Bedtime Monitoring for Fall Detection and Prevention in Older Adults

Off-body Sleep Analysis for Predicting Adverse Behavior in Individuals with Autism Spectrum Disorder

Poor sleep quality in Autism Spectrum Disorder (ASD) individuals is linked to severe daytime behaviors. This study explores the relationship between a prior night's sleep structure and its predictive power for next-day behavior in ASD individuals. The motion was extracted using a low-cost near-infrared camera in a privacy-preserving way. Over two years, we recorded overnight data from 14 individuals, spanning over 2,000 nights, and tracked challenging daytime behaviors, including aggression, self-injury, and disruption. We developed an ensemble machine learning algorithm to predict next-day behavior in the morning and the afternoon. Our findings indicate that sleep quality is a more reliable predictor of morning behavior than afternoon behavior the next day. The proposed model attained an accuracy of 74% and a F1 score of 0.74 in target-sensitive tasks and 67% accuracy and 0.69 F1 score in target-insensitive tasks. For 7 of the 14, better-than-chance balanced accuracy was obtained (p-value<0.05), with 3 showing significant trends (p-value<0.1). These results suggest off-body, privacy-preserving sleep monitoring as a viable method for predicting next-day adverse behavior in ASD individuals, with the potential for behavioral intervention and enhanced care in social and learning settings.

Edge Computing Transformers for Fall Detection in Older Adults

The global trend of increasing life expectancy introduces new challenges with far-reaching implications. Among these, the risk of falls among older adults is particularly significant, affecting individual health and the quality of life, and placing an additional burden on healthcare systems. Existing fall detection systems often have limitations, including delays due to continuous server communication, high false-positive rates, low adoption rates due to wearability and comfort issues, and high costs. In response to these challenges, this work presents a reliable, wearable, and cost-effective fall detection system. The proposed system consists of a fit-for-purpose device, with an embedded algorithm and an Inertial Measurement Unit (IMU), enabling real-time fall detection. The algorithm combines a Threshold-Based Algorithm (TBA) and a neural network with low number of parameters based on a Transformer architecture. This system demonstrates notable performance with 95.29% accuracy, 93.68% specificity, and 96.66% sensitivity, while only using a 0.38% of the trainable parameters used by the other approach.

The Role of Autopsy and Post-Mortem Investigations in Falling Traumas in the Hospital Environment

Falls in a hospital setting are a global public health problem. Despite the production of sensors and various preventive tools to reduce the risk, falls remain a dangerous event with a significant impact on the morbidity and mortality of patients. Despite numerous prevention strategies, falling in the hospital are not always investigated and the autopsy is not always performed in these cases, so it is often not known whether the death is related to the fall or to other causes, inevitably affecting the assessment of any profiles of medical liability for health personnel or for the hospital in the accident. We describe three cases of falls that occurred in different hospitals, in which the autopsy allowed to diagnose with certainty the extent of the trauma and to reconstruct its dynamics. Along with the circumstantial and documentary analyses, deficiencies both in the trauma diagnostics and in the application of the safety measures on the patients were showed. Together with the description of our cases, we propose the post-mortem investigations of these events, both for judicial and risk management purposes.