Author: Che-Chang Yang (2010-08-16); recommend: Yeh-Liang Hsu (2011-02-21)
Note: This article is the Chapter 2 of Che-Chang Yang’s doctoral thesis “Development
of a Home Telehealth System for Telemonitoring Physical Activity and Mobility
of the Elderly”.
Chapter 2. A review of accelerometry-based wearable motion
detectors for physical activity monitoring
for physical activity monitoring
activity (PA) is regarded as any bodily movement produced by skeletal muscles
which results in energy expenditure [Caspersen et al., 1985]. PA has been studied in epidemiological research for
investigating human movements and the relationship to health status, especially
in the area of cardiovascular diseases, diabetes mellitus and obesity. A
declined PA level represents a major factor in multiple illnesses and symptoms
related to functional impairment [Steele et
al., 2000]. The organization Healthy People 2020 [http://www.healthypeople.gov/HP2020/]
led by the U.S. government has recognized PA as one of the leading health
indicators (LHI), which are a measurement of health of the nation’s population.
of subjective and objective PA assessments have been developed. Subjective
methods, such as diaries, questionnaires and surveys, are inexpensive PA
assessment tools. However, these methods often depend on individual observation
and subjective interpretation, which make the assessment results inconsistent
[Meijer et al., 1991]. Some standard
tests for PA assessment also require subjective judgments. For example, the
timed up-and-go test (TUG-T) is a simple test for evaluating one’s ability to
perform a sequence of basic activities, and the result of the TUG-T can be a
predictor for risk of falling [Podsiadlo et
al., 1991]. Distinguishing postural transitions in the TUG-T, however,
depends on subjective judgment that counts the time taken for each posture
transition. The Berg Balance Scale (BBS), a valid measure to evaluate balance
control of the elderly individuals, also requires subjective observation and
determination for scoring some test items [Berg et al., 1989].
On the other
hand, objective techniques use wearable, or body-fix motion sensors, which
range from switches, pedometers, actometers, goniometers, accelerometers and
gyroscopes, for PA assessment. Mechanical pedometers, or so-called “step
counters”, are the simplest wearable sensors to measure human motion. The
pedometer uses spring-loaded mass or other switch mechanisms to detect the
obvious impacts produced by steps during locomotion. The number of steps during
motion can be registered to estimate the distance walked and the energy
expenditure. Though pedometers are cheap and simple, the major drawbacks are
that pedometers cannot reflect intensity of movement and therefore results in
inaccurate energy expenditure estimation [Saris et al., 1977]. PA can also be objectively measured by means of
magnetic systems, optical systems, or video recording. Magnetic and optical
systems for PA monitoring are costly and require complex instrumentation and
environment setting. Privacy concern is a major drawback in monitoring systems
based on video recording. These systems may not be practical for monitoring
subjects in free-living environment.
are sensors which measure the accelerations of objects in motion along
reference axes. Measuring PA using accelerometry is preferred because
acceleration is proportional to external force and hence can reflect intensity
and frequency of human movement. Accelerometry data can be used to derive
velocity and displacement information by integrating accelerometry data with
respect to time [Chen et al., 2005].
Some accelerometers can respond to gravity to provide tilt sensing with respect
to reference planes when accelerometers rotate with objects. The resulting
inclination data can be used to classify body postures (orientations). With
these characteristics, accelerometry is capable of providing sufficient
information for measuring PA and a range of human activities. Accelerometers
have been widely accepted as useful and practical sensors for wearable devices
to measure and assess PA in either clinical/laboratory settings or a
free-living environment [Mathie et al.,
were first investigated in the 1950s to measure gait velocity and acceleration
[Saunders et al., 1953].
Accelerometry measurement of human motion was studied in more detail during the
1970s due to technological advances [Morris, 1973]. It was also shown that
accelerometers had advantages over other techniques in quantitatively measuring
human movement. Micro-electromechanical system (MEMS) technology has reduced
the cost of accelerometers in smaller form factors. In the mean time, the
sensor performance is enhanced while the power consumption is greatly reduced.
