Corrigendum: SemNet: Learning semantic attributes for human activity recognition with deep belief networks.

[This corrects the article DOI: 10.3389/fdata.2022.879389.].

SemNet: Learning semantic attributes for human activity recognition with deep belief networks.

Human Activity Recognition (HAR) is a prominent application in mobile computing and Internet of Things (IoT) that aims to detect human activities based on multimodal sensor signals generated as a result of diverse body movements. Human physical activities are typically composed of simple actions (such as "arm up", "arm down", "arm curl", etc.), referred to as semantic features. Such abstract semantic features, in contrast to high-level activities ("walking", "sitting", etc.) and low-level signals (raw sensor readings), can be developed manually to assist activity recognition. Although effective, this manual approach relies heavily on human domain expertise and is not scalable. In this paper, we address this limitation by proposing a machine learning method, SemNet, based on deep belief networks. SemNet automatically constructs semantic features representative of the axial bodily movements. Experimental results show that SemNet outperforms baseline approaches and is capable of learning features that highly correlate with manually defined semantic attributes. Furthermore, our experiments using a different model, namely deep convolutional LSTM, on household activities illustrate the broader applicability of semantic attribute interpretation to diverse deep neural network approaches. These empirical results not only demonstrate that such a deep learning technique is semantically meaningful and superior to its handcrafted counterpart, but also provides a better understanding of the deep learning methods that are used for Human Activity Recognition.

Genetic Identification of all Four Mangrove Mud Crab Species (genus Scylla) Using Multiple Molecular Markers.

Misleading identification and subsequent publications on biological, molecular, and aquaculture data of mangrove mud crab (genus Scylla de Hann 1833) is a major concern in many countries. In this study, multiple molecular markers were used for genetic identification of all four known mud crab species under genus Scylla collected from India, Philippines, Myanmar, Malaysia and Indonesia. Internal Transcribed Spacer (ITS-1), Polymerase chain reaction (PCR)-Restriction Fragment Length Polymorphism (PCR-RFLP) and PCR-based species-specific markers were used to resolve taxonomic ambiguity. PCR-RFLP techniques using NlaIV and BsaJI restriction endonucleases were efficient to differentiate four different mud crab species under genus Scylla with specific fragment profile. The results also justified the use of ITS-1 and PCR-based species-specific markers to identify mud crab species available in many countries quite rapidly and effectively. Several new molecular markers generated during the study are reported here to resolve the taxonomic ambiguity of Scylla species and the results reconfirmed that India is only having two commonly available mud crab species which was reported by the authors earlier.