Towards a methodology for acceptance testing and validation of monitoring bodyworn devices

In hardware deployments, it is often necessary to test platforms for suitability for particular purposes. As lengthy data collection processes often outlive specific iterations of hardware and firmware, it is likely that migration between platforms may become necessary. In this short paper we describe a practical approach employed for acceptance testing, comparison and validation of two iterations of a wearable accelerometer and localisation platform, based on an annotated 15-task activity script. We present an analysis on the data generated for the different activities, and compare device performance using common machine learning algorithms for activity recognition.