# New Eldercare Robot with Path-Planning and Fall-Detection Capabilities
Another approach for eldercare robots is presented in [18], in which a modular robotic platform with a fixed exoskeleton arm is used for elders who suffer from disabilities like stroke and multiple sclerosis. Such a design provides indoor mobility and assistance and negates the fall possibility. However, such robotic mountable platforms are not our main concern in this research. In conclusion to the previous eldercare robots, it is evident that Matilda, Hobbit, Nao, and Giraff stand out with their dedicated fall-detection capabilities. These robots utilize different sensing technologies, including cameras, depth sensors, and motion sensors, combined with sophisticated algorithms to accurately detect falls and provide prompt assistance. User feedback supports the effectiveness of these robots in fall detection and the positive impact on older adults' safety. In comparison to other robots, one potential limitation in the fall-detection algorithms of PARO, Pepper, Mabu, Zora, Care-O-bot, and ElliQ is the lack of real-time and accurate detection of falls. These robots rely on sloweralgorithms or less advanced techniques for identifying fall events, which can result in delayed response times or false alarms. In this research, a new eldercare robot is presented. Complete software and hardware frameworks are developed. The software framework has two main features: the first is SLAM (simultaneous localization and mapping) and indoor path planning for the robot to be able to follow the elders continuously while avoiding indoor obstacles. The implemented path planning technique is RRT*[19]. The second feature is fall and posture detection using real-time image processing. The YOLOv7 [20] image processing algorithm is used in the fall detection model. This algorithm utilizes the convolutional neural networks (CNNs) machine-learning technique to provide real-time and precise detection of various objects in addition to falling and standing postures. By integrating the YOLOv7 and RRT* algorithms into the system, the robot controller can swiftly analyze video feeds from cameras and identify fall events with a high precision in addition to continuous human tracking. This allows for immediate notification and prompt assistance, minimizing the time between the occurrence of a fall and the initiation of aid. Additionally, a hardware architecture is developed with high-level and low-level controllers in addition to the sensors, motor drivers, and power supply unit. A custom-designed electronic board (Robot Control Unit--RCU) is developed to act as the low-level controller with SPI, UART, I\({}^{2}\)C, and Bluetooth communication capabilities. To implement the generated prediction model on the high-level controller, two optimization techniques are used to decrease the model size by 60% to enable enhanced real-time implementation of the model on the high-level controller. Furthermore, the new care giver robot is based on a cheap modular mechanical design, which makes it easy to customize the robot's capabilities to meet the specific needs of the elderly person. By integrating these features, the intended robot can not only enhance the quality of life for the elderly but also provide crucial assistance in their day-to-day activities.