Authors：Che-Chang Yang, Yeh-Liang Hsu(2008-12-19)；recommend：Yeh-Liang
Note：This paper is published on Telemedicine and e-Health, vol. 15,
no.1, pp.1035-1045, January, 2009.
Development of a wearable motion detector for
tele-monitoring and real-time identification of physical activity
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 presents the design of a portable microprocessor-based
accelerometry measuring device to implement real-time physical activity
identification. An algorithm was developed to process real-time tri-axial
acceleration signals produced by human movement to identify targeted still
postures, postural transitions, and dynamic movements. Fall detection was also
featured in this algorithm to meet the increasing needs of elderly care in
free-living environments. High identification accuracy was obtained in performance
evaluation. This device is technically viable for tele-monitoring and real-time
identification of physical activity, while providing sufficient information to
evaluate a person’s activity of daily living and her/his status of physical
mobility. Limitations regarding real-time processing and implementation of the
system for tele-monitoring in the home environment were also observed.
activity, fall detection, accelerometry, tele-monitoring, home telehealth
activity can be regarded as any bodily movement or posture produced by skeletal
muscles and resulting in energy expenditure [Capspersen et
al., 1985]. Aging
and various aspects of behavioral variables directly affect one’s physical
activity level [Pennathur
et al., 2003]. For elderly people, changes in physical
activity are highly related to the transition from relatively independent
living to ill and declined functional health status. This transition process is
often subtle and cannot be easily perceived by care-givers or even the elderly
themselves [Celler et al., 1995]. Therefore, it is important to provide objective and accurate assessment approaches to one’s long-term physical
activity level in a free-living environment (e.g., home) for evaluation of
quality of life and overall functional health status [Lyons et al., 2005].
in assessing physical activity are primarily due to the subtle and complex
nature of body movement, which requires accurate and reliable measuring techniques.
A majority of assessment methods in the past relied on questionnaires or
observation by regular visits, which may lead to inconsistent assessment
results and inability to be applied in large-scale studies [Meijer et al., 1991]. Advances in sensors, microprocessors
and wireless communication technologies have been the driving factors to facilitate
continuous tele-monitoring of physical activity [Aminian et al., 2004]. Accelerometers have been regarded as appropriate
motion sensors for the use of human movement measurement. These accelerometry-based
systems can provide significant information on the details of physical
activities, and further quantify the subject’s mobility level at acceptable
motion sensor-based monitoring systems capitalize on off-line data processing
techniques to analyze recorded human movements [Najafi et al., 2002, 2003]. In these systems, portable data loggers with
sensor units are commonly used to record the continuous data of human movement.
The recorded data is then uploaded to a personal computer (PC) and is analyzed by
complex signal processing techniques, such as fast Fourier transform (FFT),
wavelet transform or other frequency-domain processing methods. However, these off-line
systems are unable to provide real-time identification of human movements, let
alone tele-monitoring of physical activities.
monitoring with real-time signal processing has been developed recently. Mathie
et al  proposed a generic
framework for automated human movement classification using accelerometry data.
The movement classification within that framework does not require complex signal
processing, making it applicable to embedded systems and portable devices. Karantonis
et al  also presented a
real-time system for physical activity classification in home environments. All
the movements are classified in the real-time processing of a wearable system.
However, a PC-based program is still required to identify walking. The
advantages of such approaches are the capability of providing not only
real-time monitoring data but also the potential of instant and timely response
to emergent events [Scanaill et al.,
is an emerging research area which utilizes information and communication
technologies to enable effective delivery and management of health services at
a patient’s residence. Home telehealth systems can provide access to the users’
monitored data for users themselves, contributing to health self-management and
reduction in in-person home visits [Bensink et
al., 2006; Finkelstein et al.,
A centralized framework
is used in most home telehealth systems, in which a centralized database is
used for data storage and analysis. Figure 1 shows the structure of the decentralized
home telehealth system developed by the authors [Hsu et al., 2007]. Instead of using a centralized database that gathers
data from many households, a single household is the fundamental unit for
sensing, data transmission, storage and analysis. The core of the system is the
“Distributed Data Server (DDS)” inside a household, which is a thin server
designed specifically for the decentralized home telehealth system. As shown in
Figure 1, sensing data from sensors embedded in the home environment are
transmitted to the DDS, and are then processed and stored in the
Multi-Media-Card (MMC) of the DDS. Authorized remote users can request data
from the DDS using an Internet web browser (running Java applets) or a Visual
Basic (VB) program. Event-driven messages (mobile phone text messages or
e-mails) can be sent to specified caregivers when an urgent situation is
Figure 1. The structure of the decentralized home
several advantages of the decentralized structure over the traditional
centralized database structure:
The scale of the home
telehealth system is much smaller, which makes it economically viable and
acceptable to the end-users.
