The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.

The massive deployment of smart meters and other customized meters has motivated the development of nonintrusive load monitoring (NILM) systems. This is the process of disaggregating the total energy consumption in a building into individual electrical loads using a single-point sensor. Most literature is oriented to energy saving. Nevertheless, activity of daily livings monitoring through NILM is recently receiving much interest. This proposal presents an event-based NILM algorithm of high performance for activity monitoring applications. This is divided into two stages: 1) an event detector and 2) an event classification algorithm. The first one does not need to be trained and shows a detection rate up to 94%. The event classification algorithm uses a novel load signature based on trajectories of active, reactive, and distortion power (PQD) to obtain general models of appliance classes using principal component analysis. The F1 score and the F0.5 score (the last one is more relevant to activity monitoring) draw values of 90.6% and 98.5, respectively.

Acoustic local positioning systems (ALPSs) are an interesting alternative for indoor positioning due to certain advantages over other approaches, including their relatively high accuracy, low cost, and room-level signal propagation. Centimeter-level or fine-grained indoor positioning can be an asset for robot navigation, guiding a person to, for instance, a particular piece in a museum or to a specific product in a shop, targeted advertising, or augmented reality. In airborne system applications, acoustic positioning can be based on using opportunistic signals or sounds produced by the person or object to be located (e.g., noise from appliances or the speech from a speaker) or from encoded emission beacons (or anchors) specifically designed for this purpose. This work presents a review of the different challenges that designers of systems based on encoded emission beacons must address in order to achieve suitable performance. At low-level processing, the waveform design (coding and modulation) and the processing of the received signal are key factors to address such drawbacks as multipath propagation, multiple-access interference, near-far effect, or Doppler shifting. With regards to high-level system design, the issues to be addressed are related to the distribution of beacons, ease of deployment, and calibration and positioning algorithms, including the possible fusion of information obtained from maps and onboard sensors. Apart from theoretical discussions, this work also includes the description of an ALPS that was implemented, installed in a large area and tested for mobile robot navigation. In addition to practical interest for real applications, airborne ALPSs can also be used as an excellent platform to test complex algorithms (taking advantage of the low sampling frequency required), which can be subsequently adapted for other positioning systems, such as underwater acoustic systems or ultrawideband radio-frequency (UWB RF) systems.

Ultra-Wideband (UWB) has a high interest in research and industry for accurate indoor positioning. This technology comprises signals with a bandwidth of at least 500 MHz at a power decay of -10dB. This large bandwidth conferes the capability of resolving multipath, penetrating through obstacles and accurate ranging. However, NLOS (Non-Line-Of-Sight) conditions produced by obstacles in indoor environments severely degrade performance, as they introduce a positive bias in ranging estimation. In this paper we present a robust UWB indoor positioning which is able to accurately operate in a highly complex indoor scenario, where NLOS condition is predominant. For that purpose, the system uses a NLOS detection algorithm based on the skewness of the estimated channel impulse response and it mitigates NLOS by using an Extended Kalman Filter. The use of this detection/mitigation algorithm shows an improvement of the RMSE in positioning about 67%.

The ageing of the population, and their increasing wish of living independently, are motivating the development of welfare and healthcare models. Existing approaches based on the direct heath-monitoring using body sensor networks (BSN) are precise and accurate. Nonetheless, their intrusiveness causes non-acceptance. New approaches seek the indirect monitoring through monitoring activities of daily living (ADLs), which proves to be a suitable solution. ADL monitoring systems use many heterogeneous sensors, are less intrusive, and are less expensive than BSN, however, the deployment and maintenance of wireless sensor networks (WSN) prevent them from a widespread acceptance. In this work, a novel technique to monitor the human activity, based on non-intrusive load monitoring (NILM), is presented. The proposal uses only smart meter data, which leads to minimum intrusiveness and a potential massive deployment at minimal cost. This could be the key to develop sustainable healthcare models for smart homes, capable of complying with the elderly people’ demands. This study also uses the Dempster-Shafer theory to provide a daily score of normality with regard to the regular behavior. This approach has been evaluated using real datasets and, additionally, a benchmarking against a Gaussian mixture model approach is presented.