Feature Generation for Physical Activity Classification

  • Роман Владимирович Исаченко МФТИ
  • Илья Николаевич Жариков МФТИ
  • Артём Максимович Бочкарёв МФТИ
  • Вадим Викторович Стрижов МФТИ
Ключевые слова: wearable devices, accelerometer, time series, local approximation, classification


The paper investigates human physical activity classification problem. Time series obtained from accelerometer
of a wearable device produce a dataset. Due to the high dimension of object description and low computational resources
one has to state a feature generation problem. The authors propose to use the parameters of the local approximation
models as informative features. The experiment is conducted on two datasets for human activity recognition using accelerometer:
WISDM and USC-HAD. It compares several superpositions of various generation methods and classification

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