  Physical activity recognition (PAR) using wearable devices can provide valued
information regarding an individual's degree of functional ability and
lifestyle. In this regards, smartphone-based physical activity recognition is a
well-studied area. Research on smartwatch-based PAR, on the other hand, is
still in its infancy. Through a large-scale exploratory study, this work aims
to investigate the smartwatch-based PAR domain. A detailed analysis of various
feature banks and classification methods are carried out to find the optimum
system settings for the best performance of any smartwatch-based PAR system for
both personal and impersonal models. To further validate our hypothesis for
both personal (The classifier is built using the data only from one specific
user) and impersonal (The classifier is built using the data from every user
except the one under study) models, we tested single subject validation process
for smartwatch-based activity recognition.
