# Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions
## 1 Introduction
### Health Monitoring Using AI-Based Wearable Devices  
Modern wearable devices also analyze historical data. This analysis is carried out using artificial intelligence (AI) models [17]. [PERSON] and [PERSON] [18] and [PERSON] [19] mention the following advantages of integrating AI models: early diagnosis, extraction of information from electronic health records, analysis of medical images, virtual assistant chatbots, predictive analyses for patient conditions, and personalized medicine [20]. [PERSON] et al. [21] document the use of the Long short-term memory (LSTM) model for monitoring vital signs. The model achieved an accuracy of 96.78%. [PERSON] [22] mentions a series of disadvantages of integrating AI models in the medical context. Among these disadvantages is the black-box approach of AI algorithms, which does not provide explicit data interpretations. Additionally, the author also notes advantages such as early arrhythmia diagnosis.  
[PERSON] et al. [23] focus on analyzing patients' medical history and genetic profiles using AI methods, with the compromise of data confidentiality [24]. The study by [PERSON] et al. [25] presents the level of patient satisfaction with using a conversational system for pain assessment in spinal conditions. According to the authors, the satisfaction score obtained was 5.5 \(\pm\) 1.4 out of 7 points, indicating high satisfaction.  
[PERSON] et al. [26] outline four major challenges of wearable devices in the context of big data analysis: computational cost, communication cost, storage cost, and energy consumption. For this purpose, smart bracelets include accelerometers, heart rate sensors, and a Global Positioning System (GPS) for data collection [27,28]. The Support Vector Machine (SVM) algorithm from the paper [27] achieved an accuracy of 94.47% with a training time of 45.7 s and a testing time of 0.0242 s. The research by [PERSON] et al. [29] analyzed 45 elderly adults from three nursing care centers in Hong Kong who wore a telemedicine bracelet for three months. The results of this study are encouraging regarding the monitoring of elderly individuals. The cardiopulmonary monitoring system introduced by [PERSON] et al. [30] offers data analysis through Edge Computing. This approach reduces the burden of multiple cloud service accesses, leading to faster data transfer speeds [31].  
[PERSON] et al. [32] propose using data classification methods for monitoring fetal heart rate. In tests, the Multi-Kernel Network (MKNet) algorithm achieved the highest accuracy, 94.70%, with a training time of 19 s per epoch over a set of 70 epochs. The accuracy of the values provided by these sensors is analyzed by [PERSON] et al. [33], who note a high degree of reliability at rest but a decrease when activity changes occur. [PERSON] et al. [34] present the results of a survey in which 118 clinicians were questioned about their trust in wearable devices, with 80% rating them as useless. Conversely, 55.8% of patients, or 11,121 respondents, use such devices for cardiac conditions [35]. [PERSON] and [PERSON] [36] classify health monitoring devices into smartwatches, ear sensors (which measure oxygen saturation and heart rate), smart lenses, patches, QuardioCore, and smart shirts. These smart devices are generally connected to smartphones for detailed monitoring [37].