Instead of sending the health
monitoring data to a centralized database in a home healthcare provider, health
monitoring data are stored within the household. Only authorized caregivers can
access the data. Privacy is better protected.
The route from the sensor to
server is much shorter. Data transmission is easier and more reliable. When the
Internet communication fails, the local system can still function normally and
keep collecting data. Thus data integrity is better preserved.
The home telehealth provider
only needs to maintain an application server to provide email, short message,
and Java services. The DDS can also be used as a gateway if a centralized
database is needed.
tele-monitoring applications have been developed based on this decentralized
structure by the authors, including a portable device for tele-monitoring of
snoring and obstructive sleep apnea syndrome (OSAS) [Cheng et al., 2008], a portable device for tele-monitoring of physical
activities during sleep [Cheng et al.,
2008], and a telepresence robot for interpersonal communication with the elderly
in a home environment [Tsai et al.,
presents the design of a portable microprocessor-based motion detector to implement
tele-monitoring and real-time identification of physical activity based on the
decentralized home telehealth platform described above. An
algorithm is developed for real-time physical activity identification in
free-living environments, including 3 still postures (sitting still, standing still and lying still), 4 postural transitions (sit-to-stand, stand-to-sit, lie-to-sit and sit-to-lie), and 2
dynamic movements (turning on bed and walking). In addition, possible
falls can also be detected by this algorithm. Good
identification accuracy was obtained in the performance evaluation. Limitations
regarding real-time processing and tele-monitoring of physical activities were also
observed during the performance evaluation and implementation of the system in
the home environment.
2.1 Instrumentation design
Figure 1, the “sensor” in the physical activity monitoring system developed in
this research is a wearable motion detector shown in Figure 2. This device
mainly consists of a PIC (programmable interface controller) microprocessor (PIC18F6722, Microchip
Inc.), an RF encoder/transmitter (PT2262, Princeton Technology, TWS-CS-2,
Wenshing Electronics Co., LTD, Taipei
Taiwan) and a motion sensor module. The device is packaged in a 100mm×60mm×25mm
exterior (excluding the battery cartridge) and is 140g in total weight. The motion sensor module utilizes
a miniature MEMS-fabricated tri-axial accelerometer (KXM52-1050, Kionix Inc., Ithaca, NY), which functions
on the principle of differential capacitance in response to motion and constant
gravity to measure both acceleration and inclination. The motion detector is attached
to the pants’ belt at waist level. Waist level has been considered as an
appropriate position for motion sensor attachment because it is close to the
center of gravity of the body [Bouten et
al., 1997; Meijer et al, 1991;
Karantonis et al., 2006].
As shown in
Figure 2, the analog signals of the accelerometer outputs are initially low-pass
filtered at 50Hz to limit the signal bandwidth in response before the following
60Hz analog-to-digital conversion (ADC) data sampling process. Real-time
computation is processed in the PIC microprocessor to cyclically identify one
of the targeted physical activities. Each identified activity is immediately transmitted
to the DDS via wireless RF amplitude shift keying (ASK) 433.92MHz for data
Figure 2. The functional diagram and the
configuration of the wearable motion detector
2.2 General processes of the algorithm
earlier, the algorithm developed in this research aims to identify 3 still
postures (lying still, sitting still, and standing still), 4 postural transitions (sit-to-stand, stand-to-sit,
sit-to-lie and lie-to-sit), and walking.
