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.

In [42], a rollator-based ambulatory assistive device with an integrated non-obtrusive monitoring system was introduced, aiming to enhance the functionality and capabilities of traditional rollators. The Smart Rollator prototype incorporates multiple subsystems, including distance/speed measurement, acceleration analysis, force sensing, seat usage tracking, and physiological monitoring. Collected data are stored locally within a microprocessor system and periodically transferred to a remote data server via a local data terminal. This enables remote access and analysis of the data, contributing to improved monitoring and support for individuals using rollators.

The research work in [43] presents a fall detection system designed specifically for smart walkers. The system combines signal processing techniques with the probability likelihood ratio test and sequential probability ratio test (PLT-SPRT) algorithm to achieve accurate fall detection. Simulation experiments were conducted to identify the control model of the walker and analyze limb movements, while real-world experiments were performed to validate the system’s performance.

The study in [44] demonstrates the experimental results of fall detection and prevention using a cane robot. Non-disabled male participants walked with the cane robot, and their “normal walking” and “abnormal walking” data were recorded. The fall detection rate was evaluated using the center of pressure-based fall detection (COP-FD) and leg-motion-based fall detection (LM-FD) methods. COP-FD detected falls by calculating the center of pressure (COP) during walking and comparing it to predefined thresholds. LM-FD used a laser range finder (LRF) to measure the relative distance between the robot and the user’s legs, enabling the detection of leg motion abnormalities associated with stumbling. The results showed successful fall detection for both COP-FD and LM-FD, with some instances of false positives and false negatives.

While various machine learning techniques using sensor data have been explored for fall detection systems, a major obstacle in these projects is the lack of reliable data. Obtaining large and diverse datasets that capture different fall scenarios and environmental conditions is crucial for training robust models, posing a significant challenge that researchers and developers must address for effective machine learning-based fall detection systems.

The study in [33] presents a comprehensive analysis of publicly available datasets used for research on wearable fall detection systems. The study examines twelve datasets and compares them based on various factors, including experimental setup, sensor characteristics, movement emulation, and data format. The datasets primarily use accelerometers, gyroscopes, and magnetometers as the main sensors for capturing movement and orientation data. However, there is a lack of consensus and standardization among the datasets regarding the number and position of sensors, as well as the specific models and characteristics of the sensors employed. This heterogeneity makes it challenging to compare and evaluate fall detection systems effectively.