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Title: Accelerometer Technologies, Specifications, and Limitations
Author: Jeffery Miller
Institution: ActiGraph
Introduction: Technology advances and manufacturing efficiency improvements have drasticallyincreased the options and flexibility available to users of accelerometer-based products. Theseadvancements have led to confusion among activity monitor users, and many have been led to believethat device output normalization is an obvious and easy step. In truth, there are many engineering
obstacles that make normalization extremely complicated.
Purpose: The purpose of this presentation is to educate users on the differences between integrated
circuit accelerometer technologies and the tradeoffs and burdens that may be incurred by selecting one
type over another, regardless of manufacturer. This paper will work to dispel the myth that
accelerometers and/or activity monitors can easily be used interchangeably while furthering theunderstanding of accelerometers and their capabilities within the arena of human activity monitoring.
Summary: Until recent years, researchers interested in monitoring and quantifying human activity only
had one viable option. The technology of Microelectromechanical systems (MEMS) was not yet costeffective for everyday use, and as a result, accelerometer-based activity monitors relied primarily on
piezoelectric bimorph beams. These beams, while functional, are typically expensive to manufacture,require periodic calibration, and are limited to measurement of time varying acceleration, which
precludes positional information such as subject posture. Advances in silicon wafer and manufacturing
processing have enabled MEMS based accelerometers to become prevalent in many applications,
including nearly every smartphone manufactured today. This technology is extremely stable, exhibits
negligible measurement drift due to temperature, and requires only a single calibration. Furthermore,because they are capable of measuring static acceleration, positional information can be harvested forvarious applications.
These advances in the field of MEMS technology, coupled with the decreasing cost per bit for nonvolatile memory, have led to a fundamental shift in the way activity data are collected. Researchers are
no longer limited by the long standing filtered/epoch level data collection that dominated the arena forso many years. They are now free to collect raw acceleration data. This approach maximizes flexibility,
allowing researchers to post process and reprocess data as new algorithms become available. With thisadditional flexibility comes a growing interest by the research community in normalizing outputs acrossdevices, thereby removing the unique value provided by individual manufacturers. While this idea holds
great promise among those tasked with harvesting useful information from the collected data, there are
numerous hurdles that prevent it from being easily achieved. Proper education can establish
appropriate data collection expectations, reduce confusion about the data collected, and allow forrealisticcomparisons
betweendifferent
accelerometer
based
products.

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Introduction
The measurement of human activity continues
to be a growing area of interest within research,clinical, and personal health arenas.
Information gathered and derived from devices
that capture activity related measurements canprovide valuable insight into many healthrelated outcomes. Within the research andclinical markets, this information is often
captured subjectively by way of patientreported outcomes. Not only is this subjective
method inherently unreliable, but the
information collected lacks the intrinsic longterm value associated with objective data setsthat may be shared, processed, and studied.

differences
betweenintegrated
circuitaccelerometer technologies and the tradeoffs
and burdens that may be incurred by selecting
one type over another. Proper education canestablish
appropriate
data
collection
expectations, reduce data confusion, and allow
for realistic comparisons between different
accelerometer based products. Furthermore,this paper will discuss how the increasing desire
among the research community to normalize
outputs is difficult at best and puts device
manufacturers in the undesirable position of
having to share intricate design nuances andpotentially proprietary trade secrets.

Accelerometer Technologies

As with any tool, the intended use and desired
output/results play a very large role in theselection process. Over the course of the last
several years, a wide array of miniature, lowcost activity monitoring tools targeted atvarying markets have become available.
Primarily driven by improvements in
manufacturing
efficiencies
andoveralltechnology advances, these devices have grownin complexity and feature set to betteraccommodate a rapidly evolving field of study.
As complexity increases, use cases begin todiverge, measurement populations become
more focused, and the need for improved user
education on the varying parameters andnuances that differentiate one device fromanother becomes more imperative.

There are many technology types that yieldtransducers capable of converting acceleration
into a quantifiable and measurable signal, witheach varying in maturity, capability, cost, andmanufacturability. In the broadest sense andmost relevant to those interested in humanactivity, accelerometers can be classified into
one of two primary categories: AC coupled,which are only capable of measuring timevarying accelerations, and DC coupled, whichmeasure both static and dynamic accelerations.

