# Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions
## 1 Introduction
### Anomaly Detection Employing IoT-Based Wearable Devices  
Several works in the specialized literature have previously proposed different IoT configurations to analyze complex scenarios related to anomaly detection in vital parameters. Their identification was carried out by running the query TS = (\"anomaly detection\" AND (\"physiological data\" OR \"health monitoring\") AND (\"wearable devices\" OR \"IoT\")) AND PY = (2020-2025) AND DT = Article in the Web of Science (WOS) Core Collection. Out of the 18 results provided, the works that directly address the common elements of the present research are analyzed in the following. The paper by [PERSON] et al. [52] analyzes architectures for continuously monitoring physiological parameters through wearable devices. They reduce latency by integrating flexible piezoelectric sensors, Autoencoder-type algorithms, and Edge Computing. The paper highlights scenarios such as chronic disease monitoring, real-time anomaly detection, and community assistance for the elderly, which align with the common purpose of the present research. This research examines the issue of algorithm execution at the wearable device level (on-device AI) versus its implementation in a centralized system to which the wearable device reports data (on-cloud). The paper employs the encoders algorithm and reports an accuracy of 99.9% for the two scenarios, using a dataset of five measurements/sample and a time of 3 ms in the best scenario. [PERSON] et al. [53] designed an anomaly detection system for critical health events of patients in hospitals. The system aims to minimize false alerts by simulating scenarios associated with detecting a decrease in heart rate. The authors use the One-Class SVM algorithm and Edge Computing implementation in this paper. The reported accuracy for heart attacks is 82%. A dataset of 1000 records was used for training, and 200 records were used for testing. The latency is mentioned as being low, but without an exact numerical value. [PERSON] et al. [54] combined LSTM and Convolutional neural network (CNN) models for remote patient monitoring using wearable IoT devices. The objective is to detect anomalies and trends in physiological data, such as heart rate, blood pressure, or oxygen levels, with goals similar to those of the present research. The Ensemble LSTM-CNN model achieved an accuracy of 97.23%, with an anomaly detection rate of 95% and a response time of 2.5 s. The results demonstrate a 20% reduction in hospital readmissions and early identification of health issues in 92% of cases. Another research proposes a ResNet-LSTM model for blood pressure monitoring in the context of personalized health [55]. The model achieves a mean absolute error (MAE) of 6.2 mmHg and a root mean square error (RMSE) of 8.9 mmHg. Anomalies are detected through data analysis in an IoT and cloud infrastructure for storage and analysis. The paper by [PERSON] et al. [55] does not mention specific times. However, the dataset volume is not explicitly stated, but it emphasizes the importance of large datasets for anomaly detection. Another cloud-based patient monitoring system for real-time anomaly detection is presented in the paper by [PERSON] and [PERSON] [56]. This paper aims to reduce risks for elderly individuals through a concept-proof IoT device similar to the one presented in the current research. The system employs a Random Forest (RF) and SVM. The authors declare an accuracy of 100%. The average decision time is 16.3 s for 46 features. The infrastructure used is also cloud-based. This research [56] does not mention the volume of the dataset. A real-time monitoring system for the elderly that uses IoT sensors and commercial devices is analyzed by [PERSON] et al. [57]. The system's objective is to learn the users' daily routines. The paper aims to detect behavioral anomalies and notify these deviations via SMS in near real-time, similar to the prototype proposed in the current research. The system analyzed 1000 activities (meals, bathroom visits) and sent notifications to 9 locations.