In this paper, we propose a sensory cues guided robotic walker for improving gaits of Parkinson Disease (PD) patients. A completely non-intrusive, 3D real-time leg pose tracking and gait analysis are proposed by using a depth camera mounted on the rear of the robotic walker. It has been studied that the sensory cues can serve as effective stimuli to the PD patients for gait improvement. In our work, the sensory cues including visual and auditory cues are incorporated into the robotic walker. More specifically, both sensory cues are gait-adaptive, of which the visual cue in particular is projected onto the ground by a projector installed on the walker, in order to stimulate patients walking gait more easily. Since the adjustable cues can improve patients gaits and reduce their uncomfortableness simultaneously, the hereby developed robotic walker serves as a gait rehabilitation mechanism. To demonstrate the performance of the developed walker, several real experiments have been conducted. First, the accuracy of the proposed 3D leg pose tracking is verified by a standard motion capture system. Next, seven participants (4PD patients and 3 healthy elders) are recruited to test the system three days for verifying the effectiveness of participants gait improvement. The experimental results confirm the potential of the walker serving as a rehabilitation device for PD patients. Note to Practitioners-We proposed a completely non-intrusive, relatively inexpensive and real-time gait analyzer integrated with sensory cues guided rehabilitation system on an active robotic walker. Our goal is to provide a smart robotic walker with safety, reliability, and with rehabilitation function for elders whoever suffered from chronic disease or health problem. In this work, a reliable 3D leg pose tracking is proposed. Then, a gait analysis for acquiring spatio-temporal gait parameters such as stride length and gait velocity is proposed based on the tracking to analyze the walking gait of the patients. On the other hand, improving Parkinson disease patients gaits by using sensory cues which are known to have remarkable effects for Parkinson disease patients is in many research. Therefore, we concentrate on the sensory cues include visual cue and a rhythmic auditory cue cooperating with the robotic walker which aimed to stimulate and provide walkingassistance in their ambulation. For the sensory cues, an adaptive gait mechanism is proposed to improve patients gait based on their personal gait pattern as well as to reduce uncomfortableness of them by doing the rehabilitation. The experimental results confirm the potential of the walker serving as a rehabilitation device for Parkinson disease patients. We hope the new invention of this kind assistive robotic walker will become more popular and may provide more living aid facilities to elders.

In this paper, we propose a gait tracking and analysis method using a depth image sensor installed on robotic walker. Nowadays, robotic walker not only can assist elders who have suffered deteriorating mobility but also help to provide rehabilitating function to people who have crippled walking ability. This approach is meant to be sufficiently accurate, non-intrusive, and low-cost. The goal of this research is to enable the robotic walker to become more active in terms of walker control and comfortable user experiences thorough gait analysis. In experiment, the accuracy of the proposed 3D leg pose tracking method was evaluated by a motion capture system and the result is quite promising. After actual trials, the proposed method has the potential to be used in a safer and more reliable application such as those assisting elders and specific group of patient subjects.

In this paper, the authors proposed a local motion planner with robot controller of the mobile manipulator to grasp the object in dynamic environments without any prior knowledge of environments. Our local motion planner is based on the concept of potential field and composed of the attractive and repulsive vectors. Then, the local motion planner decides the potential vector according to the attractive and repulsive vectors in various situations. Also, an approach to deal with the drawback of local minima is contained. The attractive and repulsive vectors are generated by the distances between the target, obstacles and the manipulator. For robot control, we take the end-effector as the control point and apply the potential vector with joint-level control, and moreover evaluate the mobility and the kinematic constraints of the robot to modify the joint velocities. The experiment platform is a wheeled mobile robot with a 5-DOF manipulator using Softkinetic DS325 which is a close range RGB-D camera as our sensor. Through several experiments, the results show that our framework is fast enough and valid to grasp the object in dynamic environments.

Studies on frequent pattern mining have often considered the existence of an item in a transaction but ignored its profit and purchase quantity. As a result, utility mining has been introduced to resolve this issue. However, the resulting patterns simply show the correlation among high utility items without quantity information of such items. In this study, a novel method is proposed to discover high utility fuzzy itemsets from quantitative databases which considers both profits and quantities of items. The quantities are fuzzified into linguistic regions, thus the quantitative concept is consistent with human cognition and easy to interpret. In addition, we provide theoretical and empirical analysis of the proposed method. The simulation results demonstrated that our method can efficiently and effectively mine high utility fuzzy itemsets from quantitative databases.

Falsification of human faces using face photos has been an arising problem for face recognition and verification systems. In this paper, we propose a system to distinguish face photos from true faces by their motion models. In order to enhance the difference between the two classes, we design an enhanced optical flow method which generates a larger difference between the motion model of true faces and that of face photos. The feature vector we adopted is the dense optical flow field across a short period of time. An LDA-based training method is adopted to separate the projection of the training data into two classes, and a Bayes classifier is used to classify the testing samples. Under the specified motion of true faces and face photos, our proposed method can effectively distinguish the two classes with high verification rate. Even if the motion is arbitrary for both classes, the proposed system can also report satisfying results.