Virtual Obstacle Avoidance Strategy: Navigating through a Complex Environment While Interacting with Virtual and Physical Elements.

Robotic walking devices can be used for intensive exercises to enhance gait rehabilitation therapies. Mixed Reality (MR) techniques may improve engagement through immersive and interactive environments. This article introduces an MR-based multimodal human-robot interaction strategy designed to enable shared control with a Smart Walker. The MR system integrates virtual and physical sensors to (i) enhance safe navigation and (ii) facilitate intuitive mobility training in personalized virtual scenarios by using an interface with three elements: an arrow to indicate where to go, laser lines to indicate nearby obstacles, and an ellipse to show the activation zone. The multimodal interaction is context-based; the presence of nearby individuals and obstacles modulates the robot's behavior during navigation to simplify collision avoidance while allowing for proper social navigation. An experiment was conducted to evaluate the proposed strategy and the self-explanatory nature of the interface. The volunteers were divided into four groups, with each navigating under different conditions. Three evaluation methods were employed: task performance, self-assessment, and observational measurement. Analysis revealed that participants enjoyed the MR system and understood most of the interface elements without prior explanation. Regarding the interface, volunteers who did not receive any introductory explanation about the interface elements were mostly able to guess their purpose. Volunteers that interacted with the interface in the first session provided more correct answers. In future research, virtual elements will be integrated with the physical environment to enhance user safety during navigation, and the control strategy will be improved to consider both physical and virtual obstacles.

Lure Monitoring for Mediterranean Fruit Fly Traps Using Air Quality Sensors.

Effective pest population monitoring is crucial in precision agriculture, which integrates various technologies and data analysis techniques for enhanced decision-making. This study introduces a novel approach for monitoring lures in traps targeting the Mediterranean fruit fly, utilizing air quality sensors to detect total volatile organic compounds (TVOC) and equivalent carbon dioxide (eCO2). Our results indicate that air quality sensors, specifically the SGP30 and ENS160 models, can reliably detect the presence of lures, reducing the need for frequent physical trap inspections and associated maintenance costs. The ENS160 sensor demonstrated superior performance, with stable detection capabilities at a predefined distance from the lure, suggesting its potential for integration into smart trap designs. This is the first study to apply TVOC and eCO2 sensors in this context, paving the way for more efficient and cost-effective pest monitoring solutions in smart agriculture environments.

"She has become my best friend": a qualitative study on the perspective of elderly with type 2 diabetes regarding the use of an interactive virtual assistant device for diabetes care and mental health promotion.

AIMS: To qualitatively evaluate the experiences and emotional responses of elderly individuals with type 2 diabetes regarding the use of an interactive virtual assistant device. METHODS AND RESULTS: This qualitative study included elderly individuals who were diagnosed with type 2 diabetes and who had been using the Smart Speaker EchoDot 3rd Gen (Amazon Echo®) device for three months. A structured face-to-face interview with open-ended questions was conducted to evaluate their experiences and emotional responses associated with the device. Data analysis was performed using inductive thematic content analysis with deductive coding followed by narrative synthesis to present the overall perceptions of the participants. Thirty individuals with a mean diabetes duration of 17.1 ± 9.45 years and a mean age of 71.9 ± 5.1 years were interviewed to ensure saturation of responses. Three major themes were identified through response analysis: (1) Emotional response to user experience; (2) Humanization feelings in human-device interactions; (3) Diabetes-related self-care. Overall, participants experienced a wide range of feelings regarding the use of the interactive virtual assistant device, predominantly with positive connotations, highlighting aspects of humanization of technology and its use, and experiencing assistance in self-care related to diabetes. CONCLUSION: Our results highlight the overwhelmingly positive emotional responses and strong sense of humanization expressed by elderly individuals with diabetes toward an interactive virtual assistant device. This underscores its potential to improve mental health and diabetes care, although further studies are warranted to fully explore its impact.

Automated classification of mixed populations of Aedes aegypti and Culex quinquefasciatus mosquitoes under field conditions.

