This project presents the development and evaluation of a fall detection hexapod, designed to address fall management in an ageing population. The hexapod, powered by a Raspberry Pi, incorporates various features including a guiding night light, a camera utilizing computer vision algorithms for person tracking, artificial intelligence (AI) for fall detection, Wi-Fi connectivity for emergency call functionality, and voice command recognition. The primary objective of this project was to construct and test the robot as a proof-of-concept fall management solution.
The study focuses on the practical implementation and assessment of the fall detection hexapod, rather than expanding the field of robotics. Through the construction and evaluation of the robot, important insights were gained regarding its potential applications in social care and fall prevention. The robot demonstrates the feasibility of using accessible technologies, such as a Raspberry Pi, to develop effective and customizable care solutions.
Discussions include the performance of the fall detection hexapod, the effectiveness of the guiding night light and the potential impact on addressing the challenges of falls in an ageing population. Additionally, it highlights future research directions, including user testing and cost analysis, which could enhance the understanding and practicality of this technology. With the continued advancements in AI and the widespread availability of platforms like Raspberry Pi, there is an opportunity to further explore and refine the capabilities of robots in providing personalized and effective care solutions.