In this work, we present a novel framework for on-line human gait stability prediction of the elderly users of an intelligent robotic rollator using Long Short Term Memory (LSTM) networks, fusing multimodal RGB-D and Laser Range Finder (LRF) data from non-wearable sensors. A Deep Learning (DL) based approach is used for the upper body pose estimation. The detected pose is used for estimating the body Center of Mass (CoM) using Unscented Kalman Filter (UKF). An Augmented Gait State Estimation framework exploits the LRF data to estimate the legs' positions and the respective gait phase. These estimates are the inputs of an encoder-decoder sequence to sequence model which predicts the gait stability state as Safe or Fall Risk walking. It is validated with data from real patients, by exploring different network architectures, hyperparameter settings and by comparing the proposed method with other baselines. The presented LSTM-based human gait stability predictor is shown to provide robust predictions of the human stability state, and thus has the potential to be integrated into a general user-adaptive control architecture as a fall-risk alarm.
This paper proposes a control approach for an active robotic walking support system based on environment feedback. The support system is controlled using imposed apparent dynamics and its parameters are varied by the environment information. The environment information will not cause any motion but will only change the characteristics or maneuverability of the support system. This approach leads to a passive behavior for an active walking support system. In addition, the stability of the support system based on the apparent dynamics is also discussed. This is important as a guideline on what parameters to vary that will not cause instability to the system. Experimental results are presented to show the validity of the control algorithm with environment feedback. An intelligent walking-aid cane robot is developed for assisting the elderly and the physically challenged with walking. A motion control method is proposed for the cane robot based on human walking intention estimation. Moreover, the safety is investigated for both the cane robot and the elderly. The fall detection and prevention concepts are proposed to guarantee the safety of the elderly while walking with the cane robot. However, the deficiency of the cane robot is that it can be overturned easily because of its small size and light weight. Therefore, a controllable universal joint is designed for adjusting the tilted angle of its stick. The stability of the cane robot during the fall prevention procedure can then be enhanced by controlling the tilted angle of stick to an optimal position. A center of pressure (COP)-based fall detection (COP-FD) method is used to detect the risk of falling. In this method, the user's COP is calculated in real time using an integrated force sensory system, which comprises a six-axis force torque sensor and an inshoe load sensor. When the COP reaches the boundary of the specified safety area, i.e., the support polygon, it is assessed that the user is going to fall down. The COP-FD method can be used in various cases of falling. However, for cases of stumbling, a rapid fall detection method is proposed based on leg motion detection, and Dubois' fuzzy possibility theory is applied to adapt to different users. When the risk of falling has been detected, a fall prevention impedance control is executed considering both the interaction compliance and system stability. In the study, a control simulation platform was established to obtain the optimal controller parameters, and all the proposed methods were finally verified through simulations and experiments. According to nihseniorhealth.gov (a website for older adults), falling represents a great threat as people get older, and providing mechanisms to detect and prevent falls is critical to improve people's lives. Over 1.6 million U.S. adults are treated for fall-related injuries in emergency rooms every year suffering fractures, loss of independence, and even death. It is clear then, that this problem must be addressed in a prompt manner, and the use of pervasive computing plays a key role to achieve this. Fall detection (FD) and fall prevention (FP) are research areas that have been active for over a decade, and they both strive for improving people's lives through the use of pervasive computing. This paper surveys the state of the art in FD and FP systems, including qualitative comparisons among various studies. It aims to serve as a point of reference for future research on the mentioned systems. A general description of FD and FP systems is provided, including the different types of sensors used in both approaches. Challenges and current solutions are presented and described in great detail. A 3-level taxonomy associated with the risk factors of a fall is proposed. Finally, cutting edge FD and FP systems are thoroughly reviewed and qualitatively compared, in terms of design issues and other parameters. 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 Mobility assistance robots provide support to elderly or patients during walking. The design of a safe and intuitive assistance behavior is one of the major challenges in this context. We present an integrated approach for the context-specific, on-line adaptation of the assistance level of a rollator-type mobility assistance robot by gain-scheduling of low-level robot control parameters. A human-inspired decision-making model, the drift-diffusion Model, is introduced as the key principle to gain-schedule parameters and with this to adapt the provided robot assistance in order to achieve a human-like assistive behavior. The mobility assistance robot is designed to provide (a) cognitive assistance to help the user following a desired path towards a predefined destination as well as (b) sensorial assistance to avoid collisions with obstacles while allowing for an intentional approach of them. Further, the robot observes the user long-term performance and fatigue to adapt the overall level of (c) physical assistance provided. For each type of assistance a decision-making problem is formulated that affects different low-level control parameters. The effectiveness of the proposed approach is demonstrated in technical validation experiments. Moreover, the proposed approach is evaluated in a user study with 35 elderly persons. Obtained results indicate that the proposed gain-scheduling technique incorporating ideas of human decision-making models shows a general high potential for the application in adaptive shared control of mobility assistance robots.  An intelligent cane robot is designed for assisting the elderly or physically challenged people walking in daily life. The cane robot is driven by an Omni-directional mobile base; an aluminous stick is fixed on the base. A variety of sensors are installed on the cane robot for estimating the user's intention and status (normal walking or falling). The user's intentions of to which user want to move can be estimated by analyzing the active force on cane robot. As a nursing-care robot, the safety is a most important concern; before the elderly fall over, the cane robot should detect the sign of the falling and control the robot to assist the elderly to prevent it. Therefore a fall detection concept is proposed to estimate the risk of the falling based on the theory of zero moment point (ZMP) stability. An on-shoe sensor is designed to measure the ground reaction force and calculate the ZMP based on distributed force. The safety walking status is defined in the case of the ZMP is in the boundary of the support polygon. While the ZMP moving out of that boundary, the user will fall over. Various assistive machines have been developed to prevent falling accidents of the elderly. In order to achieve advanced support using robot technology, it is important to acquire data or real-time state estimation of user's various motions. However, a lot of expensive and sophisticated sensors utilized to estimate user's state accurately are difficult to use in general households or institutions. In this article, we propose a method to estimate the user's state utilizing a few inexpensive and simple sensors. We focused on CoG (Center of Gravity) to estimate user's state, but when utilizing less sensors than required to calculate the human link model parameters, the position of CoG is underspecified. Then we considered the range of value of unknown parameters to calculate candidates of CoG. The range of CoG candidates can become narrow enough to estimate human state in real-time by properly selecting and placing the sensors. Therefore, the evaluation of CoG candidates allows us to determine where and which sensors to set when designing assistive robots. We firstly selected some sensors which can be generally found on assistive machines, and we created sets of measurements using the number of unknown parameters. From the result of the experiment using a motion capture system, we confirmed that the range of the candidates was considerably narrow when using some of the created measurement sets. We validated the proposed method to estimate user's CoG candidates by actually placing the sensors according to the designed measurement sets and confirmed that the CoG candidates corresponded to those obtained using the motion capture system. The concept of a physical and cognitive HRI for walker-assisted gait was presented in the previous chapter. The HRI is implemented by means of a multimodal interface, which is used to develop a natural human-robot interaction in the context of human mobility assistance. That way, both cHRI and pHRI were included in this interface. Specifically, this chapter describes the cHRI component, which combines two sensor modalities: active ranging sensing (LRF) and human motion capturing (IMU) to perform the human tracking. This sensor combination presents important advantages to monitor the human gait from a mobile robot point of view, such as mentioned in the previous last chapter.