Limitations of current approaches

In 2.2.1 it was discussed that obese gait differs from that of lower BMI individuals. However, most previous research into estimating walking speed from accelerometry has not considered the effect of obesity on the outcomes. It is possible, for example, that the vertical displacement of the centre of mass in obese individuals is lessened due to wider strides and greater mediolateral sway when walking (119-121). It is not clear, therefore, whether a speed estimation algorithm which uses this displacement, such as that employed by Zijlstra and Hof (90), would generalise across BMI groups. Again, the study by Bishop and Li (206) focussed on developing a novel method to predict walking speed from accelerometry based on a biomechanical model, but did not consider the effects of altered gait patterns observed in obese individuals. It is, therefore, unclear whether speed estimates would be equally accurate for obese individuals as for non-obese individuals when employing these techniques. Approaches using artificial neural networks may be able to compensate for differences in gait, as the accelerometer signals contain tacit information pertaining to gait characteristics, which may be recognised by an aNN. The aNN would, however, need sufficient training with data from a heterogeneous group of walkers, and this has not previously been investigated. Studies or clinical interventions which require measurements of walking speed may involve many participants. For this reason, the measurement procedure needs to be practical and cost effective. A limitation of certain previous walking speed studies involving accelerometry is that prediction algorithms have been developed on an intra-subject basis (198, 200). This approach requires an initial calibration phase where the participant provides sample accelerometer data by walking at a number of speeds in the laboratory. The speed estimation algorithm can subsequently make predictions for that particular individual based on this calibration data. This approach is useful in tailoring the prediction algorithm to the subject, resulting in higher accuracy rates, and as a result it may neutralise the problem posed by

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obese gait. However, performing this initial phase adds cost in terms of calibration time and data processing time, and also requires the participant to attend a laboratory appointment. It is therefore not practical in larger studies. Conversely, inter-subject speed prediction models aim to be applied to the wider population without an individual calibration phase. In order to achieve this, the algorithms need to be adequately pre-trained with a suitable heterogeneous subject group, and the prediction models must account for inter-subject differences in gait. One study was identified which involved many obese participants. Schutz et al. (200) conducted a number of experiments in order to develop and test their speed prediction model, which correlated the RMS of a belt-worn uniaxial accelerometer signal with speed. In one of these experiments, a significant number of obese participants (n=50 females, BMI: 31.4 ± 5.1 kg/m2) took part. However, the aim of this experiment was to demonstrate that speed prediction models require individual calibration due to inter-subject differences. This was successfully demonstrated for the model in question as, although correlations between RMS and speed were high for each individual in isolation, there was a large amount of variability observed across the subject group. Consequently, they chose to employ individual calibrated algorithms in their model. A subsequent experiment validated their speed prediction model against six non-obese male subjects (BMI: 23.6 ± 2.5 kg/m2). The final experiment applied the model in a free living environment to a group containing a number of obese members (n=28 females, BMI: 30.0 ± 3.8 kg/m2). This experiment aimed to demonstrate how the model could be applied to both normal and obese women. However, there was nothing in place to measure actual walking speed in the period of testing, which means that it is uncertain whether the results returned were accurate. Although this study involved several participants with high BMIs, it did not validate the speed prediction model for obese individuals. Also, individual calibration was required by the model, which, as discussed, is impractical in large scale studies or interventions.

Many approaches to walking speed estimation require accelerometers to be placed at specific body sites, and these may not be practical in a free-living environment. The lower back close to the centre of mass is a common placement (85, 91, 94, 198). However, this may prove uncomfortable for individuals when sitting. The method employed by Bishop and Li (206) produced accurate speed predictions, but the two accelerometers need to be carefully placed so that both align with gravity when the shank is vertical, and the algorithm accuracy was also affected by the distance between the two devices. In a study or intervention employing this approach the perceived burden to a participant of having to carefully affix two

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accelerometers to the shank may result in reduced compliance. Another study investigated the use of a chest-mounted accelerometer (204), but again this is not an optimum site for long term studies outside the laboratory. The hip is an ideal accelerometer placement site for walking speed estimation in the field due to its proximity to the centre of mass and relative unobtrusiveness, but there are few studies which have investigated whether accelerometer data collected at this site can be used to accurately predict walking speed..

A study by Panagiota et al. (199) used hip-mounted accelerometers and applied a multi-linear model to predict walking speed. The study also acknowledged that height and weight can affect gait. For this reason the prediction model incorporated height, weight and BMI in addition to accelerometer features. However, there was not sufficient diversity in the subject group to test for the effects of BMI on the model, and a comparison of results between BMI groups was not made. A previous study by Vathsangam et al. (118) also used a hip-mounted accelerometer and tested three linear regression approaches to walking speed estimation. Subjects were selected for this study with varying BMIs; values ranged from 22kg/m2 to 34.5kg/m2 with a mean of 26.4 ± 5.3 kg/m2. However, there were only eight participants in total, which means that there were insufficient obese participants to test the effects of BMI on the algorithms. Additionally, the analysis was performed on an intra-subject basis, and, therefore, may not generalise for applications where this training phase is not practical.