How do Physiotherapists and Patients talk? Developing and annotating RiMotivAzione dialogue corpus.
Authors: Andrea Bolioli, Francesca Alloatti, Mariafrancesca Guadalupi, Roberta Iolanda Lanzi, Giorgia Pregnolato, Andrea Turolla (CELI Language Technology, Università di Torino, San Camillo IRCCS, 2019)
Topic: Multimodal

Abstract
The research project RiMotivAzione aims at helping post-stroke patients who are following an arm and hand rehabilitation path. In this paper we present the RiMotivAzione corpus, the first collection of dialogues between physiotherapists and patients recorded in an Italian hospital and annotated following the RIAS annotation protocol. We describe the dataset, the methodologies applied and our first investigations on relevant features of the dialogue process. The corpus was the basis for the design of a conversational interface integrated with a wearable device for rehabilitation, to be used by the patient during the exercises that he or she may perform independently.

Introduction
In recent years, computational linguistics and medical research have started to collaborate in order to analyze communication in the healthcare domain, particularly between clinicians and patients. From a medical perspective, linguistic analysis and dialogue modeling can be used to better understand and potentially enhance communication in different healthcare settings (Sen et al., 2017; Chang et al., 2013; Marzuki et al., 2017), as well as to identify 'preclinical' or 'presymptomatic' diseases for specific ranges of patients, such as discovering early linguistic signs of cognitive decline (Beltrami et al., 2018). Natural Language Processing (NLP) technologies are also used to develop new communicative tools, such as virtual assistants, to alleviate the burden on medical personnel or shift to a home-based, patient-centered model of care. Through mHealth (mobile health), for example, people can receive assistance at home, and monitoring devices can check the well-being of a person (Sezgin et al., 2018). A recent review of scientific literature about Artificial Intelligence and IoT in healthcare can be found in (Shah and Chircu, 2018). The research project RiMotivAzione aims to help patients who have suffered a stroke and are following an arm and hand rehabilitation path. The goal is to motivate the patients to follow the assigned exercises through the use of a new wearable device with motion sensors developed by the Istituto Italiano di Tecnologia (IIT), integrated with a visual app and a conversational interface. This last component guides the user through the therapeutic path, proposing exercises, giving advice, and asking for feedback. The implementation of voice technologies in the healthcare domain allows patients with motor impairments to interact with devices through spoken language (Moore et al., 2018), while their arms and hands are busy performing the assigned exercises. The interaction is seamless and spontaneous. The patient can continue the therapy autonomously, guided by the voice assistant, while the physiotherapist can monitor their progress from a distance, evaluate their performance, and prevent neglect of the therapy. This system helps the patient stay motivated and reach their rehabilitation goals on time. Needless to say, these digital assistants are not meant to substitute the clinician.

Conclusion
We created a corpus of conversations between patients and clinicians, in Italian, and annotated the dialogue turns according to the Roter Interaction Analysis System (RIAS). This corpus was the first step in the design of a conversational interface integrated with a smart wearable device to guide and assist patients through the exercises assigned by the physiotherapist. The first step in future work will be to deepen the linguistic analysis conducted on the corpus, especially regarding the tagged dialogue acts. A more in-depth qualitative investigation over the data will be carried out. The second step will be to enrich the dataset: unfortunately, only two patients were deemed appropriate for the experimentation, while a corpus ideally should contain dialogues from more speakers. The RiMotivAzione corpus can be requested from the authors for research purposes. The system prototype will be tested at San Camillo Hospital by a set of stroke patients, following clinical trial procedures. Thanks to the results of the test, we will produce experimental data to investigate if and how a voice assistant integrated with a wearable device can increase the effectiveness of the therapy.