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Calibration and Validation of Wearable Monitors


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Medicine & Science in Sports & Exercise: January 2012 - Volume 44 - Issue 1S - p S32-S38
doi: 10.1249/MSS.0b013e3182399cf7
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Wearable physical activity monitors are now widely used in research studies. These devices work by sensing either physiological or mechanical responses to bodily movement, and they use these signals to estimate variables that reflect physical activity. Calibration and validation of wearable activity monitors are the first steps to obtaining accurate and objective information. A detailed understanding of the best methods for doing this will ensure that the best possible data are collected. This article is aimed primarily at the physical activity measurement specialist, rather than the end user seeking to use “off-the-shelf” devices for assessing physical activity.

This article will discuss the process of calibrating and validating wearable physical activity monitors. First, we define validity and identify the types of validity analyses that are most appropriate for wearable activity monitors. Second, we review methods of calibration, paying attention to both “unit calibration” (to ensure interinstrument reliability) and “value calibration” (i.e., conversion of raw signals into metabolic units) of wearable monitors. Third, we describe how to investigate the validity of wearable activity monitors to determine whether they accurately estimate energy expenditure, time spent in various intensity categories, and the type of activities performed. We also discuss the strengths and weaknesses of various calibration methods. We conclude by summarizing what we consider to be the best practices for calibrating and validating wearable activity monitors and priorities for future research.

Definitions of terms

In 2004, at a conference entitled “Objective Monitoring of Physical Activity” held at the University of North Carolina, Welk (26) distinguished between two types of calibration in reference to wearable activity monitors. “Unit calibration” is performed to reduce interinstrument variability and to ensure that individual activity monitors are correctly measuring the direct signals (acceleration, HR, body posture, heat flux). “Value calibration” of wearable monitors refers to the process used to convert the direct signals into other established measurement units. Value calibration is a validity issue and is performed to ensure that a wearable monitor gives the intended values for outcome variables (i.e., “derived” variables).

“Validity” is defined as the extent to which an instrument measures what it is intended to measure (7). There are several different types of validity, including criterion-referenced validity, content validity, and construct validity. Physical activity researchers are most interested in criterion-referenced validity with wearable physical activity monitors because the variables that we are attempting to measure are highly objective (as opposed to subjective). Content and construct validity have lesser value when it comes to establishing evidence of validity for wearable physical activity monitors; however, they are often used in validating physical activity questionnaires.

Criterion-referenced validity is composed of two types: concurrent validity and predictive validity. Concurrent validity is determined by comparing or correlating data collected at the same time from a measure (wearable monitor) and a criterion measure. The criterion measure should be a “gold standard” or a measure with the highest accuracy and precision. Predictive validity is the extent to which a physical activity monitor is able to predict scores obtained using a criterion instrument.


Unit calibration of direct signals from wearable activity monitors.

Wearable monitors typically measure acceleration, physiological signals, body posture, or some combination of these factors. The validity of these direct signals can be checked by comparison with a gold standard. For accelerometers, a simple check on unit calibration would be to spin it in a circle with a known radius and frequency (revolutions per minute), so that it is exposed to a known acceleration. This technique can be used to verify that the accelerometer displays values within the manufacturer’s stated tolerance limits. Unit calibration checks are important with some older activity monitors (e.g., ActiGraph 7164 (ActiGraph LLC, Fort Walton Beach, FL)) that use cantilever beam sensors with analog filtering. They are not necessary with newer monitors (e.g., ActiGraph model GT1M), which tend to use a direct compression sensor integrated into a solid-state microelectromechanical system accelerometer with digital filtering. Because of tighter manufacturing tolerances with microelectromechanical system accelerometers, the digital filter, and the initial unit calibration performed at the factory, the sensitivity varies little between the newer devices. They should remain calibrated for the life of the device, according to the manufacturers.

