3 research outputs found
Risk management based medical device GMP(Good Manufacturing Practice) applying ISO 14971:2019(3rd) etc
prohibition์
Convolutional and LSTM Neural Networks for Sensor-Based Activity Classification and Novel Class Detection
MasterActivity recognition aims to recognize human activities, building a model to
label segments in multivariate time series collected from multiple sensors. Most
of methods address a closed set recognition problem where the prediction is made
within pre-specified labels which the model is trained on. For real-world applications,
however, a novel class which has not seen in the training phase may be
provided to the model. In this thesis, we present a method for activity recognition
which integrates a novel class detection into convolutional and LSTM classifiers,
enabling automatic detection of novel classes as well as classification of
pre-specified classes. To this end, we combine the activity recognition with the
LSTM-based temporal anomaly detection by adopting two convolutional-LSTM
neural networks which cooperate together, where one is trained as a classifier
and the other is trained to produce class-specific prediction errors that are used
to assess the likelihood of novel class. Experiments on the Skoda and the OPPORTUNITY
datasets demonstrate the high performance of our deep learning
method for novel class detection in activity recognition