Enhanced Fall Detection and Prediction Using Heterogeneous Hidden Markov Models in Indoor Environment

Falls among the elderly pose a significant risk, often leading to serious injuries and a decline in overall well-being. This study employs an Heterogenous Hidden Markov Model (HHMM) that utilizes 3D vision-based body articulation data to propose an innovative method for fall detection and prediction. To ensure the precision and reliability of our model, we preprocessed the data to eliminate noise and extract pertinent features. This involved using a ZED camera to capture joint locations and body movements at a high frequency. The dataset was divided into 40% for testing and 60% for training the HHMM model, which comprised four states representing different body positions. The model achieved a 61% prediction rate with an accuracy of 81.51%. The Viterbi algorithm facilitated real-time recognition of body postures and fall predictions. The study suggests that HHMM can improve safety monitoring systems in healthcare and senior living facilities. To enhance prediction accuracy, future research could focus on incorporating additional data sources and expanding the dataset. In Conclusion, the study concludes that HHMM has the potential to effectively recognize and predict falls, thus contributing to fall prevention measures.