Monitoring Of Behavior Research Articles

Spectral Fingerprinting Coupled Machine Learning: An Optimized Tool for Cellular Phenotype Identification

Spectral fingerprinting has emerged as a powerful tool for unraveling intricate interactions between the intracellular environment and nanomaterials. This study leverages spectral fingerprinting to deepen our comprehension of the interplay between macrophages and nanotubes, utilizing their Near Infrared fluorescence to discern cellular characteristics. This study employs Raman microscopy to showcase significant differences in defect ratio and intracellular processing of nanotubes, among macrophage phenotypes. The near infrared features of DNA-SWCNTs serve as distinctive markers for identifying different macrophage phenotypes. Here we use a support vector machine learning model to accurately identify M1 and M2 macrophages, achieving an impressive accuracy of 95%. The implications of this research extend to the development of nanomaterial-based platforms for cellular identification, holding promise for potential applications in real time in-vivo cell differentiation monitoring.This study underlines the pivotal role of nanotubes in delineating the diverse micro-environments of distinct macrophage phenotypes for clinical research. It emphasizes the potential of short DNA sequences and machine learning in characterizing and classifying cell phenotypes, offering valuable insights into the intersection of nanotechnology and biomedical research.This innovative approach holds great promise in advancing our understanding of dynamic cellular processes, disease states, disease progression, and response to stimuli in-vivo. The platform’s sensitivity to minute changes in the Near Infrared spectrum positions it as a valuable tool for researchers and clinicians. It facilitates the real time monitoring of cellular behavior within living systems, offering insights that extend to both fundamental research and clinical applications. The implications of this research pave the way for future developments in nanotechnology and biomedicine, bridging the gap between spectral analysis and cellular dynamics for enhanced diagnostic and therapeutic interventions. Figure 1

Leveraging social computing for epidemic surveillance: A case study

Social media platforms have become a popular source of information for real-time monitoring of events and user behavior. In particular, Twitter provides invaluable information related to diseases and public health to build real-time disease surveillance systems. Effective use of such social media platforms for public health surveillance requires data-driven AI models which are hindered by the difficult, expensive, and time-consuming task of collecting high-quality and large-scale datasets. In this paper, we build and analyze the Epidemic TweetBank (EpiBank) dataset containing 271 million English tweets related to six epidemic-prone diseases COVID19, Flu, Hepatitis, Dengue, Malaria, and HIV/AIDs. For this purpose, we develop a tool of ESS-T (Epidemic Surveillance Study via Twitter) which collects tweets according to provided input parameters and keywords. Also, our tool assigns location to tweets with 95% accuracy value and performs analysis of collected tweets focusing on temporal distribution, spatial patterns, users, entities, sentiment, and misinformation. Leveraging ESS-T, we build two geo-tagged datasets of EpiBank-global and EpiBank-Pak containing 86 million tweets from 190 countries and 2.6 million tweets from Pakistan, respectively. Our spatial analysis of EpiBank-global for COVID19, Malaria, and Dengue indicates that our framework correctly identifies high-risk epidemic-prone countries according to World Health Organization (WHO) statistics.

Spatial Models of Solar and Terrestrial Radiation Budgets and Machine Learning: A Review

Currently, spatial modeling is of particular relevance as it enables the understanding of the patterns and spatial variability of an event, the monitoring and prediction of the spatial behavior of a variable, the optimization of resources, and the evaluation of the impacts of a phenomenon of interest. Research carried out recently on variables related to solar energy budgets has been of special relevance due to its applications and developments in machine learning (ML) and deep learning (DL). These algorithms are crucial to improve the efficiency, precision, and applicability of remote sensing, allowing greater decision making with more reliable and timely data. Thus, this work proposes a systematic and rigorous methodology for searching research articles about the latest advances and contributions related to the modeling of radiative energy budgets using novel techniques and algorithms in some of the most relevant international scientific databases (Scopus, ScienceDirect, ResearchGate). Search parameters were applied using tracking methods through various filters, specific classifiers, and Boolean operators. The results allowed for an analysis of search trends and citations in the last 5 years related to the topic of interest and the number of most relevant articles for this research, analyzed through specialized metrics and graphs. Additionally, this methodology was classified into four categories of importance for refined and articulated searches in this evaluation: (i) according to the applied interpolation methods, (ii) according to the remote sensors used, (iii) according to the applications in different fields of knowledge. As a relevant fact and with an essentially prospective purpose, a subchapter of this review was dedicated to the latest advances and developments applied to (iv) spatial modeling of terrestrial radiation through ML, this method being a tool that opens multiple alternatives for data processing and analysis in the development and achievement of objectives in the field of geotechnologies. A quantitative comparison was conducted on the predictive performance results between the classification/regression algorithms found in the studies explored in this review. The evaluation confirmed the existence of a persistent shortage of studies in recent years within the geotechnologies field, particularly concerning the comparison of spatial distribution modeling techniques developed and implemented through ML for incident solar and terrestrial radiation. Therefore, this work provides a synthesis and analysis of the most used and novel techniques in the modeling of solar energy budgets, their limitations, and biggest challenges.

Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges.

The proliferation of wearable technology enables the generation of vast amounts of sensor data, offering significant opportunities for advancements in health monitoring, activity recognition, and personalized medicine. However, the complexity and volume of these data present substantial challenges in data modeling and analysis, which have been addressed with approaches spanning time series modeling to deep learning techniques. The latest frontier in this domain is the adoption of large language models (LLMs), such as GPT-4 and Llama, for data analysis, modeling, understanding, and human behavior monitoring through the lens of wearable sensor data. This survey explores the current trends and challenges in applying LLMs for sensor-based human activity recognition and behavior modeling. We discuss the nature of wearable sensor data, the capabilities and limitations of LLMs in modeling them, and their integration with traditional machine learning techniques. We also identify key challenges, including data quality, computational requirements, interpretability, and privacy concerns. By examining case studies and successful applications, we highlight the potential of LLMs in enhancing the analysis and interpretation of wearable sensor data. Finally, we propose future directions for research, emphasizing the need for improved preprocessing techniques, more efficient and scalable models, and interdisciplinary collaboration. This survey aims to provide a comprehensive overview of the intersection between wearable sensor data and LLMs, offering insights into the current state and future prospects of this emerging field.

Multiphysics framework for cost-effective, long-term monitoring of carbon storage in a carbonate reservoir

We evaluate an effective and cost-efficient multiphysics approach for long-term monitoring of carbon storage in carbonate reservoirs. A key focus of our study is to advocate for a cost-effective, long-term monitoring strategy for carbon storage projects through the integration of surface and borehole multiphysics measurements. By leveraging technologies related to electromagnetic (EM) and gravity methods, we present a framework that addresses the monitoring of the dynamic behavior of injected CO2 over an extended period. The modeling setup integrates realistic static reservoir models with simulated dynamic saturation models to provide results as close as possible to real experimental conditions. Results demonstrate the high sensitivity of EM fields to changes in reservoir CO2 saturation, especially when surface-to-reservoir acquisition configurations are adopted. Additionally, gravity simulations, employing three-axis vector surface gravity measurements and borehole vector gravity, effectively capture density variations over time. Our proposed framework highlights the sequential use of seismic imaging for preinjection site characterization and the transition to EM and gravity surveys for continuous monitoring during and after the injection phases. The multiphysics simulation results performed on realistic carbon storage conditions contribute to the evolving landscape of effective geophysical CO2 monitoring technology for reliable long-term reservoir management.

Impacts of heat stress on the accuracy of an ear-tag accelerometer for monitoring rumination and eating behavior in dairy-beef cross cattle using an automated gold standard

