Human Activity Recognition Using Sensor Data from Smartphones and Smartwatches
Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning
Human activity recognition and classification can be obtained using manysophisticated data recording setups, but there is also a need in observing how performance varies among models that are strictly limited to using sensor datafrom easily accessible devices: smartphones and smartwatches.
This paper presents the findings of different models that are limited to train using such sensors.
Results show promise for models trained strictly using limited sensordata collected from only smartphones and smartwatches coupled with traditional machine learning concepts and algorithms.
Authors
Rushit Dave, Naeem Seliya, Mounika Vanamala, Wei Tee