Cyber Physical System for Continuous Evaluation of Fall Risks to Enable Aging-In-Place

Every year, one out of three adults over the age of 65 falls, and about 30% of the falls result in moderate to severe injuries. The high rate of fall-related hospitalizations and the fact that falls are a major source of morbidity and mortality in older adults have motivated extensive interdisciplinary clinical and engineering research with a focus on fall prevention. This research is aimed at developing a medical Cyber Physical System (CPS) composed of a human supervised mobile robot and ambient intelligence sensors to provide continuous evaluation of environmental risks in the home. As a preventive measure to avoid falls, we propose use of mobile robots to detect possible fall risks inside a house. As a step-up to that, we also define a control framework for intelligent, networked mobile robots to semi-autonomously perform assistive and preventive tasks. This framework is integrated in a smart home that provides monitoring and control capabilities of environmental conditions such as objects blocking pathways or uneven surfaces. The main outcome of this work is the realization of this system at Worcester Polytechnic Institute\u27s (WPI) @Home testbed

Robotics Enabled In-Home Environment Screening for Fall Risks

Our overarching goal is to investigate, design, create and validate the fundamental scientific and engineering framework for intelligent, networked mobile robots to semi-autonomously perform environmental fall risk assessment in the home. Motivated by the facts that (1) aging in place improves the overall health and well-being of individuals, (2) falls are the leading cause of mortality in older adults, (3) home environmental fall risk assessment is an effective preventive strategy, and (4) extreme costs and shortage of trained personnel are huge barriers for effective and efficient delivery of fall risk home assessments by health care providers, we are iteratively developing user-centric designs for a new class of robotic systems that can be assembled easily and cost-effectively to detect environmental hazards and, as a result, preventively and proactively minimize falls in the home. The tight integration of the research thrusts in robot design and control, task and motion planning under uncertainty, and human-on-the-mesh control of networked robots is aimed at advancing the theory and practice of robotics and lead to the demonstration of innovative approaches to transform healthcare delivery with a focus on wellbeing. In this poster presentation, we will present our preliminary results from developing this framework. We present the communication and control framework for a semi-autonomous mobile robot that can be controlled over an internet connection via a web interface. We will discuss the opportunities and challenges associated with a human-robot team completing the HEROS (http://www.temple.edu/older_adult/) environment safety checklist. Our preliminary results demonstrate that this technology can be helpful to effectively prevent the in-home falls among elderly

A Walking Controller for Humanoid Robots using Virtual Force

Current state-of-the-art walking controllers for humanoid robots use simple models, such as Linear Inverted Pendulum Mode (LIPM), to approximate Center of Mass(CoM) dynamics of a robot. These models are then used to generate CoM trajectories that keep the robot balanced while walking. Such controllers need prior information of foot placements, which is generated by a walking pattern generator. While the robot is walking, any change in the goal position leads to aborting the existing foot placement plan and re-planning footsteps, followed by CoM trajectory generation. This thesis proposes a tightly coupled walking pattern generator and a reactive balancing controller to plan and execute one step at a time. Walking is an emergent behavior from such a controller which is achieved by applying a virtual force in the direction of the goal. This virtual force, along with external forces acting on the robot, is used to compute desired CoM acceleration and the footstep parameters for only the next step. Step location is selected based on the capture point, which is a point on the ground at which the robot should step to stay balanced. Because each footstep location is derived as needed based on the capture point, it is not necessary to compute a complete set of footsteps. Experiments show that this approach allows for simpler inputs, results in faster operation, and is inherently immune to external perturbing and other reaction forces from the environment. Experiments are performed on Boston Dynamic\u27s Atlas robot and NASA\u27s Valkyrie R5 robot in simulation, and on Atlas hardware