Although the newer microelectromechanical system–based accelerometer technologies are more robust than their predecessors, they are not infallible. For example, little is known about the threshold detection levels of the existing accelerometer models, which could have a marked effect on sedentary time outcomes or wear time algorithms that look for consecutive epochs of zeros (20). In addition, the robustness of the different manufacturers’ unit calibrations is unclear. For example, it has not been established that a single-point unit calibration (one G force and one frequency) is adequate to test the entire dynamic range. With these issues in mind, we still recommend that all accelerometer-based activity monitors undergo a unit calibration check with a mechanical shaker across a range of accelerations and frequencies before deployment.

Typically, unit calibration is not performed with wearable HR monitors and body posture devices. This is because the HR values obtained from heart monitors have previously been shown to be valid compared with ECG. Body posture monitors have been shown to be valid by placing them in different orientations (horizontal, vertical, tilted) and comparing this measurement with the output from the monitors. Thus, there does not seem to be a need for unit calibration with these devices.

Value calibration of wearable activity monitors.

Value calibration of wearable monitors (e.g., metabolic calibration) refers to the process by which measurement researchers obtain data that allow them to convert direct signals from monitors into estimates of energy expenditure, time spent in various intensity categories, and activity type. This process involves collecting data on multiple individuals as they perform different activities and simultaneously collecting criterion data.

The data collected by accelerometer-based activity monitors are called “activity counts.” These activity counts are derived from the raw acceleration-versus-time curve. For instance, when an accelerometer is placed on a belt worn tightly around the waist, a sinusoidal acceleration-versus-time curve is observed. These acceleration data are filtered, full-wave rectified (meaning that the absolute value of the accelerations is used), and then integrated (i.e., the area under the curve is determined) over a predetermined period. The resultant activity counts are displayed for discrete periods or epochs (e.g., 1 min). The activity counts are then used to predict energy expenditure (8).

In 2004, Welk (26) summarized the principles for designing accelerometry-based value calibration studies. He recommended that during calibration:

  • The sample population must be representative of the intended population in demographics and size.
  • The value calibration study should sample a sufficient number of wearable monitors because monitors have interunit variability.
  • The activities included in the value calibration study should range in intensity from sedentary to vigorous and incorporate both ambulatory and nonambulatory free-living activities typically performed by the intended population.

Historically, most value calibration studies on accelerometers have used a single linear regression approach. After collecting energy expenditure and activity count data on multiple individuals performing a range of physical activities, the relationship between these variables is plotted graphically, and linear regression is used to determine the line of best fit. Once a single-regression equation has been developed, the activity counts obtained by an individual performing an unknown activity can be used to estimate his or her energy expenditure. Activity count cutpoints denoting the dividing line between light- and moderate-intensity physical activity (3 METs) and moderate- and vigorous-intensity physical activity (6 METs) are typically identified. These cutpoints are then used to tally up the amount of time spent in light, moderate, and vigorous physical activity.

Montoye et al. (18) performed one of the first value calibration studies of a uniaxial accelerometer (a prototype to the Caltrac (Muscle Dynamics, Torrance, CA)). They had individuals perform level treadmill walking, treadmill running, bench stepping, knee bends, and floor touches. They used linear regression to determine the line of best fit relating acceleration to energy expenditure. Freedson et al. (12) used a similar value calibration approach with only treadmill walking and jogging. Hendelman et al. (14) and Swartz et al. (25) calibrated the ActiGraph by having subjects perform a variety of moderate-intensity lifestyle activities. In all, more than a dozen regression equations have been developed for the ActiGraph alone (Table 1).

Characteristics of adult single-regression models using the ActiGraph accelerometer, from Crouter et al. (10).

Because single-regression equations cannot accurately determine energy expenditure across a wide range of activities, Crouter et al. (11) developed a two-regression equation that discriminates between walking/running and intermittent lifestyle activities on the basis of the variability in counts across successive epochs. They calibrated the ActiGraph model 7164 on 20 different physical activities that ranged from seated rest to vigorous exercise. The method then uses one of two regression equations to predict energy expenditure, thus achieving a closer estimate than previous single-regression models. Newer approaches to conducting value calibration studies that make use of “pattern recognition” have been developed, and some of them are even more accurate than the two-regression model of Crouter et al.

