ABSTRACT Alzheimer's Disease (AD) is a progressive neurodegenerative disease diagnosed through cognitive impairment, and an early diagnosis is essential to improve treatment and care options. Current diagnostic approaches of AD, such as neuroimaging, cognitive assessments and biomarker research, are lengthy, vague and not sufficient to assess the early stages of AD. To address these problems, we introduced a novel deep learning model, ‘NeuroMixFormer’, which is based on a mixture‐of‐experts architecture for AD classification from MRI. The proposed multistage architecture employs a dynamic routing mechanism and four expert blocks per stage, each integrating dense connectivity with a spatial and channel attention module for feature extraction. To improve early feature learning, auxiliary classifiers are incorporated at intermediate stages of training. Evaluations on three datasets (ADNI, Mendeley and Kaggle Augmented Alzheimer's MRI) demonstrated the proposed model's superior performance over existing deep learning architectures and state‐of‐the‐art methods, achieving up to 99.48% accuracy on the Kaggle dataset, 90.28% on the Mendeley dataset and 99.86% on the ADNI dataset, respectively. Ablation studies confirmed the importance of dual‐attention mechanisms, and expert routing analysis showed clear specialisation patterns across AD stages, improving both classification accuracy and interpretability. These results underscore the effectiveness and generalisability of NeuroMixFormer in automated dementia detection, highlighting its potential to support early and precise AD diagnosis. However, the high computational cost and inference time associated with this high accuracy limit the practicality of the proposed approach in clinical settings.

Abstract Recent studies have suggested ways to enhance user perception of remote workspaces and robot autonomy, often at the expense of system suitability and efficiency. This research introduces a novel method, Distributed Supervisory Control (DSC), leveraging Virtual Reality (VR) to enhance teleoperation. The DSC method intelligently distributes tasks between the robot and the human operator, minimizing shared autonomy conflicts and sensory data transfer. It uses Deep Reinforcement Learning (DRL) through a Twin Delayed Deep Deterministic Policy Gradient (TD3) method for tasks like obstacle avoidance. Real-time experimentation validated the method’s effectiveness through performance metrics, including NASA-TLX, task execution time, and obstacle collision frequency. Kruskal-Wallis tests identified significant differences in task execution times and collision frequency across DSC, Direct Control (DC), and Assistive Direct Control (ADC). Dunn’s post-hoc tests indicated DSC significantly outperformed DC and ADC in both execution time and collision frequency. Similarly, NASA-TLX scores for effort, mental demand, performance, frustration, physical demand, and temporal demand also showed significant differences ( p < 0.05), supporting DSC’s lower task load. User case studies revealed enhanced user experience, as measured by the System Usability Scale (SUS) and Igroup Presence (IPQ) questionnaires for immersive experience. The non-parametric Kruskal-Wallis test was utilized to verify significant differences between group medians, followed by a Dunn’s test that revealed significant performance improvements with our Distributed Supervisory Control (DSC) method relative to Direct Control (DC) and Assistive Direct Control (ADC), thereby enhancing task efficiency and overall interface suitability. In conclusion, Distributed Supervisory Control significantly enhances task efficiency and user experience in robot teleoperation environments, demonstrating its potential as a new standard for remote operations.

Underwater Wireless Sensor Networks (UWSNs) contain several components such as vehicles and sensors that are deployed in a specific acoustic area to perform collaborative monitoring and data collection tasks. These networks are used interactively between different nodes and ground-based stations. Presently, UWSNs face issues and challenges regarding limited bandwidth, high propagation delay, 3D topology, media access control, routing, resource utilization, and power constraints. In the last few decades, research community provided different methodologies to overcome these issues and challenges; however, some of them are still open for research due to variable characteristics of underwater environment. In this paper, a survey of UWSN regarding underwater communication channel, environmental factors, localization, media access control, routing protocols, and effect of packet size on communication is conducted. We compared presently available methodologies and discussed their pros and cons to highlight new directions of research for further improvement in underwater sensor networks.

Two parameters that a breathing status monitor should provide include tidal volume (VT ) and respiration rate (RR). Recently, we implemented an optical monitoring approach that tracks chest wall movements directly on a smartphone. In this paper, we explore the use of such noncontact optical monitoring to obtain a volumetric surrogate signal, via analysis of intensity changes in the video channels caused by the chest wall movements during breathing, in order to provide not only average RR but also information about VT and to track RR at each time instant (IRR). The algorithm, implemented on an Android smartphone, is used to analyze the video information from the smartphone's camera and provide in real time the chest movement signal from N = 15 healthy volunteers, each breathing at VT ranging from 300 mL to 3 L. These measurements are performed separately for each volunteer. Simultaneous recording of volume signals from a spirometer is regarded as reference. A highly linear relationship between peak-to-peak amplitude of the smartphone-acquired chest movement signal and spirometer VT is found (r2= 0.951 ± 0.042, mean ± SD). After calibration on a subject-by-subject basis, no statistically significant bias is found in terms of VT estimation; the 95% limits of agreement are -0.348 to 0.376 L, and the rootmean-square error (RMSE) was 0.182 ± 0.107 L. In terms of IRR estimation, a highly linear relation between smartphone estimates and the spirometer reference was found (r2= 0.999 ± 0.002). The bias, 95% limits of agreement, and RMSE are -0.024 breaths-per-minute (bpm), -0.850 to 0.802 bpm, and 0.414 ±0.178 bpm, respectively. These promising results show the feasibility of developing an inexpensive and portable breathing monitor, which could provide information about IRR as well as VT, when calibrated on an individual basis, using smartphones. Further studies are required to enable practical implementation of the proposed approach.

