Purpose: Public health research requires data-intense studies over extended periods. With the advent of new technologies, there has been a resulting explosion in the amount of data generated by wearable sensors that can be used in physical activity research. Unfortunately the advances in hardware (e.g. device size), have not been matched by software to help manage, organise and analyse this data deluge. Public health research will require cross-disciplinary interactions with the computer science community in working towards solutions to automatically recognise human activities from wearable sensor data. Methods: We conducted a meta-review of contemporary computing science and information retrieval approaches such as: 1) the management and indexing of data from wearable accelerometer and image capturing devices; 2) the synchronisation and fusion of data from multiple devices (e.g. GPS, accelerometer, & SenseCam data); and 3) the representation of the meaning of image data Results: We identified: 1) Relational databases offer the most flexible solution for managing, indexing, and querying wearable sensor data information. 2) Advanced analysis of the signature of streams of data from separate devices may allow their data to be synchronised, but requires further advances. 3) The optimum method of pattern recognition in automatically annotating wearable image data is through the use of Support Vector Machine classifiers. Conclusions: Close interdisciplinary research between public health and computing science can help further our understanding of human activities. A first step may be the use of sensor web technologies to recognise a well-defined subset of activities that individuals are engaged in.