## 1 Introduction  
According to a report from the World Health Organization (WHO), the percentage of the world's population over 60 years old is projected to nearly double from 12% to 22% between 2015 and 2050, increasing from 1 billion in 2020 to 1.4 billion by 2030 and nearly 2.1 billion by 2050 [1]. One of the common problems in the elderly population is accidental falls, which are common but sometimes life-threatening. The rising demand for fall detection systems, algorithms, and techniques has been evident in the surge of interest observed via Google Trends, a platform dedicated to monitoring internet users' information search patterns since 2004. Notably, the search term \"fall detection\" has attained an unprecedented peak, registering a remarkable surge of over 500% within the past five years [2]. There are approximately 6.1 million people in the United States who use mobility assistance devices, including walking canes, orthopedic walkers, and rollators. However, life-threatening falls in the older population are a crucial health and safety issue; about 1.5 million elderly people are injured by falling each year, and about 47,300 people per year using walking aids suffer injuries from falls that require an emergency room visit [3]. In addition, life-threatening falls often occur within the rehabilitation process after major surgery to the hip or leg area, which is an increasing concern for patients and medical experts alike. The risk of repeat falls is especially high in patients who have already sustained a hip fracture [4]. Thus, there is an urgent need for a smart device that can detect falls for orthopedic walker users in real time and alert caregivers for emergency assistance. In recent years, wearable smart devices, such as Fitbit and Apple Watch, have become very popular for activity tracking and physiology monitoring; however, these smartwatches are not designed for walker users. Even if a walker user wears such a device on the wrist, the device may not accurately detect motions and hazards since both hands are rested on the walker during movement. In addition, some elderly users may not wear such devices reliably on their own. Traditional at-home health monitoring products often abandon the usage of tracking devices due to irregularities and false positives in favor of simple medical alert systems self-actuated by the subject, which do not consider the possibility that a fall may cause life-threatening injuries that may incapicate the subject and cause them to be unable to activate such systems. Inhibitions to speech and capability of motion can render medical alert systems that rely on direct communication between emergency services and the user ineffective. The primary objective of this research is to develop a novel, smart, and low-cost IoT device, namely SafeStride, that overcomes the limitations of the existing wearable and stationary fall detection systems. SafeStride is attached directly to an assistant walker, which continuously records and processes the walker's motion data in real time. SafeStride is intended to facilitate the development of a comprehensive fall detection system that efficiently tracks movement and promptly generates alerts in the event of a fall. In addition, it provides an activity log, such as walking steps and duration, standing time and stability, etc., to caregivers and physicians to help them assess a patient's progress with recovery and monitor if the patient has met the prescribed daily movement goals. This paper presents the detailed design and implementation of the hardware prototype, highlighting its potential advantages over existing solutions. Integrating this prototype into the walker holds promise for enhancing wearable fall detection capabilities, ultimately leading to improved safety and well-being of walker users. The other objective of this research is to develop a dataset for assistive walker fall detection and motion classification. Machine learning is a powerful technique for data analysis tasks such as classification and detection.