Authors: Che-Chang Yang, Yeh-Liang Hsu(2007-11-08)；Recommend:
Yeh-Liang Hsu (2009-09-09).
This paper is presented at the 33rd Annual Conference of the
IEEE Industrial Electronics Society, Nov. 5-8, 2007, Taipei, Taiwan
Algorithm Design for Real-time
Physical Activity Identification with Accelerometry Measurement
of physical activity are indicative of one’s mobility level, latent chronic
diseases and aging process. Current research has been oriented to provide
quantitative assessment of physical activity with ambulatory monitoring
approaches. This study presented the design of algorithm integrated with a
portable microprocessor-based accelerometry measuring device to implement
real-time physical activity identification. This algorithm processes real-time
tri-axial acceleration signals produced by human movement to identify targeted
still postures, postural transitions or walking. Fall detection is also
featured in this algorithm to meet the increasing needs for elderly care. High
identification accuracy was obtained during the preliminary test phase and the
observed limitations regarding real-time processing was also discussed. The
result reveals that this developed algorithm is technically viable for
real-time identification in ambulatory monitoring to provide sufficient
information in evaluating a person’s activity of daily living (ADL) and the
status of physical mobility. Possible system integration and applications in
the future were also discussed.
activity, aging, fall detection, accelerometry, elderly care, ambulatory
in sensors, microprocessors and wireless communication technologies have been
the driving factors to facilitate remote telemonitoring of physical activity
with mobile systems. These systems are potentially able to provide significant
information on details of physical activities and resulting energy expenditure,
and further to quantify the subject’s mobility level [Aminian et al., 2004].
research in recent years has focused on developing body-attached or wearable
systems. In a wearable system, the sensor units (e.g., gyroscopes or
accelerometers) and the required components are integrated into portable devices
which are attached to the belt at waist or chest level [Meijer et al., 1991; Veltink et al., 1996; Bouten et al., 1997; Foerster et al., 1999; Najafi et al., 2002; Najafi et al., 2003; Karantonis et al., 2006], or are even incorporated
into clothing [Nyan et al., 2006]. Miniature accelerometers have been
considered as the appropriate motion sensor for human movement measurement on
wearable systems [Meijer et al.,
1991; Bouten et al., 1997; Foerster et al., 1999;].
sensor-based monitoring system capitalizes on off-line data processing
technique to analyze recorded human movements [Najafi et al., 2002, 2003]. In these system, the motion sensors and data
storage medium are incorporated into a portable devices to enable ambulatory
monitoring. The recorded data is then processed and analyzed in PC-based
programs (e.g., MATLAB) using FFT, wavelet transform or other frequency-domain
manipulation to recognize human movements. Sophisticated data processing and
manipulation can be applied to yield high accuracy human movement
classification if the data is properly acquired. However, except the demanding
fall detection procedure, these systems are unable to provide real-time
response of identifying several human movements that has been expected in advanced
mobility telemonitoring systems [Nyan et
al., 2006]. Mathie et al proposed a generic framework for automated human
movement classification using accelerometry data [Mathie et al., 2002, 2003]. Karantonis et al also presented a real-time
system for physical activity classification [Karantonis et al., 2006]. All the movements are classified in real-time
process of wearable system, but identifying walking still required PC-based
presents an algorithm design for real-time physical activity identification
with accelerometric measurement. The algorithm is implemented in a
microprocessor-based portable motion measuring device to identify targeted
physical activity items among a subject’s activities in free-living environment.
These activities include three still posture (sitting still, standing still and
lying still), four postural transitions (sit-to-stand, stand-to-sit,
lie-to-sit, sit-to-lie), turn when lying and walking. In addition, fall
detection is also featured in this algorithm. All the targeted items identified
are processed in real-time process with wearable system. Preliminary
performance test was conducted to verify the identification accuracy in
laboratory-based phase. Results and further improvement was also discussed.
motion detection device was designed to measure human activities. The device
mainly consists of a PIC microprocessor (PIC18F6722, Microchip
Inc.)and a motion sensor module, and is packaged in the size of 100mm×60mm×25mm
(excluding the battery cartridge) with 140g in total weight. The motion sensor module
utilizes a miniature tri-axial accelerometer (KXM52-1050, Kionix Inc.) which functions on the principle of differential
capacitance in response to motion and constant gravity for measuring both
acceleration and inclination. The device is attached to the pants’ belt at
waist level because this position is close to the center of gravity of the body
[Meijer et al., 1991; Bouten et al., 1997; Najafi, et al., 2002; Karantonis et
The analog signals
of the sensor outputs are low pass filtered at 50Hz (-3dB) to reject noise
spikes and then directed to the PIC microprocessor for data sampling with 10-bit
60Hz A/D conversion. The PIC microprocessor cyclically processes real-time
physical activity identification with the programmed algorithm. This device is
also equipped with an RF 433.92MHz wireless data transmitter. Each identified item
is immediately forwarded to a local base station for data storage.
