Measuring gait with an accelerometer-based wearable: influence of device location, testing protocol and age.

Wearables such as accelerometers are emerging as powerful tools for quantifying gait in various environments. Flexibility in wearable location may improve ease of use and data acquisition during instrumented testing. However, change of location may impact algorithm functionality when evaluating associated gait characteristics. Furthermore, this may be exacerbated by testing protocol (different walking speed) and age. Therefore, the aim of this study was to examine the effect of an accelerometer-based wearable(s) (accW) location, walking speed, age and algorithms on gait characteristics. Forty younger (YA) and 40 older adults (OA) were recruited. Participants wore accW positioned at the chest, waist and lower back (L5, gold standard) and were asked to walk continuously for 2 min at preferred and fast speeds. Two algorithms, previously validated for accW located on L5, were used to quantify step time and step length. Mean, variability and asymmetry gait characteristics were estimated for each location with reference to L5. To examine impact of locations and speed on algorithm-dependant characteristic evaluation, adjustments were made to the temporal algorithm. Absolute, relative agreement and difference between measurements at different locations and L5 were assessed. Mean step time and length evaluated from the chest showed excellent agreement compared to L5 for both age groups and speeds. Agreement between waist and L5 was excellent for mean step time for both speeds and age groups, good for mean step length at both speeds for YA and at preferred speed for OA. Step time and length asymmetry evaluated from the chest showed moderate agreement for YA only. Lastly, results showed that algorithm adjustment did not influence agreement between results obtained at different locations. Mean spatiotemporal characteristics can be robustly quantified from accW at the locations used in this study irrespective of speed and age; this is not true when estimating variability and asymmetry characteristics.