Robots that interact with humans in a physical space or application need to
think about the person's posture, which typically comes from visual sensors
like cameras and infra-red. Artificial intelligence and machine learning
algorithms use information from these sensors either directly or after some
level of symbolic abstraction, and the latter usually partitions the range of
observed values to discretize the continuous signal data. Although these
representations have been effective in a variety of algorithms with respect to
accuracy and task completion, the underlying models are rarely interpretable,
which also makes their outputs more difficult to explain to people who request
them. Instead of focusing on the possible sensor values that are familiar to a
machine, we introduce a qualitative spatial reasoning approach that describes
the human posture in terms that are more familiar to people. This paper
explores the derivation of our symbolic representation at two levels of detail
and its preliminary use as features for interpretable activity recognition.

介绍了一种基于人类肢体空间关系推理的符号表示方法，用于解释机器人看到的人体姿态，实现可解释的活动识别。