Accelerometers

Accelerometers are instruments which measure acceleration, the change in velocity of an object over time (SI unit: m.s^-2). Acceleration is directly proportional to the force acting on the object to move it (as is the mass of the object).

Physical activity is the change of position of body segments resulting from skeletal muscle contractions. A measure of acceleration of the body or its segments can therefore be used to infer intensity of physical activity over time, allowing derivation of activity dimensions such as duration, frequency and overall volume. If acceleration and mass (including any external loading) of all major body segments are measured, whole-body energy expenditure due to physical activity can be estimated using the work-energy theorem. In practice, we do not measure acceleration of the whole body or have any knowledge of external loading; this imperfect knowledge is therefore a source of error when inferring energy expenditure. Like most other objective methods, accelerometers do not provide contextual information. Methods to identify activity type from raw accelerometry are currently in development [28].

Dimension	Possible to assess?
Duration	✔
Intensity	✔
Frequency	✔
Volume	✔
Total physical activity energy expenditure	✔
Type	(✔)
Timing of bouts of activity (i.e. pattern of activity)	✔
Domain
Contextual information (e.g. location)
Posture	(✔)
Sedentary behaviour	✔

Accelerometer components

Technological advances have resulted in instruments that can measure acceleration accurately, over extended time periods (a week or longer), and that are sufficiently compact and discrete for people to wear. All accelerometers have two essential parts: 1) a transducer or sensor which senses acceleration; and 2) a data acquisition system which processes and stores the data. The options for the sensor component fall into two basic types:

1. Piezo-electric sensors

• Natural phenomenon measured is voltage
• Low power consumption
• Must be calibrated before use
• Sensitive to acceleration in dynamic, but not in static, situations. Gravitational acceleration is therefore only transiently present in the data when the monitor is rotated relative to gravity, thus not possible to assess posture
• Digitisation and filtering to reflect relevant acceleration component
• Output often stored as proprietary “counts” in reduced time resolution but can be stored as raw waveform in S.I. units

1. Seismic or inertial sensors (aka MEMS)

• Natural phenomenon measured is capacitance
• Sensitive to acceleration in static and dynamic situations
• Digitisation to reflect acceleration (can be calibrated on-the-fly using gravity in still segments)
• Output usually as raw waveform in S.I. units but can also be reduced to proprietary “counts”

Accelerometer models

Accelerometers come in various models and specifications, and from many different manufacturers (brands) [51]. It is important to emphasise that validity is tied to the overall method, rather than solely to the accelerometer make or brand. The nature of the captured data depends on number of acceleration axes, piezo-electric or MEMS-based, resolution and dynamic range, anatomical attachment site, and degree of filtering / data processing (if any) before data storage; the method includes all these components but also subsequent data processing decisions of feature extraction and inference.

It is not possible to recommend one device over another without knowing the specific aims of a particular study; however, links to further information on instruments is provided in the instrument example section below. The following should be considered when choosing between accelerometers:

• Raw or count-based accelerometry (raw is device agnostic)
• Evidence of technical reliability
• Robustness for use in field settings
• Evidence of validity for proposed inference method
• Ability to process the data to extract target variables
• Cost and budget
• Burden upon participants, e.g. size, wear location
• Data storage capacity
• Number of days sampling required

Axes

Accelerometers can measure acceleration in one, two or three directions [8]. Naturally, it is preferable to measure all three axes (otherwise the device is “blind” to movement in one or two directions), and the majority of contemporary instruments are capable of this. Inference based on uni- and bi-axial acceleration implicitly relies on cross-axes correlations to capture the complete physical movement of the body segment to which the instrument is attached. Tri-axial accelerometers are therefore more sensitive to certain types of activity where movements are more variable in three-dimensional space, such as climbing, jumping or spontaneous play [30].

Sampling frequency and data storage

Modern accelerometers have sufficient storage capacity to store acceleration signals at up to 100 Hz over multiple days. For example, the instrument used in the UK Biobank study is capable of storing tri-axial acceleration at 100 Hz for 14 days [1]. Battery life and storage capacity permitting, recording sampling frequency should be as high as possible.

Due to memory and battery limitations, some older accelerometer instruments sample acceleration at frequencies between 10-32 Hz before conducting on-board data processing and feature extraction, summarising signals ‘on-the-fly’ as counts stored at a user defined epoch. As above, the shortest epoch time possible is desirable as data can be down sampled if required; however in some instruments only minute-by-minute resolution is available [45].

Monitor placement

The body segment that the accelerometer is attached to is a strong determinant of what acceleration data is captured and recorded. The interpretation of the resulting signal has to take this information into account, because the biomechanical profile of each activity determines the relationship between one body segment and the rest of the body.