Pattern recognition.

Pattern recognition is a branch of artificial intelligence concerned with classifying or describing observations. The goal of this method is to classify data (or patterns) on the basis of previous knowledge or statistical information extracted from the data. Pattern recognition requires the following: (a) a sensor that gathers the observations to be classified or described, (b) a feature extraction mechanism that computes numeric information from the observations, and (c) a scheme that performs the task of classifying or describing observations. The classification scheme is usually based on a set of patterns (input variables and desired outputs) that have previously been classified or described. This set of patterns is termed the “training set.” The machine learning strategy in this case is termed “supervised learning.” Machine learning can use either regression, in which case the resulting output function will be a continuous variable (e.g., energy expenditure), or a statistical procedure known as “clustering,” in which case the output will be a category label.

Pattern recognition uses one of several approaches: statistical, syntactic, or neural. Statistical pattern recognition is based on statistical characteristics of the data. Syntactic pattern recognition is based on the structural interrelationships of features. Neural pattern recognition uses a computational method developed from artificial neural networks. No matter what approach is used, pattern recognition still requires “value calibration” in that models must be developed on the basis of the relationship between activity counts and a direct measure of energy expenditure or physical activity intensity. The utility of pattern recognition is highly dependent on the physical activities included in the calibration or training study. Rothney et al. (21) and Staudenmayer et al. (22) provide detailed descriptions of artificial neural network calibration studies. These studies demonstrate that pattern recognition has much greater accuracy than other methods of estimating energy expenditure using accelerometer-based activity monitors.

Many “second-generation” commercially available wearable monitors are already using pattern recognition. The Intelligent Device for Energy Expenditure and Activity monitor (MiniSun LLC, Fresno, CA) takes data from an array of five accelerometers placed in different locations of the body and uses these to predict energy expenditure and activity type on the basis of an artificial neural network that was trained on a variety of activities (28,29). The SenseWear Armband (BodyMedia, Inc., Pittsburgh, PA) measures acceleration, body temperature, and skin galvanic response and uses these to predict energy expenditure. It is repeatedly updated as new data collected with more subjects and more activities are used to train the artificial neural network.

Value calibration of HR and combined HR–accelerometry monitors

Value calibration of wearable HR monitors often involves constructing individual HR-versus–energy expenditure calibration curves. It is well known that HR is linearly related to oxygen uptake over a wide range of intensities, but HR is not a very good predictor of light-intensity physical activity. A typical method of individual calibration is the “flex method,” whereby the flex HR is identified as the average HR obtained during sitting, standing, and light exercise, and the linear relationship between HR and V˙O2 is measured during a graded exercise test. Under free-living conditions, if an individual’s HR falls below the flex HR, they are credited with 1.0 MET. Above the flex HR, the energy expenditure is estimated from the linear HR-to–energy expenditure (HR–EE) relationship (16).

Calibration studies of the combined HR–accelerometry method also have been conducted (4–6,13,24). Similar to the HR method, key parameters such as resting HR or even the individual HR–EE calibration curve must be assessed in order for this method to achieve optimal accuracy. That is, although generalized algorithms, which work for all people without the need for individual calibration, have been established, they do not have the same high level of accuracy as algorithms that use individual calibration data (5,6).

Strengths and weaknesses of calibration methods.

Each calibration method has strengths and weaknesses. The linear regression method is simple and easy to understand, which has led to its widespread adoption. Unfortunately, the large number of regression equations and devices is a major weakness that limits our ability to draw comparisons between studies. Adopting a single-regression equation could solve this problem. However, a second and even bigger problem is that no single-regression equation accurately measures energy expenditure for all physical activities. For example, equations developed on walking and jogging work reasonably well for those activities, but they severely underestimate the cost of most other activities (10).

