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This study presents a novel solution for ambient assisted living (AAL) applications that utilizes spiking neural networks (SNNs) and reconfigurable neuromorphic processors. As demographic shifts result in an increased need for eldercare, due to a large elderly population that favors independence, there is a pressing need for efficient solutions. Traditional deep neural networks (DNNs) are typically energy-intensive and computationally demanding. In contrast, this study turns to SNNs, which are more energy-efficient and mimic biological neural processes, offering a viable alternative to DNNs. We propose asynchronous cellular automaton-based neurons (ACANs), which stand out for their hardware-efficient design and ability to reproduce complex neural behaviors. By utilizing the remote supervised method ( R e S u M e ), this study improves spike train learning efficiency in SNNs. We apply this to movement recognition in an elderly population, using motion capture data. Our results highlight a high classification accuracy of 83.4 % , demonstrating the approach's efficacy in precise movement activity classification. This method's significant advantage lies in its potential for real-time, energy-efficient processing in AAL environments. Our findings not only demonstrate SNNs' superiority over conventional DNNs in computational efficiency but also pave the way for practical neuromorphic computing applications in eldercare. [ABSTRACT FROM AUTHOR]

This study validated muscle activation estimations generated by OpenSim during the gait of elderly fallers. Ten healthy elderly participants walked on an instrumented treadmill, monitored by motion capture, force platforms, and 12 surface EMG sensors. Static optimization was used to calculate muscle activations, evaluated through cosine similarity, comparing them with EMG signals from 12 muscles of the right leg. Findings revealed varied similarity levels across muscles and gait phases. During stance phase, tibialis anterior (TIBA), peroneus longus (PERL), soleus (SOL), gastrocnemius lateralis (GASL), semitendinosus (SEMI), tensor fasciae latae (TFL), and rectus femoris (RECF) demonstrated poor similarity (cosim < 0.6), while gluteus medius (GMED), biceps femoris long head (BFLH), and vastus lateralis (VL) exhibited moderate similarity (0.6 ≤ cosim ≤ 0.8), and gluteus maximus (GMAX) and vastus medialis (VASM) displayed high similarity (cosim > 0.8). During the swing phase, only SOL demonstrated inadequate similarity, while GASL, GMAX, GMED, BFLH, SEMI, TFL, RECF, and VASL exhibited moderate similarity, and TIBA, PERL, and VASM showed high similarity. Comparing the different 10% intervals of the gait cycle generally produced more favorable similarity results. For most of the muscles and intervals, good agreement was found. Moderate agreement was estimated in the cases of TIBA (0–10%), PERL (60–70%), GASL (60–70%), TFL (10–20%), RECF (0–10%, 80–100%), and GMED (50–60%). Bad agreement was found in the cases of SOL (60–70%), GASL (0–10%), and TFL (0–10%). In conclusion, the study's validation outcomes were acceptable in most cases, underlining the potential for user-friendly musculoskeletal modeling routines to study muscle output during elderly gait. [ABSTRACT FROM AUTHOR]

An estimation of the electric sources in the heart was conducted using a novel method, based on Huygens' Principle, aiming at a direct estimation of equivalent bioelectric sources over the heart's surface in real time. The main scope of this work was to establish a new, fast approach to the solution of the inverse electrocardiography problem. The study was based on recorded electrocardiograms (ECGs). Based on Huygens' Principle, measurements obtained from the surfaceof a patient's thorax were interpolated over the surface of the employed volume conductor model and considered as secondary Huygens' sources. These sources, being non-zero only over the surface under study, were employed to determine the weighting factors of the eigenfunctions' expansion, describing the generated voltage distribution over the whole conductor volume. With the availability of the potential distribution stemming from measurements, the electromagnetics reciprocity theorem is applied once again to yield the equivalent sources over the pericardium. The methodology is self-validated, since the surface potentials calculated from these equivalent sources are in very good agreement with ECG measurements. The ultimate aim of this effort is to create a tool providing the equivalent epicardial voltage or current sources in real time, i.e., during the ECG measurements with multiple electrodes. [ABSTRACT FROM AUTHOR]

