The Humble Charisma of a White-Dressed Man in a Desert Place: Pope Francis’ Communicative Style in the Covid-19 Pandemic

The context of deep uncertainty, fear, and “social distancing” characterizing the COVID-19 pandemic has led to a need for cultural anchorages and charismatic leaders who may conjointly and effectively support human beings, strengthen their identity, and empower social commitment. In this perspective, the charismatic leadership of Pope Francis, which is widely shared not only within the religious world, may play a crucial role in facing emergency with existential reasons and psychological resources. The general aim of this work is to shed light on the communicative features of the charismatic leadership of Pope Francis during the pandemic emergency; in order to better understand his effectiveness, we analyzed both the core issues and his multimodal body signals in the global TV event of the Universal Prayer with the Urbi et Orbi Blessing. The multimodal and discursive analyses of the homily enabled us to define the “humble” charisma of the Pope, which is based upon on authentic and informal presence, manifested emotional signals (and, in particular commotion) showing features of equity and familiarity. From a discursive point of view, the common and overarching affiliation is constructed through a multiple focus on the “we” pronoun, which is constructed through socio-epistemic rhetoric. The results show how this integrated methodological perspectives, which is multimodal and discursive, may offer meaningful pathways detection of effective and persuasive signals

A Multimodal Sensory Apparatus for Robotic Prosthetic Feet Combining Optoelectronic Pressure Transducers and IMU

Timely and reliable identification of control phases is functional to the control of a powered robotic lower-limb prosthesis. This study presents a commercial energy-store-and-release foot prosthesis instrumented with a multimodal sensory system comprising optoelectronic pressure sensors (PS) and IMU. The performance was verified with eight healthy participants, comparing signals processed by two different algorithms, based on PS and IMU, respectively, for real-time detection of heel strike (HS) and toe-off (TO) events and an estimate of relevant biomechanical variables such as vertical ground reaction force (vGRF) and center of pressure along the sagittal axis (CoPy). The performance of both algorithms was benchmarked against a force platform and a marker-based stereophotogrammetric motion capture system. HS and TO were estimated with a time error lower than 0.100 s for both the algorithms, sufficient for the control of a lower-limb robotic prosthesis. Finally, the CoPy computed from the PS showed a Pearson correlation coefficient of 0.97 (0.02) with the same variable computed through the force platform

An Underactuated Active Transfemoral Prosthesis With Series Elastic Actuators Enables Multiple Locomotion Tasks

Robotic lower limb prostheses have the power to revolutionize mobility by enhancing gait efficiency and facilitating movement. While several design approaches have been explored to create lightweight and energy-efficient devices, the potential of underactuation remains largely untapped in lower limb prosthetics. Taking inspiration from the natural harmony of walking, in this article, we have developed an innovative active transfemoral prosthesis. By incorporating underactuation, our design uses a single power actuator placed near the knee joint and connected to a differential mechanism to drive both the knee and ankle joints. We conduct comprehensive benchtop tests and evaluate the prosthesis with three individuals who have above-knee amputations, assessing its performance in walking, stair climbing, and transitions between sitting and standing. Our evaluation focuses on gathering position and torque data recorded from sensors integrated into the prosthesis and comparing these measurements to biomechanical data of able-bodied locomotion. Our findings highlight the promise of underactuation in advancing lower limb prosthetics and demonstrate the feasibility of our knee–ankle underactuated design in various tasks, showcasing its ability to replicate natural movement

A Classification Approach Based on Directed Acyclic Graph to Predict Locomotion Activities with One Inertial Sensor on the Thigh

Current state-of-the-art locomotion mode classifiers for controlling robotic lower-limb prostheses rely on multiple sensors to achieve high accuracy, prediction performance, and robustness to both speed changes and subject-specific gait patterns. However, multiple sensors placed on different body parts usually entail discomfort and poor usability for the user. This paper presents an intention detection method that relies on the features extracted from an inertial measurement unit worn on the thigh and an online phase estimator. The algorithm classifies the locomotion mode of the upcoming stride among the three modes of ground-level walking, stair ascent, and stair descent. A two-stage classification process first distinguishes between transient and steady-state strides and then classifies the locomotion mode of the impending stride based on directed acyclic graphs of binary classifiers. The classification is performed at 75% or 85% of the previous stride phase, respectively for steady-state and transient strides. Data were gathered from 10 healthy subjects and processed offline. Feature design and selection were based on the data of all subjects, while the classification performance was assessed by leave-one-subject-out cross-validation. Results presented a median recognition accuracy of 98.7% for steady-state strides and 95.6% for transitions, suggesting that the method was inherently robust to variations in gait cadence, since all of the features were phase-based and not dependent on fixed time intervals. These results inform the design of control strategies for active transfemoral prostheses able to predict the user's locomotion intention during the next stride, using minimum sensors