New mathematical models, including hidden Markov models (19), artificial neural network (21), and classification trees (3), use the rich information contained in the acceleration-versus-time curve to arrive at even more accurate estimates of energy expenditure. However, a potential weakness with the current applications of pattern recognition is the reliance on data collected during 1 min. Although models are being developed from acceleration data collected during 1 s, the parameters entered into the neural network require analysis of 1 min’s worth of 1-s data. In addition, each minute of 1-s data is collected from highly orchestrated and controlled calibration studies in which activity is being performed in a consistent fashion to ensure steady-state energy expenditure. Approaches that use raw acceleration data collected at 20 to 30 Hz may not be subject to this limitation.

Currently, it is not known how these newer mathematical models will handle transitions in activities or the use of data collected during shorter periods that will allow one to effectively monitor more sporadic physical activity patterns (such as those exhibited by children). The issue of misclassification due to transitions from one activity type to another has surfaced with the two-regression approach of Crouter et al. (17), and this could be problematic for pattern recognition, given that artificial neural networks typically use inputs like measures of variability and autocorrelation of counts within a predetermined period. Thus, future calibration studies may need to focus as much on transitions between activities as on the activities themselves. Modifications to the two-regression approach of Crouter et al. (11) can address the transition limitation, but it is not certain whether similar solutions can be applied to pattern recognition.

An important calibration issue related to the use of an artificial neural network is the selection of inputs from the activity monitor used to predict activity type and/or intensity. Some researchers are using the raw acceleration data, whereas others are using activity counts recorded during 1-s epochs. There is no clear consensus on what parameters or signals to “extract” and feed into the neural network. The inputs may need to vary according to the prediction goal (activity type, METs, or the combination of both) and the population under study. This will be an important area for future research. The field may need to reach a consensus on what inputs are required to predict energy expenditure or physical activity intensity when using artificial neural networks or a related statistical classification technique for data reduction purposes.

Receiver operating characteristic curves have been used to determine cutoff points for differing intensities of activity (sedentary, light, moderate, vigorous) to minimize false negatives and false positives, as discussed by Welk (26). This has the potential advantage of allowing the researcher to select cutpoints that maximize sensitivity at the cost of specificity or vice versa. This does not seem to have happened in practice to date, but these options may be appropriate for certain research questions. In addition to the receiver operating characteristic methodology for creating cutpoints, other methodologies include decision boundaries (15) and reference activity calibration (9). Although these methods all have their advantages, they could further “muddy the water” regarding multiple cutpoints and limit comparability across studies.

One final point about new data reduction approaches is that they must be shown to be superior to previous approaches. For continuous variables, this may be accomplished by comparing SEE values, root mean square error, or 95% prediction intervals. However, new methods that involve broadly classifying the intensity of physical activity also should be compared with existing methods.

The strength of the individualized HR–EE calibration method is that it accounts for the fact that fit (and younger) individuals have lower HR values than unfit (and older) individuals, when they exercise at the same exercise intensity. However, a major weakness of this method is the need to conduct individual HR–EE calibration curves on each person, which limits its application in large-scale studies. Although this can be overcome by using generalized HR–EE regressions, the accuracy of the method decreases until it is in the same range as the best methods using accelerometer-based activity monitors. The HR–EE calibration method itself is also subject to errors that result from psychological stress, excitement, changes in ambient temperature, dehydration, and other variables.


Once calibration has taken place, the next step is to validate the wearable monitor by comparing it against a gold standard. It is important to note that researchers do not validate the measurement instrument (or physical activity monitor) per se. Instead, they validate the instrument in relation to the purpose for which it is being used. The practical implication is that a given wearable monitor (and associated data processing rules) may be valid for measuring one outcome variable but not another. For example, a waist-mounted uniaxial accelerometer may provide valid estimates of time spent in moderate-to-vigorous physical activity but not valid estimates of energy expenditure. Furthermore, the monitor may provide valid estimates of a given outcome in one group but not another. Estimates of time spent in different intensity categories may be valid for adults age 20 to 50 yr but not children or adults older than the age of 70 yr. Estimates of total daily energy expenditure may not be valid in any of the groups.