Detection of possible falls is also featured in this algorithm. Figure 3 shows
the main algorithm flowchart. There are 5 processing steps: sampling (Cx),
pre-processing (Px), dynamic postural transition
identification (DBx), still posture identification (DAx)
and possible fall detection (DCx). Each data sample is processed in the
time domain to generate one of the 9 events described above. In the case where
there is no definite result determined by the algorithm, the event will be
recorded as an uncertain movement or
an uncertain posture.
Figure 3, acceleration and trunk tilt data in x-, y-, and z-axis are collected in the data
sampling process which consists of the primary stage C1 (0.5s) and the secondary stage C2 (1.5s). The use of this dual-stage data sampling strategy
ensures that the data of a dynamic event can be captured within the sampling
interval. After the primary data sampling stage C1, decision D1
determines whether any sign of dynamic movement exists by inspecting apparent acceleration
changes in each axis. If no dynamic movement is detected (D1=No), the 0.5s trunk tilt data is used to identify one of the
three possible still postures in the processes DAx. If a dynamic
movement is detected (D1=Yes), the
secondary data sampling stage C2 is
activated to collect the subsequent 1.5s data which follows the previously
buffered 0.5s data in C1. The
median-filtering (length n=3) and
moving-averaging are also applied to the C1-C2
combined data to reduce signal spikes and high frequency noises, respectively.
The resulting processed data P
represents a dynamic event for the following step-by-step identification
Figure 3. Main algorithm flowchart
2.3 Identifying dynamic activities
mapping” technique which functions as a high-pass filter is used in many steps
in the algorithm to register whether there are apparent changes in the data
sequences. This process maps the data P
into a binary sequence C according
to Equation (1) and (2). The entry ci
in C is assigned “1” if the “slope” si between two consecutive data points and exceeds a
specific threshold sthr.
Otherwise, ci is assigned “0”. Figure 4 shows an example of applying the
slope mapping technique to a trunk tilt data of a sit-to-stand transition. Note that the binary sequence is expressed
as a histogram to highlight the entries in “1”.
, i=1,2,3,…,149 (1)
, i=1,2,3,…,150 (2)
Figure 4. An example of applying slope mapping to a
trunk tilt data
The process D3 and DA1 examine the trunk tilt values according to the trunk tilt
orientation shown in Figure 5. Decision D3
is used to examine the end posture (trunk tilt) state from the data segment of
1.5s to 2.0s out of P. An upright
state here is defined as the trunk tilt within the range of 60°forward and backward with reference
to vertical. Decision DA1 determines
a lying posture which is defined as the trunk tilt within the range of 60°relative to the horizontal, or
within the range of 60° to 120° as illustrated in Figure 5.
Figure 5. Illustration of the trunk tilt orientation
Identifying sit-stand postural
in vertical direction is used to identify sit-stand
postural transitions because the y-axis
signal is more sensitive to external motions through empirical tests. Coherent
properties of the acceleration patterns were also observed in an extensive test
with 15 recruited ostensibly healthy subjects of various ages (11 males and 4
females, 5 young subjects under 35 years old, 5 middle-aged subjects between 35
to 65, and 5 elder subjects over 65). The acceleration patterns of sit-stand postural transitions can be
characterized by its peak order, peak distance and peak amplitudes. Figure 6 shows
an example of a vertical acceleration pattern during sit-to-stand and stand-to-sit
postural transitions in slow, normal and fast motion. In this figure, it can be
observed that a positive acceleration peak appears first followed by a negative
acceleration peak for sit-to-stand. A
reverse order for stand-to-sit was
also observed. The peak amplitude is defined as the maximum positive or
negative acceleration in the data sample, and the peak distance is the time
interval (in seconds) between the positive peak and the negative peak. Faster
transition generates shorter peak distance with higher peak amplitudes, and
slow transition generates longer peak distance with lower peak amplitudes.
Figure 6. Examples of vertical acceleration
patterns of slow, normal and fast stand-to-sit transition
As shown in the
main algorithm flowchart in Figure 2, the following decisions DB1 and DB2 identify a sit-stand postural transition if a dynamic movement
with upright trunk posture is recognized (D2=Yes,
D3=Yes). Figure 7 further illustrates
the decomposed procedure of the decisions DB1
and DB2 which embodies several
required thresholds and parameters for distinguishing sit-stand transitions.
