Affective states are psycho-physiological constructs connecting mental and physiological processes. They can be represented in terms of arousal and valence according to the Russel’s model and can be extracted from physiological changes in human body. However, a well-established optimal feature set and a classification method effective in terms of accuracy and estimation time are not present in the literature. This paper aims at defining a reliable and efficient approach for real-time affective state estimation. To obtain this, the optimal physiological feature set and the most effective machine learning algorithm, to cope with binary as well as multi-class classification problems, were identified. ReliefF feature selection algorithm was implemented to define a reduced optimal feature set. Supervised learning algorithms, such as K-Nearest Neighbors (KNN), cubic and gaussian Support Vector Machine, and Linear Discriminant Analysis, were implemented to compare their effectiveness in affective state estimation. The developed approach was tested on physiological signals acquired on 20 healthy volunteers during the administration of images, belonging to the International Affective Picture System, conceived for inducing different affective states. ReliefF algorithm reduced the number of physiological features from 23 to 13. The performances of machine learning algorithms were compared and the experimental results showed that both accuracy and estimation time benefited from the optimal feature set use. Furthermore, the KNN algorithm resulted to be the most suitable for affective state estimation. The results of the assessment of arousal and valence states on 20 participants indicate that KNN classifier, adopted with the 13 identified optimal features, is the most effective approach for real-time affective state estimation.

Physical therapy keeps exploiting more and more the capabilities of the robot of adapting the treatments to patients’ needs. This paper aims at presenting a psychophysiological-aware control strategy for upper limb robot-aided orthopedic rehabilitation. The main features are the capability of i) generating point-to-point trajectories inside an adaptable workspace, ii) providing assistance in guiding the patients’ limbs in accomplishing the assigned task allowing them to freely move with a certain degree of spatial and temporal autonomy and iii) tuning the control parameters according to the patients’ kinematics performance and psychophysiological state. The implemented control strategy is validated in a real clinical setting on eight orthopedic patients undergoing twenty daily robot-aided rehabilitation sessions. The psychophysiological-aware control strategy evidenced a positive impact on the enrolled participants since they are effectively conducted in a calmer condition with respect to the patients who did not receive the psychophysiological adaptation. Moreover, clinical performance indicators suggest that the proposed tailored control strategy improves motor functions.

Pedicle screw fixation is a fundamental surgical procedure which requires high accuracy and strength from the surgeon who may be exposed to uncomfortable postures and muscular fatigue. The objective of this paper is to propose a novel approach to robot-aided pedicle screw fixation based on a Surgeon-Robot Shared control that makes the tapping procedure i.e. threading the patient's pedicle, semi-autonomous. The surgeon continues to have a full control over the surgical intervention by i) accurately move the robot end-effector using a hands-on control interface, i.e. the surgical tapper, along a pre-planned axis, ii) continuously controlling the forces exerted onto the patient spine during the tapping phase, iii) modulating the torque about the tapping axis by adequately tuning the tool-bone interaction force along the same axis. Furthermore, the procedure appears to be more comfortable and less tiring for the surgeon. The proposed approach was tested on eight subjects who were asked to perform the tapping procedure onto an anthropomorphic spine phantom. A comparative analysis among the proposed approach and the ones typically adopted in literature to perform pedicle screw placement was carried out. The experimental results demonstrated that proposed approach, with respect to the traditional robot-aided procedure, guarantees comparable accuracy and efficiency in the screw placement and lower fatigue and more comfortable postures for the surgeon.

The communication channels between physiotherapists and patients are many and varied. Rehabilitation robots are able to deliver intensive treatments and improve the patient's quality of life. However, rehabilitation robots in the literature do not integrate physical manipulation with natural verbal communication yet. This article proposes an innovative integrated system for motor rehabilitation based on the combination of physical and cognitive components to emulate the natural interaction between physiotherapists and patients. The proposed approach was validated in a laboratory setting with 20 healthy subjects. The cognitive system's ability to interact linguistically as well as the participants' kinematic performance and the emotional impact generated by two different robotic systems were assessed. The former integrates advanced linguistic capabilities and the latter lacks any verbal communication. The results showed that the presence of linguistic interaction promotes the quality of interaction, leading to improvements both in the execution of movements and in emotional terms.

This paper wants to stress the importance of human movement monitoring to prevent musculoskeletal disorders by proposing the WGD—Working Gesture Dataset, a publicly available dataset of assembly line working gestures that aims to be used for worker’s kinematic analysis. It contains kinematic data acquired from healthy subjects performing assembly line working activities using an optoelectronic motion capture system. The acquired data were used to extract quantitative indicators to assess how the working tasks were performed and to detect useful information to estimate the exposure to the factors that may contribute to the onset of musculoskeletal disorders. The obtained results demonstrate that the proposed indicators can be exploited to early detect incorrect gestures and postures and, consequently to prevent work-related disorders. The approach is general and independent on the adopted motion analysis system. It wants to provide indications for safely performing working activities. For example, the proposed WGD can also be used to evaluate the kinematics of workers in real working environments thanks to the adoption of unobtrusive measuring systems, such as wearable sensors through the extracted indicators and thresholds.