code triggers the USCI A port
BIODEVICES 2012 - International Conference on Biomedical Electronics and Devices
86
ISR, which wakes up the MCU. Depending on the se-
lected exercise, some initializations are carried out,
the ADC input channel is set to the corresponding in-
put pin and it is started (except for the finger tapping
exercise). After that the MCU goes in LPM again,
waiting the start signal (by pushing the white button),
which unlocks the execution. From now on the pro-
cessing is timed by the timer A. In the corresponding
ISR, either the value at the ADC input is sampled and
stored or the new digital word from the tapping board
is read. A global counter is incremented, to keep track
of the number of samples gathered and hence to ex-
tract time measurements from it. Then the actual sig-
nal processing takes place on a sample-by-sample ba-
sis, in different ways depending on the specific exer-
cise, as explained in the following session. The cur-
rent sample is sent to the host machine through the
Bluetooth link, and only every second (150 samples)
a vector containing statistics which characterize the
execution is sent too. If the stop conditionwhich iden-
tifies the end of an exercise is not met, the core enters
the LPM again from which it will be released by the
acquisition of a new sample, otherwise the processing
steps back to the main loop, entering in LPM until the
system gets triggered again from the GUI.
4.3.1 The Processing Algorithms
For all the exercises but the tapping one, the samples
are first low-pass filtered by an 8-tap moving average
filter in order to further smooth the signal.
For the extension and isometric rotation exercises
the algorithm simply detects the signal peaks corre-
sponding respectively to a hand extension or a torque
application. This is done by comparing each sample
with a threshold, which is computed by averaging the
first ten samples acquired. This value is stored and
used as lower limit for the threshold, which is updated
after the detection of a new peak to 0.3× peak
value.
All the values are referred to a zero represented by
the initial condition of the sensorized device when
the user is ready to start. The peak event is validated
only if at least 75 consecutive samples are above the
threshold and only as soon as the samples go under
the threshold again. The peak maximum value, its du-
ration and position are determined and used to com-
pute their incremental mean values as:
¯m
N
=
( ¯m
N1
(N 1) + s)
N
(2)
where ¯m
i
is the mean value computed over i samples,
and s is the value of the new sample. The system also
stores the absolute maximum and minimum values for
the peak amplitude. It is worth to underline that the
variables which hold the average valuesare float num-
bers, though the CPU is a 16 bit platform and floating
point is not supported in hardware. Nevertheless all
these operations are translated by the compiler in the
proper microcode without additional coding effort.
The algorithm is different in the case of the dy-
namic rotation, since different features are needed.
The typical signal has a terraced waveform, where the
edges correspond to the spinning of the potentiome-
ter whereas the plateaus indicate that the transducer
is still. The duration of both edges and plateaus, and
the amplitude of each edge, are computed. To de-
tect both onset and end of an edge, a simple detection
mechanism based on thresholds has been designed,
exploiting the smoothness of the filtered signal. A
FIFO buffer of 14 samples is linearly updated at ev-
ery new sample. The mean value of the oldest 4 sam-
ples is computed and compared with the most recent
sample. If the difference is greater than an empiri-
cally determined threshold, the algorithm detects an
edge and marks the onset n samples before the most
recent one. When the difference falls back under the
threshold, the edge end is marked and the processing
is repeated, until the potentiometer reaches the limit.
By using absolute values, the processing is the same
for both clockwise and counter-clockwise exercises.
The finger tapping exercise differs from the oth-
ers because there are no analogue signals involved.
The MCU on the main board acts as the master of the
I2C channel, requesting the 8-bit word provided by
the sensorized device whenever the sampling timer
expires. For this exercise, the timer A has been dif-
ferently set, in order to have a sampling frequency
of 50Hz, which is in line with the state of the art
(Jobb´agy et al., 2005) and allows the complete scan-
ning of the 8 keys in a sampling period. As a new
word is received, it is mirrored, if necessary, in order
to have the least significant bit always referred to the
thumb key. When the first not null data is received,
the algorithm detects the less significant bit set to 1
and creates a mask used, at the next touch, to check if
the next key tapped corresponds to a less significant
bit or not. If this is true, the mask is updated and the
processing goes on, otherwise an error flag is set. The
sequence terminates when the thumb touch is detected
(lsb = 1). If the number of touches is equal to ve
the valid sequence counter is incremented or, if ei-
ther the error flag is set or the sequence length differs
from five, the bad sequence counter is. This process-
ing is performed in real-time and when the exercise is
complete, an additional routine computes the relevant
statistics, including average touch duration for each
finger, average distance between them, total consecu-
tive touches and total duration of the exercise.
