@misc{10481/103715,
year = {2024},
month = {12},
url = {https://hdl.handle.net/10481/103715},
abstract = {Accurate recognition of daily activities is vital for applications in healthcare, fitness tracking, and
assisted living. Wrist-worn accelerometers, embedded in smartwatches and fitness trackers, provide an
effective means of continuously monitoring user movements. Therefore this project explores the
application of machine learning (ML) techniques to analyze accelerometer data from wrist-worn
devices for precise daily activity recognition. The goal is to develop robust ML models that can process
complex and noisy accelerometer data to identify and classify a wide range of activities such as walking,
running, sitting, and sleeping. The proposed system utilizes advanced ML algorithms, to interpret the
accelerometer data. These models are trained on extensive datasets containing labeled activity data,
enabling them to learn intricate patterns and variations in user movements. The system's performance
was rigorously tested and validated, demonstrating significant improvements in accuracy and reliability
over traditional methods, such as manual logs, pedometers, and simple threshold-based algorithms. The
ML-based approach offers comprehensive analysis and real-time monitoring capabilities, providing
users with precise and continuous activity recognition. Personalized insights and recommendations are
generated based on individual activity patterns, enhancing user experience and promoting better health
outcomes. Additionally, the system supports assisted living by monitoring daily activities of the elderly
and individuals with disabilities, ensuring their safety and well-being.},
publisher = {Universidad de Granada},
keywords = {Daily activity recognition},
keywords = {Wrist Watches},
keywords = {Fitness tracker},
keywords = {Accelerometer data},
title = {Enhancing activity monitoring of smart homes using IOT sensors},
author = {Deepanjali and Sreejanani, T. and Keerthana, R. and Veena Reddy, S.},
}