Early anomaly detection in smart home: A causal association rule-based approach

An Algorithm for Mining the Living Habits of Elderly People Living Alone Based on AIoT

With the global aging population on the rise, the health and safety of elderly individuals living alone have become increasingly critical. This study introduces a novel AIoT-based habit mining algorithm designed to enhance activity monitoring in smart home environments. The proposed method integrates a one-dimensional U-Net neural network for accurate behavioral classification and an FP-Growth-based temporal association rule analysis for uncovering meaningful living patterns. By leveraging environmental sensor data, the algorithm first classifies daily activities and then uses timestamps to detect time-sensitive dependencies in behavior sequences, identifying the long-term habits of the elderly. Experimental validation on CASAS datasets (ARUBA and MILAN) demonstrates superior performance, achieving a precision of 84.77%. Compared to traditional techniques, this approach excels in behavior recognition and habit mining, offering a precise and adaptive framework for AIoT-driven smart home safety and health monitoring systems. The results highlight its potential to improve the quality of life and safety for elderly individuals living alone.

DualAttlog: Context aware dual attention networks for log-based anomaly detection

Most existing log-driven anomaly detection methods assume that logs are static and unchanged, which is often impractical. To address this, we propose a log anomaly detection model called DualAttlog. This model includes word-level and sequence-level semantic encoding modules, as well as a context-aware dual attention module. Specifically, The word-level semantic encoding module utilizes a self-matching attention mechanism to explore the interactive properties between words in log sequences. By performing word embedding and semantic encoding, it captures the associations and evolution processes between words, extracting local-level semantic information. while The sequence-level semantic encoding module encoding the entire log sequence using a pre-trained model. This extracts global semantic information, capturing overall patterns and trends in the logs. The context-aware dual attention module integrates these two levels of encoding, utilizing contextual information to reduce redundancy and enhance detection accuracy. Experimental results show that the DualAttlog model achieves an F1-Score of over 95% on 7 public datasets. Impressively, it achieves an F1-Score of 82.35% on the Real-Industrial W dataset and 83.54% on the Real-Industrial Q dataset. It outperforms existing baseline techniques on 9 datasets, demonstrating its significant advantages.

Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review

BACKGROUND

Telecare and telehealth are important care-at-home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues. However, there has been a recent drive to make better use of the data from these services to facilitate more proactive and predictive care.

OBJECTIVE

This review seeks to explore the ways in which predictive data analytics techniques have been applied in telecare and telehealth in at-home settings.

METHODS

The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was adhered to alongside Arksey and O'Malley's methodological framework. English language papers published in MEDLINE, Embase, and Social Science Premium Collection between 2012 and 2022 were considered and results were screened against inclusion or exclusion criteria.

RESULTS

In total, 86 papers were included in this review. The types of analytics featuring in this review can be categorized as anomaly detection (n=21), diagnosis (n=32), prediction (n=22), and activity recognition (n=11). The most common health conditions represented were Parkinson disease (n=12) and cardiovascular conditions (n=11). The main findings include: a lack of use of routinely collected data; a dominance of diagnostic tools; and barriers and opportunities that exist, such as including patient-reported outcomes, for future predictive analytics in telecare and telehealth.

CONCLUSIONS

All papers in this review were small-scale pilots and, as such, future research should seek to apply these predictive techniques into larger trials. Additionally, further integration of routinely collected care data and patient-reported outcomes into predictive models in telecare and telehealth offer significant opportunities to improve the analytics being performed and should be explored further. Data sets used must be of suitable size and diversity, ensuring that models are generalizable to a wider population and can be appropriately trained, validated, and tested.

Application of artificial intelligence in active assisted living for aging population in real-world setting with commercial devices - A scoping review

BACKGROUND

The aging population is steadily increasing, posing new challenges and opportunities for healthcare systems worldwide. Technological advancements, particularly in commercially available Active Assisted Living devices, offer a promising alternative. These readily accessible products, ranging from smartwatches to home automation systems, are often equipped with Artificial Intelligence capabilities that can monitor health metrics, predict adverse events, and facilitate a safer living environment. However, there is no review exploring how Artificial Intelligence has been integrated into commercially available Active Assisted Living technologies, and how these devices monitor health metrics and provide healthcare solutions in a real-world environment for healthy aging. This review is essential because it fills a knowledge gap in understanding AI's integration in Active Assisted Living technologies in promoting healthy aging in real-world settings, identifying key issues that require to be addressed in future studies.