The first step (R1) is to check the type of peak orders. The
following two steps (R2 and R3)
check the peak distance (DP) and peak amplitudes
(positive peak VP and
negative peak VN) to
further confirm whether the dynamic event is indeed a sit-to-stand or stand-to-sit
transition. The threshold values used in this identification process were
experimentally obtained in the test with 15 subjects mentioned above. R1,
or R3 must be all satisfied to identify sit-to-stand or stand-to-sit transition.
Figure 7. The decomposed flowchart of sit-stand
postural transition identification
Identifying walking movement
Figure 8 shows an
example of the acceleration pattern in vertical direction at level walking. As shown
in this figure, walking movement is characterized
by its fast and repeated oscillating changes in the vertical acceleration. Previous
studies revealed that the frequencies of normal level walking are between 0.7Hz
to 4Hz with the peak acceleration values between 0.4g to -0.3g
[Aminian et al., 2002; Mathie et al.,
2003; Nyan et al., 2006; Sekine et al.,
2000]. Those parameters were adopted in this identification process. The
decision DB3 also uses slope mapping
which processes the vertical acceleration data to identify walking movement if DB2 fails to recognize a stand-to-sit postural
Figure 8. An example of acceleration patterns in vertical
direction at level walking
Identifying lie-sit postural
Tilt data and
its end posture state (trunk orientation) are required to identify and
distinguish lie-sit type postural transitions. By applying a slope mapping
technique, a lie-to-sit transition
produces positive increments in trunk tilt variation and ends with upright posture.
On the contrary, 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 trunk
tilt orientation. Decision DB4
identifies whether a lie-to-sit
transition if walking movement is not recognized in DB3. For a dynamic movement with recognized lying end state (D2=Yes, D3=No), DB5 is used to
identify sit-to-lie transition if the
sign of fall is not recognized (DC1=No).
2.4 Identifying possible falls and still postures
A fall can be
intuitively regarded as a movement accompanied by unusually high acceleration
peaks in a short time interval, which is followed by a period of lying still
posture. Therefore, fall detection in this algorithm is designed with two identification
stages. For the first identification stage, the tri-axial acceleration data of
the dynamic (D2=Yes), non-upright (D3=No) posture event is processed in
the decision DC1 to determine
whether a sign of fall exists. The sign of fall is defined in this
algorithm to indicate an existence of peak acceleration greater than Vf=±1.5g captured in the
tri-axial acceleration data. For the second identification stage, the algorithm
starts to register the duration of lying still posture in the following identification
loop. Decision DA1 examines the
trunk tilt data to determine a lying posture. The previously recognized sign of fall event can be raised to indicate
a possible fall if prolonged TD=20s period of lying still
distinguishing sitting still or standing still posture requires not only
the trunk tilt orientation data, but also the information of previously identified
postural transitions or walking movement. If a still event is not lying posture
(DA1=No) and its previous event is
either a sit-to-stand transition or walking movement, this event can be
recognized as standing still (DA3=Yes). On the other hand, the event
is sitting still if the previous event
is either a stand-to-sit or lie-to-sit transition (DA2=Yes).
Performance evaluation of the system
3.1 Basic performance test: sensitivity and specificity
In order to evaluate the performance of the device in
identifying still postures and dynamic activities, 10 subjects were recruited
for the laboratory-based test. Sensitivity and specificity for identifying
postural transitions (sit-stand
transitions, lie-sit transitions) and
walking were recorded. In the
sensitivity test, the subjects were asked to perform
separate test items according to the facilitator’s
instruction. The test items include the reciprocal postural transitions
(sit-stand transitions and sit-lie transitions) and continuous walking movements. The subjects repeated
each postural transition 20 times. In addition, the sensitivity of walking was obtained from 50s of continuous
walking steps performed by each subject.
For the specificity test, 5 test
items were arranged into phase A (sit-to-stand
and sit-to-lie), phase B (stand-to-sit and walking) and phase C (lie-to-sit).