A PORTABLE REAL-TIME MONITORING SYSTEM FOR KINESITHERAPIC HAND REHABILITATION
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87
5 THE PHYSICIAN GUI
By using a user friendly GUI developed in Matlab, the
physician can monitor in real-time on a host PC the
execution quality of the kinesitherapic exercises, also
extracting useful information to evaluate the rehabil-
itation progress over time. As already said, the link
between the system and the host PC exploits a Blue-
tooth technology. The Bluetooth device driver exports
a serial interface towards the user applications, which
is easy to manage using the built-in Matlab functions.
At launch, the very first window contains a list
of radio buttons enabling the selection of the exer-
cise and the hand to use. By pushing the start but-
ton on the GUI, a numerical code which identifies the
chosen exercise and the hand to use is sent to the de-
vice. The callback function associated to this button
also creates a new window which is specific for the
selected exercise. All the exercise windows, except
that of finger tapping one, contain an area where the
raw signal acquired by the sensor can be plotted over
time. The signal is sent to the host PC on a sample-by-
sample basis. Since every sample is a 16 bit integer,
before reading the GUI waits inside a while loop the
availability of at least 2 bytes in the serial port input
buffer. The received samples are then converted to
the corresponding real physical quantity by means of
the calibration values. For the sake of efficiency, the
time plot is refreshed only when a block of 75 input
samples has been acquired, shifting towards left the
previous blocks in the plot linear buffer: the oldest
block is overwritten and the new one is inserted on
the right. All the received samples are logged thus, at
the end of the execution, the user can visualize a static
plot of the whole signal.
Figure 6: GUI screenshot.
The GUI also receives the peaks position detected
in real-time by the system and used to analyse the
signal, together with other relevant parameters (e.g.
speed of execution, position and the amplitude of the
last peak, the maximum, the minimum and the mean
value of the executions). These values are sent by
the system to the host PC only every 150 samples of
the signal and the most important ones are presented
on the GUI (Figure 6). Counting the received sam-
ple, the GUI is able to properly receive such param-
eters as a data chunk. A flag at the end of the chunk
is used by the system to signal the end of the exer-
cise. The interface uses this flag to allow the visual-
ization of the whole signal plot, including the markers
to the peaks found by the system during the execution.
The interface enables the visualization of the “speed-
value plot” (Figure 7), which overprints to a bar graph
showing the peak values, a line graph representing the
frequency of the repetitions. This information can be
useful to evaluate how much the performance is de-
pendent by the execution speed, being important to
know if smaller values achieved by the patient are
caused by a higher execution speed or by fatigue.
Figure 7: GUI speed-value plot.
In every window there is the possibility to stop
the execution by using a push button, whose callback
function sends a numerical code to the system in order
to signal the premature end of the exercise. Another
push button enables going back to the main window,
where the user can select a new exercise.
6 CONCLUSIONS
The portable kinesitherapic monitoring system pre-
sented in this paper is proposed as a support tool to ex-
ploit along with the latest treatment techniques in the
rheumatic patients hand rehabilitation practice. Com-
BIODEVICES 2012 - International Conference on Biomedical Electronics and Devices
88
pared to other devices at the state of the art, the pro-
posed system presents several advantages. In fact it
embeds the sensorized devices necessary to execute
different kinds of exercisesin a single low-cost frame-
work, exporting the main real-time monitoring fea-
tures to a host PC via a wireless connection. Here,
an accurate analysis of the patient’s performances can
be easily performed, thanks to a user-friendly GUI.
In particular the real-time performance simplifies the
physician’s task of evaluating and correcting the pa-
tient’s training, being immediately available quantita-
tive measurements also involving the time-related as-
pects of the exercise. In the next future the system is
going to be employed in clinical trials on rheumatic
patients, with the aim of verifying the effectiveness of
the approach and the usability. The system could be
further expanded including additional sensorized de-
vices, in order to offer a wider selection of exercises.
Furthermore, being a compact and portable device, it
could represent a good solution to delivery rehabilita-
tion services in the patient’s home.
ACKNOWLEDGEMENTS
The research leading to these results has received
funding from the Region of Sardinia, Fundamen-
tal Research Programme, L.R. 7/2007 “Promotion
of the scientific research and technological innova-
tion in Sardinia” under grant agreement CRP2 584
Re.Mo.To. Project. The authors wish to thank V.
Lussu, L. Piras, I. Secci, N. Zaccheddu and F. Boi
for their collaboration. A special acknowledgementto
Michele Crabolu for the development of the first pro-
totypes of the finger tapping unit and the mechanical
realization of both the extension one and the briefcase
structure.
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A PORTABLE REAL-TIME MONITORING SYSTEM FOR KINESITHERAPIC HAND REHABILITATION
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