BACKGROUND: The recent rise in the transmission of mosquito-borne diseases such as dengue virus (DENV), Zika (ZIKV), chikungunya (CHIKV), Oropouche (OROV), and West Nile (WNV) is a major concern for public health managers worldwide. Emerging technologies for automated remote mosquito classification can be supplemented to improve surveillance systems and provide valuable information regarding mosquito vector catches in real time. METHODS: We coupled an optical sensor to the entrance of a standard mosquito suction trap (BG-Mosquitaire) to record 9151 insect flights in two Brazilian cities: Rio de Janeiro and Brasilia. The traps and sensors remained in the field for approximately 1 year. A total of 1383 mosquito flights were recorded from the target species: Aedes aegypti and Culex quinquefasciatus. Mosquito classification was based on previous models developed and trained using European populations of Aedes albopictus and Culex pipiens. RESULTS: The VECTRACK sensor was able to discriminate the target mosquitoes (Aedes and Culex genera) from non-target insects with an accuracy of 99.8%. Considering only mosquito vectors, the classification between Aedes and Culex achieved an accuracy of 93.7%. The sex classification worked better for Cx. quinquefasciatus (accuracy: 95%; specificity: 95.3%) than for Ae. aegypti (accuracy: 92.1%; specificity: 88.4%). CONCLUSIONS: The data reported herein show high accuracy, sensitivity, specificity and precision of an automated optical sensor in classifying target mosquito species, genus and sex. Similar results were obtained in two different Brazilian cities, suggesting high reliability of our findings. Surprisingly, the model developed for European populations of Ae. albopictus worked well for Brazilian Ae. aegypti populations, and the model developed and trained for Cx. pipiens was able to classify Brazilian Cx. quinquefasciatus populations. Our findings suggest this optical sensor can be integrated into mosquito surveillance methods and generate accurate automatic real-time monitoring of medically relevant mosquito species.

Analysis of Gait Kinematics in Smart Walker-Assisted Locomotion in Immersive Virtual Reality Scenario.

The decline in neuromusculoskeletal capabilities of older adults can affect motor control, independence, and locomotion. Because the elderly population is increasing worldwide, assisting independent mobility and improving rehabilitation therapies has become a priority. The combination of rehabilitation robotic devices and virtual reality (VR) tools can be used in gait training to improve clinical outcomes, motivation, and treatment adherence. Nevertheless, VR tools may be associated with cybersickness and changes in gait kinematics. This paper analyzes the gait parameters of fourteen elderly participants across three experimental tasks: free walking (FW), smart walker-assisted gait (AW), and smart walker-assisted gait combined with VR assistance (VRAW). The kinematic parameters of both lower limbs were captured by a 3D wearable motion capture system. This research aims at assessing the kinematic adaptations when using a smart walker and how the integration between this robotic device and the VR tool can influence such adaptations. Additionally, cybersickness symptoms were investigated using a questionnaire for virtual rehabilitation systems after the VRAW task. The experimental data indicate significant differences between FW and both AW and VRAW. Specifically, there was an overall reduction in sagittal motion of 16%, 25%, and 38% in the hip, knee, and ankle, respectively, for both AW and VRAW compared to FW. However, no significant differences between the AW and VRAW kinematic parameters and no adverse symptoms related to VR were identified. These results indicate that VR technology can be used in walker-assisted gait rehabilitation without compromising kinematic performance and presenting potential benefits related to motivation and treatment adherence.

IoT-Based Wireless System for Gait Kinetics Monitoring in Multi-Device Therapeutic Interventions.

This study presents an IoT-based gait analysis system employing insole pressure sensors to assess gait kinetics. The system integrates piezoresistive sensors within a left foot insole, with data acquisition managed using an ESP32 board that communicates via Wi-Fi through an MQTT IoT framework. In this initial protocol study, we conducted a comparative analysis using the Zeno system, supported by PKMAS as the gold standard, to explore the correlation and agreement of data obtained from the insole system. Four volunteers (two males and two females, aged 24-28, without gait disorders) participated by walking along a 10 m Zeno system path, equipped with pressure sensors, while wearing the insole system. Vertical ground reaction force (vGRF) data were collected over four gait cycles. The preliminary results indicated a strong positive correlation (r = 0.87) between the insole and the reference system measurements. A Bland-Altman analysis further demonstrated a mean difference of approximately (0.011) between the two systems, suggesting a minimal yet significant bias. These findings suggest that piezoresistive sensors may offer a promising and cost-effective solution for gait disorder assessment and monitoring. However, operational factors such as high temperatures and sensor placement within the footwear can introduce noise or unwanted signal activation. The communication framework proved functional and reliable during this protocol, with plans for future expansion to multi-device applications. It is important to note that additional validation studies with larger sample sizes are required to confirm the system's reliability and robustness for clinical and research applications.

