Automated pre-impact fall detection in real-time using raw data acquired from wearable sensors, such as tri-axial accelerometers, remains an open research problem in the context of elderly care. This paper presents a comparative study of nine neural network models, including Dense, CNN, LSTM, GRU, BiLSTM, BiGRU, CNN Dense, CNN LSTM, and CNN...
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2024 (v1)Journal articleUploaded on: February 25, 2024
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January 2022 (v1)Journal article
This study presents a low-power wearable system able to predict a fall by detecting a pre-impact condition, performed through a simple analysis of motion data (acceleration) and height of the subject. The system can detect a fall in all directions with an average consumption of 5.91 mA; i.e., it can monitor the activity of daily living (ADL),...
Uploaded on: December 4, 2022 -
April 14, 2023 (v1)Journal article
This paper presents the development of an electronic system that converts an electrically assisted bicycle into an intelligent health monitoring system, allowing people who are not athletic or who have a history of health issues to progressively start the physical activity by following a medical protocol (e.g., max heart rate and power output,...
Uploaded on: May 12, 2023