The first batch-fabricated MEMS accelerometers were reported in 1979 [Roylance et al., 1979]. Since then various
research and commercial applications have used MEMS accelerometers in wearable
systems for PA monitoring.
provides a comprehensive review on the working principles, capabilities, and
various applications of accelerometry-based wearable motion detectors for PA
monitoring and assessment. The authors searched for published literature after
year 2000 using a range of related keywords such as “accelerometry”,
“accelerometer”, “wearable”, “physical activity”, “human motion”, “human
movement”, “activity classification”, “energy expenditure”, “fall detection”,
“balance stability” and “gait”. Selected literatures before year 2000 are also
included. This paper first discusses the principles and fundamentals of
accelerometry, along with different sensor placements. Various research using
accelerometry-based wearable motion detectors for PA monitoring and assessment,
including posture and movement classification, estimation of energy
expenditure, fall detection and balance control evaluation, are then reviewed.
Finally this paper reviews and compares existing commercial products to provide
a comprehensive outlook of current development status and possible emerging
2.2 Design Fundamentals for Accelerometry-Based Wearable
2.2.1 Accelerometry: Principles and sensors
are basically force sensors to sense linear acceleration along one or several
directions, or angular motion about one or several axes. The former is referred
to as an accelerometer, and the later a gyroscope. The common operation
principle of accelerometers is based on a mechanical sensing element which
consists of a proof mass (or seismic mass) attached to a mechanical suspension
system with respect to a reference frame. Inertial force due to acceleration or
gravity will cause the proof mass to deflect according to Newton’s Second Law.
The acceleration can be measured electrically with the physical changes in
displacement of the proof mass with respect to the reference frame.
Piezoresistive, piezoelectric and differential capacitive accelerometers are
the most common types [Öberg et al.,
2004; Godfrey et al., 2008].
(1) Piezoresistive accelerometers
element consists of a cantilever beam and its proof mass is formed by
bulk-micromachining. The motion of the proof mass due to acceleration can be
detected by piezoresistors in the cantilever beam and proof mass. The
piezoresistors are arranged as a Wheatstone bridge to produce a voltage
proportional to the applied acceleration. Piezoresistive accelerometers are
simple and low-cost. The piezoresistive accelerometers are DC-responsive that
can measure constant acceleration such as gravity. The major drawbacks of
piezoresistive sensing are the temperature-sensitive drift and the lower level
of the output signals.
(2) Piezoelectric accelerometers
piezoelectric accelerometer, the sensing element bends due to applied
acceleration which causes a displacement of the seismic mass, and results in an
output voltage proportional to the applied acceleration. Piezoelectric
accelerometers do not respond to the constant component of accelerations.
(3) Differential capacitive accelerometers
of the proof mass can be measured capacitively. In a capacitive sensing
mechanism, the seismic mass is encapsulated between two electrodes. The
differential capacitance is proportional to the deflection of the seismic mass
between the two electrodes. The advantages of differential capacitive
accelerometers are low power consumption, large output level, and fast response
to motions. Better sensitivity is also achieved due to the low noise level of
capacitive detection. Differential capacitive accelerometers also have DC
response. Currently this kind of accelerometer has widely been used in most
applications, especially in mobile and portable systems and consumer
2.2.2 Sensor placement
Gemperle et al.  proposed the ergonomic
guideline of “wearability” to describe the interaction between the human body
and wearable objects. The “wearability map” was generalized to indicate the
proper locations of a human body for unobtrusive sensor placement. These
locations include collar area, rear of upper arm, forearm, front and rear sides
of ribcage, waist, thighs, shin, and top of the foot. These locations have
common characteristics of similar area for men and women, a relatively larger
continuous surface, and low movement and flexibility.
placement of wearable devices refers to the locations where the sensors are
placed, and how the sensors are attached to those locations. Wearable activity
sensors can be placed at different parts of a human body whose movements are
being studied. In many cases, it is necessary to measure the whole-body
movement. Therefore, the sensors are commonly placed on the sternum [Najafi et al., 2003], lower back [Meijer et al., 1991], and waist [Karantonis et al., 2006]. Most studies adopted
waist-placement for motion sensors because of the fact that the waist is close
to the center of mass of a whole human body, and the torso occupies the most
mass of a human body. This implies that the accelerations measured by a single
sensor at this location can better represent the major human motion. From an
ergonomic point of view, the torso can better bear extra weight when carrying
wearable devices. Sensors or devices can be easily attached to or detached from
a belt around waist level. Therefore, waist-placement causes less constraint in
body movement and discomfort can be minimized as well. A range of basic daily
activities, including walking, postures and activity transitions can be
classified according to the accelerations measured from a waist-worn
accelerometer [Karantonis et al.,
2006; Yang et al., 2009; Sekine et al., 2000]. An approach using a
chest-worn accelerometer was presented to detect respiratory and snoring
features for apnea diagnosis during sleep [Kawada et al, 2008].