OBJECTIVE

The aim of this overview is to outline current understanding, identify potential research opportunities, and highlight research gaps from published studies regarding the use of Artificial Intelligence in commercially available Active Assisted Living technologies that assists older individuals aging at home.

METHODS

A comprehensive search was conducted in six databases-PubMed, CINAHL, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science-to identify relevant studies published over the past decade from 2013 to 2024. Our methodology adhered to the PRISMA extension for scoping reviews to ensure rigor and transparency throughout the review process. After applying predefined inclusion and exclusion criteria on 825 retrieved articles, a total of 64 papers were included for analysis and synthesis.

RESULTS

Several trends emerged from our analysis of the 64 selected papers. A majority of the work (39/64, 61%) was published after the year 2020. Geographically, most of the studies originated from East Asia and North America (36/64, 56%). The primary application goal of Artificial Intelligence in the reviewed literature was focused on activity recognition (34/64, 53%), followed by daily monitoring (10/64, 16%). Methodologically, tree-based and neural network-based approaches were the most prevalent Artificial Intelligence algorithms used in studies (32/64, 50% and 31/64, 48% respectively). A notable proportion of the studies (32/64, 50%) carried out their research using specially designed smart home testbeds that simulate the conditions in real-world. Moreover, ambient technology was a common thread (49/64, 77%), with occupancy-related data (such as motion and electrical appliance usage logs) and environmental sensors (indicators like temperature and humidity) being the most frequently used.

CONCLUSION

Our results suggest that Artificial Intelligence has been increasingly deployed in the real-world Active Assisted Living context over the past decade, offering a variety of applications aimed at healthy aging and facilitating independent living for the older adults. A wide range of smart home indicators were leveraged for comprehensive data analysis, exploring and enhancing the potentials and effectiveness of solutions. However, our review has identified multiple research gaps that need further investigation. First, most research has been conducted in controlled testbed environments, leaving a lack of real-world applications that could validate the technologies' efficacy and scalability. Second, there is a noticeable absence of research leveraging cloud technology, an essential tool for large-scale deployment and standardized data collection and management. Future work should prioritize these areas to maximize the potential benefits of Artificial Intelligence in Active Assisted Living settings.

Occupational health knowledge discovery based on association rules applied to workers' body parts protection: a case study in the automotive industry

Occupational Health Protection (OHP) is mandatory by law and can be accomplished by considering the participation of others besides occupational physicians. The data shared can originate knowledge that might influence other processes related to occupational risk prevention. In this study, we used Artificial Intelligence (AI) methods to extract patterns among records shared under these circumstances over two years in the automotive industry. Records featuring OHP data against physical working conditions were selected, and a database of 383 profiles was designed. As Occupational Health Protection profiles under study are associated with work functional ability reduction, the body part(s) (n = 14) where it occurred were identified. Association Rules (ARs) coupled with Natural Language Processing techniques were applied to find meaningful hidden relationships and to identify the occurrence of protection profiles being assigned to at least two body parts simultaneously. After filtering ARs using three metrics (support, confidence, and lift), 54 ARs were found. The distribution of simultaneous body parts is presented as being higher in Special projects (n = 5). The results can use in: (i) design a multi-site body parts functional work ability (loss) model; (ii) model the capacity of organizations to retain workers in their working settings and (iii) prevent work-related musculoskeletal symptoms.

Open-set recognition of breast cancer treatments

Open-set recognition generalizes a classification task by classifying test samples as one of the known classes from training or "unknown." As novel cancer drug cocktails with improved treatment are continually discovered, classifying patients by treatments can naturally be formulated in terms of an open-set recognition problem. Drawbacks, due to modeling unknown samples during training, arise from straightforward implementations of prior work in healthcare open-set learning. Accordingly, we reframe the problem methodology and apply a recent Gaussian mixture variational autoencoder model, which achieves state-of-the-art results for image datasets, to breast cancer patient data. Not only do we obtain more accurate and robust classification results (14% average F1 increase compared to recent methods), but we also reexamine open-set recognition in terms of deployability to a clinical setting.

