Analyzing Gait Adaptation with Hemiplegia Simulation Suits and Digital Twins

To advance the development of assistive and rehabilitation robots, it is essential to conduct experiments early in the design cycle. However, testing early prototypes directly with users can pose safety risks. To address this, we explore the use of condition-specific simulation suits worn by healthy participants in controlled environments as a means to study gait changes associated with various impairments and support rapid prototyping. This paper presents a study analyzing the impact of a hemiplegia simulation suit on gait. We collected biomechanical data using a Vicon motion capture system and Delsys Trigno EMG and IMU sensors under four walking conditions: with and without a rollator, and with and without the simulation suit. The gait data was integrated into a digital twin model, enabling machine learning analyses to detect the use of the simulation suit and rollator, identify turning behavior, and evaluate how the suit affects gait over time. Our findings show that the simulation suit significantly alters movement and muscle activation patterns, prompting users to compensate with more abrupt motions. We also identify key features and sensor modalities that are most informative for accurately capturing gait dynamics and modeling human-rollator interaction within the digital twin framework.

Safe and Socially Aware Multi-Robot Coordination in Multi-Human Social Care Settings

This research investigates strategies for multi-robot coordination in multi-human environments. It proposes a multi-objective learning-based coordination approach to addressing the problem of path planning, navigation, task scheduling, task allocation, and human-robot interaction in multi-human multi-robot (MHMR) settings.

Human strategies for correcting `human-robot' errors during a laundry sorting task

Mental models and expectations underlying human-human interaction (HHI) inform human-robot interaction (HRI) with domestic robots. To ease collaborative home tasks by improving domestic robot speech and behaviours for human-robot communication, we designed a study to understand how people communicated when failure occurs. To identify patterns of natural communication, particularly in response to robotic failures, participants instructed Laundrobot to move laundry into baskets using natural language and gestures. Laundrobot either worked error-free, or in one of two error modes. Participants were not advised Laundrobot would be a human actor, nor given information about error modes. Video analysis from 42 participants found speech patterns, included laughter, verbal expressions, and filler words, such as ``oh'' and ``ok'', also, sequences of body movements, including touching one's own face, increased pointing with a static finger, and expressions of surprise. Common strategies deployed when errors occurred, included correcting and teaching, taking responsibility, and displays of frustration. The strength of reaction to errors diminished with exposure, possibly indicating acceptance or resignation. Some used strategies similar to those used to communicate with other technologies, such as smart assistants. An anthropomorphic robot may not be ideally suited to this kind of task. Laundrobot's appearance, morphology, voice, capabilities, and recovery strategies may have impacted how it was perceived. Some participants indicated Laundrobot's actual skills were not aligned with expectations; this made it difficult to know what to expect and how much Laundrobot understood. Expertise, personality, and cultural differences may affect responses, however these were not assessed.