# Fall Detection Based on Recurrent Neural Networks and Accelerometer Data from Smartphones
## 1 Introduction
### Related Work  
Existing fall detection systems leverage various technologies, including wearable devices, ambient sensors, and smartphones. Among these, smartphones (or rather their accelerometers and gyroscopes) are particularly promising due to their widespread availability. This has led to the exploration of various approaches for fall detection. Many traditional machine learning solutions, such as support vector machines (SVMs) [4] and \(k\)-Nearest Neighbors (\(k\)-NNs) [5], have been explored [6], but their performance is often limited by the need for handcrafted features and their inability to capture temporal dependencies in sequential data. Their research was based on the UR Fall Detection Dataset (URFD) [7], for which samples were recorded using an accelerometer hidden in two Microsoft Kinect cameras, which seems to be unpractical in real-life scenarios, in our opinion. In the course of the aforementioned studies, SVM gave better results. The authors of [8] addressed the fall detection problem using a comparable methodology. They constructed a dedicated dataset comprising six activities and 45 samples collected from three subjects. Several machine learning classifiers, including naive Bayes, random forest, and support vector machines (SVMs), were evaluated, with the SVM achieving the highest performance. Nevertheless, the dataset was limited in size and diversity. In [3], the authors conducted a review of the most popular datasets on the performance of various activities available on the Internet. The postulate was made that signals should be collected using smartphones. They compared 13 datasets and found them insufficient to build an effective fall detection system. Only some of them contained information on both falls and activities of daily living (ADLs) that required movement. In addition, a few lacked details about study participants, such as gender, age, or height. The researchers decided to supplement the existing datasets with the missing cases by conducting controlled simulations of seven types of falls and nine types of movement-related activities. They collected data using an accelerometer inside a smartphone, placing it alternately in the participant's left and right pockets. The created UniMiB SHAR Dataset was shared publicly and has been widely used in subsequent research (including this paper). The highest accuracy score achieved in this study was 98.34%. Later, [PERSON] et al. [9] applied various machine learning techniques, including deep learning, for human activity recognition. The deep neural network implemented in their study outperformed traditional classifiers such as support vector machines (SVMs) and random forests (RFs) in terms of accuracy achieving 96.73% on the UniMiB SHAR dataset. In 2018, [PERSON] et al. [10] introduced an evaluation framework aimed at comparing different feature extraction methods. Their experiments, conducted on the Opportunity [11]and UniMiB SHAR datasets, involved assessing multiple feature extraction approaches and comparing the performance of various machine learning algorithms, including Long Short-Term Memory (LSTM) networks. The solution achieved 74.66% on the UniMiB SHAR dataset. Another LSTM-based work was presented by [PERSON] [12]. The author proposed the idea of building an autoencoder with LSTM layers achieving 98.17% accuracy on the UniMiB SHAR dataset. The model was trained using a combination of autoencoder as the feature extractor and SVM as the classifier. In 2020, an interesting solution involving a layer-wise CNN (Convolutional Neural Network)-based model was presented [13]. In this paper, the local loss was applied to the Human Activity Recognition (HAR) problem achieving 98.82% accuracy. [PERSON] and [PERSON] [14] used various classifier topologies to recognize human activities. The aim was to find out what affects the inaccurate learning of classification models by analyzing the architectures of deep neural networks. This approach gave the highest accuracy of 99.82% for the UniMiB SHAR dataset. [PERSON] et al.