An Empirical Study on the Influence of Smart Home Interface Design on the Interaction Performance of the Elderly

The concept of the smart home has been widely recognized and accepted, but the differentiated characteristics of elderly smart products in terms of demand and use are becoming more and more prominent. The lack of an efficient navigation design of the smart product interface increases the cognitive burden of elderly users, and how to better meet the needs of the elderly with smart products gradually becomes the focus of attention. This study was conducted for the elderly group, using the scenario-based design method to analyze the needs of elderly users, combining the research results of scenario theory with the smart home interaction design research method, focusing on how to make the style of interface navigation, sliding layout and button size more suitable for the cognitive behavior of elderly users. The purpose of this research is to realize an age-friendly smart home interaction design in terms of functional design and interface design. The experiment is divided into two stages: in stage 1, two different layouts and operation methods are commonly used for the age-friendly smart home interface: up and down sliding and left and right sliding; in stage 2, the functional buttons are square, where 4 styles are selected, and the side lengths are set to 10 mm, 12 mm, 15 mm, 18 mm and 22 mm. The sliding and retrieval test and retrieval and click test results show that for different sliding layout methods, the interactive performance and subjective evaluation of the interface with the up-and-down sliding layout are better. Among all functional button styles, the interaction performance and subjective evaluation of the simple button style with lines are the best. Among the function keys with a size of 10–22 mm, the interaction performance is better from 12 mm to 18 mm. The conclusion of the better interface data information obtained from this experiment improves the rationality of the age-friendly smart home interface and makes the smart home interface better for the age-friendly scenario.

Optimization assisted deep learning based intrusion detection system in wireless sensor network with two‐tier trust evaluation

Nowadays, owing to the openness of transmission medium, wireless sensor networks (WSNs) suffer from a variety of attacks, together with DoS attacks, tampering attacks, sinkhole attacks, and so on. Therefore, an effectual system is necessary for recognizing the intrusions in WSN. This paper aims to set up a novel intrusion detection system (IDS) via a deep learning model. Initially, optimal cluster head (CH) is selected among the sensor nodes, from which the sensor nodes that have high energy will be prioritized to act as CH. In this proposed work, the CH selection is evaluated optimally by not only considering the energy parameter, further under the constraints like delay and distance. For optimal selection, a novel approach named as self‐improved sea lion optimization (SI‐SLnO) model is introduced in this work. As per the proposed strategy, the trust of CH and nodes is evaluated based on a multidimensional two‐tier hierarchical trust model by considering content trust, honesty trust, and interactive trust. Finally, the deep learning‐based intrusion detection takes place via optimized neural network (NN), where the training is done by the proposed SI‐SLnO algorithm via the optimal weight tuning process. At last, the supremacy of the developed approach is examined via evaluation over numerous extant techniques.

Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns

The monitoring of the daily life activities routine is beneficial, especially in old age. It can provide relevant information on the person's health state and wellbeing and can help identify deviations that signal care deterioration or incidents that require intervention. Existing approaches consider the daily routine as a rather strict sequence of activities which is not usually the case. In this paper, we propose a solution to identify flexible daily routines of older adults considering variations related to the order of activities and activities timespan. It combines the Gap-BIDE algorithm with a collaborative clustering technique. The Gap-BIDE algorithm is used to identify the most common patterns of behavior considering the elements of variations in activities sequence and the period of the day (i.e., night, morning, afternoon, and evening) for increased pattern mining flexibility. K-means and Hierarchical Clustering Agglomerative algorithms are collaboratively used to address the time-related elements of variability in daily routine like activities timespan vectors. A prototype was developed to monitor and detect the daily living activities based on smartwatch data using a deep learning architecture and the InceptionTime model, for which the highest accuracy was obtained. The results obtained are showing that the proposed solution can successfully identify the routines considering the aspects of flexibility such as activity sequences, optional and compulsory activities, timespan, and start and end time. The best results were obtained for the collaborative clustering solution that considers flexibility aspects in routine identification, providing coverage of monitored data of 89.63%.