Such a rapid evolution in product capability andofferings is often coupled with confusion by
those charged with harvesting useful
information from the devices on a day to day
basis. This white paper will work to alleviate
this confusion and educate users on the

Piezoelectric2|PageIn order to maintain brevity, only thetechnologies that are currently used or havevalue within the human activity markets will be
discussed. In the most basic of terms we will
discuss piezoelectric and MEMS based
capacitive accelerometers.

Piezoelectric based accelerometers have beencommercially available since the first half of thetwentieth century, and they have been used fora broad range of applications with great
success. They are based on the phenomenon ofactigraphcorp.com

crystalline structures, which yield an electricalsignal proportional to the amount ofacceleration they experience.
These sensors are typically manufactured with amass carefully situated at the end of a
cantilevered beam.
This weighted beam
deflects in relation to the acceleration
experienced, yielding an electrical signal
proportional to the acceleration experienced.
This is a very effective solution for measuring
human activity, but there are a number offactors that limit its functionality within thecurrent activity economy.
Until recent years, most devices intended forend user consumption utilized a piezoelectricbi-morph beam based accelerometer. These
devices have a significant advantage over theircounterparts in that they require zero power tooperate, providing for an end user device with avery long battery life. Unfortunately, bi-morph
beam based devices are difficult tomanufacture, which typically increases the costto the end user. Additionally, because they aresusceptible to drift due to environmental
conditions and mechanical shocks, regular fieldcalibration is generally required.
Anotherdrawback to the bi-morph beam type
accelerometer is the fact it provides an AC
coupled output only. In practice this means
only transient, or time varying, accelerations
can be measured, thus precluding the ability toharvest positional or posture information.
MEMS Capacitive
Recent years have witnessed the proliferationof Microelectromechanical Systems (MEMS)
based accelerometers. Most, if not all, MEMS
devices utilized in human activity monitors are
3|Pagebased on a capacitive response. With thesedevices, an internal capacitance changes
proportionately
totheacceleration
experienced, yielding a very stable output
signal. Unlike bi-morph beams, which areconsideredpassivecomponents,
these
accelerometers are active semiconductors. As aresult, they require power in order to operate,yielding a net negative effect on activity
monitor battery life. The MEMS accelerometer
manufacturing process produces a very stabletransducer that does not suffer from the sameenvironmental drift as its piezoelectriccounterpart. This translates into a single
calibration requirement that is typicallyachieved during the activity monitor
manufacturing process, resulting in a reduced
operational burden to the end user.
MEMS accelerometers have very broad usage
and adoption across many markets, including
air bag technology, home appliance, mobile
phones, and game controllers, as well as thedeveloping human activity market. Because
they are packaged in standard semiconductor
packaging, they are easily machine placed
during the manufacturing process, lowering
overall product cost. MEMS accelerometers arealso typically DC coupled outputs, allowing forthe measurement and capture of both staticand transient accelerations. From this, it ispossible to pull positional or posture related
information. Furthermore, these devices can bepurchased in a myriad of configurations andoptions that include analog or digital output
and programmable dynamic ranges.

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Data Collection
Unfortunately, as the number of accelerometry
device options and choices increase, so does
confusion.
Historically, researchers only
needed to concern themselves with a handful ofparameters easily achieved by most devices.
However the increased availability of MEMS
devices has fueled a shift in paradigm related tomeasuring and collecting data in recent years.
Rather than the previously limiting method ofstoring the activity data in filtered format inpredetermined epochs, researchers are taking
advantage of significant increases in memory
densities and battery life improvements to store
data in a raw format. This method offers far
more flexibility because the data can be postprocessed repeatedly as new algorithms
become available. Researchers are just now
beginning to reap the benefits of this shift inmeasurement style, through the emergence of
new areas of interest such as activity patternrecognition.
Regardless of the technology type employed or
the source of the acceleration to be measured,
all accelerometer devices must perform the twobasic functions of pre-filtering, or anti-aliasing
filter, and sampling to achieve activity
measurements.

