Intelligent Recognition of Multimodal Human Activities for Personal Healthcare

Nowadays, the advancements of wearable consumer devices have become a predominant role inhealthcare gadgets. There is always a demand to obtain robust recognition of heterogeneous human activitiesin complicated IoT environments. The knowledge attained using these recognition models will be thencombined with healthcare applications. In this way, the paper proposed a novel deep learning frameworkto recognize heterogeneous human activities using multimodal sensor data. The proposed framework iscomposed of four phases: employing dataset and processing, implementation of deep learning model,performance analysis, and application development. The paper utilized the recent KU-HAR database witheighteen different activities of 90 individuals. After preprocessing, the hybrid model integrating ExtremeLearning Machine (ELM) and Gated Recurrent Unit (GRU) architecture is used. An attention mechanismis then included for further enhancing the robustness of human activity recognition in the IoT environment.Finally, the performance of the proposed model is evaluated and comparatively analyzed with conventionalCNN, LSTM, GRU, ELM, Transformer and Ensemble algorithms. To the end, an application is developedusing the Qt framework which can be deployed on any consumer device. In this way, the research shedslight on monitoring the activities of critical patients by healthcare professionals remotely. The proposedELM-GRUaM model achieved supreme performance in recognizing multimodal human activities with anoverall accuracy of 96.71% as compared with existing models

Isolation and identification of chitin in three-dimensional skeleton of Aplysina fistularis marine sponge

The recent discovery of chitin within skeletons of numerous marine and freshwater sponges (Porifera) stimulates further experiments to identify this structural aminopolysaccharide in new species of these aquatical animals. Aplysina fistularis (Verongida: Demospongiae: Porifera) is well known to produce biologically active bromotyrosines. Here, we present a detailed study of the structural and physico-chemical properties of the three-dimensional skeletal scaffolds of this sponge. Calcofluor white staining, Raman and IR spectroscopy, ESI-MS as well as chitinase digestion test were applied in order to unequivocally prove the first discovery of α-chitin in skeleton of A. fistularis