## 1 Introduction
The utilization of machine learning (ML) represents significant potential for fall detection, including Support Vector Machines (SVMs), Random Forest (RF), and Hidden Markov Models (HMM) [33]. These models rely on handcrafted features and require extensive feature engineering [34]. Hidden Markov Models (HMMs) are based on the concept of a Markov process, which is a stochastic process with the property that the future state depends only on the current state and not on the past states [33]. [PERSON] et al. introduced an alarm system that utilizes an HMM-based Support Vector Machine (SVM) [35]. The model was trained and evaluated using a dataset consisting of 180 fall instances. [PERSON] et al. proposed an innovative method for human activity recognition that involves partitioning activities into meaningful phases called motion units, similar to phonemes in speech recognition [36]. [PERSON] et al. developed and assessed a concise set of six high-level features (HLFs) on the CSL-SHARE and UniMiB SHAR datasets [37]. They demonstrated that HLFs can be effectively extracted using ML methods, allowing for activity classification across datasets, even in imbalanced and limited training scenarios. Additionally, they identified specific HLF extractors responsible for classification errors. However, DL models, in particular convolutional neural networks (CNNs) [38; 39], recurrent neural networks (RNNs) [40], and their variations, have evolved as enhanced fall detection methods. The requirement for manual feature extraction was eliminated by the capacity of DL models to automatically extract pertinent features from unprocessed sensor data, which allows for quicker and more precise detection. [PERSON] et al. employed high-level handcrafted features, including human pose estimation and optical flow, as inputs for individual VGG-16 classifiers [38]. [PERSON] et al. developed a visual-based system that utilizes video information captured via a Kinect camera [39]. Multiple frames from the video are stacked to form a cube, which is inputted into a 3D CNN. The 3D CNN effectively incorporates spatial and temporal characteristics, encoding both appearance and motion characteristics across frames [39]. [PERSON] et al. introduced a system for fall detection utilizing video data, employing a recurrent neural network (RNN) with two layers of Long Short-Term Memory (LSTM) [40]. The approach involved performing 2D pose estimation using the OpenPose [41] algorithm to provide body joint information. The extracted pose vectors were then input into a two-layer LSTM network, enabling fall detection. We endeavored to identify bed exiting and other dangerous activities, which would serve as a measure to prevent falls and injuries and be more effective and desirable. Previously, we developed a depth camera and ultrawideband radar system, named \"eNightLog\",to monitor and classify the night wandering behaviors of older adults. We demonstrated that it outperformed the integrated pressure mattress and infrared fence system [42] and was effective in managing wandering behaviors in a field test [43]. We then developed deep learning models for the depth camera [44; 45] and ultrawideband (UWB) radar [46; 47] to better classify sleep postures. However, eNightLog had some limitations. It could not distinguish between users and medical staff, especially when they were taking care of the users. False alarms also happened when the bed was raised over the threshold height of the patient's head or the patient's service table height changed. In view of this, we aimed to optimize the existing eNightLog system and extend its functions to detect potential fall events. We dubbed this new system \"eNightTrack\" as the succession of our previous system with enhanced tracking functions.