According to the algorithm, when a subject is sitting still, his possible
posture transition from that state is either sit-to-stand transition or sit-to-lie
transition. Therefore in phase A, the subjects were initially in the sitting
still posture and they can perform any movement other than the sit-to-stand or sit-to-lie transitions. Similarly in phase B, the subjects were
initially standing still, and then performed any movements other than stand-to-sit transition or walking movement. In phase C, the
subjects performed any movements other than lie-to-sit
transition as they were initially lying. Each subject performed a total of 50
movements Note that during the test the subjects were
allowed to vary their movement speeds or time intervals arbitrarily, and they were
informed to avoid performing ambiguous movements. For example, in phase A, the
subjects were asked to avoid performing a movement similar to sit-to-lie transition even though they
subjectively considered it a free movement.
identification accuracy was obtained in the performance evaluation. Table 1
shows the sensitivity and specificity obtained from 200 and 500 data samples
respectively. Note that the evaluation did not include sitting still or standing
still postures because both still postures are associated with the results
of previously identified postural transitions or walking.
Table 1. Evaluation of sensitivity and specificity
of the algorithm in identifying still postures and dynamic movements
3.2 Demonstration of long-term tele-monitoring at home
activity monitoring system developed in this research was implemented in a home
environment to demonstrate the possibility of long-term tele-monitoring of
physical activity at home. Figure 9 is the activity chronograph which shows the
continuously recorded activity data from a test subject at home in a typical
day. This figure contains 6.75-hour data in the 15-hour monitoring period. The tester
could not wear the sensor continuously in some situations, such as taking a
shower, going outside, etc., therefore the vertical time axis is not continuous.
In this figure, lines of various lengths represent respective events (event 1
to 9) identified by the system. For example, the subject was lying from 4:00 to
9:00, and mostly sitting from 9:00 to 12:00.
activity data can be statistically classified as in Figure 9, which shows the
numbers and percentages of each dynamic activity or static posture within the
entire monitoring period. The subject also made a report to register his actual
activities during this period, and the monitored data reveals good correlation
with the report. While the chronograph in Figure 9 shows the pattern of the
subject’s daily activity, the quantitative data in Figure 10 can be used to
form indices of the mobility level of the subject.
Event number 1: lying still, no.2: sitting still, no.3:
standing still, no.4: walking,
no.5: sit-to-stand, no.6: stand-to-sit, no.7:
lie-to-sit, no.8: sit-to-lie, no.9: possible fall
Figure 9. Example of activity chronograph of the continuously
recorded data in the ambulatory monitoring at home
Figure 10. Statistical results of the recorded
system using a single wearable motion detector for physical activity
identification was presented in this paper. A miniature tri-axial accelerometer
was used as the motion sensor due to its superiority of accurate
acceleration/tilt sensing, fast response and low power consumption. The
identification algorithm was specifically developed for microprocessor-based
wearable systems to identify the target human static postures, level walking
and dynamic postural transitions. The potential capability of fall detection
was also presented. Good identification accuracy was obtained in the performance
evaluation, though the nature of actual human movements in daily living is more
complex than what is considered and characterized in the algorithm.
This system is
based on the decentralized home telehealth system structure described in the
first section, which can be easily implemented in a home environment and is economically
viable and acceptable to the end-users. This system can be used for long-term physical
activity and mobility monitoring in the home environment. As depicted in Figure
1, alert messages will be sent out to the caregivers in the form of mobile
phone text messages (short message service, SMS) or e-mails if potential urgent
situations are detected (falls, lack of movement, etc.). Moreover, multiple
sensors (wearable motion detectors) with different sensor identification
numbers can be connected to the same DDS in the situation when there are more
than one users at home.
performance evaluation and implementation of the system in the home
environment, the following limitations regarding real-time processing and
tele-monitoring of physical activities were also observed:
capacitance tri-axial accelerometer was used to measure both the acceleration
and trunk tilt of the human body. For such a gravity-responsive accelerometer,
the most precise tilt sensing can be maintained when the accelerometer is at
static, or under constant acceleration. Degradation of tilt sensing accuracy in
changing acceleration magnitude is inevitable [Elble, 2005]. Therefore, the
acceleration data acquired from the wearable motion detector cannot be used to
precisely interpret the complex nature of human motions. In spite of this
constraint, tilt sensing and acceleration measurement using one tri-axial
accelerometer is still valid for normal physical activity because the resulting
sensor outputs still preserve apparent characteristics for either trunk tilt or
The wearable motion detector
motion detector was designed to be clipped onto a waist/pant belt. However,
carrying the device might limit some movements in lying posture and therefore
further influences the wearer’s comfort, and the tester could not wear the
device in some situations, such as taking a shower, going outside, etc. This
has been an important usability issue for fall detector in general, as these
are often the situations where users are at great risk of fall at home (getting
out of the shower, getting out of bed to go to the bathroom at night, etc.)