This paper proposes accurate respiratory rate estimation using nasal breath sound recordings from a smartphone. Specifically, the proposed method detects nasal airflow using a built-in smartphone microphone or a headset microphone placed underneath the nose. In addition, we also examined if tracheal breath sounds recorded by the built-in microphone of a smartphone placed on the paralaryngeal space can also be used to estimate different respiratory rates ranging from as low as 6 breaths/min to as high as 90 breaths/min. The true breathing rates were measured using inductance plethysmography bands placed around the chest and the abdomen of the subject. Inspiration and expiration were detected by averaging the power of nasal breath sounds. We investigated the suitability of using the smartphone-acquired breath sounds for respiratory rate estimation using two different spectral analyses of the sound envelope signals: The Welch periodogram and the autoregressive spectrum. To evaluate the performance of the proposed methods, data were collected from ten healthy subjects. For the breathing range studied (6-90 breaths/min), experimental results showed that our approach achieves an excellent performance accuracy for the nasal sound as the median errors were less than 1% for all breathing ranges. The tracheal sound, however, resulted in poor estimates of the respiratory rates using either spectral method. For both nasal and tracheal sounds, significant estimation outliers resulted for high breathing rates when subjects had nasal congestion, which often resulted in the doubling of the respiratory rates. Finally, we show that respiratory rates from the nasal sound can be accurately estimated even if a smartphone's microphone is as far as 30 cm away from the nose.

In this article, we present a wearable intelligence device for activity monitoring applications. We developed and evaluated algorithms to recognize physical activities from data acquired using a 3-axis accelerometer with a single camera worn on a body. The recognition process is performed in two steps: at first the features for defining a human activity are measured by the 3-axis accelerometer sensor and the image sensor embedded in a wearable device. Then, the physical activity corresponding to the measured features is determined by applying the SVM classifier. The 3-axis accelerometer sensor computes the correlation between axes and the magnitude of the FFT for other features of an activity. Acceleration data is classified into nine activity labels. Through the image sensor, multiple optical flow vectors computed on each grid image patch are extracted as features for defining an activity. In the experiments, we showed that an overall accuracy rate of activity recognition based our method was 92.78%.

This paper presents a child activity recognition approach using a single 3-axis accelerometer and a barometric pressure sensor worn on a waist of the body to prevent child accidents such as unintentional injuries at home. Labeled accelerometer data are collected from children of both sexes up to the age of 16 to 29 months. To recognize daily activities, mean, standard deviation, and slope of time-domain features are calculated over sliding windows. In addition, the FFT analysis is adopted to extract frequency-domain features of the aggregated data, and then energy and correlation of acceleration data are calculated. Child activities are classified into 11 daily activities which are wiggling, rolling, standing still, standing up, sitting down, walking, toddling, crawling, climbing up, climbing down, and stopping. The overall accuracy of activity recognition was 98.43% using only a single- wearable triaxial accelerometer sensor and a barometric pressure sensor with a support vector machine.

The authors present an approach that extracts human activity patterns of daily living and represents spatiotemporal relations between activities intuitively. In general, customised services are provided based on activity patterns of users. This study focuses on extracting and determining activities that occur simultaneously. In order to determine simultaneous activities, the authors analysed the daily activities that are collected from device applications such as location sensors and electronics. In addition, a context model using the incremental statistical method is organised and temporal relations between the activities patterns are analysed. Furthermore, information visualisation of the spatiotemporal topology with duration and frequency is demonstrated. Also, the authors have experimented on a test-bed called the ubiquitous smart space and compared the accuracy of the incremental statistical method with that of the non-incremental method.

We address a new inherent limitation of potential field methods, which is symmetrically aligned robot-obstacle-goal (SAROG). The SAROG involves one critical risk of local minima trap. For dealing with the problem, we investigate the way how the local minima trap is recognized, and present our random force algorithm. The force algorithm has two categories of random unit total force (RUTF) and random unit total force with repulsion removal (RUTF-RR) which are selected based on the conditions of a robot, an obstacle and a goal.

In this paper, we present an approach for providing dynamic object association and identification in heterogeneous sensor networks where identification sensors have coverage uncertainty. Detection uncertainty of identifications by the coverage uncertainty is managed by grouping unassociated identifications. In the system, visual sensors find corresponding objects between cameras by using homographic lines and track them by using multi-camera localization scheme. Identification sensors (i.e., RFID system, fingerprint or iris recognition system) are incorporated into the tracking system for objects identification. This paper elaborates possible identification cases and necessary conditions with the coverage uncertainty of identification sensors. Finally, the proposed association method is evaluated with a realistic simulation.

This demonstration presents a content-based leaf image retrieval system that supports wired/wireless access. For example, if we want to know about a plant that we encounter in a mountain or field, we might look it up in an illustrated book. But, it will take a long time to search due to the lack of appropriate indexing or search clues and huge amounts of similar plants. In order to solve this problem, we developed a content-based leaf image retrieval system called mCLOVER that supports both wired and wireless access and includes a set of novel features for easy querying and efficient retrieval.