A typical scenario in which the intended use of a measurement instrument should be considered is the validation of accelerometer energy expenditure prediction equations. Researchers often attempt to evaluate the validity of accelerometer regression equations by correlating predicted and measured energy expenditure. However, in many cases, the equations were developed so as to derive cutpoints to broadly classify the intensity of the physical activity. In this case, validity should be judged by evaluating how well the cutpoints classify physical intensity categories, not whether they accurately predict energy expenditure for the period. Similarly, when validating a device that measures the posture of a subject (sitting or standing), validity should be judged on the basis of how well it classifies sitting or standing. It is important to distinguish between “measurement error” (mismeasurement of something continuous) and “misclassification error” (when the target is categorical). The data analysis and statistical procedures required to validate continuous and categorical data differ markedly. Further details on this are presented in the article by Staudenmayer et al. (23) in this supplement.

Validating derived variables from wearable monitors

Virtually all wearable monitors use direct signals to estimate derived variables, the primary one being energy expenditure. Several methods can be used to validate devices that predict energy expenditure:

  • Indirect calorimetry. This method measures respiratory gas exchange (oxygen uptake and carbon dioxide production) to allow calculation of energy expenditure.
  • Room calorimetry. This method involves a specially designed chamber that houses an individual for extended periods. Energy expenditure can be determined either through indirect calorimetry (gas exchange) or through direct calorimetry (heat production).
  • Doubly labeled water (DLW). In this method, a person drinks water containing stable isotopes of hydrogen and oxygen, which then equilibrate in the body. Carbon dioxide production is determined from the differential rate of elimination of 2H and 18O.
  • Direct observation. In this method, investigators keep a log of the time spent doing different activities, and the energy cost of these activities is then determined from the Compendium of Physical Activities (1).

Indirect calorimetry is an appropriate criterion for minute-by-minute energy expenditure. The COSMED K4b2 (Rome, Italy) and the Jaeger Oxycon Mobile (Viasys Health Care, Hoechberg, Germany) are examples of portable metabolic measurement systems that measure gas exchange (V˙O2 and V˙CO2) and calorie expenditure. Because these breath-by-breath systems are more prone to error than are mixing chamber systems, a good practice is to periodically ensure the validity of the instrument by measuring the oxygen cost on two to three individuals at rest and at work rates of 50, 100, 150, and 200 W on a cycle ergometer. The V˙O2 measurements should be within 100 mL·min−1 of expected values.

The DLW validation method is an excellent method for assessing total daily energy expenditure. It is important to note, however, that the DLW validation method suffers from a lack of temporally linked intensity information. In other words, it cannot provide any information on bout frequency, intensity, and duration of physical activity.

A relatively new derived variable is the type of physical activity, which can be validated against direct observation. Researchers are now using pattern recognition systems, such as artificial neural networks and classification trees, to predict types of activity (e.g., sitting, standing, walking, running, and bicycling). This is important because researchers want to know how much time people spend doing different types of activities. Knowing the type of activity could also lead to more accurate estimates of energy expenditure because activity-specific predictions could then be applied.


A wide range of physical activities should be used during calibration procedures. Given that most people spend the majority of the 24-h day lying, sitting, and standing, it makes sense to include these activities. In addition, activities should span the entire range from sedentary to vigorous activities. Light, moderate, and vigorous activities that are typical of the types of activities performed by the population of interest should be included (26). It is helpful to think of physical activities as falling into several domains: transportation, housework, occupation, and leisure time sports/recreation.

Core activities that should be examined include lying, sitting, standing, car driving, slow walking, brisk walking, bicycling, stair climbing, stair descending, slow running, and fast running. Other activities may include television watching, vacuuming, sweeping/mopping, washing windows, washing dishes, doing laundry, lawn mowing, raking, one-on-one basketball, singles racquetball, and singles tennis (2,11,14,27). Selection of the activities used for metabolic calibrations has not been highly scientific up to this point. To take a more scientific approach, researchers may be able to use data from time use surveys or physical activity logs to get a more accurate picture of the most common activities performed by the population of interest.