Fulgide Derivatives as Photo-Switchable Coatings for Cathodes of Lithium Ion Batteries - A DFT Study.

Photo-switchable coatings for lithium ion batteries (LIB) can offer the possibility to control the diffusion processes from the electrode materials to the electrolyte and thus, for example, reducing the energy loss in the fully charged state. Fulgide derivatives, as known photo-switches, are investigated concerning their use as coating for vanadium pentoxide, a potential cathode material for LIB. With the help of Density Functional Theory calculations, two fulgide derivatives are characterized with respect to their photophysics, their aggregation behaviour on the cathode material and the ability to form self-assembled monolayers (SAM). Furthermore, the two states of the photo-switchable coating are tested with respect to lithium diffusion from the cathode material, passing the SAM and entering the electrolyte. We found a difference for the energy barriers depending on the state of the photo-switch, preferring its closed form. This behaviour can be used to prevent the loss of charge in batteries of portable devices.

The effect of self-care education with smart phone applications on the severity of nausea and vomiting after stem cell transplantation in leukemia patients.

INTRODUCTION: Common side effects after stem cell transplantation (SCT), such as anorexia, nausea, and vomiting, can disrupt the quality of life of patients. Therefore, this study aimed to determine the effect of self-care education with smart phone applications on the severity of nausea and vomiting after SCT in leukemia patients. MATERIALS AND METHODS: In this clinical trial study, using the blocked randomization method 104 leukemia patients undergoing SCT were assigned to two groups, intervention and control. The patients of the Control Group received routine care, and the Intervention Group received self-care education with a smart mobile phone application, in addition to routine care. Two weeks, one month, and three months after the start of the intervention, the severity of nausea and vomiting was evaluated using the visual analog scale (VAS) and the Khavar Oncology scale, both of which were completed by both Control and Intervention Groups. Data were analyzed using chi-square, Fisher's exact, Mann-Whitney, and Friedman tests using the Statistical Package for Social Sciences version 25 software. RESULTS: The severity of nausea and vomiting in leukemia patients undergoing SCT was significantly different in the two groups at all three timepoints (two weeks, one month, and three months) after transplantation (p-value = 0.000). CONCLUSION: The severity of nausea and vomiting after SCT in leukemia patients was improved by self-care education with a smart phone application. Therefore, this method is recommended to reduce the severity of nausea and vomiting in leukemia patients who undergo transplantation.

Advances in Light-Responsive Smart Multifunctional Nanofibers: Implications for Targeted Drug Delivery and Cancer Therapy.

Over the last decade, scientists have shifted their focus to the development of smart carriers for the delivery of chemotherapeutics in order to overcome the problems associated with traditional chemotherapy, such as poor aqueous solubility and bioavailability, low selectivity and targeting specificity, off-target drug side effects, and damage to surrounding healthy tissues. Nanofiber-based drug delivery systems have recently emerged as a promising drug delivery system in cancer therapy owing to their unique structural and functional properties, including tunable interconnected porosity, a high surface-to-volume ratio associated with high entrapment efficiency and drug loading capacity, and high mass transport properties, which allow for controlled and targeted drug delivery. In addition, they are biocompatible, biodegradable, and capable of surface functionalization, allowing for target-specific delivery and drug release. One of the most common fiber production methods is electrospinning, even though the relatively two-dimensional (2D) tightly packed fiber structures and low production rates have limited its performance. Forcespinning is an alternative spinning technology that generates high-throughput, continuous polymeric nanofibers with 3D structures. Unlike electrospinning, forcespinning generates fibers by centrifugal forces rather than electrostatic forces, resulting in significantly higher fiber production. The functionalization of nanocarriers on nanofibers can result in smart nanofibers with anticancer capabilities that can be activated by external stimuli, such as light. This review addresses current trends and potential applications of light-responsive and dual-stimuli-responsive electro- and forcespun smart nanofibers in cancer therapy, with a particular emphasis on functionalizing nanofiber surfaces and developing nano-in-nanofiber emerging delivery systems for dual-controlled drug release and high-precision tumor targeting. In addition, the progress and prospective diagnostic and therapeutic applications of light-responsive and dual-stimuli-responsive smart nanofibers are discussed in the context of combination cancer therapy.

Smart Spare Parts (SSP) in the Context of Industry 4.0: A Systematic Review.