The most appropriate position of an accelerometer depends on the study question and feasibility considerations. The hip or lower back allows for tracking the movement of the largest and most central part of the body, the trunk. A seismic accelerometer attached to the thigh may be used to estimate posture from its direction with respect to gravity. The wrist is generally considered to be the most acceptable wear location to participants, and is therefore increasingly used in surveillance systems where representativeness of the sample is a particular concern.

During some activities, the acceleration of one body segment may not be representative of other body segments or the body as a whole; one such example of this is upper body movement whilst cycling, where measurement of acceleration at the wrist or trunk would likely result in under-estimating energy expenditure [19].

Number of monitors

As indicated above, there is a finite amount of information about an individual’s physical activity that can be captured by a single accelerometer. More information can be collected by measuring acceleration at two or more body locations simultaneously, which if interpreted in an appropriate manner could be used to enhance inferences about physical activity. However, it is only recently that accelerometer hardware and price have reached the point where multi-monitor measurement might be considered a feasible option, so it has not yet been implemented in any large scale studies [56]; accordingly, development of the methodology for interpreting multiple acceleration signals in a complementary manner has attracted considerably less attention [32, 41].

Using labelled data collected in a laboratory, it has been demonstrated that machine learning approaches can utilise raw acceleration signals recorded at multiple body sites to better discriminate between different activity types [2, 34]. Another study used linear regression analyses to show that acceleration intensity measured at both the hip and wrist is slightly more predictive of activity energy expenditure than either signal alone [44]. Further methodological work is required to understand how best to implement and interpret different monitor configurations.

Naturally, the increase in information data capture has to be weighed against the increased burden upon the participant, which can cause issues with reactivity and reduce protocol adherence.

Duration of measurement

If inferences are to be made about habitual physical activity (whether this is in terms of volume, type, intensity etc.), it is necessary to consider the number of days of measurement required to capture this, given day-to-day within-person variation in physical activity. Minimum criteria can be applied based on the within-and between-individual variation of the variable of interest, as described in the section on physical activity variability.

Accelerometry is the most common objective method used to measure physical activity; it has been used extensively in field settings for:

• Large-scale observational cohort studies
• Studies undertaking association analyses between exposure(s) and outcome(s)
• Interventions and randomised controlled trials to examine intervention or treatment efficacy
• Describing temporal patterns of the intensity of physical throughout the day

Accelerometers were initially used as an outcome measure in small studies and a criterion measure to compare with self-report data. As accelerometers have become cheaper they have increasingly been used in large studies [56], including thousands of participants in the  National Health and Nutrition Examination Survey (NHANES) [45], Pelotas birth cohorts [12] and UK Biobank containing raw accelerometry in more than 100k individuals [13]

When raw acceleration data is provided in SI units (m.s^-2), processing data and estimation of dimensions of physical activity follows a series of user-defined steps, all of which affect the validity of the final inference.

The measured value of a raw acceleration signal contains three components:

1. Acceleration as a result of human movement – the critical component when estimating physical activity
2. Acceleration as a result of gravitational force - in static situations this tells us the orientation of the (triaxial) accelerometer
3. Noise

In order to assess physical activity, it is necessary to isolate the human movement component of the signal. Interpreting this signal in a reliable way, and the methodology for doing so is still under active development. While there is no consensus yet on standard procedures for making such inferences, an example pipeline is summarised in Figure D.3.3, which is described in detail below.

Data preparation

Preparatory steps involved in processing raw accelerometry data include [47]:

1. Calibration of acceleration signals to local gravity, to compensate for hardware inconsistencies and make them equally sensitive to movement in all directions.
2. Highlight recording errors, interruption of signal and instances when the sensor’s dynamic range is exceeded.
3. Resampling the measured signal to a chosen rate (sometimes sampling rate can fluctuate around a defined rate).

Vector magnitude processing

These steps summarise the high frequency tri-axial acceleration movement component as a single unit average over an epoch duration defined by the researcher.

1. Combining the x, y and z axes to calculate the vector magnitude (VM) using the formula VM = , see Figure P.3.4. VM thus represents the intensity of acceleration the device is subjected to at any time point, irrespective of direction.
2. Removing gravity from VM using one of two approaches (see Figure P.3.5):
   • Euclidean norm minus one (ENMO): subtract 1g (one gravitational unit) from the vector magnitude and truncate resulting negative values to zero [39, 49].
   • High pass filtered VM (HPFVM): use a high pass filter to remove low frequency components of the signal, including gravity.
3. Removing noise using a low pass filter. Signal chances above a certain frequency are assumed not to be the result of bodily movement. This noise could be a result of external mechanical forces or the instrument itself. Human movement is typically in the range 0-15 Hz, so a low pass filter can remove the high frequency noise from the desired physical activity signals. To apply a low-pass filter with a cut-off of 15 Hz, the sampling frequency must be >30 Hz (twice the cut off).
4. (Optional) Summarising the final intensity signal over time. The average of all vector magnitudes within the defined time interval is calculated (e.g. each five seconds).

