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.

Towards the Simulation of a Realistic Large-Scale Spiking Network on a Desktop Multi-GPU System.

The reproduction of the brain 'sactivity and its functionality is the main goal of modern neuroscience. To this aim, several models have been proposed to describe the activity of single neurons at different levels of detail. Then, single neurons are linked together to build a network, in order to reproduce complex behaviors. In the literature, different network-building rules and models have been described, targeting realistic distributions and connections of the neurons. In particular, the Granular layEr Simulator (GES) performs the granular layer network reconstruction considering biologically realistic rules to connect the neurons. Moreover, it simulates the network considering the Hodgkin-Huxley model. The work proposed in this paper adopts the network reconstruction model of GES and proposes a simulation module based on Leaky Integrate and Fire (LIF) model. This simulator targets the reproduction of the activity of large scale networks, exploiting the GPU technology to reduce the processing times. Experimental results show that a multi-GPU system reduces the simulation of a network with more than 1.8 million neurons from approximately 54 to 13 h.

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.

The lack of 4-hydroxynonenal in otosclerotic bone tissue in Ethiopian population.

In Ethiopians, like in other Africans, the incidence of otosclerosis is lower than in Western and Asian populations. Unfortunately, due to the lack of available otorhinolaryngology specialists many patients are not treated and suffer the progression of the disease and severe hearing loss. This program of the Global ENT Outreach Organization (GEO) together with the Ethiopian partners was done to help some of these patients and in parallel to evaluate the presence of the oxidative stress bioactive marker 4-hydroxynonenal (HNE), which is known as major lipid peroxidation product and the second messenger of free radicals, in the otosclerotic bone specimens. Namely, we described recently that as HNE acts as a bone growth regulator associated with pathogenesis of otosclerosis. The prospective study conducted at the ENT Department of the Migbare Senay General Hospital, Addis Ababa, Ethiopia in June 2012, under the auspices of the Global ENT Outreach Organization, USA. Altogether 36 patients (male = 12, female = 24) underwent surgery due to the previous otosclerosis diagnosis based on the clinical and audiometric findings. The bone samples were harvested from patients with intraoperatively confirmed otosclerosis diagnosis. Immunohistochemistry for HNE-modified proteins was carried out on formalin-fixed paraffin-embedded specimens. The presence of HNE was found in almost all bone samples analyzed, without particular difference in the HNE distribution pattern between the otosclerotic and respective control bone specimens. Although there was no significant association between the HNE appearance and otosclerotic bone outgrowth observed, several cases have shown tendency of higher HNE expression in patients with more severe hearing loss. The results of the present study are in contrast with our previous findings obtained on European patients most likely due to the differences between studied population groups.