The implementation of Industry 4.0 has integrated manufacturing, electronics, and engineering materials, leading to the creation of smart parts (SPs) that provide information on production system conditions. However, SP development faces challenges due to limitations in manufacturing processes and integrating electronic components. This systematic review synthesizes scientific articles on SP fabrication using additive manufacturing (AM), identifying the advantages and disadvantages of AM techniques in SP production and distinguishing between SPs and smart spare parts (SSPs). The methodology involves establishing a reference framework, formulating SP-related questions, and applying inclusion criteria and keywords, initially resulting in 1603 articles. After applying exclusion criteria, 70 articles remained. The results show that while SP development is advancing, widespread application of AM-manufactured SP is recent. SPs can anticipate production system failures, minimize design artifacts, and reduce manufacturing costs. Furthermore, the review highlights that SSPs, a subcategory of SPs, primarily differs by replacing conventional critical parts in the industry, offering enhanced functionality and reliability in industrial applications. The study concludes that continued research and development in this field is essential for further advancements and broader adoption of these technologies.

Open collaborative smart plugs for energy management.

Given the growth of domotics and home automation, there is a need to use smart devices that integrate energy management systems and enable the automation of the environment. Considering the need to study the relationship between the environmental parameters in which the equipment is located and the energy parameters, an Environmental Awareness smart Plug (EnAPlug) is proposed with the application of machine learning (Tiny ML).This article presents a demonstration of EnAPlug applied to a refrigerator for predictions on internal humidity and activation motor for 5 min-ahead prediction on its operation, i.e., turning on or off. The two models for forecasting humidity presented Root Mean Squared Error (RMSE) results of 0.055 and 0.058 and a Coefficient of determination (r2 score) of 0.97 and 0.99, respectively. For the motor activation prediction, the results obtained were an accuracy of 94.74% and 94.84%, an F1 score of 0.97 for OFF, 0.94 for ON for Forecast 1 and 0.97 for OFF and 0.93 for ON for Forecast 2. Although the prototype does not have commercial purposes, what differs from existing smart plugs is the option to store data locally. The results are promising, as it allows for better energy management with implementation of machine learning.

A Brazilian native bee (Tetragonisca angustula) dataset for computer vision.

Jataí is a pollinator of some crops; therefore, its sustainable management guarantees quality in the ecosystem services provided and implementation in precision agriculture. We acquired videos of natural and artificial hives in urban and rural environments with a camera positioned at the hive entrance. In this way, we obtained videos of the entrance of several colonies for multiple bee tracking and removed images from the videos for bee detectors. This data, their respective labels, and metadata make up the dataset. The dataset displays potential for utilization in computer vision tasks such as comparative studies of deep learning models. They can also integrate intelligent monitoring systems for natural and artificial hives.

OnionFoliageSET: Labeled dataset for small onion and foliage flower crop detection.

Digital image datasets for Precision Agriculture (PA) still need to be available. Many problems in this field of science have been studied to find solutions, such as detecting weeds, counting fruits and trees, and detecting diseases and pests, among others. One of the main fields of research in PA is detecting different crop types with aerial images. Crop detection is vital in PA to establish crop inventories, planting areas, and crop yields and to have information available for food markets and public entities that provide technical help to small farmers. This work proposes public access to a digital image dataset for detecting green onion and foliage flower crops located in the rural area of Medellín City - Colombia. This dataset consists of 245 images with their respective labels: green onion (Allium fistulosum), foliage flowers (Solidago Canadensis and Aster divaricatus), and non-crop areas prepared for planting. A total of 4315 instances were obtained, which were divided into subsets for training, validation, and testing. The classes in the images were labeled with the polygon method, which allows training machine learning algorithms for detection using bounding boxes or segmentation in the COCO format.

A Systematic Review and Meta-analysis of Mobile Health Applications and Telemonitoring in Atopic Dermatitis Self-Management.