can also be attached to wrists, thigh, or ankles. Sleep time duration can be
determined from a wrist-worn accelerometer [Liszka-Hackzell et al., 2005] and activity levels during
sleep can be measured [Morillo et al.,
2010]. Ankle-attached accelerometers can significantly reflect gait-related
features during locomotion or walking. Steps, travel distance, velocity, and
energy expenditure can be estimated by an ankle-worn accelerometer [Park et
al., 2006; Kuo et al., 2009]. A special placement in which an accelerometer
unit integrated into hearing aid housing was used for detecting falls [Lindemann
et al., 2005]. The rationale of this
sensor placement was based on the author’s hypothesis that the individual intends
to protect the head against higher acceleration caused by abnormal activities.
Accelerometers have also been placed at the top of head for measuring balance
during walking [Menz et al., 2003].
consideration for sensor placement is how to attach sensors to the human body.
Wearable sensors can be directly attached to skin [Najafi et al., 2003; Lindermann et
al., 2005], or in the form of indirect attachment by using straps, pant
belts and wristbands, or other accessories [Liszka-Hackzell et al., 2005; Park et al.,
2006; Menz et al.,
2003]. Sensors and wearable devices can also be integrated into clothing [Noury
et al., 2004]. In principal, the
accelerometers or motion sensors should be securely fitted and attached to the
human body in order to prevent relative motion between the sensors and the
parts of the human body. Loose attachment or unsecured fit causes vibration and
displacement of the wearable systems, and this is liable to produce extraneous
signal artifacts and to degrade sensing accuracy.
of Wearable Systems Using Accelerometry Measurement
can be used in ambulatory monitoring to continuously measure long-term
activities of subjects in a free-living environment. The recorded longitudinal
activity data can be used to identify postures and to classify several daily
movements which are related to an individual’s functional status. Signal
analysis and algorithm are used to classify daily human movements that are of
interest, and adverse activity, such as falls can be detected as well.
Important features extracted from posture sway and gait pattern have also been
studied for the purposes of evaluating risks of falling and mobility. In
addition, energy expenditure is the typical application featured by most commercially
2.3.1 Posture and movement classification
classification using accelerometry-based methodologies has been widely studied.
Approaches to movement classification can be threshold-based or using
statistical classification schemes. Threshold-based movement classification
takes advantage of known knowledge and information about the movements to be
classified. It uses a hierarchical algorithm structure (like decision tree) to
discriminate between activity states. A set of empirically-derived thresholds
for each classification subclass are required. Kiani et al. [K1997] presented a systematic approach to movement
classification based on a hierarchical decision tree that enables automatic
movement detection and classification. Mathie et al.  further presented a generic classification framework
consisting of a hierarchical binary tree for classifying postural transitions,
falling, walking, and other movements using signals from a wearable triaxial
accelerometer. This modular framework also allows modifying individual
classification algorithm for particular purposes.
Tilt sensing is
a basic function provided by accelerometers which respond to gravity or
constant acceleration. Therefore, human postures, such as upright and lying,
can be distinguished according to the signal magnitude of accelerations along
sensitive axes from only one accelerometer worn at the waist and torso [Karantonis
et al., 2006; Yang et al., 2009]. However,
single-accelerometer approach has difficulty in distinguishing between standing
and sitting as both are upright postures, although a simplified scheme with
tilt threshold to distinguish standing and sitting has been proposed [Karantonis
et al., 2006]. Standing and sitting
postures can be distinguished by observing different orientations of body
segments where multiple accelerometers are attached. For example, two
accelerometers can be attached to the torso and thigh to distinguish standing
and sitting postures from static activities [Veltink et
al., 1996; Foerster
et al., 1999; Lyons et al., 2005].
