  The technological advancement in wireless health monitoring through the
direct contact of the skin allows the development of light-weight wrist-worn
wearable devices to be equipped with different sensors such as
photoplethysmography (PPG) sensors. However, the motion artifact (MA) is
possible to occur during daily activities. In this study, we attempted to
perform a post-calibration of the heart rate (HR) estimation during the three
possible states of average daily activity (resting, \textcolor{red}{laying
down}, and intense treadmill activity states) in 29 participants (130
minutes/person) on four popular wearable devices: Fitbit Charge HR, Apple Watch
Series 4, TicWatch Pro, and Empatica E4. In comparison to the standard
measurement (HR$_\text{ECG}$), HR provided by Fitbit Charge HR
(HR$_\text{Fitbit}$) yielded the highest error of $3.26 \pm 0.34$ bpm in
resting, $2.33 \pm 0.23$ bpm in \textcolor{red}{laying down}, $9.53 \pm 1.47$
bpm in intense treadmill activity states, and $5.02 \pm 0.64$ bpm in all states
combined among the four chosen devices. Following our improving HR estimation
model with rolling windows as feature (HR$_\text{R}$), the mean absolute error
(MAE) was significantly reduced by $33.44\%$ in resting, $15.88\%$ in
\textcolor{red}{laying down}, $9.55\%$ in intense treadmill activity states,
and $18.73\%$ in all states combined. This demonstrates the feasibility of our
proposed methods in order to correct and provide HR monitoring post-calibrated
with high accuracy, raising further awareness of individual fitness in the
daily application.
