Evaluation of Various State of the Art Head Pose Estimation Algorithms for Clinical Scenarios.

Head pose assessment can reveal important clinical information on human motor control. Quantitative assessment have the potential to objectively evaluate head pose and movements' specifics, in order to monitor the progression of a disease or the effectiveness of a treatment. Optoelectronic camera-based motion-capture systems, recognized as a gold standard in clinical biomechanics, have been proposed for head pose estimation. However, these systems require markers to be positioned on the person's face which is impractical for everyday clinical practice. Furthermore, the limited access to this type of equipment and the emerging trend to assess mobility in natural environments support the development of algorithms capable of estimating head orientation using off-the-shelf sensors, such as RGB cameras. Although artificial vision is a popular field of research, limited validation of human pose estimation based on image recognition suitable for clinical applications has been performed. This paper first provides a brief review of available head pose estimation algorithms in the literature. Current state-of-the-art head pose algorithms designed to capture the facial geometry from videos, OpenFace 2.0, MediaPipe and 3DDFA_V2, are then further evaluated and compared. Accuracy is assessed by comparing both approaches to a baseline, measured with an optoelectronic camera-based motion-capture system. Results reveal a mean error lower or equal to 5.6∘ for 3DDFA_V2 depending on the plane of movement, while the mean error reaches 14.1∘ and 11.0∘ for OpenFace 2.0 and MediaPipe, respectively. This demonstrates the superiority of the 3DDFA_V2 algorithm in estimating head pose, in different directions of motion, and suggests that this algorithm can be used in clinical scenarios.

Usability Evaluation of the SmartWheeler through Qualitative and Quantitative Studies.

BACKGROUND: Intelligent powered wheelchairs remain a popular research topic that can improve users' quality of life. Although our multidisciplinary research team has put a lot of effort into adding features based on end-users needs and impairments since 2006, there are still open issues regarding the usability and functionalities of an intelligent powered wheelchair (IPW). METHODS: For this reason, this research presents an experience with our IPW followed by a study in two parts: a quantitative one based on the System Usability Scale (SUS) questionnaire and a qualitative one through open questions regarding IPW functionalities with novice users, e.g., IPW non-users. These users never used an IPW before, but are users and aware of the impacts of the technology used in our IPW, being undergraduate to postdoctoral students and staff (faculty, lecturers, research engineers) at the Faculty of Engineering of Université de Sherbrooke. RESULTS: The qualitative analyses identified different behaviours among the novice users. The quantitative analysis via SUS questionnaire done with novice users reports an "okay" rating (equivalent with a C grade or 68 SUS Score) for our IPW's usability. Moreover, advantages and disadvantages opinions were gathered on the IPW as well as comments which can be used to improve the system. CONCLUSIONS: The results reported in these studies show that the system, e.g., IPW, was judged to be sufficiently usable and robust by novice users, with and without experience with the software used in developing the IPW.

OpenTera: A microservice architecture solution for rapid prototyping of robotic solutions to COVID-19 challenges in care facilities.

As telecommunications technology progresses, telehealth frameworks are becoming more widely adopted in the context of long-term care (LTC) for older adults, both in care facilities and in homes. Today, robots could assist healthcare workers when they provide care to elderly patients, who constitute a particularly vulnerable population during the COVID-19 pandemic. Previous work on user-centered design of assistive technologies in LTC facilities for seniors has identified positive impacts. The need to deal with the effects of the COVID-19 pandemic emphasizes the benefits of this approach, but also highlights some new challenges for which robots could be interesting solutions to be deployed in LTC facilities. This requires customization of telecommunication and audio/video/data processing to address specific clinical requirements and needs. This paper presents OpenTera, an open source telehealth framework, aiming to facilitate prototyping of such solutions by software and robotic designers. Designed as a microservice-oriented platform, OpenTera is an end-to-end solution that employs a series of independent modules for tasks such as data and session management, telehealth, daily assistive tasks/actions, together with smart devices and environments, all connected through the framework. After explaining the framework, we illustrate how OpenTera can be used to implement robotic solutions for different applications identified in LTC facilities and homes, and we describe how we plan to validate them through field trials.