Specification of an integrated information architecture for a mobile teleoperated robot for home telecare

Robots and the Possibility of Humanistic Care

Care robots are likely to perform increasingly sophisticated caring activities that some will consider comforting and valuable. They will get increasingly humanlike and lifelike. This paper addresses the conceptual question: Even if robots can assist and ease people's suffering, can such machines provide humanistic care? Arguably, humanistic care is the most humanly distinctive and deepest form of care there is. As such, it may be thought to show most starkly the gulf between human and robot caregiving. The paper argues that humanistic caregiving is indeed a distinctive form of 'affective' care dependent on certain uniquely human characteristics or aspects of our humanity which can provide a profound kind of comfort to suffering people. It then argues that there is an important conceptual sense in which robots cannot provide humanistic care. Nonetheless, the paper subsequently suggests that we may recognize a useful sense in which robots, of a suitably anthropomorphic type, can provide humanistic care. Robots might 'express' to people with physical, social, or emotional needs the kind of humanistic care that only human beings can provide but that sufferers can nonetheless receive comfort from precisely because of what is expressed to them. Although this sense of humanistic robot care is derivative from uniquely human care, and although it is wide open to social and ethical criticism, it is nonetheless an idea worth clarifying for anyone interested in the possibilities and limits of robot care.

The Route of Motor Recovery in Stroke Patients Driven by Exoskeleton-Robot-Assisted Therapy: A Path-Analysis

Background: Exoskeleton-robot-assisted therapy is known to positively affect the recovery of arm functions in stroke patients. However, there is a lack of evidence regarding which variables might favor a better outcome and how this can be modulated by other factors. Methods: In this within-subject study, we evaluated the efficacy of a robot-assisted rehabilitation system in the recovery of upper limb functions. We performed a path analysis using a structural equation modeling approach in a large sample of 102 stroke patients (age 63.6 ± 13.1 years; 61% men) in the post-acute phase. They underwent 7 weeks of bilateral arm training assisted by an exoskeleton robot combined with a conventional treatment (consisting of simple physical activity together with occupational therapy). The upper extremity section of the Fugl–Meyer (FM-UE) scale at admission was used as a predictor of outcome, whereas age, gender, side of the lesion, days from the event, pain scale, duration of treatment, and number of sessions as mediators. Results: FM-UE at admission was a direct predictor of outcome, as measured by the motricity index of the contralateral upper limb and trunk control test, without any other mediating factors. Age, gender, days from the event, side of lesion, and pain scales were independently associated with outcomes. Conclusions: To the best of our knowledge, this is the first study assessing the relationship between clinical variables and outcomes induced by robot-assisted rehabilitation with a path-analysis model. We define a new route for motor recovery of stroke patients driven by exoskeleton-robot-assisted therapy, highlighting the role of FM-UE at admission as a useful predictor of outcome, although other variables need to be considered in the time-course of disease.

Exploring the factors that influence physician technostress from using mobile electronic medical records

This paper proposes an integrated model for investigating how physicians' perceived individual and technology characteristics affect their technological stress (technostress) that is derived from using mobile electronic medical records (MEMRs). Individual characteristics comprise constructs of mobile self-efficacy and technology dependence, whereas perceived technology characteristics comprise constructs of perceived usefulness, complexity, and reliability. We employed the survey method to collect 158 valid questionnaires from physicians working at three branch hospitals to determine perceptions regarding MEMRs, yielding a response rate of 33.62%. Partial least squares, a structural equation modeling technique, was used for model examination and hypothesis testing. The results show that physicians have a low perception of MEMR dependence and technostress. Furthermore, physicians' perceived MEMR technology dependency, mobile self-efficacy, and complexity were proven to significantly affect physician technostress when using MEMRs, whereas perceived usefulness and reliability were not. The explanatory power of the research model reached 67.8%. The results of this study provide valuable insights and significant knowledge for technostress in health care, particularly from academic and practical perspectives.