This award supports an integrative, collaborative project to develop a personalized lower-limb assistive wearable robot that reduces human effort during physically intensive activities, such as lifting. The robot works by sensing a user’s physical effort using soft-wearable electronics and responding accordingly to reduce this effort. This work has significant applications in factory labor involving weights, as overexertion injuries are both costly and frequently disabling. This research investigates the effectiveness of this wearable robot strategy in reducing human effort and develops strategies to improve the utilization of wearable robots and soft wearable electronics. The robot assistance to each individual human wearer is customized using an estimation method based on various metrics. The estimation method can also be used to design a training method using a wearable robot. The soft wearable sensors will be useful in robotics as well as medical applications related to diagnosis, monitoring, and therapeutics. The proposed project integrates research and education by developing a project-based course on wearable robotics and supporting graduate and undergraduate student mentoring in independent research and thesis studies. The project strengthens the infrastructure for education and research by helping maintain wearable robot testbeds. The research results will be broadly disseminated through publications, software, and data sets.
The research team members have three objectives that contribute to the goal of customizability in wearable robot personalized assistance. First, the customization process will be improved by identifying alternative optimization criteria to efficiently estimate the user’s physical effort during physically intensive activities. This will be accomplished through a rapid and robust estimate of the user effort using a conventional physiological sensor, such as a muscle activity sensor, followed by an estimate using new soft wearable electronics. Second, the work will enhance soft-wearable electronics with the goal of improving on and replacing conventional sensors. Associated tasks will explore the feasibility of using existing soft wearable electronics as sensors and then iteratively improve the electronics and estimation method to accurately sense and estimate physiological status. Third, the study will integrate and evaluate the personalized assistance achieved using soft wearable sensor measurements in a physically intensive activity, such as lifting using an ankle exoskeleton. This task will use appropriate metrics such as energy expenditure rate of the task and muscle activity. The work will result in customized (personalized) assistance available from a wearable robot for physically intensive activities and a soft wearable sensor system to evaluate the physical status of the user and provide real-time feedback. The evaluation outcomes can be applied in interventions to mitigate or prevent existing hazards and resulting injuries to workers; thus, the results of this research will benefit human laborers in factories, warehouses, and other industrial workplaces.
This proposal was funded with the National Institute for Occupational Safety and Health (NIOSH) in the Center for Disease Control and Prevention (CDC).
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
