1,721,026 research outputs found
A combination of template matching and Bayesian estimation to detect and classify activities of daily living
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Respiration and postural sway: detection of phase synchronizations and interactions
No full textThe aim of the central nervous system in upright stance is to control an intrinsically unsta- ble plant. Internal disturbances, such as hæmodynamics and respiration, constitute an endog- enous threat to equilibrium. The way CNS reacts to those perturbations was studied in this work, through the analysis of summary scores taken from posturographic and pneumographic data. Signals were recorded simultaneously during trials administered on a sample population of healthy young adults, while sitting and standing and at paced and spontaneous uncon- trolled breathing. The extraction of posturographic and pneumographic parameters was accompanied by the utilization of techniques for the detection of phase synchronization in bivariate data, and the extraction of an interaction index, the mutual information MI. The effects of the biomechanical condition and respiratory amplitude on MI and summary meas- ures were tested with a two-way ANOVA. Summary scores clearly depend on posture condi- tion. Synchronization between breath and postural sway is always present, depends on both biomechanical condition and respiratory threat, and cannot be reduced to a simple linear relation.The aim of the central nervous system in upright stance is to control an intrinsically unstable plant. Internal disturbances, such as haemodynamics and respiration, constitute an endogenous threat to equilibrium. The way CNS reacts to those perturbations was studied in this work, through the analysis of summary scores taken from posturographic and pneumographic data. Signals were recorded simultaneously during trials administered on a sample population of healthy young adults, while sitting and standing and at paced and spontaneous uncontrolled breathing. The extraction of posturographic and pneumographic parameters was accompanied by the utilization of techniques for the detection of phase synchronization in bivariate data, and the extraction of an interaction index, the mutual information MI. The effects of the biomechanical condition and respiratory amplitude on MI and summary measures were tested with a two-way ANOVA. Summary scores clearly depend on posture condition. Synchronization between breath and postural sway is always present, depends on both biomechanical condition and respiratory threat, and cannot be reduced to a simple linear relation. (C) 2004 Elsevier B.V. All rights reserved
Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review
No full textThis article presents a systematic review aimed at mapping the literature published in the last decade on the use of machine learning (ML) for clinical decision-making through wearable inertial sensors. The review aims to analyze the trends, perspectives, strengths, and limitations of current literature in integrating ML and inertial measurements for clinical applications. The review process involved defining four research questions and applying four relevance assessment indicators to filter the search results, providing insights into the pathologies studied, technologies and setups used, data processing schemes, ML techniques applied, and their clinical impact. When combined with ML techniques, inertial measurement units (IMUs) have primarily been utilized to detect and classify diseases and their associated motor symptoms. They have also been used to monitor changes in movement patterns associated with the presence, severity, and progression of pathology across a diverse range of clinical conditions. ML models trained with IMU data have shown potential in improving patient care by objectively classifying and predicting motor symptoms, often with a minimally encumbering setup. The findings contribute to understanding the current state of ML integration with wearable inertial sensors in clinical practice and identify future research directions. Despite the widespread adoption of these technologies and techniques in clinical applications, there is still a need to translate them into routine clinical practice. This underscores the importance of fostering a closer collaboration between technological experts and professionals in the medical field
Elaborazione di un insieme minimo di segnali accelerometrici per il riconoscimento di attività motorie
No full textA reverse engineering schema to monitor 3-D control of upper limbs while playing the Wii
No full textCalibration of a measurement system for the evaluation of effectiveness index in bicycle training”
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