2.2 Algorithm design
Figure 1 shows
the framework of the algorithm which is organized with five processing steps:
Data sampling (Cx), pre-processing (Px),
dynamic postural transition identification (DBx), still posture
identification (DAx) and possible fall detection (DCx). All signals are
processed in the time-domain analysis due to the limited computation capability
of the PIC microprocessor and the constraints regarding real-time processing. A
batch of sampled data is called an “event” to represent one physical activity
item. This algorithm identifies one still postures or postural
transitions/dynamic movement with 0.5s or 2.5s collected data in a processing
cycle. In the case where there is no definite result determined throughout the
processes, the event will be recorded as an “Uncertain movement” or an
Figure 1. Algorithm framework
inclination (tilt) data in each tri-axial channel (x-, y-, and z-axis) are calculated prior to the
data sampling process. The data sampling process consists of the primary stage
(C1) and the secondary stage (C2) respectively. The use of this
dual-stage data sampling strategy ensures that the data of one event can be
captured within the same sampling interval. After primary sampling (C1), process D1 determines whether any sign of dynamic movement exists by
investigating accelerations in each axis. If no dynamic movement be detected (D1=No), the 0.5s tilt data relative to
the vertical axis is used to identify one of the three possible still postures
in the processes DAx. If dynamic movement is detected (D1=Yes), the secondary sampling (C2) is immediately activated to collect the subsequent 2.0s data.
The data collected in both stages is combined (2.5s) and is then
median-filtered (P1, window length
n=3) to reject noise spikes. Moving average is further applied to the
median-filtered accelerations data series. After the above processes, a dynamic
event with six data series (accelerations of x-, y-, and z- axes, and their respective tilts) is
to be identified in the following procedure.
mapping” technique is commonly employed to register apparent changes over the data
series by mapping the pre-processed data series into a binary sequence. The slope
sequences of each data series are intermediately generated to register the
difference between the two neighboring sample values (Equation 1). The binary
sequence data is then obtained from the slope sequence as Equation. 2. The entry
ci in the binary sequence is
assigned “1” if the
slope entry si exceeds a
specific threshold sthr.
Otherwise, ci is “0”. The inherent properties of the binary
sequence are the determinants to indicate the possible form of the original
data. Figure 2 shows an example of applying slope mapping to a trunk tilt data
of a sit-to-stand postural transition. Note that the binary sequence is
expressed as a histogram to highlight the entries in “1”.
Figure 1. An example of applying slope mapping to
the trunk tilt data sequence
2.3 Identification of targeted physical activity items
(1) Sit-stand postural transitions
vertical acceleration is used to identify sit-stand postural transitions as its
apparent sensitivity over the other two components. The acceleration patterns
of sit-stand postural transitions can be characterized by three rules: R1:
peak order; R2: peak distance (time interval) and R3: peak values. Figure.
3 shows an example of the vertical acceleration patterns from a test subject
during slow, normal and fast sit-stand postural transitions. By comparing the
upper (sit-to-stand) and the lower (stand-to-sit) pattern, the two postural
transitions have opposite peak orders (a positive peak appears first and is
followed by a negative peak for sit-to-stand, and the reverse order for
stand-to-sit case). The peak distance (R2) indicates the time interval (in
sec.) between that two peaks. Faster transition speeds generate shorter peak
distance. The peak values for R3 represent the respective
acceleration peaks in positive and negative regions. It is also evident for
both sit-stand transitions that faster postural transitions produce shorter
peak distance and higher peak amplitudes.
properties has also been observed in an extensive test with 15 recruited ostensibly
healthy subjects of various ages (11 males and 4 females, 5 for young,
middle-aged and elder, respectively). Practical threshold values for R2
and R3, were also extracted from test
results. Either of sit-stand postural transitions can only be identified when
the condition of all the three rules are satisfied.
Figure. 3. Example of vertical acceleration
patterns of slow, normal and fast stand-to-sit transition acquired by the same
(2) Walking and body turning when lying
dynamic event is not upright (D3=No),
the tilt data of the antero-posterior and the medio-lateral directions is used
in the decision D4 to determine a
body turning movement when lying.As shown in Figure 4, walking is characterized
by fast and repeated oscillating changes in the vertical acceleration. Previous
study revealed that the frequencies of normal walking are between 0.7Hz to 4Hz
with the peak acceleration values between 0.4g to -0.3g
were reported [Sekine et al., 2000; Mathie
et al., 2003, 2004]. Slope mapping is
applied to the vertical acceleration data to recognize walking movement.
Figure 4. An Example of acceleration patterns in vertical
and horizontal (antero-posterior) directions at level walking
(3) Lie-sit postural transition
postural transitions can be identified and further distinguished by
investigating both the trunk tilt variation and the final posture orientation.
Figure 5 illustrates the coordinate of trunk orientation. Upright stance
posture is defined as trunk orientations within -20° to 20° with reference to vertical. Lying
posture is regarded as the trunk tilt greater than 70° or less than -70°. By applying slope mapping, a
lie-to-sit transition produces positive increments in trunk tilt variation and
ends with upright orientation. Similarly, a sit-to-lie transition produces
negative increments in trunk tilt variation and ends with lying posture. Note
that reverse stance (handstand) posture is not considered in the posture
Figure 5. Trunk orientation for still posture
(4) Still postures
still postures requires the trunk orientation and the information of previously
known postural transitions or walking. A still event can be recognized as a
lying still posture according to the trunk orientation or a previous known sit-to-lie
postural transition. Sitting still or standing still postures have upright
orientation. Therefore, they can be distinguished by the existence of the types
of previous sit-stand transitions or walking movement. If a still event is not
lying and its previous posture transition is “sit-to-stand” or “walking”,
this event is identified as “standing
still”. On the other hand, the event is identified as “sitting still” if the previous posture transition is “stand-to-sit” or “lie-to-sit”.
