Indoor Group Activity Recognition using Multi-Layered HMMs

Integration of Ontology Transformation into Hidden Markov Model

“…The sensibility of this approach is based on the value of 𝜀. Since authors [21][22][23][24][25][26][27][28][29][30][31][32] based on populating ontologies through HMM and mixed HMM and ontologies, in this work, a strictly relationship is outlined between ontology and HMM. As precise in Section 3.2, in the case of multiple ontologies, a single HMM can represent them using this approach.…”

“…The ontology is used to map knowledge components and HMM are used to identify the most suitable resource queried by user based on semantic relations among resources. Recently, to recognize group activities based on imageries data, Elangovan [31] proposed an approach where he considered the groups of human activities as ontologies. Then, these ontologies are used as sequences.…”

“…These works are summarized in Table 1. Camara [24] 2017 Extract knowledge from ontology (Java source) using HMM Rani et al [29] 2017 Use ontology to personalize system of E-learning based on FPN and HMM Karmegam [30] 2019 Combine HMM and ontology to build answering system for agricultural domain Monika and Raju [25] 2019 Build ontology using algorithms based on HMMs Elangovan [31] 2021 Train HMM based on ontologies to compute sequence evaluation Muthukumar et al [32] 2021…”

“…The HMM is therefore designed to learn ontology concepts and all of their attributes. This concept was backed by two sets of tasks: (1) using HMMs to populate ontologies [21][22][23][24][25]; and (2) combining HMMs and ontologies to develop systems [26][27][28][29][30][31][32].…”

A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions

“…Classification and Fusion Approach: After feature extraction, the next step is classification, for which many works of literature have used classical machine learning algorithms such as SVMs [279] and HMMs [280] for HAR [28,278]. There are various approaches for fusing information from different modality sensors, generally, the most common approaches are data level, feature level, and decision level fusion [281].…”