This paper presents a review of different classification techniques used to recognize human
activities from wearable inertial sensor data. Three inertial sensor units were used in this …

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness
monitoring and data analysis using various algorithmic techniques have been subjects of …

In last few decades, human activity recognition grabbed considerable research attentions
from a wide range of pattern recognition and human–computer interaction researchers due …

This work presents the Transition-Aware Human Activity Recognition (TAHAR) system
architecture for the recognition of physical activities using smartphones. It targets real-time …

This paper adopts different supervised learning methods from the field of machine learning
to develop multiclass classifiers that identify the transportation mode, including driving a car …

The ubiquity of communication devices such as smartphones has led to the emergence of
context-aware services that are able to respond to specific user activities or contexts. These …

Transportation and locomotion mode recognition from multimodal smartphone sensors is
useful for providing just-in-time context-aware assistance. However, the field is currently …

The increasing dependence on smartphones with advanced sensors has highlighted the
imperative of precise transportation mode classification, pivotal for domains like health …

Developments in information and communication technologies have led to the wider usage
of Internet of Things (IoT). In the modern health care applications, the usage of IoT …

Real-time transit tracking is gaining popularity as a means for transit agencies to improve the
rider experience. However, many transit agencies lack either the funding or initiative to …