Figure 1 Filtering and Sampling Process

The manners in which these functions are
performed are determined by the designer and4|Pageare typically driven by a multitude of variables
includingtheintendedmeasurement
requirements, cost, battery life requirements,
device size, environmental demands, andinteroperability with previously manufactured
devices. There are many tradeoffs made by thedesigner during the process in order to achieve
the end result while simultaneously meeting as
many of the design requirements as possible.
The remainder of this paper will focus onproviding education on the key specifications ofaccelerometers, filtering methods, and how
they impact various measurements. After
presentation of these parameters, it will beobvious how device output normalization is nota trivial endeavor.
Sampling
One of the many new accelerometer features
implemented in recent years is the introductionof varying sample rates. Rather than being
limited to the historical sample rates of 10 or 30
Hz, mainstream devices now support rates up
to and beyond 100 Hz. The introduction of thisfeature alone has created the misconception
among end users that 'more information' isnaturally contained within a signal that is
sampled at a higher rate. Depending on theintended use and how it is implemented, thismay not be the case and could result in
unnecessarily large files and related data
storage issues.
More activity monitors are beginning to utilize
MEMS accelerometers with a digital output
because of the flexibility and rapid design cycles
they provide.
One thing that must be
accounted for is the fact that digital
accelerometers generally have finite sample

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rates to select from, and therefore numerical
techniques must be performed in order toachieve the desired end user sample rate. For
instance, if a desired end user sample rate of 30
Hz is required and the accelerometer only
samples at octaves of 200 Hz (very common),sample rate shifting must be implemented toachieve the desired 30 Hz result. While further
discussion of the varying shifting techniques is
outside the scope of this white paper, it isimportant to realize that each method impacts
the overall measurement output in its own way.
An additional consideration in regards tosample rate is the accuracy and stability of thetiming source for the sampling. Many digital
accelerometers have internal timing sources,
yielding little to no flexibility to the designer. If
not taken into consideration, these crude
oscillators can vary from device to device by asmuch as +/-15%. This means that a desired 30
Hz sampling rate can yield values ranging
between 34.5 Hz and 25.5 Hz, and two identicalactivity monitors can be configured identicallyand yet have sampling rates that vary by asmuch as 9 Hz. Having a repeatable andpredictable sample rate is paramount for
accurate post processing and further digital
filtering.
Anti-Aliasing Filtering
An additional consideration is that of the antialiasing filter, or pre-filter. This filter sets theinitial bandwidth of the signal to be sampled. It
is important to note that all activity datacollected is pre-filtered with an anti-aliasing
filter. All data, even raw, is filtered at least
once. This is driven by the requirement of the
Nyquist-Shannon sampling theorem which
5|Pagestates that any frequency band-limited signal
can be perfectly reconstructed from a sequenceof samples if the bandwidth, B, is no greaterthan ½ the sampling rate, fs. Simply stated, thesampling rate must be twice the highest
frequency component of the signal to besampled.
fs > 2B
If this criterion is not met, a phenomenon called‘aliasing’ occurs, which corrupts the sampled
data so that it no longer accurately represents
the original signal. The ability to harvest usefulactivity information from an aliased signal issignificantly compromised. In addition, thesefilters vary from one component manufacturer
to another; digital accelerometers typically havea preset filter bandwidth much wider than isneeded for human activity measurement,
requiring further filtering within the digitaldomain to achieve the desired bandwidth.
While this dual filtering approach is a perfectly
valid solution in this circumstance, there are
numerous ways in which it can be
implemented. Each solution presents itself witha number of tradeoffs that must be made, andin the scenario of digital filtering the issue ofthe repeating spectrum is introduced. Unlike its
analog filter counterpart, digital filters exhibit arepeating frequency spectrum that repeats atthe sample rate. This must be taken into
account during the system design to ensure
these repeating spectrums do not inadvertently
allow undesired acceleration noise to impact
the final output.

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consistently normalize outputs becomes very
difficult. Further discussions will show that key
specifications of the accelerometer Integrated
Circuit (IC) itself can vary, also introducing
output variations between devices.