Accelerometer-based technologies can be utilized for precision monitoring of feeding behaviors, but limited information is available regarding the impact of varying environmental conditions on sensor performance. The objective of this study was to determine if a commercially available ear-tag sensor (CM; CowManager SensOor, Agis Automatisering BV) could accurately quantify eating and rumination time under heat stress conditions. Data obtained from CM sensors was compared with data collected using an automated gold standard (RW; Rumiwatch System; Itin+Hoch). Automated measurements were obtained from 2 experiments in which cattle were exposed to heat stress conditions. In the principal study (Experiment 1), 3428 h of data were collected from 9 Holstein × Angus steers (470.9 ± 23.9 kg) subjected to either thermoneutral (TN; 21.0°C; 64.0% humidity; temperature-humidity index [THI] = 67; 12- and 12-h light and dark cycle; n = 1714 h), or heat stress conditions (HS; cyclical daily temperatures to mimic diurnal patterns; 0800 - 2000 h: 33.6°C, 40.0% RH, THI: 83.5; 2000 - 0800 h: 23.2°C, 70.0% RH; THI: 70.3; n = 1714 h). Data (n = 719 h) from 6 Holstein x Angus steers (487.9 ± 9.1 kg) were obtained from a subsequent experiment (Experiment 2) to confirm consistency of ear-tag accelerometer performance under elevated THI (HS conditions as described above). In Experiment 1, CM was capable of quantifying rumination time with high accuracy under TN conditions (concordance correlation coefficient [CCC]: 0.75 - 0.81). Overall, agreement between CM and the automated gold standard declined 6 - 7% during HS, which was most apparent later in the day when cattle had been subjected to HS for multiple hours (moderate agreement; CCC: 0.68). Accuracy for rumination time was also only moderate for data collected during Experiment 2 (CCC: 0.55 - 0.61). In contrast, CM reported total eating (eating with the head down + head up while masticating) time with moderate accuracy for TN (CCC: 0.53 - 0.54), only achieved negligible to low accuracy during HS (CCC: 0.39 - 0.44 [Experiment 1] and 0.17 - 0.34 [Experiment 2]). Sensor performance did improve when CM eating time was compared specifically to the time spent with the head down reported by RW; HS still negatively influenced sensor performance, however, with high agreement during TN (CCC: 0.72 - 0.73) but low to moderate agreement during HS (CCC: 0.65 - 0.69 [Experiment 1] and 0.40 - 0.58 [Experiment 2]). Results of this study suggest accuracy of ear-tag accelerometers may be impaired when cattle are subjected to heat stress.

Dynamic Monitoring of Rail Behavior under Passenger Train Loading Using Distributed Fiber Optic Sensors

Dynamic Monitoring of Rail Behavior under Passenger Train Loading Using Distributed Fiber Optic Sensors

Millisecond-scale behaviours of plankton quantified invitro and insitu using the Event-based Vision Sensor.

The Event-based Vision Sensor (EVS) is a bio-inspired sensor that captures detailed motions of objects, aiming to become the 'eyes' of machines like self-driving cars. Compared to conventional frame-based image sensors, the EVS has an extremely fast motion capture equivalent to 10,000-fps even with standard optical settings, plus high dynamic ranges for brightness and also lower consumption of memory and energy. Here, we developed 22 characteristic features for analysing the motions of aquatic particles from the EVS raw data and tested the applicability of the EVS in analysing plankton behaviour. Laboratory cultures of six species of zooplankton and phytoplankton were observed, confirming species-specific motion periodicities up to 41 Hz. We applied machine learning to automatically classify particles into four categories of zooplankton and passive particles, achieving an accuracy up to 86%. At the insitu deployment of the EVS at the bottom of Lake Biwa, several particles exhibiting distinct cumulative trajectory with periodicities in their motion (up to 16 Hz) were identified, suggesting that they were living organisms with rhythmic behaviour. We also used the EVS in the deep sea, observing particles with active motion and periodicities over 40 Hz. Our application of the EVS, especially focusing on its millisecond-scale temporal resolution and wide dynamic range, provides a new avenue to investigate organismal behaviour characterised by rapid and periodical motions. The EVS will likely be applicable in the near future for the automated monitoring of plankton behaviour by edge computing on autonomous floats, as well as quantifying rapid cellular-level activities under microscopy.

Testing Smoker Detection Using Google Cloud Services and Infrastructure

Smoking remains a significant public health challenge globally, contributing to a wide range of detrimental health outcomes including cardiovascular diseases, cancer, and respiratory disorders. Despite concerted efforts to curb smoking rates through policy interventions, effective monitoring and enforcement remain complex and resource-intensive tasks for health authorities and organizations. Innovative approaches leveraging advanced technologies such as visual detection systems powered by deep learning offer promising solutions to enhance smoking behavior detection and monitoring. Integrating the Google Cloud Vision API enables real-time identification of smoking indicators and discrimination from complex visual backgrounds. This capability not only supports proactive health monitoring but also strengthens the enforcement of public health policies aimed at reducing smoking prevalence. The research methodology utilizes a dataset of 600 images sourced from the Kaggle platform, encompassing diverse scenarios to optimize model training. Techniques such as image segmentation, feature extraction, and machine learning-based classification are employed to achieve high levels of precision and recall in identifying smokers and cigarette smoke. Despite the advantages of scalability, robust infrastructure, and high availability facilitated by cloud computing, the study acknowledges challenges such as bandwidth constraints and security risks associated with handling sensitive health data. Nevertheless, technological innovations in visual detection systems and cloud services are underscored as pivotal in mitigating the health impacts of smoking and advancing public health initiatives.