(5) Possible falls
A fall can
be intuitively regarded as a movement accompanied by unusual high acceleration
peaks in a very short time interval. A fall is detected with two phases in this
algorithm. A “sign of fall” event can be recognized if there exists
acceleration greater than Vf=±1.5g. In the second phase,
the previously recognized “sign of fall” event could be raised to a “possible
fall” if it is followed by a prolonged lying posture of which time duration
Preliminary tests of the
3.1 Sensitivity and specificity test
In order to
evaluate the sensitivity and specificity of the algorithm in identifying targeted
physical activities, 10 subjects were recruited in a laboratory-based test.
During the test, the subjects worn the measuring device and meanwhile were
asked to perform each test items repeatedly as they habitually do in their
free-living condition. They were allowed to be free to vary their movement
styles (e.g., speeds or time intervals) arbitrarily. In addition, the test subjects
were also informed of avoiding performing ambiguous movements.
The test items
and the results are listed in the Table 1. The obtained results also reveal
good performance compared with other research [Karantonis et al., 2006]. Note that sitting still or standing still postures were
not included in this evaluation phase because this item is associated with
other identified movement as described above. In addition, falling was not included
because there is no unified form associated with falling.
Table 1. Evaluation of sensitivity and specificity
of the algorithm in identifying still postures and dynamic vements
3.2 Demonstration of continuous monitoring at home
Figure 6 is the
activity chronograph which chronologically displays more than 9000 recorded physical
activity items over the 16-hour monitoring period in his home. The lines with
different values represent respective identified items. The subject was not
expected to use this system continuously throughout the monitoring period for
some situations, such as taking a shower, going outside, etc. A report was made
by the subject to register his actual activities/living behaviors during that
monitoring period. The result of the monitored data reveals good correlation
with reference to the subject’s actual activities.
Figure 6. Activity chronograph of 16-hour
Discussion and conclusion
The algorithm for real-time physical activity
identification was developed and implemented in a portable microprocessor-based
measuring device. It achieves good performance in still posture and dynamic
activity identification. However, some inherent factors associated with
identification accuracy are worth discussing here.
4.1 Instrumental factor
A tri-axial accelerometer was used in this
study to measure the acceleration and body orientation caused by human movement.
In fact, the most precise tilt sensing can be maintained when the accelerometer
is at static, or under constant acceleration. Declined tilt sensing accuracy has
been reported in combination of varying acceleration magnitude [Elble, 2005].
However, the measured tilt data still preserves sufficient information of body
orientation. The attachment and personal fit of the device as well as power
consumption of the device are the major factors affecting the overall
performance. Note that two false possible falling events were detected in the
continuous monitoring. Such condition might be avoided by lowering the sensing
sensitivity to reduce the inference caused by the environment or improper
4.2 Personal factor
As for the personal factors regarding the
identification accuracy, it is certain that the characteristics of the
activities vary from person to person. Even those patterns in a person are not
uniformly the same. The algorithm would have difficulty in identifying mixed or
ambiguous movement which is the combination of multiple activities in
continuous phase. Therefore, identification accuracy of the algorithm could be limited
when applied in a in one’s free-living environment, despite that fact that it
achieves good performance from the laboratory-set tests.
4.3 Real-time processing factor
The relatively limited functional capability of
the microprocessor, coupled with the fact that that real-time data processing
must be synchronized within very short time period (e.g., less than 200ms), are
the major factors concerning the identification performance. Most off-line
systems capitalize on PC-based FFT, fuzzy inference or wavelet transform to
analyze the recorded data. Therefore, PC-based data analysis techniques usually
have higher identification accuracy over the real-time identification
approaches [Najafi et
al., 2003]. The
vertical acceleration was found to has major significance over both
antero-posterior and medio-lateral components. The performance of
microprocessor-based real-time processing could be better enhanced if the
required processes for the other data are properly reduced.
accelerometry-based data processing algorithm for real-time human physical
activity identification was developed and presented in this paper. This algorithm
was specifically developed for microprocessor-based human motion measuring
devices to facilitate real-time identification of several targeted physical
activities in a home environment. The development work in this study also
highlights the advantage of implementing real-time approach for physical
activity monitoring. The accuracy of the algorithm was evaluated in a
laboratory-based test and the demonstration of continuous monitoring shows the
functional feasibility to recognize real-time physical activities. This
developed algorithm can be incorporated into activity tele-monitoring
applications to evaluate a person’s activity of daily living (ADL) and his
status of physical mobility.
was sponsored by Medical Mechatronics Education
in Chang Gung University.
This support is gratefully acknowledged.
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