Trunk tilt variation due to sit-stand postural transitions can be measured by
integrating the signal from a gyroscope attached to the chest of the subject [Najafi
et al., 2002]. Sit-stand postural
transitions can be identified according to the patterns of vertical
acceleration from an accelerometer worn at the waist [Yang et al., 2009].
signals can be used to determine walking in ambulatory movement. Walking can be
identified by frequency-domain analysis [Karantonis et al., 2006; Ohtaki et al.,
2005]. It is characterized by a variance of over 0.02g in vertical acceleration
and frequency peak within 1-3 Hz in the signal spectrum [Ohtaki et al., 2005]. Discrete wavelet
transform is used to distinguish walking on a level ground and walking on a
stairway [Sekine et al., 2000].
classification using statistical schemes utilize a supervised machine learning
procedure, which associates an observation (or features) of movement to
possible movement states in terms of the probability of the observation. Those
schemes include, for example, k-nearest neighbor (kNN) classification [Foerster et al., 1999; Bussmann et al., 1998],
support vector machines (SVM) [Lau et al.,
2009; Zhang et al., 2006], Naive
Bayes classifier [Huynh et al., 2006;
Long et al., 2009], Gaussian mixture
model (GMM) [Allen et al., 2006] and
hidden Markov model (HMM) [Mannini et al.,
2010; Pober et al., 2006]. Naive
Bayes classifier determines activities according to the probabilities of the
signal pattern of the activities. In GMM approach, the likelihood function is
not a typical Gaussian distribution. The weights and parameters describing
probability of activities are obtained by the expectation-maximization
algorithm. Transitions between activities can be described as a Markov chain
that represents the likelihood (probability) of transitions between possible
activities (states). The HMM is applied to determine unknown states at any time
according to observable activity features (extracted from accelerometry data)
corresponding to the states. After the HMM is trained by example data, it can
be used to determine possible activity state transitions.
2.3.2 Estimation of energy expenditure
expenditure (EE) can be estimated by measuring physical activities. The doubly
labeled water method (DLW) and indirect calorimetry that measures oxygen
uptake, carbon dioxide production and cardiopulmonary parameters are regarded
as gold-standard references of EE. Though accurate, gas analyzers for indirect
calorimetry are expensive and they require specialized skills to operate. The
isotopes analysis and production for DLW method are costly and are not suitable
for large-scale studies [Vanhees et al.,
2005]. Accelerometers provide an alternative method of estimating energy
expenditure in a free-living environment. EE due to physical activity can be
better predicted from the acceleration integral in anterior-posterior direction
of an accelerometer [Bouten et al.,
1994], though vertical acceleration is most sensitive to major activities like
walking or running. The signal integral of triaxial acceleration outputs has
been found to have linear relationship with the metabolic energy expenditure
due to several daily activities [Bouten et
accelerometers usually convert the magnitude of accelerations to provide
“activity counts” per defined period of time (epoch). The activity counts
represent the estimated intensity of measured activities during each time
period. Therefore, the recorded activity counts can be compared with
questionnaires, or more accurately, the DLW method [Plasqui et al., 2007] or indirect calorimetry to
estimate the energy expenditure due to activities [Crouter et al., 2006]. Several regression equations can be derived or
validated for different accelerometers to better match exact EE of physical
activities among subjects.
affecting the accuracy of EE estimation using accelerometry are the location
and attachment of accelerometers, external vibration, gravitational artifact,
and the types of activity performed in a free-living environment. Sensor
attachment to trunk, lower back or second lumbar vertebra is preferred because
the trunk represents the major part of body mass and moves with most
activities. Gravitational effect is also relatively small on this body segment
[Bouten et al., 1997]. On the other
hand, waist-mounted accelerometers are unable to measure upper limb movement
and have inaccurate EE estimation when the subjects carry different loads of
weight during activity [Mathie et al.,
2004]. Moreover, EE during walking may be inaccurately estimated when the
locomotion is not horizontal, e.g., slope climbing and walking up and
downstairs. A barometer that measures the atmosphere pressure was integrated
with a triaxial accelerometer [Ohtaki et
al., 2005]. This approach can use the added information of altitude changes
to determine movement with vertical displacement, such as taking elevator,
walking upstairs and downstairs.
