# New Eldercare Robot with Path-Planning and Fall-Detection Capabilities  
[PERSON]  
1 Mechanical Engineering Department, Arab Academy for Science and Technology and Maritime Transport, Smart-Village Branch, Cairo 11736, Egypt; [EMAIL_ADDRESS] (A.E.j. [EMAIL_ADDRESS] (Y.E.-S.)  
[PERSON]  
2  
[PERSON]  
1 Mechanical Engineering Department, Arab Academy for Science and Technology and Maritime Transport, Smart-Village Branch, Cairo 11736, Egypt; [EMAIL_ADDRESS] (A.E.j. [EMAIL_ADDRESS] (Y.E.-S.)  
[PERSON]  
2 Mechanical Engineering Department, Arab Academy for Science and Technology and Maritime Transport, Sheraton Branch, Cairo 11757, Egypt
in robot design, sensing technologies, indoor path planning, fall-detection techniques, and human-robot interactions in real-world scenarios. Examples of eldercare robots include the robot Matilda [7] which utilizes a fall-detection algorithm that analyzes sensor data from cameras, microphones, and depth sensors. This algorithm is designed to detect specific indicative fall patterns such as sudden changes in body position, impact, or abnormal behavior. Upon detecting a potential fall event, Matilda triggers immediate alerts for assistance. Another research study was conducted on the robot Hobbit [8], in which advanced algorithms are applied to monitor the environment using cameras, temperature sensors, and RGB-D cameras. Several algorithms were used to analyze the sensor data to detect falls based on different criteria, such as changes in body posture, rapid movements followed by a sudden stop, or collisions with objects. Once a fall is detected, Hobbit promptly notifies caregivers or emergency services. Another fall-detection algorithm is used for the Nao humanoid robot [9] which utilizes its sensor suite, including motion sensors, cameras, and depth perception. This algorithm recognizes key fall-related patterns, such as sudden changes in orientation, significant acceleration or deceleration, or rapid descent. Additionally, the robot Giraff provides remote communication and social interaction, and its fall-detection capabilities are achieved using relatively cheap hardware [10]. While it does not have a dedicated fall-detection algorithm, Giraff's video and audio capabilities enable remote caregivers to visually monitor older adults and provide immediate assistance if a fall occurs. Another robot named PARO [11], which is a therapeutic robot resembling a baby harp seal, focuses on emotional support for older adults but does not have fall-detection capability. However, PARO's presence and interaction with older adults have shown positive effects on emotional well-being, potentially reducing the risk of falls caused by psychological factors. Pepper [12], another humanoid robot, contributes to the emotional well-being of older adults. It helps in potentially reducing the fall risks associated with social isolation and depression but without a fall-detection feature. Moreover, Mabu [13], an AI-powered robot designed to provide personalized healthcare assistance, does not have specific fall-detection capabilities. However, Mabu's continuous monitoring of older adults' health conditions and proactive intervention can contribute to the prevention of falls by addressing underlying health issues. The robot Zora [14; 15] is also used in healthcare settings. It focuses on entertainment and therapy and has a dedicated fall-detection capability using a pose-estimation technique. In addition, its interactive activities and exercises contribute to cognitive stimulation and physical well-being, potentially reducing the risk of falls through enhanced mobility and engagement. Other examples of robots in healthcare settings are the robots Care-O-bot [16] and ElliQ [17]. These robots provide social interaction, cognitive activities, and object-detection capabilities and can potentially reducing the risk of falls through enhanced mobility and engagement. However, neither of them has a fall-detection feature.