Hands-Off Therapist Robot Behavior Adaptation to User Personality for Post-Stroke Rehabilitation Therapy

This paper describes a hands-off therapist robot that monitors, assists, encourages, and socially interacts with post-stroke users in the process of rehabilitation exercises. We developed a behavior adaptation system that takes advantage of the users introversion-extroversion personality trait and the number of exercises performed in order to adjust its social interaction parameters (e.g., interaction distances/proxemics, speed, and vocal content) toward a customized post-stroke rehabilitation therapy. The experimental results demonstrate the robot's autonomous behavior adaptation to the user's personality and the resulting user improvements of the exercise task performance

Encouraging User Autonomy through Robot-Mediated Intervention

In this paper, we focus on the question of promoting user autonomy at a healthcare task. During a robot-mediated intervention, socially assistive robot should seek to encourage users to learn skills and behaviors that will generalize and persist beyond the duration of the intervention. Treating a care-receiver as an apprentice rather than a dependent results in greater proficiency at self-management [2]. This philosophy must be incorporated into the design and implementation of robot-mediated healthcare interventions in order for them to be accepted by real-world users. Our approach toward encouraging user autonomy and promoting generalized skill learning was to model the occupational therapy technique of graded cueing [1]. Graded cueing involves giving a patient the minimum required feedback while guiding them through a task. This method promotes generalized skill learnin

Where am i? scene recognition for mobile robots using audio features

Automatic recognition of unstructured environments is an important problem for mobile robots. We focus on using audio features to recognize different auditory environments, where they are characterized by different types of sounds. The use of audio information provides a complementary means of scene recognition that can effectively augment visual information. In particular, audio can be used toward both the analysis and characterization of the environment at a higher level of abstraction. We begin our investigation of recognizing different auditory environments with the audio information. In this paper, we utilize low-level audio features from a mobile robot and investigate using highlevel features based on spectral analysis for scene characterization, and a recognition system was built to discriminate between different environments based on these audio features found. 1