The predictive validity of wearable monitors must be shown by cross-validation. One method of cross-validation involves dividing the data into two subsets. A researcher performs the metabolic calibration on one subset (the training set) and then validates the analysis on the other (the validation set). An alternative method of cross-validation procedure is the “leave-one-out” approach. This involves leaving out a single observation to use as the validation data and using the remaining observations for metabolic calibration. This is repeated until every observation in the sample serves as the validation data one time.

Ideally, further cross-validations should be conducted in a simulated free-living environment across activities that are similar to but different from those included in the validity study. This maintains a degree of experimental control but is more similar to what the monitor would cope with in a real-world setting. For example, a bank of tasks could be divided into categories; each category would contain tasks of a similar nature/intensity. A given number from each category could be selected for calibration, and different ones (but from the same general categories) could be put together into a simulated daily routine (punctuated by sitting time, TV viewing, etc.) for the cross-validation. V˙O2 could be assessed continually during the semistructured routine using a portable system. The performance of the monitor during the entire period, as well as for individual tasks, could then be evaluated.


In calibrating and validating wearable activity monitors, it is helpful to keep these points in mind:

  • Researchers must check to see that the wearable monitor is correctly measuring the direct signals (e.g., acceleration, HR) used to derive estimates of energy expenditure. In other words, researchers should conduct unit calibration studies to ensure that the signals are being properly monitored.
  • When performing value calibrations of the wearable monitor, it is important to use a wide variety of activities, ranging from rest to vigorous exercise.
  • The population used to calibrate the monitor should include both sexes and a wide range of ages, adiposity classifications, and fitness levels.
  • Validity of V˙O2 measurements should be demonstrated by ensuring that the values obtained at fixed power outputs on a cycle ergometer are within known limits.
  • Value calibration (i.e., metabolic calibration) studies that use pattern recognition approaches will yield more accurate data than single-regression models because they take advantage of the rich mathematical information contained in the direct signals obtained by the wearable monitor.
  • Monitors should be evaluated on the basis of their accuracy in estimating energy expenditure for many different activities and for their ability to accurately estimate energy expenditure during free-living activity, over extended periods.
  • Validation studies should use methods such as indirect calorimetry, DLW, and direct observation. Room calorimeters may be useful for light intensity and indoor activities but severely restrict the types of activities that can be performed.
  • Value calibrations of existing monitors should demonstrate whether the new approach offers any advantage over existing published calibrations.
  • No single validation study can do it all; rather, multiple studies are needed to establish the validity of a wearable monitor.


Future research studies will need to answer the following questions:

  • Do triaxial accelerometers improve predictive accuracy for determining energy expenditure compared with uniaxial accelerometers?
  • Do pattern recognition approaches using raw data improve upon those using 1-s data?
  • Which locations on the body provide the best predictions of energy expenditure?
  • Do multiple sites provide greater predictive accuracy than single sites?
  • Do pattern recognition approaches accurately measure energy expenditure and accurately classify activity type, when there are transitions between activities?

Future research should determine which demographic variables (e.g., age, height, weight, sex) are confounders and need to be used in conducting value calibrations of wearable monitors. In addition, researchers should consider whether it is appropriate to use V˙O2 data for the criteria, when attempting to measure physical activity during short periods (e.g., 10 s). Finally, research laboratories should join together to design these studies and foster collaboration between groups to ensure a logical approach with comparability between the studies. Research groups should work together on these types of studies to attempt to increase the number of devices tested, to expand the serial number range tested, and to allow for different methods to be tested simultaneously.

This study was supported by National Institutes of Health grants 5 R21 CA122430 and 5 R01 HD55400.

The authors have no conflicts of interest to declare.

Results of the present study do not constitute endorsement by the American College of Sports Medicine.


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