###### Abstract
[PERSON] [5] and [PERSON] [6] have thoroughly compared the performance of basic thresholding techniques with a wide set of supervised learning solutions. It is difficult to use threshold techniques as we decide the upper threshold value of fall and non-fall activities for new test subjects. Due to this limitation, recent approaches [13] employ feature extraction engineering and machine learning classifiers to improve the detection accuracy. In the last decade, many fall-detection methods have been developed using classical machine learning techniques [14] and deep learning [15; 16; 17]. Some of the developed fall-detection systems use logistic regression [14], naive Bayes [18; 19], decision tree [18; 19], support vector machines [18], and k-nearest neighbors [18; 19; 20]. Deep learning algorithms have also been applied to gait analysis and fall-detection systems. Other methods detect falls using inertia sensor signals by converting the 1D sensor signals to 2D images of spectrotemporal footprints and then applying CNN to classify those converted images [21; 22]. As a wearable sensor-based fall-detection method generates sequential datasets, it has the advantage of using sequential models such as LSTM. The sensor fusion of the accelerometer and gyroscope data streams using a hybrid CNN-LSTM method [15], as well as LSTM-based activity recognition [16; 23], is used for the recognition of falls. The study in [16] uses a single inertial sensor placed in the trunk. The CNN and LSTM model inputs are the raw acceleration and angular velocity signals. The problem with [PERSON]'s [15] approach is that he uses KNN classifiers--it is challenging to use such algorithms for real-time fall detection due to the Euclidean distance computational time. Long short-term memory (LSTM) and transformers are popular algorithms used in sequence models to encode and process sequential data. In this article, we extend our previous works in [17; 24] by proposing a new technique for fall-detection systems. Our approach is based on a vision transformer [25; 26; 27] and deep LSTM [28], which are used for image recognition and computer vision. First, we collected fall datasets, pre-processed, segmented, and balanced minor-class data samples. Fall kinematics data have different lengths, and it is difficult to include the fall event in a single-window input sequence. Thus, we introduce different sliding-window segmentation methods of the input sequence that help us to develop a real-time fall-detection system. Recently, many researchers have been developing vision transformers (ViT) [27] and deep LSTM [28] methods for image classification. This paper explores convolutional feature extractors with deep learning feature fall-detection systems on wearable sensor datasets. Two deep learning models are used as our imminent detection model, the LSTM encoder and the transformer encoder. To balance the minor classes, several techniques--resampling, data augmentation, and customized loss functions--are used. We collect data using an accelerometer and a gyroscope sensor to detect the impending fall correctly, while reducing false positives and false negatives and collecting near-fall datasets. We also propose several sliding-window segmentation techniques that include the important signal information in a single observation window across the various features of sequential data to detect falls.