On specialized window lengths and detector based human activity recognition
Hong Li, Gregory D. Abowd, Thomas Plötz · 2018
Sliding window based activity recognition chains represent the state-of-the-art for many mobile and embedded scenarios as they are common in wearable computing. The length of the analysis frames is a crucial system parameter that directly influences the effectiveness of the overall approach. In this paper we present a method that optimizes the window length - individually for each target activity. Instead of employing a single, multi-class recognition system that is based on a generic window length, we combine individually optimized activity detectors into an Ensemble based recognition approach. We demonstrate the effectiveness of the approach through an experimental evaluation on eight benchmark datasets. The proposed method leads to significant improvements across a range of activity recognition application domains.