There is increasing evidence of the relationship between sleep and neurodegeneration, but this knowledge is not incorporated into clinical practice yet. We aimed to test whether a basic sleep parameter, as total sleep estimated by actigraphy for 1 week, was a valid predictor of CSF Alzheimer’s Disease core biomarkers (amyloid-β-42 and –40, phosphorylated-tau-181, and total-tau) in elderly individuals, considering possible confounders and effect modifiers, particularly the APOE ε4 allele. One hundred and twenty-seven cognitively unimpaired volunteers enrolled in the Valdecilla Study for Memory and Brain Aging participated in this study. Seventy percent of the participants were women with a mean age of 65.5 years. After adjustment for covariates, reduced sleep time significantly predicted higher t-tau and p-tau. This association was mainly due to the APOE ε4 carriers. Our findings suggest that total sleep time, estimated by an actigraphy watch, is an early biomarker of tau pathology and that APOE modulates this relationship. The main limitation of this study is the limited validation of the actigraphy technology used. Sleep monitoring with wearables may be a useful and inexpensive screening test to detect early neurodegenerative changes.

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In this work, a novel optical fiber sensor capable of measuring both the liquid level and its refractive index is designed, manufactured and demonstrated through simulations and experimentally. For this, a silica capillary hollow-core fiber is used. The fiber, with a sensing length of 1.55 mm, has been processed with a femtosecond laser, so that it incorporates four holes in its structure. In this way, the liquid enters the air core, and it is possible to perform the sensing through the Fabry–Perot cavities that the liquid generates. The detection mode is in reflection. With a resolution of 4 μm (liquid level), it is in the state of the art of this type of sensor. The system is designed so that in the future it will be capable of measuring the level of immiscible liquids, that is, liquids that form stratified layers. It can be useful to determine the presence of impurities in tanks.

Depth cameras are developing widely. One of their main virtues is that, based on their data and by applying machine learning algorithms and techniques, it is possible to perform body tracking and make an accurate three-dimensional representation of body movement. Specifically, this paper will use the Kinect v2 device, which incorporates a random forest algorithm for 25 joints detection in the human body. However, although Kinect v2 is a powerful tool, there are circumstances in which the device’s design does not allow the extraction of such data or the accuracy of the data is low, as is usually the case with foot position. We propose a method of acquiring this data in circumstances where the Kinect v2 device does not recognize the body when only the lower limbs are visible, improving the ankle angle’s precision employing projection lines. Using a region-based convolutional neural network (Mask RCNN) for body recognition, raw data extraction for automatic ankle angle measurement has been achieved. All angles have been evaluated by inertial measurement units (IMUs) as gold standard. For the six tests carried out at different fixed distances between 0.5 and 4 m to the Kinect, we have obtained (mean ± SD) a Pearson’s coefficient, r = 0.89 ± 0.04, a Spearman’s coefficient, ρ = 0.83 ± 0.09, a root mean square error, RMSE = 10.7 ± 2.6 deg and a mean absolute error, MAE = 7.5 ± 1.8 deg. For the walking test, or variable distance test, we have obtained a Pearson’s coefficient, r = 0.74, a Spearman’s coefficient, ρ = 0.72, an RMSE = 6.4 deg and an MAE = 4.7 deg.

The ballistocardiogram is a graphic representation of the movements of the body associated with cardiac activity. In this paper, a ten minutes ballistocardiogram have been captured for ten different volunteers with a polymer optical fiber (POF) specklegram sensor. This transducer, which is composed by a CCD camera, a laser emitting diode and two meters of POF, allows to capture the ballistocardiogram by analyzing how the induced speckle pattern changes over the time. These changes are related to cardiac activity. Several processing methods have been compared to determine which method achieve the best performance: Complex Cepstrum, Power of Spectral Density (PSD), Pam-Tompkins algorithm, Wavelet, Autocorrelation, Savitzky-Golay filter, Mean Absolute Deviation and Hilbert transform. Accuracy and resources consumption have been characterized and compared for these methods. Hilbert, PSD and Savitzky-golay exhibit both small error and computational cost. This paper describes a baseline for main frequency determination of POF-based ballistocardiogram signals in real time.