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Decoding Natural Behavior from Neuroethological Embedding
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Characterizing Word Embeddings for Zero-Shot Sensor-Based Human Activity Recognition.

Moe Matsuki1, Paula Lago2, Sozo Inoue3

  • 1Department of Applied Science for Integrated System Engineering Kyushu Institute of Technology, Kitakyushu 804-8550, Japan.

Sensors (Basel, Switzerland)
|November 23, 2019
PubMed
Summary
This summary is machine-generated.

This study explores Zero-shot learning for sensor activity recognition using word embeddings. Word embeddings offer an efficient, automated approach for recognizing unknown activities, outperforming traditional attribute methods.

Keywords:
Zero-shot machine learninghuman activity recognitionword embedding representation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Zero-shot learning (ZSL) aims to recognize activities unseen during training.
  • Existing ZSL methods primarily use attribute or embedding vectors for semantic representation.
  • Sensor-based activity recognition lacks extensive research in ZSL using embedding vectors.

Purpose of the Study:

  • To evaluate Zero-shot learning for sensor activity recognition using different semantic vector representations.
  • To compare the performance of attribute vectors, embedding vectors, and expanded embedding vectors in ZSL.
  • To investigate the efficiency and accuracy of word embedding-based ZSL for sensor data.

Main Methods:

  • Implemented Zero-shot learning framework for sensor activity recognition.
  • Utilized three types of semantic vectors: attribute, embedding, and expanded embedding vectors.
  • Compared the performance and analyzed the correlation of semantic vector types with recognition accuracy.

Main Results:

  • Performance across the three semantic vector types was found to be similar.
  • Word embedding-based Zero-shot learning proved more efficient due to automated vector generation.
  • The proposed method demonstrated higher accuracy than attribute-vector methods when semantic and sensor data information aligned.

Conclusions:

  • Word embeddings provide an efficient and effective semantic representation for sensor-based Zero-shot activity recognition.
  • The choice of semantic vector representation impacts efficiency and accuracy, particularly when data alignment is present.
  • Findings guide the selection of appropriate classes and sensor data for training effective Zero-shot recognition models.