2.3.3 Fall detection and balance evaluation
injuries cause fracture and trauma which remarkably deteriorate the health and
functional status of elderly people, leading to living dependence and higher
risk of morbidity and mortality. Falls can be conceptually deemed as a rapid
postural change from upright to reclining position to ground, or some lower
level not as a consequence of sustaining a violent blow, loss of consciousness,
sudden onset of paralysis as in stroke or an epileptic seizure [Gibson, 1987].
approach to fall detection using accelerometry is published by Williams et al. , and a fall detector was
presented after a number of pilot studies [Doughty et al., 2000]. In its design implementation, the fall detector
consisted of two piezoelectric shock sensors to detect the impact and a mercury
tilt switch to identify the orientation. A two-stage detection process which
detects both impact (acceleration) and orientation was used to better eliminate
false alarms. The two-stage detection process firstly screens if any impact
greater than a certain threshold exists (the first stage). A fall emergency is
registered after the first stage if the reclining posture remains unchanged (the
wearer does not get up) for a specific period of time. This design
implementation led to the product commercialization of the fall detector by
Tunstall Group [http://www.tunstall.co.uk/]. Similar approaches have been
incorporated into fall detection algorithms using a waist-mounted accelerometer
[Karantonis et al., 2006; Yang et al., 2009].
Lindemann et al.  evaluated a fall detector
that was fixed behind the ear. Two high-g (50g) accelerometers were
orthogonally placed in the detector such that accelerations along all the
sensitive axes could be measured. The fall detection algorithm used three
trigger thresholds of sum-vector of acceleration in a plane (>2g), the
velocity before the initial impact (>0.7 m/s), and the sum-vector of
acceleration in all spatial axes (>6g) to recognize a fall. Though high
sensitivity and specificity of the algorithm has been reported, such sensor
placement would become an issue when ergonomics and integrated design of
wearable system are considered.
or postural stability of the body while standing still or walking has been
regarded as an important predictor of risk of falling of the elderly [Berg et al., 1992]. The physiological profile
assessment (PPA) proposed by Lord et al. [Lord et al., 2003] also adopts postural sway as one of the six tests for
screening risk of falling. In the balance test of PPA, postural sway can be
measured using a sway meter that records body displacement at waist level.
Force plate or pressure mat can be used to record the trajectory of center of
pressure (COP) of body which also represents postural sway [Sherrington 2000].
The postural sway measured from the sway meter and force plate shows strong
correlation, and can provide similar information about balance sway.
Postural sway can
also be measured by using accelerometers placed at the back of a subject [Hageman
et al., 1995; Duarte et al., 2000; Nichols et al., 1995]. Triaxial accelerometers
have been used to obtain the postural sway projected on a level ground [Mayagoitia
et al., 2000]. With the known height
from the sensor to the ground, and the sensor output showing the tilt angle,
trigonometric calculation can be applied to obtain the trajectory in
anterior-posterior and medio-lateral directions projected on a level plane during
a standing posture. The advantage of this technique is that the accelerometer
is more sensitive to the difference of test conditions and is fully portable
without the use of a force plate. Studies also showed a moderate correlation
between trunk acceleration and COP pattern [Adlerton et al., 2003].
parameters have been presented to assess balance control, functional ability,
and risk of falling. Gait parameters during free walking can be measured by
using accelerometers. Accelerometry data can be used to identify heel strike [Evans
et al., 1991], gait cycle frequency,
stride symmetry and regularity [Auvinet et
al., 1999]. Measurement of temporal parameters of gait during long periods
of walking using accelerometers was presented [Aminian et al., 1999], and the spatial-temporal parameters were also
measured using a miniature gyroscope [Aminian et al., 2002]. Moe-Nilssen et al.
[2003, 2004] estimated the gait cycle characteristics of the subjects during
timed walking. A triaxial accelerometer was attached to the lower trunk (the L3
region of the spine), and the signals were analyzed by an autocorrelation
procedure to obtain cadence, step length, and gait regularity and symmetry.
between young and elder subjects have been compared by investigating
accelerometry data. Vector magnitude (root mean square) values of accelerations
obtained from the pelvis and head (vertical component) of elder subjects are
smaller comparing with those obtained from young subjects [Menz et al., 2003]. Elder subjects showed
slower velocity, shorter step length, and larger step timing variability during
both walking on level and irregular surfaces from the temporal-spatial gait
parameters between young and elder subjects. The harmonic ratio has been proposed
as a measure of smoothness of walking, and is defined as the ratio of the
summed amplitudes of the even-numbered harmonics to the summed amplitudes of
odd-numbered harmonics both obtained from finite Fourier transform [Smidt et al., 1971]. Older people with
elevated risk of falling exhibited lower harmonic ratio [Menz et al., 2003].
