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. 2014 Apr 9;14(4):6474–6499. doi: 10.3390/s140406474

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© 2014 by the authors; licensee MDPI, Basel, Switzerland.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

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Figure 2. — Different stages of the activity recognition chain (ARC). An example of the correlation of the windowing approach and subsequent levels of the ARC is shown. Here, different window sizes are depicted particularly. Concretely, M sensors deliver raw signals (u₁, u₂, …, u_M), which are subsequently processed (p₁, p₂, …, p_M). The signals are partitioned into data windows of size W_k (e.g., s₁_{W_k}, s₂_{W_k}, …, s_{MW_k}). For each window, k, a set of features are extracted and aggregated in a single feature vector (f₁(s₁_{W_k}), f₂(s₂_{W_k}), …, f_M(s_{MW_k})) that is used as the input to a classifier. The classifier yields a class (c_{W_k}) that represents the identified activity.