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Falls are dangerous for the elderly population; therefore, many fall detection systems have been developed. However, previous methods are bulky for elderly people or only use a single sensor to isolate falls from daily living activities, which makes a fall difficult to distinguish. In this paper, we present a cost-effective and easy-to-use portable fall-detection sensor and algorithm. Specifically, to detect human falls, we used a three-axis accelerator and a three-axis gyroscope in a mobile phone. We used the Fourier descriptor-based frequency analysis method to classify both normal and falling status. From the experimental results, the proposed method detects falling status with 96.14% accuracy. Introduction Falling, which means losing one’s balance or tripping over something, occurs in all age groups, but especially in the elderly. In Asia, senior citizens over the age of 65 experience over half of the body injuries caused by falling [1]. In spite of extensive preventive efforts, falls remain the leading cause of accidental deaths among the elderly. Research estimates that over a third of the people aged over 65 suffer from a fall each year, and the number of falls increases progressively with age in both genders and in all racial and ethnic groups [2]. Because of vision and other problems, the falls typically occur in certain environmental conditions. Falls cause serious damage, including fractures, brain damage, and anxiety toward exercise. Quickly dealing with a fall injury such as a broken bone will reduce the time the bone fragment remains in the body. Moreover, some fractured bone regions never completely heal if they do not receive proper care within four hours [3]. If a person falls and bumps his or her head, he or she can black out, and any resulting brain damage should be cared for in a short amount of time to reduce side effects such as paralysis. Reducing the time until treatment can also help prevent a second injury and pain. For rapid treatment, the most important challenge is fall detection, which simultaneously provides communication of the fall. Therefore, fall detection is critical. A mobile phone with a three-axis accelerometer and gyroscope is a device that can easily carried by humans. In this paper, a mobile phone is used to classify human falls from normal walking motion. The three-axis accelerometer is used to measure three direction activity data. A gyroscope sensor is used to detect angular momentum; it is also useful in determining the user’s rotation when he or she falls. To verify the accuracy of the proposed method, the system and algorithm were validated with a real human data set. Materials and Method Data Acquisition Subjects either walked or fell down while holding a mobile phone in their hand. A three-axis accelerometer was used to detect users’ activity such as walking, falling, sitting, etc. When a person falls, one’s body rotates on the body’s center. A gyroscope that measures angular momentum was used to detect this rotation. The sampling frequency of each data was 20 Hz. The device used for the data acquisition was the Galaxy Note 1 (Samsung Electronics, Seoul, South Korea). The total data set

The Novel Computer Aided Falling Detection System Using Mobile Phone for Elderly Person