Review of current products
There are many
step counters available at very low prices that provide basic step counting and
EE calculation. On the other hand, only a few commercial activity monitors use
accelerometers. This section reviews several commercially available activity
monitors using accelerometers, which are commonly used, compared and validated
in research literatures, to provide a comprehensive outlook of current
development status and how the activity monitors perform in various
applications. Primary specification of the surveyed products are summarized and
compared in Table 2.1.
Armband (BodyMedia Inc.,) is an activity monitor worn on the upper limb to
measure physical activities. The SenseWear Armband combines a dual-axial
accelerometer to measure motion and multiple sensors to measure skin
temperature, heat flux and galvanic skin response. This system can report the
total EE, metabolic equivalent of tasks (METs), total number of steps, and
sleep duration. The SenseWear armband was used in a program of weight
intervention [Polzien et al., 2007].
Compared with other products and indirect calorimetry, the SenseWear armband
accurately assessed EE across slow to normal walking, but showed
underestimation of EE during increased walking speeds [Arvidsson et al., 2009]. The SenseWear armband in
connection with a fuzzy inference system was also used to distinguish motion
states and emergency situations [Jin et
CT1 and RT3 (StayHealthy Inc.)
has two motion monitor products, CT1 Calorie Tracker and RT3. Both products can
be worn with a clip at the waist. CT1 is a FDA cleared Class II medical device
for accurate EE estimation. RT3 is an activity monitor that uses a
piezoelectric triaxial accelerometer to provide METs for clinical and research
applications. RT3 also replaces the previous version Tritrac-R3D, which has
been widely used in a number of studies and research applications.
A validation of
RT3 for the assessment of PA reported that RT3 was a good measure of PA for
boys and men [Rowlands et al., 2004].
RT3 has been used in recording temporal patterns of activity in chronic obstructive
pulmonary disease (COPD) patients [Hecht et
al., 2009]. A study on the effect of a telehealth intervention for patients
after coronary artery bypass surgery (CABS) used RT3 to measure PA and EE of
the patients [Barnason et al., 2009].
AMP 331 (Dynastream Innovations
The AMP331 is an
activity monitor positioned on the back of the ankle. With the proprietary
“SpeedMax” technology, AMP 331 uses accelerometers to measure the forward and
vertical accelerations to determine the position of the foot in space. Major
gait parameters, such as stride length, speed and travelled distance during
walking or running can be calculated. The recorded data in AMP 331 can be
downloaded to PCs via a 916 MHz wireless radio receiver.
showed that the accuracy in distance computation is about 97% and even 99%
after proper calibration. A study was conducted to validate the AMP 331 in
assessing EE. This study recruited 41 subjects whose 12-hour daily activities
in a field environment were recorded. The EE estimate from the AMP 331 and
diary record were compared and the Pearson correlation coefficient is 0.651 [Park
et al., 2006]. The AMP 331 was
reported to better estimate EE than other wearable sensors (comparing with the
reference EE from indirect calorimetry) during walking with the manufacturer’s
estimation equation [Crouter et al.,
2006]. The accuracy of the AMP 331 to detect atypical gait was also studied.
The AMP 331 performed better than other sensors (comparing with data obtained
from video recording) in detecting structured walking and stair ascent/descent
[Kuo et al., 2009].
GT3X, GT1M (ActiGraph LLC)
The GT1M uses a
uniaxial accelerometer and measures acceleration at 30 Hz sampling rate and
12-bit resolution in response to 0.05 to 2.5g. The sampled signals are then
bandpass-filtered between 0.25 to 2.5 Hz. The GT1M can be worn at the waist to
measure activity counts, step counts, activity levels and EE. It can also be
worn on the wrist for sleep monitoring. The data can be downloaded to the PC
software “ActiLife” via USB connection.
GT1M has been
used in evaluating PA levels in children and adolescent [Martinez-Gómez et al., 2009]. This device can
accurately measure step counts and EE level between subjects in various ages [Focht
et al., 2003]. de Vries 
reported that the ActiGraph series was the most studied activity monitor, and
many studies have validated its reliability and performance. The latest model
GT3X uses a triaxial accelerometer for more accurate PA monitoring. GT3X is new
and has been used in a study of physical activity in association with vascular
function [García-Ortiz et al., 2010].
