Showing papers by "Paolo Bonato published in 2023"

The Back Muscle Surface Electromyography-Based Fatigue Index: A Digital Biomarker of Human Neuromuscular Aging?

Abstract: As part of our quest for digital biomarkers of neuromuscular aging, and encouraged by recent findings in healthy volunteers, this study investigated if the instantaneous median frequency (IMDF) derived from back muscle surface electromyographic (SEMG) data monitored during cyclic back extensions could reliably differentiate between younger and older individuals with cLBP. A total of 243 persons with cLBP participated in three experimental sessions: at baseline, one to two days after the first session, and then again approximately six weeks later. During each session, the study participants performed a series of three isometric maximal voluntary contractions (MVC) of back extensors using a dynamometer. These were followed by an isometric back extension at 80% MVC, and—after a break—25 slow cyclic back extensions at 50% MVC. SEMG data were recorded bilaterally at L5 (multifidus), L2 (longissimus dorsi), and L1 (iliocostalis lumborum). Linear mixed-effects models found the IMDF-SEMG time-course changes more rapidly in younger than in older individuals, and more prominently in male participants. The absolute and relative reliabilities of the SEMG time–frequency representations were well compared between older and younger participants. The results indicated an overall good relative reliability, but variable absolute reliability levels. IMDF-SEMG estimates derived from cyclic back extensions proved to be successful in reliably detecting differences in back muscle function in younger vs. older persons with cLBP. These findings encourage further research, with a focus on assessing whether an IMDF-SEMG-based index could be utilized as a tool to achieve the preclinical detection of back muscle aging, and possibly predict the development of back muscle sarcopenia.

Assessment of Demand-Side Management on the Performance of a Single-Dwelling Mechanical Ventilation Plus Radiant Floor System

Abstract: This paper focuses on the profitability of demand-side management strategies developed for a single-dwelling mechanical ventilation plus radiant floor system. Energy savings and comfort indicators are quantified for a number of control options, including demand-controlled ventilation and temperature setbacks. The assessment is based on numerical energy simulations conducted in TRNSYS for the climate of Bolzano (Italy). To perform the simulations, numerical models of the energy system and the reference dwelling were developed. Based on the analysed climate and building, it was found that demand-side management strategies can have a significant impact on the energy consumption and time distribution of energy loads: demand control ventilation allows the achievement of consistent energy savings in the electrical consumption of the fans (up to 37 %), whereas the use of an adaptive dehumidification setpoint can lead to savings within the range of 10 % in summer electrical consumption. The use of non-occupancy temperature setbacks does not show a significant impact on the annual thermal demand, although the time pattern of the loads is considerably affected, with a cascade effect on the performance of the air-to-water heat pump. The use of the climatic curve parameters at the generator allows an improvement of the electrical performance of the heat pump, increasing the SCOP of more than 20 %.

An Avatar Robot Overlaid with the 3D Human Model of a Remote Operator

Abstract: Although telepresence assistive robots have made significant progress, they still lack the sense of realism and physical presence of the remote operator. This results in a lack of trust and adoption of such robots. In this paper, we introduce an Avatar Robot System which is a mixed real/virtual robotic system that physically interacts with a person in proximity of the robot. The robot structure is overlaid with the 3D model of the remote caregiver and visualized through Augmented Reality (AR). In this way, the person receives haptic feedback as the robot touches him/her. We further present an Optimal Non-Iterative Alignment solver that solves for the optimally aligned pose of 3D Human model to the robot (shoulder to the wrist non-iteratively). The proposed alignment solver is stateless, achieves optimal alignment and faster than the baseline solvers (demonstrated in our evaluations). We also propose an evaluation framework that quantifies the alignment quality of the solvers through multifaceted metrics. We show that our solver can consistently produce poses with similar or superior alignments as IK-based baselines without their potential drawbacks.

Language Control in Robotics

Abstract: For robots performing a assistive tasks for the humans, it is crucial to synchronize their speech with their motions, in order to achieve natural and effective human-robot interaction. When a robot's speech is out of sync with their motions, it can cause confusion, frustration, and misinterpretation of the robot's intended meaning. Humans are accustomed to using both verbal and nonverbal cues to understand and coordinate with each other, and robots that can align their speech with their actions can tap into this natural mode of communication. In this research, we propose a language controller for robots to control the pace, tone, and pauses of their speech along with it's motion in the trajectory. The robot's speed is adjusted using an admittance controller based on the force input from the user, and the robot's speech speed is modulated using phase-vocoders.