Unsupervised statistical concept drift detection for behaviour abnormality detection

Abnormal behaviour can be an indicator for a medical condition in older adults. Our novel unsupervised statistical concept drift detection approach uses variational autoencoders for estimating the parameters for a statistical hypothesis test for abnormal days. As feature, the Kullback–Leibler divergence of activity probability maps derived from power and motion sensors were used. We showed the general feasibility (min. F1-Score of 91 %) on an artificial dataset of four concept drift types. Then we applied our new method to our real–world dataset collected from the homes of 20 (pre–)frail older adults (avg. age 84.75 y). Our method was able to find abnormal days when a participant suffered from severe medical condition.

A dual-layer context-based architecture for the detection of anomalous instructions sent to medical devices

Complex medical devices are controlled by instructions sent from a host personal computer (PC) to the device. Anomalous instructions can introduce many potentially harmful threats to patients (e.g., radiation overexposure), to physical device components (e.g., manipulation of device motors), or to functionality (e.g., manipulation of medical images). Threats can occur due to cyber-attacks, human error (e.g., using the wrong protocol, or misconfiguring the protocol's parameters by a technician), or host PC software bugs. Thus, anomalous instructions might represent an intentional threat to the patient or to the device, a human error, or simply a non-optimal operation of the device. To protect medical devices, we propose a new dual-layer architecture. The architecture analyzes the instructions sent from the host PC to the physical components of the device, to detect anomalous instructions using two detection layers: (1) an unsupervised context-free (CF) layer that detects anomalies based solely on the instruction's content and inter-correlations; and (2) a supervised context-sensitive (CS) layer that detects anomalies in both the clinical objective and patient contexts using a set of supervised classifiers pre-trained for each specific context. The proposed dual-layer architecture was evaluated in the computed tomography (CT) domain, using 4842 CT instructions that we recorded, including two types of CF anomalous instructions, four types of clinical objective context instructions and four types of patient context instructions. The CF layer was evaluated using 14 unsupervised anomaly detection algorithms. The CS layer was evaluated using six supervised classification algorithms applied to each context (i.e., clinical objective or patient). Adding the second CS supervised layer to the architecture improved the overall anomaly detection performance (by improving the detection of CS anomalous instructions [when they were not also CF anomalous]) from an F1 score baseline of 72.6%, to an improved F1 score of 79.1% to 99.5% (depending on the clinical objective or patient context used). Adding, the semantics-oriented CS layer enables the detection of CS anomalies using the semantics of the device's procedure, which is not possible when using just the purely syntactic CF layer. However, adding the CS layer also introduced a somewhat increased false positive rate (FPR), and thus reduced somewhat the specificity of the overall process. We conclude that by using both the CF and CS layers, a dual-layer architecture can better detect anomalous instructions to medical devices. The increased FPR might be reduced, in the future, through the use of stronger models, and by training them on more data. The improved accuracy, and the potential capability of adding explanations to both layers, might be useful for creating decision support systems for medical device technicians.

Indirectly-Supervised Anomaly Detection of Clinically-Meaningful Health Events from Smart Home Data

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly-Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection. This optimization is driven by a small amount of example anomalies, driving an indirectly-supervised approach to anomaly detection. Additionally, we enhance the approach by introducing a warm start method that reduces optimization time between similar problems. We validate the feasibility of Isudra to detect clinically-relevant behavior anomalies from over 2 million sensor readings collected in 5 smart homes, reflecting 26 health events. Results indicate that indirectly-supervised anomaly detection outperforms both supervised and unsupervised algorithms at detecting instances of health-related anomalies such as falls, nocturia, depression, and weakness.

