## 2 Related Work
### Explainable and Interpretable Fall Detection  
In recent years, there has been a growing interest in developing explainable and interpretable approaches in healthcare for human activity recognition. Several studies have proposed innovative techniques to provide insights into the reasoning behind the fall and enhance the trustworthiness of the models for patients and healthcare professionals. To provide a reliable method for detecting falls, the authors of [29] proposed a strategy involving placing three sensors on the subject's body in various locations. The information gathered by these sensors was utilized for training purposes. The UMAFall dataset collected sensor readings and trained five distinct models for each sensor. To ascertain the final output, a majority voting classifier was employed. The authors also incorporated LIME, an explainable AI (XAI) technique, to improve the interpretability of the model's outputs. The proposed method yielded high accuracies for each sensor model and the majority voting classifier. The addition of LIME enhanced the model's outputs' interpretability. The authors of [30] presented a solution, PRF-PIR, which integrates multimodal sensor fusion with passive and interpretable monitoring for long-term monitoring. The system comprised a software-defined radio (SDR) device and a novel passive infrared (PIR) sensor system. The proposed solution utilized the HIAR model, incorporating a recurrent neural network (RNN) to effectively address the captured data's temporal dependence. The efficacy of the PRF-PIR system was validated by collecting data on various academic office activities performed by human subjects. The system exhibited its capacity as a monitoring system that is non-invasive, resilient, and precise by attaining a high level of accuracy in both human identification and activity recognition. Furthermore, the findings were corroborated by explainable artificial intelligence (XAI) techniques, which further authenticate the supremacy of sensor fusion compared to single-sensor alternatives. The PRF-PIR system exhibited its ability to function as a monitoring system for intricate human activities that are passive, interpretable, and highly precise. The authors of [31] explored the integration of CNN-based self-attention models to predict fall events by analyzing balance test scores. Their study utilizes a semi-supervised learning approach that leverages data from ankle-mounted IMU sensors (Analog Devices Inc., Wilmington, MA, USA) to detect high-risk fall moments. This method not only anticipates when a fall is likely to occur but also explains which specific moments and movements are most associated with increased fall risk. By highlighting these critical moments, their approach aids in preventive healthcare strategies, enabling interventions before falls occur, thereby enhancing patient safety and reducing the incidence of fall-related injuries. The authors of [23] introduced XAI-Fall, an advanced explainable AI system tailored for fall detection using wearable devices. The system strategically places multiple sensors across different body locations to capture a comprehensive dataset of user movements. The integration of LIME within their framework makes the AI's decisions transparent, allowing caregivers and patients to understand why certain movements may be flagged as potential falls. This transparency is crucial for trust and facilitates targeted interventions based on the system's feedback, potentially leading to more personalized and effective fall-prevention strategies. The authors of [32] utilized explainable artificial intelligence combined with wearable sensor technology to perform gait analysis aimed at identifying risks of osteopenia and sarcopenia among the elderly. Their approach employs advanced machine learning algorithms such as XGBoost and Random Forest to analyze the statistical parameters of gait data.