## 2 Related Work  
A wide range of approaches have been explored to design effective fall detection systems, including the use of wearable devices [7; 8; 9; 10; 11] and smartphones [12; 13; 14; 15] with microphones [16], cameras [17], accelerometers and gyroscopes [18; 19], GPS [20], and combinations of multiple sensors [21]. In addition to wearable devices, alternative approaches have been extensively investigated in fall detection systems. Depth sensors, such as Microsoft Kinect [22] or time-of-flight cameras [23], have been implemented with the primary goal of enhancing the accuracy and effectiveness of fall detection systems. Infrared sensors have been used to detect changes in infrared radiation within specific environments [24]. Doppler radar systems provide a non-invasive and privacy-preserving approach for timely and accurate fall detection among the elderly, analyzing unique time-frequency characteristics to identify fall events regardless of lighting conditions [25]. Vision-based methods analyze video data from cameras, employing techniques such as optical flow analysis [26], object tracking [27], and human pose estimation to detect falls based on changes in motion or posture [28]. Acoustic sensors, such as microphones or sound arrays, capture audio signals and employ signal processing techniques to identify sudden impact sounds, screams, or other acoustic patterns associated with falls [29]. Signal processing techniques play a crucial role in all of the aforementioned fall detection systems, as they extract meaningful features from sensor data, facilitating the accurate detection of fall events across diverse system types. Multiple signal processing techniques have been employed in fall detection systems, including orientation filters [9], quaternions [10], thresholding techniques [13; 18; 19; 20], histograms of oriented gradients [17], clustering algorithms [21], Bayesian segmentation approaches [23], spatiotemporal analysis methods [27], Kalman Filters [30], sensor data fusion [31], and wavelet transforms [32]. Furthermore, machine learning methods have been applied extensively in recent years. In this field, a variety of machine learning models have been employed, ranging from simpler approaches, like decision trees [22] and k-nearest neighbors (kNN) [7; 11; 14; 21; 24; 29], to more complex methods, such as Bayesian classifiers [23; 25], support vector machines (SVM) [28], neural networks [16], and deep learning models [15; 26]. A closely related research field is human activity recognition (HAR) by classifying human activities from motion sensor data. Several public datasets for fall detection using wearable sensors [33] are available. A dataset [34] in which data were labeled as fall, near fall, and activities of daily living (ADL) were most useful for us. Both the signal processing method [35] and the machine learning approach [5; 36] are widely used in this field. In signal processing approaches, methods based on preset thresholds to detect a step [37], methods based on peak detection count steps by counting the peaks of sensor readings [38], and methods based on correlation analysis count steps by calculating and comparing the correlation coefficients between two neighboring windows of sensor readings [39]. In machine learning methods, authors design HAR algorithms based on convolutional neural networks (CNN) [40; 41], and researchers in [36] have developed an algorithm based on long short-term memory (LSTM) to recognize human activities. It is worth mentioning that all existing HAR datasets were based on wearable IMU sensors directly attached to the human body. In contrast, this project involves fixing the sensor to a walker, so the data characteristics are quite different. The research and development of fall detection systems specifically for assistive walkers and rollators is relatively limited. However, there have been several notable endeavors to create technology-integrated devices to improve the safety and functionality of these mobility aids.