  Exchanging data as character-separated values (CSV) is slow, cumbersome and
error-prone. Especially for time-series data, which is common in Activity
Recognition, synchronizing several independently recorded sensors is
challenging. Adding second level evidence, like video recordings from multiple
angles and time-coded annotations, further complicates the matter of curating
such data. A possible alternative is to make use of standardized multi-media
formats. Sensor data can be encoded in audio format, and time-coded
information, like annotations, as subtitles. Video data can be added easily.
All this media can be merged into a single container file, which makes the
issue of synchronization explicit. The incurred performance overhead by this
encoding is shown to be negligible and compression can be applied to optimize
storage and transmission overhead.
