  Human activity recognition has grown in popularity with its increase of
applications within daily lifestyles and medical environments. The goal of
having efficient and reliable human activity recognition brings benefits such
as accessible use and better allocation of resources; especially in the medical
industry. Activity recognition and classification can be obtained using many
sophisticated data recording setups, but there is also a need in observing how
performance varies among models that are strictly limited to using sensor data
from easily accessible devices: smartphones and smartwatches. This paper
presents the findings of different models that are limited to train using such
sensors. The models are trained using either the k-Nearest Neighbor, Support
Vector Machine, or Random Forest classifier algorithms. Performance and
evaluations are done by comparing various model performances using different
combinations of mobile sensors and how they affect recognitive performances of
models. Results show promise for models trained strictly using limited sensor
data collected from only smartphones and smartwatches coupled with traditional
machine learning concepts and algorithms.
