Human gait is mainly related to the foot and leg movements but, obviously, the entire motor system of the human body is involved. We hypothesise that movement parameters such as dynamic balance, movement harmony of each body element (arms, head, thorax…) could enable us to finely characterise gait singularities to pinpoint potential diseases or abnormalities in advance. Since this paper deals with the preliminary problem pertaining to the classification of normal and abnormal gait, our study will revolve around the lower part of the body. Our proposal presents a functional specification of gait in which only observational kinematic aspects are discussed. The resultant specification will confidently be open enough to be applied to a variety of gait analysis problems encountered in areas connected to rehabilitation, sports, children's motor skills, and so on. To carry out our functional specification, we develop an extraction system through which we analyse image sequences to identify gait features. Our prototype not only readily lets us determine the dynamic parameters (heel strike, toe off, stride length and time) and some skeleton joints but also satisfactorily supplies us with a proper distinction between normal and abnormal gait. We have performed experiments on a dataset of 30 samples.

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Frailty and senility are syndromes that affect elderly people. e ageing process involves a decay of cognitive and motor functions which often produce an impact on the quality of life of elderly people. Some studies have linked this deterioration of cognitive and motor function to gait patterns. us, gait analysis can be a powerful tool to assess frailty and senility syndromes. In this paper, we propose a vision-based gait analysis approach performed on a smartphone with cloud computing assistance. Gait sequences recorded by a smartphone camera are processed by the smartphone itself to obtain spatiotemporal features. ese features are uploaded onto the cloud in order to analyse and compare them to a stored database to render a diagnostic. e feature extraction method presented can work with both frontal and sagittal gait sequences although the sagittal view provides a better classification since an accuracy of 95% can be obtained.

Advances in embedded electronic systems, the development of new communication protocols, and the application of artificial intelligence paradigms have enabled the improvement of current automation systems of energy management. Embedded devices integrate different sensors with connectivity, computing resources, and reduced cost. Communication and cloud services increase their performance; however, there are limitations in the implementation of these technologies. If the cloud is used as the main source of services and resources, overload problems will occur. There are no models that facilitate the complete integration and interoperability in the facilities already created. This article proposes a model for the integration of smart energy management systems in new and already created facilities, using local embedded devices, Internet of Things communication protocols and services based on artificial intelligence paradigms. All services are distributed in the new smart grid network using edge and fog computing techniques. The model proposes an architecture both to be used as support for the development of smart services and for energy management control systems adapted to the installation: a group of buildings and/or houses that shares energy management and energy generation. Machine learning to predict consumption and energy generation, electric load classification, energy distribution control, and predictive maintenance are the main utilities integrated. As an experimental case, a facility that incorporates wind and solar generation is used for development and testing. Smart grid facilities, designed with artificial intelligence algorithms, implemented with Internet of Things protocols, and embedded control devices facilitate the development, cost reduction, and the integration of new services. In this work, a method to design, develop, and install smart services in self-consumption facilities is proposed. New smart services with reduced costs are installed and tested, confirming the advantages of the proposed model.

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