INTRODUCTION: Up to 25% of children and 5.6% of adults in the USA have atopic dermatitis (AD), with substantial impacts on quality of life. Effective control can be challenging despite therapy efforts. The emergence of information and communication technologies (ICT) in AD management prompted this study to assess its impact on self-management. We conducted a meta-analysis to assess outcomes from peer-reviewed clinical trials evaluating the effectiveness of teledermatology, mobile health (mHealth) apps, and electronic devices for managing AD. METHODS: We searched PubMed, Web of Science, Scopus, and Embase for articles written in English and published until May 2023. RESULTS: Twelve trials with 2424 participants were selected from 811 studies. A meta-analysis of 1038 individuals reported a mean difference (MD) of -1.57 [95% confidence interval (CI): -2.24, -0.91] for the Patient Oriented Eczema Measure (POEM). A meta-analysis of 495 individuals reported a Dermatology Life Quality Index (DLQI) MD of -0.59 [95% CI: -0.95, -0.23]. Despite heterogeneity (I2 = 47% and I2 = 74%), the impact was significant (P ≤ 0.001). SCORing Atopic Dermatitis (SCORAD) showed an insignificant MD of -0.12 (P = 0.91). CONCLUSION: mHealth applications and telemonitoring show significant improvement in patients' quality of life (DLQI) and self-management (POEM) but no significant impact on AD severity (SCORAD).

Navigated augmented reality through a head-mounted display leads to low deviation between planned, intra- and postoperative parameters during glenoid component placement of reverse shoulder arthroplasty: a proof-of-concept case series.

BACKGROUND: Navigated augmented reality (AR) through a head-mounted display (HMD) has led to accurate glenoid component placement in reverse shoulder arthroplasty (RSA) in an in-vitro setting. The purpose of this study is to evaluate the deviation between planned, intraoperative, and postoperative inclination, retroversion, entry point, and depth of the glenoid component placement during RSA, assisted by navigated AR through an HMD, in a surgical setting. METHODS: A prospective, multicenter study was conducted. All consecutive patients undergoing RSA in 2 institutions, between August 2021 and January 2023, were considered potentially eligible for inclusion in the study. Inclusion criteria were as follows: age >18 years, surgery assisted by AR through an HMD, and postoperative computed tomography (CT) scans at 6 weeks. All participants agreed to participate in the study and informed consent was provided in all cases. Preoperative CT scans were undertaken for all cases and used for 3-dimensional (3D) planning. Intraoperatively, glenoid preparation and component placement were assisted by a navigated AR system through an HMD in all patients. Intraoperative parameters were recorded by the system. A postoperative CT scan was undertaken at 6 weeks, and 3D reconstruction was performed to obtain postoperative parameters. The deviation between planned, intraoperative, and postoperative inclination, retroversion, entry point, and depth of the glenoid component placement was calculated. Outliers were defined as >5° for inclination and retroversion and >5 mm for entry point. RESULTS: Seventeen patients (9 females, 12 right shoulders) with a mean age of 72.8 ± 9.1 years (range, 47.0-82.0) met inclusion criteria. The mean deviation between intra- and postoperative measurements was 1.5° ± 1.0° (range, 0.0°-3.0°) for inclination, 2.8° ± 1.5° (range, 1.0°-4.5°) for retroversion, 1.8 ± 1.0 mm (range, 0.7-3.0 mm) for entry point, and 1.9 ± 1.9 mm (range, 0.0-4.5 mm) for depth. The mean deviation between planned and postoperative values was 2.5° ± 3.2° (range, 0.0°-11.0°) for inclination, 3.4° ± 4.6° (range, 0.0°-18.0°) for retroversion, 2.0 ± 2.5 mm (range, 0.0°-9.7°) for entry point, and 1.3 ± 1.6 mm (range, 1.3-4.5 mm) for depth. There were no outliers between intra- and postoperative values and there were 3 outliers between planned and postoperative values. The mean time (minutes : seconds) for the tracker unit placement and the scapula registration was 03:02 (range, 01:48 to 04:26) and 08:16 (range, 02:09 to 17:58), respectively. CONCLUSION: The use of a navigated AR system through an HMD in RSA led to low deviations between planned, intraoperative, and postoperative parameters for glenoid component placement.

Hybrid crystalline bioparticles with nanochannels encapsulating acemannan from Aloe vera: Structure and interaction with lipid membranes.