EFFICACY OF VIRTUAL REALITY EXPOSURE THERAPY FOR THE TREATMENT OF POST TRAUMATIC STRESS DISORDER IN ADULTS

The emergence of new technologies like enhanced immersion and realism providing realistic stimulations, biometric sensor integration having physiological monitoring like heart rate monitors and conductance sensors which allow the observation and monitoring of patients behaviour and their responses and combining the reality with VR technology have altogether made the Virtual Reality Exposure Therapy more effective for the treatment of phobias and Post Traumatic Stress Disorder [1]. The efficacy of VRET in treatment of PTSDcan be observed through various studies conducted on war veterans, general population and people suffering from stressful traumatic memories of the past.Astudy was conducted to assess the treatment ofPTSD through VRET in the survivors of 9/11 attacks revealed significant reductions in symptoms with large effect size (d=1.50).

Big Data in online education

From classifying learners to predicting learner behaviour, the application of Big Data in online education has been vast. Besides the potential benefits of Big Data in education, it is necessary to critically engage with some ethical and social challenges that Big Data presents to the field of online learning. The increasing use of big data by large institutional actors and corporations raises questions not only about data privacy and ownership, but whether this data is used to genuinely improve learner and teacher online learning experiences, or primarily for commercial profits and institutional benefits. When addressing ethical concerns regarding the use of Big Data in education, critiques often follow a reasoning that is in line with corporate interests and neoliberal logic of marketization of education. Given the importance of the pursuit for democratic online education, the need for critical perspectives in the field is ever-more essential. This research tries to critically address the role and impact of Big Data on labour relations and economic fairness in online education by examining both corporate and institutional data practices in online learning. The study puts forward a provisional theory of the use of Big Data in two large online learning platforms (Coursera and Blackboard) using critical grounded theory. The core category of Exploitation of the learning community, the three constituent concepts; the Vendor-Institutional Complex, Use of learner generated value for profit, and the Behavioral monitoring and engineering; and the sustaining category, the Magic Trick, were the foundational blocks for developing an emancipatory theory that addressed ethical issues of economic fairness regarding the use of big data in online education.

Multifunctional Ta4C3Tx MXene/HTPP - PBS composites with multi cross-linking systems to cope with complex nuclear environments

The development of multifunctional radiation protection materials is crucial to ensure the safety of personnel and equipment in complex nuclear environments. Herein, a flexible radiation protection material integrating self-healing, thermal management, and behaviour response was prepared from Ta4C3Tx MXene, Hydrogen-terminated phenyl polysiloxane, and Polyborosiloxane through esterification, “thiol-ene click” hydrosilylation reaction using L(+)-Cysteine as a bridge. The results showed that when Ta4C3Tx MXene reached 20 wt%, the tensile strength of composite was 0.6321 MPa with the self-healing rate at 90.22 %. And the gauge factor of the material reached 25.83 at 100 % strain. This compensated for the gap between low strain and high sensitivity. Additionally, the multilayer structure of Ta4C3Tx MXene and the absorption properties of Tantalum enable the material to attenuate γ-rays through absorption and scattering mechanisms. The attenuation capability of Ta2O5 and Ta4C3Tx MXene fillers for 59.6 KeV γ-rays was compared at the same Tantalum content. When the Tantalum content reaches 23.81 wt%, the Ta4C3Tx MXene composite contains 20 wt% filler and has an attenuation coefficient of 0.757. Similarly, the Ta2O5 composite contains 22.5 wt% filler and has an attenuation coefficient of 0.734. These results are significant for improving temperature and behaviour monitoring of workers in low-temperature nuclear environments.

Revenue forecasting in smart retail based on customer clustering analysis

Understanding your customers is among one of the most important strategies to boost retail profit. In this research, we propose a WiFi-based sensing method to analyze customer behaviors. The monitoring of customer behaviors may lead to revenue growth. Specifically, the strategy is focused on understanding and grouping customers’ behaviors in which we track customers who share similar visiting patterns through WiFi sensing. Accordingly, we can have group-based prediction done for customers who own similar behaviors. We extract customers’ visiting patterns including the customers’ Service Set Identifier list and related information. After all, the proposed system is realized in a cafeteria place where we have the deployed WiFi access points continuously collect data over a time horizon of three months to serve as the inputs for data analysis. The data samples include the number of customers’ devices, number of products and revenue amounts. The dataset also integrates group information and weather conditions. We adopt several machine learning methods including Support Vector Regression and Random Forest for model induction. We conduct these models in terms of three main prediction tasks consisting of coffee shop’s revenue, the number of products, and the number of customers’ devices for evaluation. Furthermore, considering these predictions, we separate between the staying-in and to-go parts. Based on the experiment result, customers’ group information helps, as well as weather conditions. Overall, we can achieve the best prediction result when both the group information and weather conditions are included where we can enjoy as good as 6% to 10% in MAPE.

