Infertility and assisted reproduction: legislative and cultural evolution in Italy. Infertilità e procreazione assistita: evoluzione legislativa e culturale in Italia

The social representation of the infertility coming out from the national newspapers has been explored in relation to the enactment of the Law 40/2004 and the 2005 referendum. All the articles (n=731), published in the last 15 years by the highest circulation Italian newspapers, have been collected in a corpus (token=360345) that underwent a multivariate analysis of textual data. Results show a difficulty in complementing together the mind and the body. The “biologic” is represented as the place of the technical and medical intervention while the “psycho-social” is conceived as the place of the family storytelling of the personal experiences of infertility and the public ethical debate on it. Before the law, newspapers deal with the theme of the family experience probably supporting the law enactment. After the referendum this thematic is dismissed and the theme of infertility as a pathology to treat emerges, bringing back infertility to the medical issu

Human Hand Motion Analysis and Synthesis of Optimal Power Grasps for a Robotic Hand

Biologically inspired robotic systems can find important applications in biomedical robotics, since studying and replicating human behaviour can provide new insights into motor recovery, functional substitution and human-robot interaction. The analysis of human hand motion is essential for collecting information about human hand movements useful for generalizing reaching and grasping actions on a robotic system. This paper focuses on the definition and extraction of quantitative indicators for describing optimal hand grasping postures and replicating them on an anthropomorphic robotic hand. A motion analysis has been carried out on six healthy human subjects performing a transverse volar grasp. The extracted indicators point to invariant grasping behaviours between the involved subjects, thus providing some constraints for identifying the optimal grasping configuration. Hence, an optimization algorithm based on the Nelder-Mead simplex method has been developed for determining the optimal grasp configuration of a robotic hand, grounded on the aforementioned constraints. It is characterized by a reduced computational cost. The grasp stability has been tested by introducing a quality index that satisfies the form-closure property. The grasping strategy has been validated by means of simulation tests and experimental trials on an arm-hand robotic system. The obtained results have shown the effectiveness of the extracted indicators to reduce the non-linear optimization problem complexity and lead to the synthesis of a grasping posture able to replicate the human behaviour while ensuring grasp stability. The experimental results have also highlighted the limitations of the adopted robotic platform (mainly due to the mechanical structure) to achieve the optimal grasp configuration

Exploring the EMG transient: the muscular activation sequences used as novel time-domain features for hand gestures classification

IntroductionMuscular activation sequences have been shown to be suitable time-domain features for classification of motion gestures. However, their clinical application in myoelectric prosthesis control was never investigated so far. The aim of the paper is to evaluate the robustness of these features extracted from the EMG signal in transient state, on the forearm, for classifying common hand tasks.MethodsThe signal associated to four hand gestures and the rest condition were acquired from ten healthy people and two persons with trans-radial amputation. A feature extraction algorithm allowed for encoding the EMG signals into muscular activation sequences, which were used to train four commonly used classifiers, namely Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Non-linear Logistic Regression (NLR) and Artificial Neural Network (ANN). The offline performances were assessed with the entire sample of recruited people. The online performances were assessed with the amputee subjects. Moreover, a comparison of the proposed method with approaches based on the signal envelope in the transient state and in the steady state was conducted.ResultsThe highest performance were obtained with the NLR classifier. Using the sequences, the offline classification accuracy was higher than 93% for healthy and amputee subjects and always higher than the approach with the signal envelope in transient state. As regards the comparison with the steady state, the performances obtained with the proposed method are slightly lower (<4%), but the classification occurred at least 200 ms earlier. In the online application, the motion completion rate reached up to 85% of the total classification attempts, with a motion selection time that never exceeded 218 ms.DiscussionMuscular activation sequences are suitable alternatives to the time-domain features commonly used in classification problems belonging to the sole EMG transient state and could be potentially exploited in control strategies of myoelectric prosthesis hands

Novel fragile X syndrome 2D and 3D brain models based on human isogenic FMRP-KO iPSCs

