Zusammenfassung
Background/Objectives: Activities of Daily Living (ADLs) are crucial for assessing
an individual’s autonomy, encompassing tasks such as eating, dressing, and moving
around, among others. Predicting these activities is part of health monitoring, elderly
care, and intelligent systems, improving quality of life, and facilitating early dependency
detection, all of which are relevant components of ...
Zusammenfassung
Background/Objectives: Activities of Daily Living (ADLs) are crucial for assessing
an individual’s autonomy, encompassing tasks such as eating, dressing, and moving
around, among others. Predicting these activities is part of health monitoring, elderly
care, and intelligent systems, improving quality of life, and facilitating early dependency
detection, all of which are relevant components of personalized health and social care.
However, the automatic classification of ADLs from sensor data remains challenging due
to high variability in human behavior, sensor noise, and discrepancies in data acquisition
protocols. These challenges limit the accuracy and applicability of existing solutions. This
study details the modeling and evaluation of real-time ADL classification models based
on batch learning (BL) and stream learning (SL) algorithms. Methods: The methodology
followed is the Cross-Industry Standard Process for Data Mining (CRISP-DM). The models
were trained with a comprehensive dataset integrating 23 ADL-centric datasets using
accelerometers and gyroscopes data. The data were preprocessed by applying normalization
and sampling rate unification techniques, and finally, relevant sensor locations
on the body were selected. Results: After cleaning and debugging, a final dataset was
generated, containing 238,990 samples, 56 activities, and 52 columns. The study compared
models trained with BL and SL algorithms, evaluating their performance under various
classification scenarios using accuracy, area under the curve (AUC), and F1-score metrics.
Finally, a mobile application was developed to classify ADLs in real time (feeding data
from a dataset). Conclusions: The outcome of this study can be used in various data
science projects related to ADL and Human activity recognition (HAR), and due to the
integration of diverse data sources, it is potentially useful to address bias and improve
generalizability in Machine Learning models. The principal advantage of online learning
algorithms is dynamically adapting to data changes, representing a significant advance in
personal autonomy and health care monitoring.
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