The development of novel hardware computing systems and methods has been a topic of increased interest for researchers worldwide. New materials, devices, and architectures are being explored as a means to deliver more efficient solutions to contemporary issues. Along with the advancement of technology, there is a continuous increase in methods available to address significant challenges. However, the increased needs to be fulfilled have also led to problems of increasing complexity that require better and faster computing and processing capabilities. Moreover, there is a wide range of problems in several applications that cannot be addressed using the currently available methods and tools. As a consequence, the need for emerging and more efficient computing methods is of utmost importance and constitutes a topic of active research. Among several proposed solutions, we distinguish the development of a novel nanoelectronic device, called a "memristor", that can be utilized both for storing and processing, and thus it has emerged as a promising circuit element for the design of compact and energy-efficient circuits and systems. The memristor has been proposed for a wide range of applications. However, in this work, we focus on its use in computing architectures based on the concept of Cellular Automata. The combination of the memristor's performance characteristics with Cellular Automata has boosted further the concept of processing and storing information on the same physical units of a system, which has been extensively studied in the literature as it provides a very good candidate for the implementation of Cellular Automata computing with increased potential and improved characteristics, compared to traditional hardware implementations. In this context, this paper reviews the most recent advancements toward the development of Cellular-Automata-based computing coupled with memristor devices. Several approaches for the design of such novel architectures, called "Memristive Cellular Automata", exist in the literature. This extensive review provides a thorough insight into the most important developments so far, helping the reader to grasp all the necessary information, which is here presented in an organized and structured manner. Thus, this article aims to pave the way for further development in the field and to bring attention to technological aspects that require further investigation. [ABSTRACT FROM AUTHOR]

Unconventional and, specifically, wave computing has been repeatedly studied in laboratory based experiments by utilizing chemical systems like a thin film of Belousov–Zhabotinsky (BZ) reactions. Nonetheless, the principles demonstrated by this chemical computer were mimicked by mathematical models to enhance the understanding of these systems and enable a more detailed investigation of their capacity. As expected, the computerized counterparts of the laboratory based experiments are faster and less expensive. A further step of acceleration in wave-based computing is the development of electrical circuits that imitate the dynamics of chemical computers. A key component of the electrical circuits is the memristor which facilitates the non-linear behavior of the chemical systems. As part of this concept, the road-map of the inspiration from wave-based computing on chemical media towards the implementation of equivalent systems on oscillating memristive circuits was studied here. For illustration reasons, the most straightforward example was demonstrated, namely the approximation of Boolean gates. [ABSTRACT FROM AUTHOR]

State-of-the-art IoT technologies request novel design solutions in edge computing, resulting in even more portable and energy-efficient hardware for in-the-field processing tasks. Vision sensors, processors, and hardware accelerators are among the most demanding IoT applications. Resistance switching (RS) two-terminal devices are suitable for resistive RAMs (RRAM), a promising technology to realize storage class memories. Furthermore, due to their memristive nature, RRAMs are appropriate candidates for in-memory computing architectures. Recently, we demonstrated a CMOS compatible silicon nitride (SiNx) MIS RS device with memristive properties. In this paper, a report on a new photodiode-based vision sensor architecture with in-memory computing capability, relying on memristive device, is disclosed. In this context, the resistance switching dynamics of our memristive device were measured and a data-fitted behavioral model was extracted. SPICE simulations were made highlighting the in-memory computing capabilities of the proposed photodiode-one memristor pixel vision sensor. Finally, an integration and manufacturing perspective was discussed. [ABSTRACT FROM AUTHOR]

The quick growth of information technology has necessitated the need for developing novel electronic devices capable of performing novel neuromorphic computations with low power consumption and a high degree of accuracy. In order to achieve this goal, it is of vital importance to devise artificial neural networks with inherent capabilities of emulating various synaptic properties that play a key role in the learning procedures. Along these lines, we report here the direct impact of a dense layer of Pt nanoparticles that plays the role of the bottom electrode, on the manifestation of the bipolar switching effect within SiO2-based conductive bridge memories. Valuable insights regarding the influence of the thermal conductivity value of the bottom electrode on the conducting filament growth mechanism are provided through the application of a numerical model. The implementation of an intermediate switching transition slope during the SET transition permits the emulation of various artificial synaptic functionalities, such as short-term plasticity, including paired-pulsed facilitation and paired-pulse depression, long-term plasticity and four different types of spike-dependent plasticity. Our approach provides valuable insights toward the development of multifunctional synaptic elements that operate with low power consumption and exhibit biological-like behavior. [ABSTRACT FROM AUTHOR]