Session 1-1:

Technology, Robotics and Healthcare

Date: Thursday 1st October 2020. 14:00-15:30

Scientific chair:

Zoom room: 

Physical room:

14:00-14:20: Fieldwork and field trials in hospitals: Co-designing a robotic solution to support data collection in geriatric assessment. Karine Lan Hing Ting, Dimitri Voilmy, Quitterie De Roll, Ana Iglesias and Rebeca Marfil.
Abstract: Comprehensive Geriatric Assessment (CGA) is a multidimensional and multidisciplinary diagnostic instrument that helps provide personalized care to the elderly, by evaluating their state of health. This evaluation is based on extensive data collection about the frail older person’s medical, psychosocial, and functional limitations, in order to develop a coordinated plan to maximize overall health with aging. Being an interdisciplinary effort, it requires the coordination of several clinical professionals. This coordination rests, for a large part, on patient data sharing. In the social and economic context of growing ageing populations, medical experts can save time and effort if provided with interactive tools to efficiently assist them in doing CGAs, managing either standardized tests or data collection. Recent research proposes the use of social robots as the central part of these tools. This paper presents the first and last step of the research around the design and evaluation of the CLARC robot, that is able to interact efficiently with the patient to gather data: needs analysis concerning clinical data management, and pilot experiment in real-life conditions in a rehab hospital.
14:30-14:50: Sound Sensor to Detect the Number of People in a Room. Sami Boutamine, Dan Istrate, Jerome Boudy, Yoann Fousseret and Yves Parmantier.
Abstract: Ambient sound monitoring is a widely used strategy to follow older adults, which could help them achieve healthy ageing with comfort and security. In a previous work, we have already developed a smart audio sensor able to recognize everyday life sounds in order to detect activities of daily living (ADL) and distress situations. In this paper, we propose a new functionality by analyzing the speech flow to detect the number of people in a room. The proposed algorithms are based on speaker diarization methods. This information can be used to better detect activities of daily life but also to know when the person is home alone. This functionality can also offer more comfort through light, heating and air conditioning adaptation to the number of people in an environment. The sensor output is sent through KNX bus to COCAPS project platform.
15:00-15:20: An Automatic Turn Phase Detection Method based on Doppler Radar System with CWT Analysis. Racha Soubra, Aly Chkeir and Farah Chehade.
Abstract: In this research, we highlight the need for in-home gait monitoring to evaluate the functional status of older adults. Most of the mobility evaluation tests are accomplished in clinics through gait and transfer tasks such as walking a certain distance and turning while walking. Accordingly, the main objective of this study focuses on providing a continuous assessment technique with daily basis records. Our methodology is based on a Doppler radar system with wavelet transform analysis. First, we intend to select the convenient mother wavelet for Doppler signals, as different wavelets can lead to different outcomes. After that, we aim to detect a transfer phase while walking in the home automatically. An experimental protocol was performed in the laboratory of the university. This experiment was based on an accurate 3D motion-capture camera system (Vicon) that is used in order to validate our results. Outcomes revealed five top rank mother wavelets and showed that the variance is a valuable parameter to distinguish between walking and turning phases. DARC algorithm was then applied to the cumulative signal of variance to automatically determine the starting and ending point of a turning phase.