A Comparison of Machine Learning and Deep Learning Techniques for Activity Recognition using Mobile Devices.
Alejandro BaldominosAlejandro CervantesYago SaezPedro IsasiPublished in: Sensors (Basel, Switzerland) (2019)
We have compared the performance of different machine learning techniques for human activity recognition. Experiments were made using a benchmark dataset where each subject wore a device in the pocket and another on the wrist. The dataset comprises thirteen activities, including physical activities, common postures, working activities and leisure activities. We apply a methodology known as the activity recognition chain, a sequence of steps involving preprocessing, segmentation, feature extraction and classification for traditional machine learning methods; we also tested convolutional deep learning networks that operate on raw data instead of using computed features. Results show that combination of two sensors does not necessarily result in an improved accuracy. We have determined that best results are obtained by the extremely randomized trees approach, operating on precomputed features and on data obtained from the wrist sensor. Deep learning architectures did not produce competitive results with the tested architecture.
Keyphrases
- deep learning
- machine learning
- artificial intelligence
- big data
- convolutional neural network
- physical activity
- electronic health record
- endothelial cells
- mental health
- clinical trial
- double blind
- open label
- randomized controlled trial
- computed tomography
- phase iii
- placebo controlled
- study protocol
- magnetic resonance imaging
- diffusion weighted imaging
- amino acid
- contrast enhanced