Specifications
Figure 2 Digital Filter Frequency Response

Oversampling
When both the bandwidth, which is set by theanti-aliasing filter, and the sample rate, which
may or may not include sample rate shifting
techniques, are considered, it becomes obviousthat two sample rates may actually have thesame bandwidth. If the Nyquist criterion is met
in both cases, then both sample sequences canfully recreate the original signal.
There can be benefit, however, to oversampling(sampling more than twice the highest signal
frequency) data, and this has been utilized in
the audio industry for years. The primary
benefit is an improvement in the Signal to Noise
Ratio (SNR). In regards to day-to-day activities
for the average person, this likely does not holdvalue. But for those subjects that do not
generate typical acceleration values in theirambulation, such as elderly who may shuffle
their feet, oversampling could improve results
as more signal is discernible in the very low
acceleration range.
It is clear from this brief discussion that thesetwo parameters, bandwidth and sample rate,
have the ability to significantly impact device
output. Without knowing precisely how otherdevices solve these core problems, the ability to
6|PageWhile there are numerous specifications that
determine a MEMS accelerometer or activity
monitor’s performance, this white paper will
only discuss dynamic range, sensitivity, noise
density, and resolution. These four parametersand how they are used have the ability to
impact device output in a significant manner.
Dynamic Range
The dynamic range of an accelerometer isdefined as the range of accelerations a device
can successfully measure while still maintaining
a linear response. Typical accelerometers on
the market today can measure +/- 2g, +/-4 g, +/6g, and +/- 8g. In some instances, these areprogrammable on the device to maximize
flexibility.
It should be noted that although anaccelerometer may be specified to a range of+/-6 g, it is possible that it will report values
outside of this range if the stimuli exceeds these
range

Figure 3 Implications of Dynamic Range Non-Linearities

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However, there is no guarantee of accuracy.
Depending on the type of subject activity andwear location (wrist and foot worn devices
generally experience higher accelerations than
waist worn devices), it wouldn’t be consideredabnormal for the device to be subjected toaccelerations outside of its printed specification
range. While not damaging to the device, this
could lead to erroneous data if not handledproperly. Some manufacturers choose to limit
the reported output to the printed specification
range of the accelerometer while others reportwhatever value is collected. This difference inapproach from manufacturer to manufacturer
can further complicate matters.
Sensitivity
The sensitivity specification of an accelerometeris typically given in milli-volts/g for analog
output devices and milli-g/LSB (Least Significant
Bit) for digital output devices. It provides ameasure of the device’s sensitivity to the input
it experiences. A more sensitive device will
provide a ‘larger’ output for a given stimulus.
Typically, the more sensitive the device, thebetter it is at measuring and discerning low
amplitude signals as the output is ‘amplified’ toovercome the inherent noise.
To the end user, this implies that two devices
with very similar overall specifications may yield
different
results
inlow
acceleration
environments.
Noise
Noise is an often overlooked specification
within the activity monitoring world, but theimportance of this parameter will continue togrow as end user requirements and subject
7|Pagepopulations become more focused in order tostudy very specific areas.
Typically provided in g/√Hz, noise density’s
impact on the overall output is dependent upon
the system bandwidth, which is set by the antialiasing filter. If not properly taken into
consideration, this parameter can impact theactivity monitor’s ability to reliably discern
between internally generated noise and low
acceleration stimuli.
Resolution
The final parameter of interest is the resolutionin which the data are presented. This is afunction of the dynamic range of theaccelerometer and the number of bits of theAnalog to Digital Converter (ADC) that are
employed. It is a measure of the size, in g’s, ofthe discrete levels used to represent theoriginal signal and provides a measure for thelowest discernible acceleration that can bemeasured, in the absence of noise. This isimportant because two devices with differingresolutions will report different values while
subjected to the same stimuli.

Conclusions
In recent years, many human activity
monitoring solutions have become available,
and their steadily increasing variety of featuresand options has the potential to result in end
user confusion. It has been discussed that mostactivity monitors manufactured today utilize
one of two types of transducers: piezoelectricor MEMS capacitive. Each type is a suitable
solution with varying pros and cons, but thegeneral consensus has been to move towards
the use of MEMS based devices due to theactigraphcorp.com

overwhelming availability of components on themarket, ease of manufacturing, and lack of fieldcalibration requirement.
The best representation of any measured signal
is the original signal, and any function
performed on the signal beyond its original
analog state impacts the output in its own
unique manner. It has been shown that there
are numerous factors and parameters that
impact the overall device output including
bandwidth, filter strategy, sampling rate,
sample rate accuracy, dynamic range,
sensitivity, noise density, and resolution. These
parameters work together in varying ways,
often times working against other functional
requirements such as battery life, size, or water
resistance. Such interaction naturally requires
tradeoffs to be made during device design.
Due to the number of parameters that have thepotential
tonegatively
impactthemeasurement result, the practicality ofnormalizing outputs from one manufacturer’s
device to another is very low. While the belief
in doing so is that devices could be intermixed
and data exchanged regardless of activity
monitor manufacturer, the reality is a high levelof collaboration would be required acrossdevice manufacturers. Furthermore, doing sowould remove the unique and intrinsic value
offered by different companies, reducing yearsof design and engineering knowledge tocommon and public knowledge.

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