Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions.

Solar energy has been one of the accessible and affordable renewable energy technologies for the last few decades. Photovoltaics and solar thermal collectors are mature technologies to harness solar energy. However, the efficiency of photovoltaics decays at increased operating temperatures, and solar thermal collectors suffer from low exergy. Furthermore, along with several financial, structural, technical and socio-cultural barriers, the limited shadow-free space on building rooftops has significantly affected the adoption of solar energy. Thus, Photovoltaic Thermal (PVT) collectors that combine the advantages of photovoltaic cells and solar thermal collector into a single system have been developed. This study gives an extensive review of different PVT systems for residential applications, their performance indicators, progress, limitations and research opportunities. The literature review indicated that PVT systems used air, water, bi-fluids, nanofluids, refrigerants and phase-change material as the cooling medium and are sometimes integrated with heat pumps and seasonal energy storage. The overall efficiency of a PVT system reached up to 81% depending upon the system design and environmental conditions, and there is generally a trade-off between thermal and electrical efficiency. The review also highlights future research prospects in areas such as materials for PVT collector design, long-term reliability experiments, multi-objective design optimisation, techno-exergo-economics and photovoltaic recycling.

It is known that parameter selection for data sampling frequency and segmentation techniques (including different methods and window sizes) has an impact on the classification accuracy. For Ambient Assisted Living (AAL), no clear information to select these parameters exists, hence a wide variety and inconsistency across today’s literature is observed. This paper presents the empirical investigation of different data sampling rates, segmentation techniques and segmentation window sizes and their effect on the accuracy of Activity of Daily Living (ADL) event classification and computational load for two different accelerometer sensor datasets. The study is conducted using an ANalysis Of VAriance (ANOVA) based on 32 different window sizes, three different segmentation algorithm (with and without overlap, totaling in six different parameters) and six sampling frequencies for nine common classification algorithms. The classification accuracy is based on a feature vector consisting of Root Mean Square (RMS), Mean, Signal Magnitude Area (SMA), Signal Vector Magnitude (here SMV), Energy, Entropy, FFTPeak, Standard Deviation (STD). The results are presented alongside recommendations for the parameter selection on the basis of the best performing parameter combinations that are identified by means of the corresponding Pareto curve.

Phase change material (PCM) based passive cooling of photovoltaics (PV) can be highly productive due to high latent heat capacity. However, the low rate of heat transfer limits its usefulness. Thus, the presented work aims at the improvement in PV cooling by using finned PCM (FPCM) heat sinks. In the present study, PCM heat sink and FPCM heat sinks were investigated numerically for PV cooling and the extracted heat is used for space heating. 4 kWp PV, PV-PCM and PV-FPCM systems were studied under the weather conditions of Southeast of England. It was observed that the PCM heat sinks can drop the peak PV temperature by 13 K, whereas FPCM heat sinks can enhance the PV cooling by 19 K. The PCM heat sinks can increase the PV electrical efficiency from 13% to 14%. Moreover, the daily electricity generation can be boosted by 7% using PCM and 8% by using FPCM heat sinks. In addition, 7 kWh of thermal output was achieved using the FPCM heat sink, and the overall efficiency of system increased from 13% to 19%.