Emergency repairs downstream of earth and rockfill dams with water spring with indication of internal erosion

During periodic dam safety assessments, various anomalies are identified through field inspections, project documentation analysis, and structural behavior monitoring. These assessments aim to detect non-conformities, and engineering design firms propose technical solutions to address these issues. The primary goal of these reviews is to evaluate the overall state of conservation and structural safety, ensuring compliance with updated design criteria, revised standards, changing weather conditions, and technological advances. Dam renovations are crucial for enhancing operational capacity and preventive care. Implementing filtering materials within the dam's body establishes a drainage system to relieve internal pressures on the embankments and foundations. This prevents the washing away of soil particles due to internal erosion, known as "piping," a major cause of dam failures. Piping occurs when soil particles are displaced towards the outside of the embankment through the percolation process, particularly in the upstream area near the dam's base, where high hydraulic gradients and flows make it susceptible. This article presents a set of emergency or long-term solutions to address anomalies related to the onset of internal erosion in dams, with a focus on preventing piping. These solutions are vital for safeguarding the structural integrity and safety of these critical infrastructure components.

Privacy versus pedagogy – students’ perceptions of using learning analytics in higher education

The rapid recent use of learning analytics (LA) in higher education, specifically during the COVID-19 pandemic, allows the monitoring of users' behavior while learning. Using LA may promote students' learning outcomes but also intrude into their privacy. This study aimed to explore students’ behaviour and perceptions towards privacy and data protection when using LA for pedagogical needs, examine the privacy trade-off of students' willingness to share personal information in exchange for pedagogical benefits and understand the predicting variables for this privacy trade-off. A model was developed containing five groups of influencing variables (demographic data, perceptions, feelings, behaviour and awareness) on the privacy trade-off. A total of 1,014 students completed an online questionnaire. The results found that students do care about their privacy but are not aware of privacy and data protection regulations. They are willing to trade off privacy for pedagogical benefits, and they trust their academic institutions, but they want transparency. Age, a sense of security in the academic institution, behaviour, data misuse concern and institution management of students' personal information are the significant predictors for a privacy trade-off. It is important to engage students in the process as they are the main beneficiaries of LA and build trust between them and the institution. Implications for practice or policy: Academic institution should actively provide information to raise privacy awareness. Academic institutions should engage students in the process of using LA to create a high degree of trust. Universities should endorse LA policy, fostering it for pedagogical purposes. Instructors may utilise LA to enrich student learning, respecting their willingness to share pedagogical data. Academic institutions should provide a high level of transparency in order to build students’ trust in their institutions.

Circadian Rhythm and Sleep Analyses in a Fruit Fly Model of Fragile X Syndrome Using a Video-Based Automated Behavioral Research System.

Fragile X syndrome (FXS) is caused by the full mutation in the FMR1 gene on the Xq27.3 chromosome region. It is the most common monogenic cause of autism spectrum disorder (ASD) and inherited intellectual disability (ID). Besides ASD and ID and other symptoms, individuals with FXS may exhibit sleep problems and impairment of circadian rhythm (CR). The Drosophila melanogaster models of FXS, such as dFMR1B55, represent excellent models for research in the FXS field. During this study, sleep patterns and CR in dFMR1B55 mutants were analyzed, using a new platform based on continuous high-resolution videography integrated with a highly-customized version of an open-source software. This methodology provides more sensitive results, which could be crucial for all further research in this model of fruit flies. The study revealed that dFMR1B55 male mutants sleep more and can be considered weak rhythmic flies rather than totally arrhythmic and present a good alternative animal model of genetic disorder, which includes impairment of CR and sleep behavior. The combination of affordable videography and software used in the current study is a significant improvement over previous methods and will enable broader adaptation of such high-resolution behavior monitoring methods.

