Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Physical Factors <s> In this paper, we introduce a passive-type walker using servo brakes referred to as RT Walker. RT Walker realizes several functions such as obstacles/steps avoidance function, path following function, gravity compensation function, variable motion characteristics function, etc., by controlling only servo brakes without using servo motors. These passive-type systems are dependable for using practically in real world environment, because of the passive dynamics with respect to the applied force/moment, simple structure, lightweight, and so on. However, the most serious problem of them is the falling accident of user, because the passive-type systems are lightweight and move easily based on the small force/moment applied by the user unintentionally. In this paper, we pay attention to a method for estimating the human state during the usage of the walker and propose a motion control algorithm for realizing a fall-prevention function based on its human state. We also implement the proposed control methods in RT Walker experimentally and illustrate the validity of them <s> BIB001 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Physical Factors <s> In this study, we propose a novel fall prevention scheme for an omni-direction type cane robot by using a DC motor driven universal joint. The cane robot which is driven by three omni-wheels is called Intelligent Cane Robot (iCane). It is designed for aiding the elderly and handicapped people walking as shown in Fig.1. The motion of cane robot is controlled for both normal and abnormal walking conditions. The user's normal walking aided by the cane robot, a concept called “Intentional Direction (ITD)” is proposed. Guided by the online estimated ITD, we apply the admittance control method in the motion control of cane robot. For the abnormal walking, we mainly studied the case of user's fall down. The center of gravity (COG) of user can be estimated from the angle of an inverted pendulum which represents human dynamic model. Fall prevention algorithm based on the relationship between user's COG and the cane is proposed. Because the size of the cane robot is small, when the robot is preventing the user falling down, firstly, the stability of the cane robot should be ensured. A universal joint which is driven by two DC motors is used to reduce the moment causing the cane robot falling over. The proposed method is verified through experiments. <s> BIB002 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Physical Factors <s> The purpose of this study is to investigate whether the acceleration of the center of gravity of body during sit-to-walk motion have a relationship with falling or not. In this study, we measured sit-to-walk motion of fall experienced and inexperienced subjects by using a portable acceleration monitor device that we have developed. The result of discriminant analysis by using indexes with a significant difference revealed a 90.3% correct prediction rate for falling. The results indicated possibility of fall prevention by this method. <s> BIB003 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Physical Factors <s> Falls are a major problem in older adults. A continuous, unobtrusive, environmentally mounted (i.e., embedded into the environment and not worn by the individual), in-home monitoring system that automatically detects when falls have occurred or when the risk of falling is increasing could alert health care providers and family members to intervene to improve physical function or manage illnesses that may precipitate falls. Researchers at the University of Missouri Center for Eldercare and Rehabilitation Technology are testing such sensor systems for fall risk assessment (FRA) and detection in older adults' apartments in a senior living community. Initial results comparing ground truth (validated measures) of FRA data and GAITRite System parameters with data captured from Microsoft(®) Kinect and pulse-Doppler radar are reported. <s> BIB004
As people get older, their bodies start to lose the vitality they once had; in addition to this, medical conditions start to arise. Due to lack of exercise and the natural deterioration that comes with age, muscles, bones and other parts of the body weaken; which leads to many problems including falling  . Over the last years, intelligent walking-aid devices have been made available to assist patients who are prone to falling in their daily life. They take advantage of robotic advances in motion control to create responses based on human intentions. These walking-aid devices not only offer physical support to the patients, but also prevent falls by detecting possible hazards around the subject  . Intelligent walkers have been used during the last decade to assist and/or rehabilitate people experiencing weakness in their lower limbs. These types of walkers offer obstacle avoidance, path following, gravity compensation, and many other features. They fall into two categories: active and passive walkers. The first one uses servo motors and the second one does not. Active walkers are robust, but they can be dangerous if not calibrated properly because they can move unintentionally. Passive walkers, on the order hand, are safer but not as sturdy as the active ones due to their light weight. RT walker BIB001 is a passive-type walker which only uses servo brakes. It employs the user's dynamics and movement directions as feedback to realize the different motion characteristics. An estimation method based on the user's state is used to get his/her motion features. Three user's states were utilized; these states are stopped, walking, and emergency. The distance between the user and the RT walker, and the walker's velocity are used to determine the current state. In the stopped and walking state, the risk of falling is at a minimum. In the emergency state, the apparent dynamics of the walker are modified by controlling the brake torques of the wheels, thus preventing the fall. The prototype consists of a support frame, two wheels with powder brakes, two passive casters, two laser range finders, and a tilt sensor and controller. Other methods to assist and/or monitor the patient's physical health include: robo-cane BIB002 , accelerometer-based gait analysis BIB003 , camera-based posture and gait analysis BIB004 . These and other studies will be further explained in Section 6.