Usually a special designed waist belt which contains the fall detector (the
wearable motion detector in this research) is provided in the situation when
the user cannot clip the fall detector to a pant belt. In addition, reduction
in the size and weight of the device is also an important design issue in
improving the comfort of the users.
capacity is an important consideration for the practicality of such wearable
devices. To extend the time for use, a battery cartridge of larger power
capacity can be selected. Proper selection of sampling rate in the microprocessor
is also important in order to maintain optimal power longevity. For example,
when the subject is at static state, the microprocessor should be set at a
lower sampling rate because higher signal resolution is not necessary.
developed in this study has the ability to provide real-time status of physical
activity. The microprocessor has sufficient computation capability for the
real-time processing. However, because each physical activity must be
identified in continuous intervals with discrete short samples, real-time
systems usually cannot provide the identification accuracy higher than that of
the off-time analysis systems [Karantonis, 2006]. The limitation concerning the
identification accuracy of both off-line and real-time systems is the complex
nature of human movement. People usually perform mixed and combined movements in
their normal activities of living, even the diverse characteristics of one
activity item can be observed from the same person. Advances in signal
processing techniques such as self-adaptive parameter tuning can be further
employed to enhance the identification accuracy.
developed in this research has been technically proven to be feasible for
continuous tele-monitoring of physical activity at home. This real-time system
can provide sufficient information in evaluating the subject’s activities of
daily living (ADL) pattern and his/her physical mobility level. The use of the
Ethernet-accessible DDS at home also enables remote real-time monitoring via
the Internet. This home tele-health system has potential applications in
rehabilitation, elderly care and personal health management.
Aminian, K., Najafi, B., Bula, C. and Leyvraz, P. F., 2002.
“Spatial-temporal Parameters of Gait Measured by an Ambulatory System Using
Miniature Gyroscope,” Journal of
Biomechanics, vol. 35, pp. 689-699.
Aminian, K., and Najafi, B., 2004. “Capturing Human Motion Using
Body-fixed Sensors: Outdoor Measurement and Clinical Applications,” Comp. Anim. Virtual Worlds, Vol. 15, pp.
Bensink M., Hailey, D., Wootton, R., 2006. “A systematic Review of
Successes and Failures in Home Telehealth: Preliminary Results,” Journal of Telemedicine and Telecare, Vol.12,
Bouten, C. V., Koekkoek, K. T. M., Verduin, M., Kodde, R., and Janssen,
J. D., 1997. “A Triaxial Accelerometer and Portable Data Processing Unit for
the Assessment of Daily Physical Activity,” IEEE
Trans. Biomed. Eng., Vol. 44, No. 3, pp.136-147.
Capspersen, C. J., Powell, K. E., and Christenson, G. M., 1985. “
Physical Activity, Exercise and Physical Fitness: Definitions and Distinctions
for Health Related Research,” Public
Health Rep., Vol. 110, pp. 126-131.
Celler, B.G., Earnshaw, W., Ilsar, E.D., Betbeder-Matibet, L.,
Harris, M.F., Clark, R., Hesketh, T., and Lowell, N.H., 1995. “Remote Monitoring
of Health Status of the Elderly at Home. A Multidisciplinary Project on Aging
at the University of New South Wales,” International Journal of Biomedical
Computing, Vol. 40, pp. 147-155.
Cheng, C. M., Hsu, Y. L., Young, C. M., Wu, C. H., 2008. “Development of
a Portable Device for Tele-monitoring of Snoring and OSAS Symptoms,” Telemedicine and e-Health, Vol. 14, No.
