Author: Che-Chang Yang, Yeh-Liang Hsu (2010-08-20);
recommended: Yeh-Liang Hsu (2010-08-25).
Note: This paper is published in Sensors, Vol. 10, No. 8, pp. 7772-7778,
Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity
of physical activity are indicative of one’s mobility level, latent chronic
diseases and aging process. Accelerometers have been widely accepted as useful
and practical sensors for wearable devices to measure and assess physical
activity. This paper reviews the development of wearable accelerometry-based
motion detectors. The principle of accelerometry measurement, sensor properties
and sensor placements are first introduced. Various research using accelerometry-based
wearable motion detectors for physical activity monitoring and assessment,
including posture and movement classification, estimation of energy
expenditure, fall detection and balance control evaluation, are also reviewed.
Finally this paper reviews and compares existing commercial products to provide
a comprehensive outlook of current development status and possible emerging
Keywords: accelerometry; accelerometer;
physical activity; human motion; energy expenditure; gait; fall detection
activity (PA) is regarded as any bodily movement produced by skeletal muscles
which results in an energy expenditure . 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 declining PA level represents a major factor in
multiple illnesses and symptoms related to functional impairment . The
organization Healthy People 2020 [http://www.healthypeople.gov/HP2020/] led by
government has recognized PA as one of the leading health indicators (LHI),
which are a measurement of health of a nation’s population.
of subjective and objective PA assessment tools have been developed. Subjective
methods, such as diaries, questionnaires and surveys, are inexpensive tools.
However, these methods often depend on individual observation and subjective
interpretation, which make the assessment results inconsistent . 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 . 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 .
On the other
hand, objective techniques use wearable, or body-fixed 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 a spring-loaded mass or some other switch mechanism 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 result in
inaccurate energy expenditure estimations . 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 concerns are a major drawback
in monitoring systems based on video recording. These systems may not be
practical for monitoring subjects in free-living environments.
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 . 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 free-living environments .
were first investigated in the 1950s to measure gait velocity and acceleration
. Accelerometry measurement of human motion was studied in more detail
during the 1970s due to technological advances . 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 meantime, sensor
performance has been enhanced while the power consumption is greatly reduced.
The first batch-fabricated MEMS accelerometers were reported in 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 technologies.
Design Fundamentals for
Accelerometry-Based Wearable Motion Detectors
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 [12,13].
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.
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.
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
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 the 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
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 on 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 , lower
back , and waist . 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 [16-18]. An
approach using a chest-worn accelerometer was presented to detect respiratory
and snoring features for apnea diagnosis during sleep .
can also be attached to wrists, thigh, or ankles. Sleep time duration can be
determined from a wrist-worn accelerometer  and activity levels during
sleep can be measured . 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 [22,23]. A special placement in which an accelerometer unit
integrated into hearing aid housing was used for detecting falls . 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 [25,26].
consideration for sensor placement is how to attach sensors to the human body.
Wearable sensors can be directly attached to the skin [15,24], or with some
form of indirect attachment by using straps, pant belts and wristbands, or
other accessories [20,22,25,26]. Sensors and wearable devices can also be
integrated into clothing . 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
Capabilities of Wearable
Systems Using Accelerometry Measurement
Accelerometers 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
3.1 Posture and Movement Classification
Movement 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.  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 magnitude of acceleration signals along sensitive axes from only one
accelerometer worn at the waist and torso [16,17]. However, the 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 . 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 [30-32]. 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 . Sit-stand postural transitions can be
identified according to the patterns of vertical acceleration from an
accelerometer worn at the waist .
Acceleration signals can be used to determine walking
in ambulatory movement. Walking can be identified by frequency-domain analysis
[16,34]. It is characterized by a variance of over 0.02 g in vertical acceleration and frequency peak
within 1–3 Hz in the signal spectrum . Discrete wavelet transform is used
to distinguish walking on a level ground and walking on a stairway .
Movement 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 [31,35], support vector machines (SVM)
[36,37], Naive Bayes classifier [38,39], Gaussian mixture model (GMM)  and hidden
Markov model (HMM) [41,42]. 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
3.2 Estimation of Energy Expenditure
Energy 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 the 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 . 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 ,
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 .
