Ambient assisted living for frail people through human activity recognition: state-of-the-art, challenges and future directions

Ambient Assisted Living is a concept that focuses on using technology to support and enhance the quality of life and well-being of frail or elderly individuals in both indoor and outdoor environments. It aims at empowering individuals to maintain their independence and autonomy while ensuring their safety and providing assistance when needed. Human Activity Recognition is widely regarded as the most popular methodology within the field of Ambient Assisted Living. Human Activity Recognition involves automatically detecting and classifying the activities performed by individuals using sensor-based systems. Researchers have employed various methodologies, utilizing wearable and/or non-wearable sensors, and employing algorithms ranging from simple threshold-based techniques to more advanced deep learning approaches. In this review, literature from the past decade is critically examined, specifically exploring the technological aspects of Human Activity Recognition in Ambient Assisted Living. An exhaustive analysis of the methodologies adopted, highlighting their strengths and weaknesses is provided. Finally, challenges encountered in the field of Human Activity Recognition for Ambient Assisted Living are thoroughly discussed. These challenges encompass issues related to data collection, model training, real-time performance, generalizability, and user acceptance. Miniaturization, unobtrusiveness, energy harvesting and communication efficiency will be the crucial factors for new wearable solutions.

Accelerating the K-Nearest Neighbors Filtering Algorithm to Optimize the Real-Time Classification of Human Brain Tumor in Hyperspectral Images

The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of information captured by the sensors. One of the stages with higher computational load is the K-Nearest Neighbors (KNN) filtering algorithm. The main goal of this study is to optimize and parallelize the KNN algorithm by exploiting the GPU technology to obtain real-time processing during brain cancer surgical procedures. This parallel version of the KNN performs the neighbor filtering of a classification map (obtained from a supervised classifier), evaluating the different classes simultaneously. The undertaken optimizations and the computational capabilities of the GPU device throw a speedup up to 66.18× when compared to a sequential implementation.

Custom FPGA processing for real-time fetal ECG extraction and identification

Monitoring the fetal cardiac activity during pregnancy is of crucial importance for evaluating fetus health. However, there is a lack of automatic and reliable methods for Fetal ECG (FECG) monitoring that can perform this elaboration in real-time. In this paper, we present a hardware architecture, implemented on the Altera Stratix V FPGA, capable of separating the FECG from the maternal ECG and to correctly identify it. We evaluated our system using both synthetic and real tracks acquired from patients beyond the 20th pregnancy week. This work is part of a project aiming at developing a portable system for FECG continuous real-time monitoring. Its characteristics of reduced power consumption, real-time processing capability and reduced size make it suitable to be embedded in the overall system, that is the first proposed exploiting Blind Source Separation with this technology, to the best of our knowledge.

TPF versus PF as induction therapy in unresectable head and neck cancer: A pharmacoeconomic analysis

6075

Background: Adding docetaxel to induction therapy with PF has been shown to improve overall survival of patients with head and neck cancer. On the basis of the raw data from the EORTC trial 24971/TAX 323 a pharmacoeconomic analysis has been performed. A National Healthcare System perspective and a 5-years temporal horizon have been adopted. Methods: A decision-analytic approach has been adopted, and a Markov model has been used to represent patient's weekly transitions among different health states, related to treatment (chemotherapy, radiotherapy, surgeries, palliative care) and disease status (complete/partial/no response, progression, recurrence). Constant or time-variant costs and quality of life adjustment factors have been assigned to each health state, together with transition probabilities to other states. A 3% discount factor has been used for costs sustained after the first year. The model allowed calculating expected life years (LYs), quality adjusted life years (QALYs) and costs. From these figures, incremental cost/effectiveness (ICER) and cost/utility (ICUR) ratios have been calculated. Robustness of results obtained with baseline values has been tested with probabilistic multivariate sensitivity analysis. Results: In the base case analysis, treatment with TPF yields an expectancy of 2.38 LYs and 1.53 QALYs at a cost of Euro (E) 23,018, which for PF were 1.93 LYs, 1.11 QALYs, and E 19,567, respectively. ICER and ICUR were E 7,669/LY and E 8,217/QALY, respectively. Sensitivity analyses were performed varying transition probabilities of 10%, costs of 30% and utility scores of 20% of their basal values. The analyses showed that 86% and 84% of the results lie below the threshold of E 50,000/LY and E 50,000/QALY, respectively. Conclusions: In our analysis TPF induction chemotherapy proved to be cost-effective with respect to PF, having an ICER and ICUR comparable to other widely accepted healthcare interventions.

[Table: see text]

Flow Rate Profiler: an instrument to measure blood velocity profiles

In this paper we present Flow Rate Profiler (FRP), an instrument for measuring the blood velocity by means of ultrasound-based techniques. The velocity is directly related to the shear rate, which is in turn proportional to the shear stress, a parameter expressing the pressure exerted by the blood on the vessel walls. The knowledge of this value is important in medicine to establish the state of the vessels, directly related to vascular diseases. FRP provides a non-invasive measure of the blood velocity by exploiting the red corpuscles property of diffusing ultrasound waves: in practice blood velocity is determined by a cross-correlation technique, which analyses the time shift between correlated subsequent echo waves, instead of frequency shift characteristic of the Doppler technique. The acquired data are then processed on a personal computer by means of mathematical techniques based on the evaluation of the correlation function, giving a reconstructed velocity profile and showing a good adherence with experimental data, since the average error is nearly the 10%. The reconstructed profile is displayed to the operator, who can follow the vessel status in real time. A few comparisons between the reconstructed and the experimental profiles are also presented, together with a study on a small set of patients suffering from artery hypertension.

Rat liver mitochondria can synthesize nicotinamide adenine dinucleotide from nicotinamide mononucleotide and ATP via a putative matrix nicotinamide mononucleotide adenylyltransferase

In order to gain some insight into the mechanism by which nicotinamide nucleotides localize in mitochondria, NMN was added to rat liver mitochondria, with NAD synthesis tested both enzymatically and by means of HPLC. Evidence is given that the mitochondrial matrix contains a specific NMN adenylyltransferase (E.C. 2.7.7.1.), inhibited by PPi, AMP and ADP-ribose. Some features of this enzyme, including the substrate, pH and temperature dependence were also investigated.

[Evaluation of force, velocity and power during muscular exercise]

Authors propose an original method for studying muscular hypotrophy considering mechanical output. Tests was performed on femoral quadriceps in order to have standardized parameters about work of normal, pathologic and retrained muscle.