A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection.

Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors' sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection.

Dedicated photoreceptor pathways in Drosophila larvae mediate navigation by processing either spatial or temporal cues.

To integrate changing environmental cues with high spatial and temporal resolution is critical for animals to orient themselves. Drosophila larvae show an effective motor program to navigate away from light sources. How the larval visual circuit processes light stimuli to control navigational decision remains unknown. The larval visual system is composed of two sensory input channels, Rhodopsin5 (Rh5) and Rhodopsin6 (Rh6) expressing photoreceptors (PRs). We here characterize how spatial and temporal information are used to control navigation. Rh6-PRs are required to perceive temporal changes of light intensity during head casts, while Rh5-PRs are required to control behaviors that allow navigation in response to spatial cues. We characterize how distinct behaviors are modulated and identify parallel acting and converging features of the visual circuit. Functional features of the larval visual circuit highlight the principle of how early in a sensory circuit distinct behaviors may be computed by partly overlapping sensory pathways.

NONO couples the circadian clock to the cell cycle.

Mammalian circadian clocks restrict cell proliferation to defined time windows, but the mechanism and consequences of this interrelationship are not fully understood. Previously we identified the multifunctional nuclear protein NONO as a partner of circadian PERIOD (PER) proteins. Here we show that it also conveys circadian gating to the cell cycle, a connection surprisingly important for wound healing in mice. Specifically, although fibroblasts from NONO-deficient mice showed approximately normal circadian cycles, they displayed elevated cell doubling and lower cellular senescence. At a molecular level, NONO bound to the p16-Ink4A cell cycle checkpoint gene and potentiated its circadian activation in a PER protein-dependent fashion. Loss of either NONO or PER abolished this activation and circadian expression of p16-Ink4A and eliminated circadian cell cycle gating. In vivo, lack of NONO resulted in defective wound repair. Because wound healing defects were also seen in multiple circadian clock-deficient mouse lines, our results therefore suggest that coupling of the cell cycle to the circadian clock via NONO may be useful to segregate in temporal fashion cell proliferation from tissue organization.