Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4475
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dc.contributor.authorRayasam, Krishna Chaitanya-
dc.contributor.authorDeshmukh, Prashant-
dc.contributor.authorKar, Sougata Kumar-
dc.contributor.authorDas, Santos Kumar-
dc.date.accessioned2024-03-14T11:27:26Z-
dc.date.available2024-03-14T11:27:26Z-
dc.date.issued2024-02-
dc.identifier.citationNational conference on Intelligent Systems, IoT, and Wireless Communication for the Society (IIWCS), National Institute of Technology Rourkela 16-17 February 2024en_US
dc.identifier.urihttp://hdl.handle.net/2080/4475-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractRecognizing activities within smart home environments is imperative for developing automated services catering to inhabitants’ needs. However, this task presents significant challenges due to the variability of environments, diverse sensorymotor systems, user behavior patterns, signal sparsity, and model redundancy. End-to-end systems often struggle to automatically extract essential features and require access to context and domain knowledge. To address the feature extraction challenges in activity recognition within smart homes, we propose an innovative approach that integrates methods from both Natural Language Processing (NLP) and Time Series Classification (TSC) domains. The efficacy of our proposed method is evaluated using aruba dataset sourced from the Center for Advanced Studies in Adaptive Systems (CASAS). WordPiece tokenizer is used to tokenize the words in the sentences. Later, the tokenized sentences are embedded, trained and classified using Fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model for sequence classification. Our method demonstrates promising results in offline activity classificationen_US
dc.subjectAmbient Sensorsen_US
dc.subjectBERTen_US
dc.subjectElderly Peopleen_US
dc.subjectHuman Activity Recognitionen_US
dc.subjectSmart Homesen_US
dc.titleEnhancing Human Activity Recognition with Language Analysis and BERTen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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