Non-wear time

1. Identification of non-wear time using a user-defined algorithm, for example 30 [49] or 60 [55] consecutive minutes of stationary time. Stationary episodes can be identified using the standard deviation of the acceleration of the three axes and a threshold value (e.g. <13 mg = non-wear) [55]. Algorithms for detecting non-wear time using count based accelerometers have also been developed [6, 9, 23]. The difference between true non-wear and physical inactivity (e.g. during sleep or rest) is shown by Figure P.3.6.
2. As with other objective methods which involve body-worn instruments, decisions must be made regarding how non-wear time is remedied once identified. Please see the dedicated section on this topic.

Note on processing proprietary “count” accelerometer data

Older accelerometer store data in proprietary formats (i.e. counts), and several processing steps occur on-board the instrument. The details of these on-board processing steps are often known only to the manufacturer and are not decided by the user. It is important to acknowledge that these data processing ‘decisions’ made by any given monitor makes the stored information fundamentally different to the original acceleration signal. It is also important to acknowledge that some older count-based accelerometers capture movement in only one or two directions.

Brand specific software is available which enables users to process data, as described in the section below. In some cases it is also possible to export data and conduct the above steps in freely available software.

Physical activity summary variables

The steps in Figure D.3.3 result in a summary of physical activity as mean acceleration (g or mg) per user-defined epoch. For count based accelerometers the unit will be in counts per user-defined epoch. Additional variables such as average acceleration by day and hour, or time spent at different acceleration levels (see Figure D.3.7) can be derived to investigate patterns of activity and compare individuals.

Further inferences

Physical activity energy expenditure

Accelerometers do not directly measure activity energy expenditure but there is a natural relationship between bodily movement and energy expenditure which can be exploited by predictive models. In practice, the characterisation of this relationship is complex because it varies by the body segment being measured and the activity being performed.

The relationship between accelerometer recordings and energy expenditure is often studied by collecting data in a laboratory, where participants can be measured contemporaneously with a gold-standard measure of energy expenditure, such as respiratory gas analysis using facemask/mouthpiece or inside a calorimeter. In a typical study design, participants are often asked to perform a set routine of activities of varying intensity [42, 44].

The overall relationship between activity energy expenditure and uniaxial acceleration measured at the waist during rest, walking, and jogging is fairly linear; however, deviations from linearity occur for high-intensity running [3, 7], for which movements are better captured with additional measurement of the antero-posterior axis of acceleration [5, 37]. Linear relationships derived for rest and ambulation show much poorer validity in biomechanically diverse activities, such as cycling or lifting weights. Non-linear statistical models have been proposed to improve prediction equations. For example, a 2-segment regression model has been shown to improve accuracy of energy expenditure estimates, compared with simple regression [11].

Those intending to use models derived by laboratory studies should critically evaluate the study population, and judge how appropriate it is to generalise from the activities they performed. Laboratory studies may not reflect relationships between acceleration and energy expenditure in free living, and laboratory-derived prediction equations have been found to substantially overestimate free-living energy expenditure [17].

The validity of different accelerometer methods have been reviewed, demonstrating large variability in mean bias and correlation with estimates of energy expenditure using the doubly labelled water (DLW) method [32]. Studies using DLW have shown that, in children, accelerometers explained 13% of DLW's PAEE variance and 31% of TEE variance. In adults, explained variance was higher, 29 and 44% for PAEE and TEE [41]. Studies have also shown differences in values both within- and between-models [4, 20, 33, 53, 54].

Using “cut-points” to estimate time spent in various intensity categories

A model of the relationship between acceleration magnitude and rate of energy expenditure (intensity) can be used to derive “cut-points” that correspond to a given energy expenditure value. For example, a researcher interested in quantifying time spent at or above “moderate” intensity may want to find acceleration intensity cut-point that best discriminates between < 4 METs and > 4 METs.

However, cut-points for defining different intensity levels in relative metabolic terms are somewhat arbitrary and the use of different cut-points can have a profound impact on estimates of physical activity [21, 29]. A researcher using accelerometry must understand the derivation of prediction equations from calibration studies and the rationales and implications of choosing a particular set of cut-points [29, 36, 52]. For example, published cut-points for sedentary behaviour (SB) from one accelerometer vary from 100 cpm to 800 cpm. Similarly, the range of cut points for moderate-intensity activity varies between 200 cpm to 3000 cpm.