Machine learning-based understanding of aquatic animal behaviour in high-turbidity waters

Inspired by the ambitions envisioned in the Fourth Industrial Revolution for aquaculture, also known as Aquaculture 4.0, the aquaculture (marine animal farming) industry is seeking to adopt data-driven Artificial Intelligence (AI) to help significantly improve business operations. One of the major barriers is the manual annotation of animal behaviour data, which is a time-consuming task that demands high levels of concentration from biologists. To address this challenge, this paper proposes novel automatic animal behaviour monitoring tailored for industrial scenarios. Our approach introduces a real-time machine-learning-based instance segmentation system that is specialised for underwater environments, where large groups of shrimp are farmed. The implemented system achieves an accuracy rate of 89% at 30 frames per second (fps) and can accurately detect shrimp in high-density areas under poor lighting conditions and high turbidity waters, despite the challenges of occlusion and overlapping. A key innovation of our method is the implementation of a new density cluster algorithm for time series and video analysis. This approach provides a more efficient and accurate way of monitoring animal behaviour, significantly saving time and effort for biologists and advancing the capabilities of automated aquaculture systems.

RpTrack: Robust Pig Tracking with Irregular Movement Processing and Behavioral Statistics

Pig behavioral analysis based on multi-object tracking (MOT) technology of surveillance videos is vital for precision livestock farming. To address the challenges posed by uneven lighting scenes and irregular pig movements in the MOT task, we proposed a pig MOT method named RpTrack. Firstly, RpTrack addresses the issue of lost tracking caused by irregular pig movements by using an appropriate Kalman Filter and improved trajectory management. Then, RpTrack utilizes BIoU for the second matching strategy to alleviate the influence of missed detections on the tracking performance. Finally, the method utilizes post-processing on the tracking results to generate behavioral statistics and activity trajectories for each pig. The experimental results under conditions of uneven lighting and irregular pig movements show that RpTrack significantly outperforms four other state-of-the-art MOT methods, including SORT, OC-SORT, ByteTrack, and Bot-SORT, on both public and private datasets. The experimental results demonstrate that RpTrack not only has the best tracking performance but also has high-speed processing capabilities. In conclusion, RpTrack effectively addresses the challenges of uneven scene lighting and irregular pig movements, enabling accurate pig tracking and monitoring of different behaviors, such as eating, standing, and lying. This research supports the advancement and application of intelligent pig farming.

Vehicle Monitoring and Tracking System

In this paper we present a vehicle monitoring and tracking system. Previous systems focus solely on either theft detection or car/driver monitoring. In this paper we provide an integrated solution that provide both theft detection, tracking and driver behavior monitoring. Our system performs well in detecting theft attempts through RFID and tracking location by GPS. It also monitors the driver behavior using OBD reader. Combining both features in one system has great advantages like cost efficiency, space saving and the possibility of adding new features.

Research on Load Decomposition and Optimization of Intelligent Elderly Care Service Based on Heuristic and Event Detection Algorithm

Against the backdrop of the digital age, the openness, equality and interaction of the Internet economy have injected new vitality into China’s traditional industries. The application of big data technology, especially in information integration and analysis, has become a key force in promoting the sustainable and healthy development of the national economy. This study focuses on the “Internet +” environment, discusses the impact of the aging problem of community workers on home care services, and proposes an optimization scheme based on a heuristic algorithm. The heuristic algorithm, inspired by the foraging behavior of ants in nature, optimizes the route selection problem by simulating an ant colony to choose the path with a high concentration of pheromones and shows outstanding application potential in the field of home care. The accuracy of the event detection algorithm is directly related to the performance of the load decomposition algorithm, and the change point detection algorithm can effectively identify the change point of the probability distribution in the time series data, which provides important input data for unsupervised clustering. Advanced computer theory, including the Hidden Markov model (HMM) and swarm intelligence optimization algorithm, is used in this research. By comparing different swarm intelligence algorithms, we find that the standard Gray Wolf optimization (SGWO) model is better than the basic Gray Wolf optimization (BGWO) algorithm and the improved Gray Wolf optimization (DGWO) algorithm in terms of stability and output results. The SGWO model significantly improves the efficiency of the load decomposition algorithm, which has been verified in the application of the smart elderly care service platform. The platform not only supports the operation of related technologies and information products but also realizes the seamless integration of information among various subjects of elderly care services. In addition, the factor hidden in the Markov model that can be selectively activated effectively monitors equipment status in the Internet of Things environment, provides real-time monitoring of user consumption behavior and fault information and further enhances the quality and efficiency of smart elderly care services.