Commercial 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  or indirect
calorimetry to estimate the energy expenditure due to activities . Several
regression equations can be derived or validated for different accelerometers
to better match exact EE of physical activities among subjects.
Factors 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 . 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
. 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 . This approach can use the added information
of altitude changes to determine movement with vertical displacement, such as
taking elevator, walking upstairs and downstairs.
3.3 Fall Detection and Balance Control Evaluation
Fall-related 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 .
The first approach to fall detection using accelerometry
is published by Williams et al. , and a fall detector was presented after a
number of pilot studies . 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
Lindemann et al.  evaluated a fall detector
that was fixed behind the ear. Two high-g (50 g) 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 (>2
g), the velocity before the initial impact (>0.7 m/s), and the sum-vector of acceleration
in all spatial axes (>6 g)
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.
Balance control 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 . The physiological profile assessment (PPA)
proposed by Lord et al.  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 . 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 [54-56]. Triaxial accelerometers
have been used to obtain the postural sway projected on a level ground .
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 .
Significant gait 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 , gait cycle
frequency, stride symmetry and regularity . Measurement of temporal
parameters of gait during long periods of walking using accelerometers was
presented , and the spatial-temporal parameters were also measured using a
miniature gyroscope . Moe-Nilssen et al. [63,64] 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.
Gait features 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 [25,26]. 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 . Older people with elevated risk of
falling exhibited lower harmonic ratio .
Review of Current Products
There are many step counters available at very low prices that provide
basic step counting and EE calculations. 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 1.
SenseWear (BodyMedia Inc.)
Armband (BodyMedia Inc.,) is an activity monitor worn on the upper limbs 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 weight intervention program
. 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 . The SenseWear
armband in connection with a fuzzy inference system was also used to
distinguish motion states and emergency situations .
Table 1. Product specification comparison.
88.4 × 56.4 × 24.1
71 × 56 × 28
71.3 × 24 × 37.5
38 × 37 × 18
75 × 50 × 20
53 × 35 × 7
70 × 54 × 17
1 uni-axis and
Waist or wrist
Chest, thigh, feet
30 Hz (12 bit)
10 Hz (8 bit)
1.5 V AAA × 1
1.5V AAA × 1
3.7 V Lithium
3 V li-polymer
1 1.5 V AA
3 days (continuous)
916 MHz RF (USB wireless adapter)
3 hours to 21 days
(dependant on data
resolution and collection)
(or 40 days)
upright time, steps,
MET, PAL, kCal
gait types, EE
CT1 and RT3 (StayHealthy Inc.)
has two motion monitor products, the CT1 Calorie Tracker and the 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 . RT3 has been used in recording temporal patterns of activity
in chronic obstructive pulmonary disease (COPD) patients . 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 .
AMP 331 (Dynastream Innovations
The AMP 331 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
is 0.651 . 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 . 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 .
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 2.5 g. 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 adolescents . This device can accurately measure step counts
and EE level between subjects in various ages . 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
. 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. 
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 , but
overestimated the steps during a 24 hour monitoring .
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.  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 .
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) .
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
, and has been validated in the study of ambulatory measurement for gait
analysis , and EE estimation of PA .
Sensor-based 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 .
Approaches that utilize diverse sensors in a
single accelerometer provide more activity information and may be expected to improve
the accuracy in PA monitoring. Altimeters (pressure sensors) have been used
along with an accelerometer to identify movements with altitude changes, such
as walking up/downstairs. The ability to classify inclined walking may enhance
the accuracy in EE estimation during PA. The measurements of human heat dissipation,
skin temperature and 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
In the future, the application of wearable
accelerometry-based activity monitors should be provided with the integration
to so-called “health smart home” monitoring systems . Accelerometry data
obtained from wearable accelerometers can be synchronized with the activity of
daily living (ADL) data recorded by such monitoring systems to better describe
the information of human mobility, physical activity, behavioral pattern, and
functional ability that encompass the important parameters regarding the
overall health status of an individual.
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