Human Activity Recognition from Accelerometer Data Using a Wearable Device

By Pierluigi Casale, Oriol Pujol, Petia Radeva. Iberian Conference on Pattern Recognition and Image Analysis, 2011

352 citations recorded by the source corpus.

How researchers see this paper

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Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks (2015, methodology): “To decrease the computational cost, researchers performed feature selection [4], sensor selection [8], contextual prediction [9] and sample frequency reduction [10].”
Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “Acceleration signals obtained from accelerometers have been used for HAR [267], due to their robustness against”
Materials that couple sensing, actuation, computation, and communication (2015, methodology): “Random forests were used to classify five activities from one second windows of accelerometer data [17], with an accuracy ranging from 88.”
Human activity recognition from accelerometer data using Convolutional Neural Network (2017, background): “Random forest is known to be a good classification method for recognizing human activities [9].”
LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes (2021, background): “Currently, HAR is becoming a challenging research topic due to the accessibility of sensors in wearable devices (e.g., smartphone, smartwatch, etc.) which are cost-effective and consume less power, including live cascading of time-series data [9].”