Miguel Ángel García González

A wireless under-mattress sensor system for sleep monitoring in people with major depression

Monitoring people during sleep time is of particular interest to patients with sleep disorders and elderly people or infants. We have developed a battery powered electronic system to monitor people in the bed, store the data and send them through a wireless link to a PC. The resulting data will be subject to further analysis using pattern recognition to evaluate the feasibility of using the system as an indicator of sleep quality. The system is able to detect presence on the bed and movements and breathing of the patient. The changes in pressure distribution on the mattress cause changes in pressure in the layers of the sensor and a voltage will appear on the electrodes. The detection ability depends strongly on the shape of the mattress and the bedbase.

Real time heart rate variability assessment from Android smartphone camera photoplethysmography: Postural and device influences

The aim of this paper is to present a smartphone based system for real-time pulse-to-pulse (PP) interval time series acquisition by frame-to-frame camera image processing. The developed smartphone application acquires image frames from built-in rear-camera at the maximum available rate (30 Hz) and the smartphone GPU has been used by Renderscript API for high performance frame-by-frame image acquisition and computing in order to obtain PPG signal and PP interval time series. The relative error of mean heart rate is negligible. In addition, measurement posture and the employed smartphone model influences on the beat-to-beat error measurement of heart rate and HRV indices have been analyzed. Then, the standard deviation of the beat-to-beat error (SDE) was 7.81 ± 3.81 ms in the worst case. Furthermore, in supine measurement posture, significant device influence on the SDE has been found and the SDE is lower with Samsung S5 than Motorola X. This study can be applied to analyze the reliability of different smartphone models for HRV assessment from real-time Android camera frames processing.

Assessment of energy expended in physical activity by a smartphone-based system

The aim of this paper is to present a smartphone based system to assess daily energy expended in physical activ- ity (PA). Nowadays, sedentary li festyle is increasingly recog- nized as a serious, worldwide public health concern, because sedentary behaviors ar e associated to cardiovascular diseases and premature death. A smartphone application has been developed for data gathering from internal motion sensor, wireless wearable heart rate moni tor and user filled out forms. The data collected have been sent to a remote server to esti- mate the total energy expenditure (TEE), which has been determined by the sum of three components: basal energy expenditure (BEE), physical activi ty (AEE) and thermic effect of food (TEF). In this study, the subjects have used the devel- oped application for seven consecutive days, which represents a typical week. Then, a profil e of their daily TEE has been obtained. The presented system is a useful tool to generate personalized recommendations to maintain energy balance through more active lifestyle and healthier eating habits.

Improvement of the actimetry agreement among sites by proper choice of signal processing stages

Physical activity (PA) has been identified as a key health indicator and has been assessed in multiple studies as the study of cardiovascular diseases, diabetes mellitus, obesity and psychological conditions. For this reason the use of accelerometers to quantify PA is widely accepted in either clinical/laboratory settings as well as in freeliving environments. The use of accelerometers in the measurement of PA is a relatively new technique and, therefore, has been not standardized. The aim of this work is to study the influence of signal processing stages in the estimation of PA while measuring simultaneously in three different sites to provide robust indicators of activity in front of changes in site measurement. With our measurement we show that a proper choice of signal processing steps can improve the agreement among activity indices measured from different sites on the same individual. The results also show that the most suited index is the time above threshold. In this case, the best combination of axes is by applying the quadratic mean, the best filtering of axes is using a cut-off frequency of around 1.5 Hz and a threshold to compute the index of 0.04 g.

The influence of preset frequency, loading condition, and exercise type on the mechanical behavior of a novel vibratory bar.

Abstract Rodríguez-Jiménez, S, Benitez, A, González, MÁG, Feliu, GM, and Maffiuletti, NA. The influence of preset frequency, loading condition, and exercise type on the mechanical behavior of a novel vibratory bar. J Strength Cond Res 28(4): 982–989, 2014—This study aimed to analyze the influence of different vibration frequencies, loading conditions, and exercise types on the mechanical behavior of a novel vibratory bar (VB). Fourteen healthy men were asked to hold the VB during lying row (pulling) and bench press (pushing) static exercise as steadily as possible for 10 seconds with loads of 20, 50, and 80% of the maximum sustained load (MSL) and at preset vibration frequencies (fin) of 20, 35, and 50 Hz. Root mean square vibration acceleration (aRMS), peak-to-peak displacement (D), and frequency (fout) were gained from a 3-dimensional accelerometer fixed to the VB. Increasing vibration frequency (from 20 to 50 Hz) resulted in a progressive and sizeable increase in VB aRMS and fout (both p ⩽ 0.001) with smaller variations of D (⩽5.9%, p ⩽ 0.001). Adding weight to the VB (progressive overload from 20 to 80% MSL) did not affect D and minimally altered aRMS (<4.2%, p = 0.014) and fout (<1.7%, p = 0.002). Altering the type of exercise (pulling vs. pushing) did not affect VB aRMS, D, and fout. In conclusion, this study establishes the validity of a novel VB and legitimates its use for effective and safe upper-body static exercise with a wide range of vibration frequencies and loading conditions in the context of physical training or rehabilitation.

Study of sleep stages by controlled inducement and measurement of drowsiness related biomedical signals.

This paper describes the development of a laboratory experiment related with drowsiness in order to find patterns in biomedical signals that allow to distinguish the periods of drowsiness, contrasted with sleep phases obtained by EEG analysis and current methods used to detect the fall of attention in drivers.