Smart nanocarrier-based bioactive delivery systems are a current focus in nanomedicine for allowing and boosting diverse disease treatments. In this context, the design of hybrid lipid-polymer particles can provide structure-sensitive features for tailored, triggered, and stimuli-responsive devices. In this work, we introduce hybrid cubosomes that have been surface-modified with a complex of chitosan-N-arginine and alginate, making them pH-responsive. We achieved high-efficiency encapsulation of acemannan, a bioactive polysaccharide from Aloe vera, within the nanochannels of the bioparticle crystalline structure and demonstrated its controlled release under pH conditions mimicking the gastric and intestinal environments. Furthermore, an acemannan-induced phase transition from Im3m cubic symmetry to inverse hexagonal HII phase enhances the bioactive delivery by compressing the lattice spacing of the cubosome water nanochannels, facilitating the expulsion of the encapsulated solution. We also explored the bioparticle interaction with membranes of varying curvatures, revealing thermodynamically driven affinity towards high-curvature lipid membranes and inducing morphological transformations in giant unilamellar vesicles. These findings underscore the potential of these structure-responsive, membrane-active smart bioparticles for applications such as pH-triggered drug delivery platforms for the gastrointestinal tract, and as modulators and promoters of cellular internalization.

Smart Anklet Use to Measure Vascular Health Benefits of Preventive Intervention in a Nature-Based Environment-A Pilot Study.

The present study aimed to investigate the associations between nature-based intervention and peripheral pulse characteristics of patients with PAOD using new smart technology specifically designed for this purpose. A longitudinal panel study performed between 1 January 2022 and 31 December 2022 included 32 patients diagnosed with peripheral arterial occlusive disease (PAOD) who were treated in the vascular surgeons' hospital "Dobb" in Valjevo. These patients were exposed for six months to moderate-intensity physical activity (MPA) in a nature-based environment. They practiced 150 to 300 min of walking 6 km/h and cycling activities (16-20 km/h) weekly as recommended for patients with chronic conditions and those living with disability. Univariate logistic regression analysis was used to identify factors associated with major improvements in peripheral pulse characteristics of patients with PAOD. After six months of MPA, half of the patients (50%, 16/32) achieved minor, and half of them major improvements in peripheral pulse characteristics. The major improvements were associated with current smoking (OR = 9.53; 95%CI = 1.85-49.20), diabetes (OR = 4.84; 95%CI = 1.09-21.58) and cardiac failure, and concurrent pulmonary disease and diabetes (OR = 2.03; 95%CI = 1.01-4.11). Our pilot study showed that patients with PAOD along with other chronic conditions and risk factors benefited more from continuous physical activity in a nature-based environment.

A comprehensive dataset integrating household energy consumption and weather conditions in a north-eastern Mexican urban city.

The prediction of domestic electricity consumption is relevant because it helps to plan energy production, among many other benefits. In this work a dataset was collected from one house in an urban city of north-east of Mexico. An ad-hoc acquisition system was implemented to collect the data using a smart meter and the open weather API. The data was collected every minute over a period of 14 months since November 5, 2022, to January 5, 2024. The dataset contains 605,260 samples of 19 variables related with energy consumption and weather data. This dataset is specifically tailored for predicting domestic energy consumption and understanding consumption behaviours, filling a void in the existing literature where such datasets for Mexico are scarce. Moreover, the multivariate nature of the dataset allows researchers to investigate and propose new techniques for forecasting or pattern classification using multivariate data collected in a real scenario.

Semi-supervised ensemble learning for human activity recognition in casas Kyoto dataset.

-The automatic identification of human physical activities, commonly referred to as Human Activity Recognition (HAR), has garnered significant interest and application across various sectors, including entertainment, sports, and notably health. Within the realm of health, a myriad of applications exists, contingent upon the nature of experimentation, the activities under scrutiny, and the methodology employed for data and information acquisition. This diversity opens doors to multifaceted applications, including support for the well-being and safeguarding of elderly individuals afflicted with neurodegenerative diseases, especially in the context of smart homes. Within the existing literature, a multitude of datasets from both indoor and outdoor environments have surfaced, significantly contributing to the activity identification processes. One prominent dataset, the CASAS project developed by Washington State University (WSU) University, encompasses experiments conducted in indoor settings. This dataset facilitates the identification of a range of activities, such as cleaning, cooking, eating, washing hands, and even making phone calls. This article introduces a model founded on the principles of Semi-supervised Ensemble Learning, enabling the harnessing of the potential inherent in distance-based clustering analysis. This technique aids in the identification of distinct clusters, each encapsulating unique activity characteristics. These clusters serve as pivotal inputs for the subsequent classification process, which leverages supervised techniques. The outcomes of this approach exhibit great promise, as evidenced by the quality metrics' analysis, showcasing favorable results compared to the existing state-of-the-art methods. This integrated framework not only contributes to the field of HAR but also holds immense potential for enhancing the capabilities of smart homes and related applications.