Research Question

There have been a number of attempts to improve on the basic linear model for estimating energy expenditure from accelerometer output, as described above. These studies have applied nonlinear equations and neural networks to this purpose. Where these studies have addressed the need for additional parameters based on demographic, physical and physiological attributes of individuals, they have not included a sufficient number of appropriate subject attributes in the prediction model to fully test their effect on prediction accuracy. Also, the effect of these additional parameters is often not distinguishable from the other innovations that each study has implemented in parallel, or is confounded by the issue of multiple activities. Further research is needed, therefore, to investigate whether the addition of anthropometric and physiological parameters to the prediction equations can improve the capacity of the basic linear model to estimate EE.

The research question is asked: can EE estimation accuracy be improved by the addition of anthropometric and physiological attributes to the prediction model?

In order to clearly assess the effects of subject attributes on the relationship between EE and accelerometer counts, a single activity needs to be considered in isolation. Walking is of primary interest as it is the most common physical activity, and is practical for obese participants to undertake in both experimental and free living settings. If physiological and anthropometric attributes are identified which improve EE prediction for walking, then it is likely that EE prediction models for other activities would benefit from the addition of such attributes, though the relevant attributes may differ between activities.

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Classification of Aerobic and Gym-Based Exercises from

Accelerometer Output

As discussed in detail in section 2.3.7, classification accuracy varies according to which activities are being tested (section 2.3.7.1), the types of accelerometer feature being used (section 2.3.7.4), and the number and placement of sensors (section 2.3.7.2). The study described in this chapter aimed to establish whether good classification accuracy could be obtained from hip- and ankle-mounted accelerometer data, for both obese and non-obese participants performing a set of activities suitable for an obesity management programme. The study also investigated whether a different approach to feature selection is needed for obese populations when compared to non-obese populations. The research questions were posed as follows:

Research question 1: can a set of aerobic exercises and free-living activities be identified from data collected by a single accelerometer mounted at the hip or at the ankle?

Different activity sets return varying classification accuracies, and this is the case even if the feature set and classification algorithm remain unchanged (160, 249). It is not clear, therefore, whether it is possible to accurately classify a particular set of activities. The study described in this chapter aimed to evaluate classification accuracy for an activity set comprising a variety of lifestyle activities and aerobic/gym exercises. The activity set is intended to be suitable for inclusion in a weight management programme aimed at obese participants. With this in mind, activities were chosen that should not be too difficult for an obese person to perform, that may be performed at low intensities and built up over time, and include common exercises that are used in the gym to lose weight. The activities are described in more detail in section 3.2.2.2.

Although the wearing of multiple sensors and cumbersome equipment may be acceptable in the laboratory, in a field setting activity monitoring equipment should be as unobtrusive as possible. For those taking part in a weight management programme, a single accelerometer placed at an unobtrusive site would minimise the burden experienced by the wearer, and thus aid compliance to the measurement regime. The study, therefore, examined classification accuracy for single-site mounted accelerometers. As discussed in 2.3.7.2, previous research does not provide consensus as to which single accelerometer placement site will provide the best overall accuracy for activity classification. However, hip and lower limb sites have proved effective for identifying activity sets involving whole-body dynamic activities (89,

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110, 180). Currently, activity monitors are around the size of a matchbox and are worn using belts and straps, or in some cases affixed directly to skin through adhesives (250). The hip is an unobtrusive body site for use under free-living conditions, as the accelerometer may be attached to a belt and worn as an item of clothing. Lower limb mounted accelerometers may be more obtrusive than those worn at the hip, but still may be acceptable in free-living conditions. Continuing advances in technology mean increased miniaturisation of sensors, and thus reduced obtrusiveness of wearing these devices. However, the subject may perceive affixing multiple sensors as a greater burden than a single sensor, and this may affect compliance. Additionally, data from more sensors contributes to the computational cost of analysing the data, which supports the rationale for a single body site. For this study, two accelerometer sites were chosen for comparison: one accelerometer was affixed to a belt worn around the waist over the right hip, and the other accelerometer was worn at the ankle. The present study addresses the research question by comparing the classification accuracies achieved for the two body sites from an activity set containing free-living activities, and aerobic and gym-based exercises.

Research question 2: Does activity classification accuracy differ between obese and normal BMI groups?

As discussed in 2.2, accelerometer signals produced by obese individuals performing physical activity may differ from signals generated by their non-obese counterparts. There are two main factors to consider: a surfeit of adipose tissue at accelerometer sites may influence the measured accelerometer signals; and body movements, such as gait, differ between obese individuals and those with lower BMIs. The waist, for example, exhibits higher adipose tissue levels for obese groups, which may affect accelerometer movement and introduce noise to the signal. Similarly, signals taken from ankle -mounted accelerometers may exhibit different characteristics which reflect the differences in how obese and non-obese persons move (119- 123), as discussed in 2.2.1. To answer the research question, the study compared classification accuracy across BMI groups for the two accelerometer sites.

Research question 3: do the same accelerometer features apply to obese and normal BMI groups, or do they require different accelerometer features to characterise their physical activities?

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For the purposes of activity classification, a feature set is chosen with the aim of exploiting the characteristic differences in the accelerometer output between activities. The prediction accuracy of the classification algorithm greatly depends on how well the feature set captures those characteristics. However, there may also be characteristic differences between obese and normal groups within single activities (as discussed in 2.2.1), and an alternative feature set may be required to effectively distinguish activities depending on BMI group. It is not clear, therefore, whether a particular set of features will apply equally to both obese and non- obese groups. To answer the research question, the study compared the effectiveness of a number of feature sets when applied to obese and normal BMI groups in order to determine whether different sets of features are better suited to one BMI group over the other.