Overall, cut-points make limited use of the wealth of detail available from raw acceleration data, often sufficient only for a crude approximation of the intended outcome [29] Given the arbitrary nature of the count-based cut-offs, and the difference in unit expression across accelerometer models, reporting accelerometry data in standardised units of acceleration (m.s^-2) is recommended [3, 10, 20]. Extraction of signal features and patterns from raw acceleration data can significantly improve PAEE estimation [27], and offers the ability to make inferences about posture from limb angles [37].

Posture

Using raw accelerometer data, it is possible to make inferences about an individual’s posture. When the instrument is stationary, it should measure 1g of acceleration in total over all 3 axes. The ratio of X:Y:Z indicates the direction gravity is acting on the device. From this, pitch and roll can be calculated:

• Pitch = atan( X / √(Y²; + Z²) ) x (180.0 / Π)
• Roll = atan( Y / √(X² + Z²) ) x (180.0 / Π)

One way to think about this is to liken the accelerometer to a spirit level. For example, placing an accelerometer on the thigh will allow us to measure its pitch, i.e. angle with horizontal; this feature has been used to discriminate between sitting and standing [16]. Others have used the orientation patterns of the wrist to infer sleep episodes [50] or describe sedentary behaviour [38].

Activity recognition [22, 25, 26]

An acceleration trace recorded by a modern raw accelerometer is of sufficiently high resolution and fine detail that individual motions and gestures leave discernible patterns and signatures; activity recognition is the process of using this data to automatically identify types of activities, such as walking or running [24, 34].

Correctly identifying activities from acceleration traces is a challenging task, and is ordinarily approached using supervised machine learning techniques such as neural networks [40] and random forests [2, 18], whereas others adopt a prescriptive approach and design classifiers from first principles [43, 48]. Supervised learning requires models to be trained with labelled data, which is typically acquired by direct observation in a laboratory setting and is expensive in terms of time, labour and equipment; this has prompted the exploration of alternative data collection methods such as wearable cameras to capture free-living behaviours [15, 18].

Activity recognition is often formulated as a short-term decision problem, where the acceleration data is chopped up into many frames of a fixed length (usually less than a minute), and a model is used to classify that short sequence [24]. A feature extraction process is applied to describe the data in that time window, and this feature vector is used as the input to the model; for example, features describing the frequency domain are a common choice because they are naturally suited to capturing repetitive motions typical of ubiquitous human activities such as walking [34, 35]. However, the most recent advances indicate that the revolutionary techniques of deep learning have the potential to supersede the present traditional machine learning approaches [22, 25], as it has in many other domains [26].

It is widely recognised that sensor data collected at multiple body sites is more informative for activity classification [2, 34], however, models reliant on so many input signals cannot be utilised by the majority of studies that typically only administer one device per participant.

Practitioners intending to rely upon the output of an activity classifier should critically evaluate its reported accuracy, and carefully consider the consequences of misclassification. It should be noted that while many lab studies are reporting high classification accuracies [40], validation in free-living is relatively scarce and notably less impressive [31], which is perhaps why activity recognition has not yet reached mainstream adoption.

Characteristics of accelerometers are described in Table P.3.6.

Strengths

• Objective data collection eliminates recall bias.
• No social desirability bias (except possibly as reactivity bias or differential non-wear time).
• No requirement for literacy and numeracy; data quality should not differ by educational attainment, ethnicity or socio-economic status, in particular for wear-and-forget protocols.
• Continuously captures movement in much finer detail than even the most detailed PA diary.
• Greater sensitivity to changes in behaviour over time, so useful for evaluating interventions.
• Easy to administer (possibility to collect via post so no need for direct contact with participants).
• Provides time-stamped data, showing durations of PA and number of transitions from one PA level to another.
• Useful for self-monitoring and providing real-time feedback to wearer in situations where behaviour change is a desirable objective - e.g. RCTs, community health interventions.

Limitations

Missing data / non-wear time / non-compliance:

• Contextual data: objective methods currently provide no information on domain or behaviour type (e.g. driving, working, TV/computer use).
• Representativeness: devices tend to be worn only for short-periods of a few days and may not capture infrequent activities like occasional participation in sports.
• Negative aesthetic effects: unsightly bulges below close-fitting clothes, or associations with criminality (e.g. ankle-worn device) may decrease wear time adherence.
• Adverse effects: hardware attachments and adhesives may cause skin irritations, especially in children.
• Water resistance: some devices are not waterproof and need to be removed prior to swimming or bathing, resulting in non-wear time/missing data.

