Rank Pooling Approach for Wearable Sensor-Based ADLs Recognition.

ABSTRACT: This paper addresses wearable-based recognition of Activities of Daily Living (ADLs) which are composed of several repetitive and concurrent short movements having temporal dependencies. It is improbable to directly use sensor data to recognize these long-term composite activities because two examples (data sequences) of the same ADL result in largely diverse sensory data. However, they may be similar in terms of more semantic and meaningful short-term atomic actions. Therefore, we propose a two-level hierarchical model for recognition of ADLs. Firstly, atomic activities are detected and their probabilistic scores are generated at the lower level. Secondly, we deal with the temporal transitions of atomic activities using a temporal pooling method, rank pooling. This enables us to encode th

SUBMITTER: Nisar MA 

PROVIDER: S-EPMC7349219 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature