# Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions
## 4 Discussion
The scalability of cloud-based solutions allows easy integration with wearable technologies and IoT devices, ensuring accessibility in various healthcare settings. The study presents some methodological constraints associated with the evaluation conducted on a single subject, which limits the generalization of the results to larger populations. Therefore, the authors acknowledge that the model's generalization has not been assessed. Although representative over the two years, the number of recordings may not cover all possible clinical variations. During the tests, the total number of aggregated daily records was 676. The number of raw records for the parameters AR, mR, and MR was 725,338, the number of records for the parameter S was 9455, and the number of records for the parameters DST and SST was 680. The patient's age at the beginning of the monitoring was 69 years, as they were born in 1954. The performance of the anomaly detection algorithm identified 18 anomalies. Of these, nine were real anomalies, and nine were false anomalies. Based on these values, the standard metrics were identified with the following scores: precision: 50%, recall: 100%, F\({}_{1}\)-Score: 33%, and accuracy: 98.5%. The final model hyperparameters include Randomized PCA. Tests showed that detecting non-urgent anomalies is preferred to avoiding missing critical ones. The final hyperparameter configuration after tests is rank = 6, ensureZeroMean = true, seed = 1. Tests confirmed that detecting non-urgent anomalies is preferable to missing urgent events, ensuring robust patient monitoring. The study's focus on a single patient was intentional to keep the scope manageable and ensure a more controlled, detailed analysis. While it is true that results based on a single individual may not fully account for inter-individual variability, the findings serve as a proof of concept. Future iterations of the system can include larger datasets that encompass more diverse patients with varying age ranges, pre-existing conditions, and medications. The detection of anomalies in physiological data is a widely studied topic. Traditional methods such as Isolation Forest and One-Class SVM have been used to identify deviations in vital signs. However, these methods are often sensitive to noise in real-time collected data. In this paper, PCA was investigated to analyze variations in data. The HADA usesRandomized PCA to demonstrate detection efficiency by integrating into an IoT-cloud infrastructure. Table 3 presents the comparative analysis between the state-of-the-art and the HADA as a proof of concept. In Table 3, NS indicates that the value is not specified. In this research, the PCA method was integrated into the HADA to study the necessity of dimensionality reduction correlated with the identification of monitored physiological parameters, which contribute to the main objective of the study. By analyzing each parameter in isolation, it was demonstrated that subtle anomalies resulting from unitary monitoring cannot be detected. Tests conducted without using PCA resulted in an accuracy of less than 90%. In comparison, the use of PCA within the HADA model resulted in an accuracy of 98.5%. This value demonstrates that the simultaneous and contextual analysis of all variables improves anomaly detection. HADA balances accuracy and response time compared to existing methods. Although the model presented by [PERSON] and [PERSON] [56] achieves 100% accuracy, it requires 16.3 s of processing time and an exclusively cloud-based infrastructure, which limits its use in IoT systems. In contrast, HADA offers 98.5% accuracy with a processing time of 3.5 s. This makes the PCA model more suitable for applications that require a quick response. Compared to RNN-based methods (Autoencoder and LSTM-CNN), HADA offers competitive performance without requiring high computational resources. Although Autoencoder is extremely fast (3 ms), it requires Edge Computing infrastructure, which is challenging to implement in standard IoT solutions.