Cost and resources:

• Purchase costs: accelerometers tend to vary in price from £100-150 to nearly £1,000 for more sophisticated models.
• Losses: the cost of lost or damaged devices may be substantial. Although in an ideal situation a study should lose no more than 2-3% of their devices, studies in special populations (e.g. young children) may lose >25%.
• Processing: measurement of raw acceleration data may require days of data processing on desktop computers or access to dedicated hardware such as a supercomputer.

Bias:

• Prone to reactivity, whereby knowledge that one is being monitored causes deviation from typical behaviour.
• Some devices or wear positions are highly-insensitive to physical activity performed in seated/reclining postures, e.g. cycling, rowing, or wheelchair use.
• Accelerometers are unable to account for additional effort required to work against resistance, e.g. lifting weights or cycling uphill or against wind resistance.

Transparency:

• Some commercial devices use proprietary algorithms, resulting in uncertainty about the precise methodology employed during data processing, which complicates comparisons between studies using different devices or software.
• Highly-variable data processing practices lead to poor comparability between studies - e.g. different policies for classifying zero count data as ‘non-wear’ may result in marked differences in PA / SB estimates.

Consideration	Comment
Number of participants	Small to very arge
Relative cost	Moderate
Participant burden	Low
Risk of reactivity bias	Low
Risk of recall bias	Low to high
Risk of social desirability bias	Yes
Risk of observer bias	No
Risk of social desirability bias	No
Risk of observer bias	No
Participant literacy required	No
Cognitively demanding	No

Considerations relating to the use of accelerometers for assessing physical activity are summarised by population in Table P.3.7.

It has been suggested that establishing the relationship between acceleration data and energy expenditure is especially problematic in children due to their growth and development which affects estimates of resting metabolic rate and energy expended (i.e. movement economy) during activity. Children’s resting metabolic rate expressed relative to body weight decreases with age and maturation, and similarly the energy expended relative to body mass during walking and running also decreases (improved movement economy) with age [46].

Population	Comment
Pregnancy	Waist worn devices may be problematic.Depending on term, may need to consider placement of monitors to avoid discomfort. Skin can also be more sensitive during pregnancy which could increase chances of irritation.
Infancy and lactation	Consider safety of attachment (should not be removable by infant). Depending on the age, posture/orientation may also be very different, especially when still crawling. When carried, activity of carer is measured rather than of infant.
Toddlers and young children	High sampling frequency recommended to capture intermittent patterns of activity. Consider safety of attachment (should not be able to be removed by infant).
Adolescents	Size and design of devices may negatively affect adherence.
Adults	There may be occupational complications (e.g. food preparation, nursing).
Older Adults	If memory impairment is a concern, low-maintenance devices are preferred. For example, a device that can be worn comfortably overnight, so it will not be forgotten in the morning.

Administration of accelerometers

• If there is a time lag between initialising the monitor and participant wearing the device, the data analysis program should take this into consideration.
• It is important to consider how accelerometers are to be distributed and returned, e.g. face-to-face or via mail. This choice will have cost/burden implications.
• Late or non-returns affect the number of instruments available.
• Participants can be asked to keep concurrent activity logs detailing wear and non-wear times (and other time flags, e.g. sleep), however this does increase burden.

Wear adherence

Achieving adequate wear adherence may be difficult in some populations, such as adolescents. The following points may enhance compliance:

• Choose an unobtrusive device such as a wrist-worn sensor
• Show participants graphical output data which includes non-wear time
• Provide incentives for adhering to wear-time protocol throughout the desired period (e.g. feedback)
• Some accelerometers are waterproof while others must be removed for bathing, showering and swimming; this may influence adherence and wear-time
• Provide encouragement and support for participants through phone calls, SMS or email messages
• Logs may help self-monitoring
• Provide clear instructions and a method to contact the study team, where possible
• Enlist the support of others such as teachers, parents, family members
• Investigate and mitigate any barriers to wearing, e.g. a waist belt may feel uncomfortable in obese participants

• Accelerometer instruments.
• Sufficient computational resources to process the data.
• Additional docking stations to charge the accelerometers.
• In the field setting, attention must be paid to the logistics of distributing and collecting and re-distributing the accelerometers.
• Data storage: file sizes vary based on the format the data is saved in and the amount of data collected. File size for a week-long 100 Hz measurement of tri-axial movement varies by device, but is typically in the range ~0.35 GB to ~0.7 GB.

A method specific instrument library is being developed for this section. In the meantime, please refer to the overall instrument library page by clicking here to open in a new page.