The purpose of this study was to evaluate the sensitivity of 16 parameters derived from acceleration to detect changes caused by age and visual conditions during quiet standing and detect and minimise possible sources of unwanted variability that could affect accelerometer measures on the trunk. Twenty-seven healthy subjects, including 16 elderly (age, 69.3 ± 3.6 years) and 11 young (age, 23.6 ± 2.2 years) subjects, were evaluated. The parameters evaluated include root-mean-square values, fractal dimensions, path length, range, frequency dispersion and power spectrum among others derived from these values. These 16 parameters evaluated for each axis of movement and/or derivations resulted in 59 sub-parameters. These 59 sub-parameters were analysed in the elderly and young groups and under the open-eye and closed-eye conditions. The results showed that 30 sub-parameters detected differences for an age effect with open eyes, 18 detected differences with closed eyes, 25 detected differences for the young group standing with closed–open eyes and 37 detected differences for the elderly with closed and open eyes (p < 0.01). We used simple signal processing for the accelerometry signals to minimise the effects of unwanted variability that could affect the results. The results showed better performance compared with those results published previously using force platforms to evaluate postural sway. The results presented here should be useful for researchers who want to use accelerometry to evaluate steady postural balance.

The present study was performed to evaluate and characterize the potential of accelerometers and angular velocity sensors to detect and assess anticipatory postural adjustments (APAs) generated by the first step at the beginning of the gait. This paper proposes an algorithm to automatically detect certain parameters of APAs using only inertial sensors.

Solar energy harvesting using Photovoltaic (PV) systems is one of the most popular sources of renewable energy, however the main drawback of PV systems is their low conversion efficiency. An optimal system operation requires an efficient tracking of the Maximum Power Point (MPP), which represents the maximum energy that can be extracted from the PV panel. This paper presents a novel control approach for the Maximum Power Point Tracking (MPPT) based on the differential flatness property of the Boost converter, which is one of the most used converters in PV systems. The underlying idea of the proposed control approach is to use the classical flatness-based trajectory tracking control where a reference voltage will be defined in terms of the maximum power provided by the PV panel. The effectiveness of the proposed controller is assessed through numerical simulations and experimental tests. The results show that the controller based on differential flatness is capable of converging in less than 0.15 s and, compared with other MPPT techniques, such as Incremental Conductance and Perturb and Observe, it improves the response against sudden changes in load or weather conditions, reducing the ringing in the output of the system. Based on the results, it can be inferred that the new flatness-based controller represents an alternative to improve the MPPT in PV systems, especially when they are subject to sudden load or weather changes.

Background: The timely detection of fall risk or balance impairment in older adults is transcendental because, based on a reliable diagnosis, clinical actions can be taken to prevent accidents. This study presents a statistical model to estimate the fall risk from the center of pressure (CoP) data.Methods: This study is a cross-sectional analysis from a cohort of community-dwelling older adults aged 60 and over living in Mexico City. CoP balance assessments were conducted in 414 older adults (72.2% females) with a mean age of 70.23 ± 6.68, using a modified and previously validated Wii Balance Board (MWBB) platform. From this information, 78 CoP indexes were calculated and analyzed. Multiple logistic regression models were fitted in order to estimate the relationship between balance alteration and the CoP indexes and other covariables.Results: The CoP velocity index in the Antero-Posterior direction with open eyes (MVELAPOE) had the best value of area under the curve (AUC) to identify a balance alteration (0.714), and in the adjusted model, AUC was increased to 0.827. Older adults with their mean velocity higher than 14.24 mm/s had more risk of presenting a balance alteration than those below this value (OR(Odd Ratio) = 2.94,p<0.001, 95%C.I.(Confidence Interval) 1.68–5.15). Individuals with increased age and BMI were more likely to present a balance alteration (OR1.17,p<0.001, 95%C.I.1.12–1.23;OR1.17,p<0.001, 95%C.I.1.10–1.25). Contrary to what is reported in the literature, sex was not associated with presenting a balance alteration (p= 0.441, 95%C.I.0.70–2.27).Significance: The proposed model had a discriminatory capacity higher than those estimated by similar means and resources to this research and was implemented in an embedded standalone system which is low-cost, portable, and easy-to-use, ideal for non-laboratory environments. The authors recommend using this technology to support and complement the clinical tools to attend to the serious public health problem represented by falls in older adults.

We present a study on using a smart phone as a tool to evaluate anticipatory postural adjustments (APA) before the beginning of normal gait in healthy subjects. The results show a significantly lower amplitude of anticipatory postural adjustments in the mediolateral axis (ML) when subjects used the non-dominant leg for the first step compared with the amplitude when using the dominant leg. The use of smart phones as a tool for measuring APA enables more personalized and active healthcare for the population without the necessity of their buying or carrying other devices.