Adriana Guedes Arrais

Abstract
Over the last few years, Man-Machine collaborative systems have been increasingly present in daily routines. In these systems, one operator usually controls the machine through explicit commands and assesses the information through a graphical user interface. Direct & implicit interaction between the machine and the user does not exist. This work presents a man-machine symbiotic concept & system where such implicit interaction is possible targeting search and rescue scenarios. Based on measuring physiological variables (e.g. body movement or electrocardiogram) through wearable devices, this system is capable of computing the psycho-physiological state of the human and autonomously identify abnormal situations (e.g. fall or stress). This information is injected into the control loop of the machine that can alter its behavior according to it, enabling an implicit man-machine communication mechanism. A proof of concept of this system was tested at the ARTEX (ARmy Technological EXperimentation) exercise organized by the Portuguese Army involving a military agent and a drone. During this event the soldier was equipped with a kit of wearables that could monitor several physiological variables and automatically detect a fall during a mission. This information was continuously sent to the drone that successfully identified this abnormal situation triggering the take-off and a situation awareness fly-by flight pattern, delivering a first-aid kit to the soldier in case he did not recover after a pre-determined time period. The results were very positive, proving the possibility and feasibility of a symbiotic system between humans and machines.

Abstract
The progressive and complex nature of Parkinson's disease (PD) may largely benefit from regular and personalised monitoring, which is beyond the current clinical practice and routinely available systems. This paper proposes a simple and effective system to address this issue by using a wearable device to quantify a key PD's motor symptom - gait impairment as a proof-of-concept for a future broader approach. In this study, 60 inertial signals were collected from the ankle in 41 PD patients during a clinical standard gait assessment exercise. Each exercise iteration was classified by a specialised neurologist based on the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). A signal processing and feature extraction pipeline was employed to characterise gait, followed by a statistical analysis of their ability to differentiate between the 5 levels of impairment. The results revealed that 4 of the 8 studied features exhibited high discriminatory power between different severity levels of gait impairment, with statistical significance. The distinguishing capability of these 4 extracted features - gait consistency, rotation angle, mean height and length of steps - holds great promise for the development of a gait severity quantification remote monitoring for PD patients at home or on the move, proving the concept of the usefulness of wearable devices for regular and personalised PD symptom monitoring.

Abstract
Agriculture work is physically demanding and the sector workers have a high incidence of musculoskeletal disorders. The shift to Agriculture 5.0 and the advancement of precision agriculture have involved the digitalization of this industry, but tend to marginalise the workers, though they are still essential to more thorough tasks that cannot be automated. In order to tackle the necessity to support the monitoring of agriculture workers, we developed quantification algorithms, incorporated in a mobile application, which calculate metrics based on the signals gathered by wearable sensors. Our proximity to the Douro region lead us to focus on metrics that could be more meaningful for viniculture, namely the quantification of trunk inclinations and shear cuts, very common in this production. The developed algorithms showed an error of 1.36 degrees for the calculus of inclination and 2.43 cuts for the prediction of cuts when tested with on-field data. These results suggest that the created system has the viability to be used by agricultures and give reliable feedback on their workers.