BACKGROUND
Activities of daily living (ADL) are essential for independence and personal well-being, reflecting an individual's functional status. Impairment in executing these tasks can limit autonomy and negatively affect quality of life. The assessment of physical function during ADL is crucial for the prevention and rehabilitation of movement limitations. Still, its traditional evaluation based on subjective observation has limitations in precision and objectivity.
OBJECTIVE
The primary objective of this study is to use innovative technology, specifically wearable inertial sensors combined with artificial intelligence (AI) techniques, to objectively and accurately evaluate human performance in ADL. It is proposed to overcome the limitations of traditional methods by implementing systems that allow dynamic and non-invasive monitoring of movements during daily activities. The approach seeks to provide an effective tool for the early detection of dysfunctions and the personalization of treatment and rehabilitation plans, thus promoting an improvement in the quality of life of individuals.
METHODS
To monitor movements (six related to the shoulder and three related to the back), wearable inertial sensors were developed that include accelerometers and triaxial gyroscopes. The initial database consisted of 53,165 activity records, which was reduced to 52,600 after processing to remove null or abnormal values. Finally, four Deep Learning models were created combining various processing layers to explore different approaches in ADL recognition.
RESULTS
The results revealed high performance of the four proposed models, with levels of accuracy, precision, recall, and F1-score ranging between 95% and 97% for all classes, and an average loss of 0.10. These results indicate the great capacity of the models to accurately identify a variety of activities, with a good balance between precision and recall. Both the convolutional and bidirectional approaches achieved slightly superior results, although the bidirectional model reached convergence in a smaller number of epochs.
CONCLUSIONS
The Deep Learning models implemented have demonstrated solid performance indicating an effective ability to identify and classify various daily activities related to the shoulder and lumbar region. These results were achieved with minimal sensorization, non-invasive and practically imperceptible to the user, which does not affect their daily routine and promotes acceptance and adherence to continuous monitoring, thus improving the reliability of the data collected. This research has the potential to have a significant impact on the clinical evaluation and rehabilitation of patients with movement limitations, by providing an objective and advanced tool to detect key movement patterns and joint dysfunctions.
