KNUST www.knust.edu.gh A SMART WEARABLE FALL GUARD SYSTEM FOR OLDER ADULTS K.B Osei 1 A. K. Asare Dakwa 1 E. Owusu 1 J.M. Ansah 1 I. Acquah, PhD *1 * iacquah.coe@knust.edu.gh 1 Department of Computer Engineering, College of Engineering Kwame Nkrumah University of Science and Technology (KNUST), Ghana INTRODUCTION METHODS Fig. 5: Graphs of Falls, ADLs and stationary motions respectively Fig. 1: Design Concept RESULTS The dataset was split into training and testing data; eighty percent training data and twenty percent testing data. Overall, we divided the data into three categories of motion so that the model we develop can classify these motions. These were the Stationary, Fall, and Activities of Daily Living (ADL) motions. These motions were presented graphically and were plotted based on accelerometer, gyroscopic and magnetometer values. DISCUSSION The simulation results and live demonstrations showed our fall detection model's reliability and effectiveness. The Arduino IDE’s serial monitor outputs precise separation between the various motion states confirming the model's capacity to recognize falls and distinguish them from normal activities. The addition of the alert system also emphasizes the applicability of our strategy in real-world situations as we targeted elderly people in nursing environments. This embraces the primary goals of this project. Future work should look at protecting other vital parts of the body which are also prone to fall injuries. Again, user-friendly interfaces, such as touchscreen displays or voice prompts can be incorporated, to provide users with real-time feedback CONCLUSION Our wearable fall guard system has proven its capability to precisely detect falls and distinguish them from ordinary activities via thorough development and optimum testing. With the help of the Edge Impulse platform, a strong machine-learning model was made possible, and when imbibed into our Arduino microcontroller, allowed for real-time analysis of motion data and quick action in the event of a fall. The system's ability to deliver prompt help and lessen the potential effects of falls is brought about by the seamless integration of sensory information, intelligent inference, and immediate alarm activation. REFERENCES 1. "Statista," [Online]. Available: https://www.statista.com/statistics/1183936/number-ofpeople- aged-65-years-and-older-in-ghana/. [Accessed 22 February 2023]. 2. "WHO global report on fall prevention in older age," [Online]. Available: https://www.who.int/publications/i/item/9789241563536 . [Accessed 23 February 2023]. 3. R. M. a. N. B. Y. Soliman, "Falls in the Elderly Secondary to Urinary Symptoms," vol. 18, no. 1, p. 28, 2016. Department of Computer Engineering – Biomedical Engineering Program mail: compeng@knust.edu.gh compengknust Aims/objectives. This project aims to: • Develop a wearable device that accurately detects falls using machine learning algorithms. • Design protective hip pads to cushion the impact of falls. • Evaluate the system's performance in real-world conditions. The world is experiencing a rapidly aging population. In Ghana, the number of individuals aged 65 and over is steadily increasing[1]. Falls pose a significant health risk to this vulnerable group, leading to severe injuries, reduced independence, and a lower quality of life[2][3]. Despite the growing prevalence of falls among the elderly, current fall detection systems suffer from limitations such as low accuracy, high false alarm rates, and reliance on cumbersome devices. These challenges hinder their widespread adoption and effectiveness in preventing fall-related injuries. Our project aims to address these limitations by developing a wearable, user-friendly fall detection system. By combining advanced machine learning algorithms with comfortable, protective hip pads, we seek to accurately detect falls and mitigate their impact. Our research is crucial for improving the safety and well-being of the elderly population.