The number of older people needing healthcare is a growing global phenomenon. The assistance in long-term care comprises a complex of medical, nursing, rehabilitation, and social assistance services. The cost is substantial, but technology can help reduce spending by ensuring efficient health services and improving the quality of life. Advances in artificial intelligence, wireless communication systems, and nanotechnology allow the creation of intelligent home care systems avoiding hospitalization with evident cost containment. They are capable of ensuring functions of recognition of activities, monitoring of vital functions, and tracking. However, it is essential to also have information on location in order to be able to promptly intervene in case of unforeseen events or assist people in carrying out activities in order to avoid incorrect behavior. In addition, the automatic detection of physical activities performed by human subjects is identified as human activity recognition (HAR). This work presents an overview of the positioning system as part of an integrated HAR system. Lastly, this study contains each technology’s concepts, features, accuracy, advantages, and limitations. With this work, we want to highlight the relationship between HAR and the indoor positioning system (IPS), which is poorly documented in the literature.

Developments in the medical and technological fields have led to a longer life expectancy

The daily use of devices generating electric and magnetic fields has led to potential human overexposure in home and work environments. This paper assesses the possible effects of electric fields on human health at low and high frequencies. It presents an electronic monitoring device that captures the incidence of specific absorption rate (SAR) and temperature variation (∆T) on the human body. The system transmits data to a cloud platform, where a feedforward neural network (FFNN) processes the received information. SAR and surface temperature values are detected in an indoor environment, monitoring stationary and moving subjects. The results effectively assess temperature distribution due to electromagnetic fields. The prototype detected temperature peaks and high SAR values when the subjects remained motionless. Predictive analysis confirms the need for workplaces with materials shielding external electromagnetic signals and attenuating internal sources. Moderate mobile phone use could lower SAR and temperature values.

A five-line (5L) photonic crystal waveguide (PCW) capable of realizing compact, optical buffering and low-distortion data interconnection of picosecond pulse is presented and studied through plane-wave expansion and finite-difference time-domain methods. By appropriately modifying the radii of defects in the 5L PCW, high buffering performance and low-distortion slow light transmission of picosecond pulses are achieved, which are guaranteed by an optimized value of normalized delay bandwidth product (NDBP) of 0.6811 for the proposed PCW. The largest buffering storage capacity and bandwidth obtained are about 219.73 bit and 19.55 nm, respectively. It is found that the distortion of pulse transmission in a PCW is greatly affected by the waveguide length. With these optimized parameters, a relative pulse distortion per unit length of 1.15×10 ^-4 μm ^-1 is obtained for a 2.19-ps pulse, meaning that for a signal pulse with a dutyfactor of 0.5, the proposed structure can process optical digital pulse signals at the speed of 0.22831 Tb/s. Moreover, the NDBP value of 0.6811 obtained is much higher than that of all other structures based on waveguides reported in the literature previously so that high performance of the proposed PCW can be achieved. The waveguide proposed is suitable for data interconnections among chips or among processing units within a chip in optical signal processing, and computations where the size of the transmission line or interconnector is highly limited.

The aging population has created a significant challenge affecting the world; social and healthcare systems need to ensure elderly individuals receive the necessary care services to improve their quality of life and maintain their independence. In response to this need, developing integrated digital solutions, such as IoT based wearable devices combined with artificial intelligence applications, offers a technological platform for creating Ambient Intelligence (AI) and Assisted Living (AAL) environments. These advancements can help reduce hospital admissions and lower healthcare costs. In this context, this article presents an IoT application based on MEMS (micro electro-mechanical systems) sensors integrated into a state-of-the-art microcontroller (STM55WB) for recognizing the movements of older individuals during daily activities. human activity recognition (HAR) is a field within computational engineering that focuses on automatically classifying human actions through data captured by sensors. This study has multiple objectives: to recognize movements such as grasping, leg flexion, circular arm movements, and walking in order to assess the motor skills of older individuals. The implemented system allows these movements to be detected in real time, and transmitted to a monitoring system server, where healthcare staff can analyze the data. The analysis methods employed include machine learning algorithms to identify movement patterns, statistical analysis to assess the frequency and quality of movements, and data visualization to track changes over time. These approaches enable the accurate assessment of older people’s motor skills, and facilitate the prompt identification of abnormal situations or emergencies. Additionally, a user-friendly technological solution is designed to be acceptable to the elderly, minimizing discomfort and stress associated with using technology. Finally, the goal is to ensure that the system is energy-efficient and cost-effective, promoting sustainable adoption. The results obtained are promising; the model achieved a high level of accuracy in recognizing specific movements, thus contributing to a precise assessment of the motor skills of the elderly. Notably, movement recognition was accomplished using an artificial intelligence model called Random Forest.