In addition, the company also releases ActiTrainer that uses the same triaxial
accelerometer as that is used in GT3X, and a heart rate monitoring is
(also known as Step Activity Monitor, SAM) is an ankle-worn,
microprocessor-controlled activity monitor for gait measurement. It records
steps in a variety of gait styles and cadence. The StepWatch has also received
FDA marketing clearance as a Class II device.
Foster et al. [Foster
et al., 2005] investigated the
accuracy in step counting of the StepWatch and found the negligible variance
over all walking speed. It was reported to have minimum difference of step
counts compared with the actual step counts during treadmill walking. The
StepWatch showed better step counting at slow treadmill walking speed [Storti et al., 2008], but overestimated the
steps during a 24 hour monitoring [Karabulut et al., 2005].
activPAL (PAL Technologies
The activPAL is
a motion sensor based on a uniaxial piezoresistive accelerometer. Worn and
positioned on the thigh by direct adhesion to the skin, the activPAL classifies
sitting, standing and walking among free-living activities. Recorded data is
transferred to a PC via USB port. Ryan et al. [Ryan et al., 2006] investigated the validity and reliability of the
activPAL, showing that it was a valid and reliable tool in measuring step and
cadence of the healthy subjects during walking. The activPAL was also compared
with a discrete accelerometer device on the same healthy adults. The study
indicated that the activPAL achieved a close match to the proven accelerometric
data [Godfrey et al., 2007]. For
older adults, the activPAL also exhibited accurate step counting and cadence compared
with two other pedometers (New-Lifestyles Digi-Walker SW-200 and NL2000) [Grant
et al., 2008].
Device for Energy Expenditure and Activity (IDEEA) is a device designed for PA
and behavior monitoring, gait analysis, EE estimation and posture detection. An
external set consisting of 5 biaxial accelerometers are attached to lower limbs
and are wire-connected to a portable recorder worn at waist. It uses a 32-bit
microprocessor that enables real-time data acquisition and processing. The
IDEEA has been used in monitoring PA of obese people in real life environment
[Benedetti et al., 2009], and has
been validated in the study of ambulatory measurement for gait analysis [Huddleston
et al., 2006], and EE estimation of
PA [Zhang et al., 2004].
Table 2.1 Product specification comparison
Number of accelerometer
Number of accelerometer axis
1 uni-axis and 1 dual-axis
Waist or wrist
Chest, thigh, feet
3.7V Lithium ion/Lithium Polymer
3V li-polymer rechargeable
1 1.5V AA
3 days (continuous)
916MHz RF (USB wireless adapter)
Data storage capacity
3 hours to 21 days (dependant on data resolution and collection)
(or 40 days)
EE estimation, activity duration, sleep duration
Activity intensity, EE, MET
Steps, cadence, walking speed, stride length, distance, EE
Activity counts, steps, MET, activity intensity level
Steps, gait characteristics
Sedentary and upright time, steps, stepping time, cadence,
sit-to-stand activities, MET, PAL, kCal
Activity types, gait types, EE
measurement of human activities can provide quantitative assessment of physical
activity. PA monitoring using accelerometry techniques enables automatic,
continuous and long-term activity measurement of subjects in a free-living
environment. All accelerometers provide basic step counting and activity counts
(intensity) that can be used to estimate the energy expenditure due to PA. This
has been widely adopted as an assistive method in the application of weight and
dietary management. Postural sway can be measured by accelerometry that offer
moderate correlation with reference to a force plate. Important gait
parameters, such as the cadence, stride length, stride regularity, walking
speed, can be measured using accelerometry to evaluate one’s risk of falling
and mobility level. Detecting unusual movement, such as falling, is applicable
to telecare or a personal emergency response system (PERS) for the elderly. In
addition, accelerometry can assist traditional assessment tools for
quantitative evaluation. For example, the TUG-T timing can be identified
automatically according to the accelerometer outputs obtained from the test
subjects. The time taken to perform each activity state can be objectively
identified and the movement characteristics can be analyzed as well [Higashi et al., 2008].
utilize diverse sensors in a single accelerometer provide more activity
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Altimeters (pressure sensors) have been used along with an accelerometer to
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conductivity have also been used in a commercial accelerometer-based activity
monitor for accurate EE and metabolism rate assessments. In addition,
accelerometers can be integrated into clothing from the ergonomics’ point of
In the future,
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