Molano CChen L.-PLI-CHEN FU2021-09-022021-09-0220191062922Xhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85076735659&doi=10.1109%2fSMC.2019.8914379&partnerID=40&md5=74fba32de7deba8776399143b8d8e2dahttps://scholars.lib.ntu.edu.tw/handle/123456789/581379Traditional robotic walkers have primarily focused on safety and navigation. In this paper, we challenge the previous work on walkers by implementing a deep learning module developed with the goal of using a robot to provide mobility assistance to the elders. Through the data collected from multiple sensors, we are capable of leveraging the maneuverability of robotic walkers under different scenarios and gait requirements. This capability is achieved by CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory) architectures. Thus, the system can provide personalized assistance to the elders performing the locomotion activities indoors accurately. Furthermore, the robot learns the optimal behavior based on the interactions with the environment in a supervised learning approach. To validate our system, we evaluated the system with some users who provided qualitative comments about the comfort degree of using the robot. ? 2019 IEEE.Long short-term memory; Robotics; Robots; Walking aids; Behavior-based; Convolutional neural network; Learning modules; Locomotion activity; Mobility assistance; Multiple sensors; Robotic walkers; Supervised learning approaches; Deep learning[SDGs]SDG3Long short-term memory; Robotics; Robots; Walking aids; Behavior-based; Convolutional neural network; Learning modules; Locomotion activity; Mobility assistance; Multiple sensors; Robotic walkers; Supervised learning approaches; Deep learningconference paper10.1109/SMC.2019.89143792-s2.0-85076735659