  Human Activity Recognition (HAR) stands as a pivotal technique within pattern
recognition, dedicated to deciphering human movements and actions utilizing one
or multiple sensory inputs. Its significance extends across diverse
applications, encompassing monitoring, security protocols, and the development
of human-in-the-loop technologies. However, prevailing studies in HAR often
overlook the integration of human-centered devices, wherein distinct parameters
and criteria hold varying degrees of importance compared to other applications.
Notably, within this realm, curtailing the sensor observation period assumes
paramount importance to safeguard the efficiency of exoskeletons and
prostheses. This study embarks on the optimization of this observation period
specifically tailored for HAR using Inertial Measurement Unit (IMU) sensors.
Employing a Deep Convolutional Neural Network (DCNN), the aim is to identify
activities based on segments of IMU signals spanning durations from 0.1 to 4
seconds. Intriguingly, the outcomes spotlight an optimal observation duration
of 0.5 seconds, showcasing an impressive classification accuracy of 99.95%.
This revelation holds immense significance, elucidating the criticality of
precise temporal analysis within HAR, particularly concerning human-centric
devices. Such findings not only enhance our understanding of the optimal
observation period but also lay the groundwork for refining the performance and
efficacy of devices crucially relied upon for aiding human mobility and
functionality.