A Framework for Detecting and Analyzing Behavior Changes of Elderly People over Time Using Learning Techniques

A sensor-rich environment can be exploited for elder healthcare applications. In this work, our objective was to conduct a continuous and long-term analysis of elderly's behavior for detecting changes. We indeed did not study snapshots of the behavior but, rather, analyzed the overall behavior evolution over long periods of time in order to detect anomalies. Therefore, we proposed a learning method and formalize a normal behavior pattern for elderly people related to her/his Activities of Daily Living (ADL). We also defined a temporal similarity score between activities that allows detecting behavior changes over time. During the periods of time when behavior changes occurred, we then focused on each activity to identify anomalies. Finally, when a behavior change occurred, it was also necessary to help caregivers and/or family members understand the possible pathology detected in order for them to react accordingly. Therefore, the framework presented in this article includes a fuzzy logic-based decision support system that provides information about the suspected disease and its severity.

CAPHAR: context-aware personalized human activity recognition using associative learning in smart environments

The existing action recognition systems mainly focus on generalized methods to categorize human actions. However, the generalized systems cannot attain the same level of recognition performance for new users mainly due to the high variance in terms of human behavior and the way of performing actions, i.e. activity handling. The use of personalized models based on similarity was introduced to overcome the activity handling problem, but the improvement was found to be limited as the similarity was based on physiognomies rather than the behavior. Moreover, human interaction with contextual information has not been studied extensively in the domain of action recognition. Such interactions can provide an edge for both recognizing high-level activities and improving the personalization effect. In this paper, we propose the context-aware personalized human activity recognition (CAPHAR) framework which computes the class association rules between low-level actions/sensor activations and the contextual information to recognize high-level activities. The personalization in CAPHAR leverages the individual behavior process using a similarity metric to reduce the effect of the activity handling problem. The experimental results on the “daily lifelog” dataset show that CAPHAR can achieve at most 23.73% better accuracy for new users in comparison to the existing classification methods.

Synthetic repurposing of drugs against hypertension: a datamining method based on association rules and a novel discrete algorithm

BACKGROUND

Drug repurposing aims to detect the new therapeutic benefits of the existing drugs and reduce the spent time and cost of the drug development projects. The synthetic repurposing of drugs may prove to be more useful than the single repurposing in terms of reducing toxicity and enhancing efficacy. However, the researchers have not given it serious consideration. To address the issue, a novel datamining method is introduced and applied to repositioning of drugs for hypertension (HT) which is a serious medical condition and needs some improved treatment plans to help treat it.

RESULTS

A novel two-step data mining method, which is based on the If-Then association rules as well as a novel discrete optimization algorithm, was introduced and applied to the synthetic repurposing of drugs for HT. The required data were also extracted from DrugBank, KEGG, and DrugR+ databases. The findings indicated that based on the different statistical criteria, the proposed method outperformed the other state-of-the-art approaches. In contrast to the previously proposed methods which had failed to discover a list on some datasets, our method could find a combination list for all of them.

CONCLUSION

Since the proposed synthetic method uses medications in small dosages, it might revive some failed drug development projects and put forward a suitable plan for treating different diseases such as COVID-19 and HT. It is also worth noting that applying efficient computational methods helps to produce better results.

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart buildings sector, improving life quality within smart homes, assessing the occupancy status information, detecting human behavior with a view to assisted living, maintaining environmental health, and preserving natural resources. The main purpose of our review consists of surveying the current state of the art regarding the recent developments in integrating supervised and unsupervised machine learning models with sensor devices in the smart building sector with a view to attaining enhanced sensing, energy efficiency and optimal building management. We have devised the research methodology with a view to identifying, filtering, categorizing, and analyzing the most important and relevant scientific articles regarding the targeted topic. To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding the above-mentioned topic within the scientific literature. After processing the obtained papers, we finally obtained, on the basis of our devised methodology, a reliable, eloquent and representative pool of 146 papers scientific works that would be useful for developing our survey. Our approach provides a useful up-to-date overview for researchers from different fields, which can be helpful when submitting project proposals or when studying complex topics such those reviewed in this paper. Meanwhile, the current study offers scientists the possibility of identifying future research directions that have not yet been addressed in the scientific literature or improving the existing approaches based on the body of knowledge. Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments, as well as purposes that have not been investigated yet.