## 2 Related Work
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. In summary, the current research on fall detection systems has focused primarily on wearable devices and stationary systems, with limited exploration of technology integration in walkers, rollators, canes, and wheelchairs. Integrating technology into walkers and rollators can improve fall detection capabilities and meet the specific needs of people who rely on these mobility aids. However, it is important to note that data availability remains a major challenge in this field, and creating reliable datasets is crucial to further progress in this research.