1, pp. 55-68.
Cheng, C. M., Hsu, Y. L., Young, C. M., 2008. “Development of a Portable
Device for Tele-monitoring of Physical Activities During Sleep,” Telemedicine and e-Health, Vol.14,
Elble, J. R., 2005. “Gravitational Artifact in Accelerometric
Measurements of Tremor,” Clinical
Neurophysiology, Vol. 116, pp. 1638-1643.
Finkelstein, S. M., Speedie, S. M., and Potthoff, S., 2006. “,Home
Telehealth Improves Clinical Outcomes at Lower Cost for Home Healthcare,” Telemedicine and e-health Vol. 12, No.
2, pp. 128-136.
Hsu, Y. L., Yang, C. C., Tsai, T. C., Cheng, C. M. and Wu, C. H., 2007,
“Development of a Decentralized Home Telehealth Monitoring System”, Telemedicine and e-Health, Vol. 13, No.1,
Karantonis, D. M., Narayanan, M. R., Mathie, M., Lovell, N. H., and
Celler, B. G., 2006. “Implementation of a Real-Time Human Movement Classifier
Using a Triaxial Accelerometer for Ambulatory Monitoring,” IEEE Trans. Info. Tech Biomed., Vol. 10, No. 1, pp. 156-167.
Lyons, G. M., Culhane, K. M., Hilton, D., Grace, P. A., and Lyons, D.,
2005. “A Description of an Accelerometer-based Mobility Monitoring Technique,” Medical Engineering & Physics, Vol.
27, pp. 497-504.
Mathie, M. J., Coster, A. C. F., Lovell, N. H. Celler, B. G., 2003.
“Detection of Daily Physical Activities Using a Triaxial Accelerometer,” Medical & Biological Engineering &
Computing, Vol. 41, pp. 296-301.
Meijer, G. A. L., Westerterp, K. R., Verhoeven, F. M. H., Koper, H. B.
M., and Hoor, F. T., 1991. “Methods to Assess Physical Activity with Special
Reference to Motion Sensors and Accelerometers, IEEE Trans. On Biomed. Eng., Vol. 38, No.3, pp. 221-229.
Najafi, B., and Aminian, K., 2002. “Measurement of Stand-Sit and
Sit-Stand Transitions Using a Miniature Gyroscope and Its Application in Fall
Risk Evaluation in the Elderly,“ IEEE
Trans. Biomed. Eng., Vol. 49, pp. 843-851.
Najafi, B., Aminian, K., Paraschiv-Ionescu, A., Loew, F., Bula, C. J.,
and Robert P., 2003. “Ambulatory system for Human Motion Analysis Using a
Kinematic Sensor: Monitoring of Daily Physical Activity in the Elderly,” IEEE Trans. On Biomed. Eng., Vol. 50, No.
6, pp. 711-723.
Nyan, M. N., Tay, F. E. H., Seah, K. H. W., and Sitoh, Y. Y., 2006.
“Classification of a Gait Patterns in the Time-frequency Domain,” Journal of Biomechanics, Vol. 39, pp.
Pennathur, A., Magham, R.,
Contreras, L R., and Dowling, W., 2003. “Daily Living Activities in Older Adults:
Part I- A Review of Physical Activity and Dietary Intake Assessment Methods”, International Journal of Industrial
Ergonomics, Vol. 32, pp. 389-404.
Scanaill, C. N., Carew, S., Barralon, P., Noury, N., Lyons D., and
Lyons, G. M., 2006. “A Review of Approaches to Mobility Telemonitoring of the
Elderly in Their Living Environment,” Annals
of biomed. Eng., Vol. 34, No.4, pp. 547-563.
Sekine, M., Tamura T., Togawa T., Fukui Y., 2000. “Classification of Waist-acceleration
Signals in a Continuous Walking Record,” Medical
Eng. and Phy., Vol. 22, pp. 285-291.
Tsai, T. C., Hsu, Y. L., Ma, A. I., King, T.,
Wu, C. H., 2007. “Developing a Telepresence Robot for Interpersonal Communication
with the Elderly in a Home Environment,” Telemedicine
and e-Health, Vol. 13, No. 4, pp. 407-424.