Fragile X syndrome (FXS) is a neurodevelopmental disorder, characterized by intellectual disability and sensory deficits, caused by epigenetic silencing of the FMR1 gene and subsequent loss of its protein product, fragile X mental retardation protein (FMRP). Delays in synaptic and neuronal development in the cortex have been reported in FXS mouse models; however, the main goal of translating lab research into pharmacological treatments in clinical trials has been so far largely unsuccessful, leaving FXS a still incurable disease. Here, we generated 2D and 3D in vitro human FXS model systems based on isogenic FMR1 knock-out mutant and wild-type human induced pluripotent stem cell (hiPSC) lines. Phenotypical and functional characterization of cortical neurons derived from FMRP-deficient hiPSCs display altered gene expression and impaired differentiation when compared with the healthy counterpart. FXS cortical cultures show an increased number of GFAP positive cells, likely astrocytes, increased spontaneous network activity, and depolarizing GABAergic transmission. Cortical brain organoid models show an increased number of glial cells, and bigger organoid size. Our findings demonstrate that FMRP is required to correctly support neuronal and glial cell proliferation, and to set the correct excitation/inhibition ratio in human brain development

Physiological Responses During Hybrid BNCI Control of an Upper-Limb Exoskeleton

When combined with assistive robotic devices, such as wearable robotics, brain/neural-computer interfaces (BNCI) have the potential to restore the capabilities of handicapped people to carry out activities of daily living. To improve applicability of such systems, workload and stress should be reduced to a minimal level. Here, we investigated the user’s physiological reactions during the exhaustive use of the interfaces of a hybrid control interface. Eleven BNCI-naive healthy volunteers participated in the experiments. All participants sat in a comfortable chair in front of a desk and wore a whole-arm exoskeleton as well as wearable devices for monitoring physiological, electroencephalographic (EEG) and electrooculographic (EoG) signals. The experimental protocol consisted of three phases: (i) Set-up, calibration and BNCI training; (ii) Familiarization phase ; and (iii) Experimental phase during which each subject had to perform EEG and EoG tasks. After completing each task, the NASA-TLX questionnaire and self-assessment manikin (SAM) were completed by the user. We found significant differences (p-value < 0.05) in heart rate variability (HRV) and skin conductance level (SCL) between participants during the use of the two different biosignal modalities (EEG, EoG) of the BNCI. This indicates that EEG control is associated with a higher level of stress (associated with a decrease in HRV) and mental work load (associated with a higher level of SCL) when compared to EoG control. In addition, HRV and SCL modulations correlated with the subject’s workload perception and emotional responses assessed through NASA-TLX questionnaires and SAM

Bio-Cooperative Approach for the Human-in-the-Loop Control of an End-Effector Rehabilitation Robot

The design of patient-tailored rehabilitative protocols represents one of the crucial factors that influence motor recovery mechanisms, such as neuroplasticity. This approach, including the patient in the control loop and characterized by a control strategy adaptable to the user's requirements, is expected to significantly improve functional recovery in robot-aided rehabilitation. In this paper, a novel 3D bio-cooperative robotic platform is developed. A new arm-weight support system is included into an operational robotic platform for 3D upper limb robot-aided rehabilitation. The robotic platform is capable of adapting therapy characteristics to specific patient needs, thanks to biomechanical and physiological measurements, and thus closing the subject in the control loop. The level of arm-weight support and the level of the assistance provided by the end-effector robot are varied on the basis of muscular fatigue and biomechanical indicators. An assistance-as-needed approach is applied to provide the appropriate amount of assistance. The proposed platform has been experimentally validated on 10 healthy subjects; they performed 3D point-to-point tasks in two different conditions, i.e., with and without assistance-as-needed. The results have demonstrated the capability of the proposed system to properly adapt to real needs of the patients. Moreover, the provided assistance was shown to reduce the muscular fatigue without negatively influencing motion execution

Hybrid brain/neural interface and autonomous vision-guided whole-arm exoskeleton control to perform activities of daily living (ADLs)

[EN] Background The aging of the population and the progressive increase of life expectancy in developed countries is leading to a high incidence of age-related cerebrovascular diseases, which affect people's motor and cognitive capabilities and might result in the loss of arm and hand functions. Such conditions have a detrimental impact on people's quality of life. Assistive robots have been developed to help people with motor or cognitive disabilities to perform activities of daily living (ADLs) independently. Most of the robotic systems for assisting on ADLs proposed in the state of the art are mainly external manipulators and exoskeletal devices. The main objective of this study is to compare the performance of an hybrid EEG/EOG interface to perform ADLs when the user is controlling an exoskeleton rather than using an external manipulator. Methods Ten impaired participants (5 males and 5 females, mean age 52 +/- 16 years) were instructed to use both systems to perform a drinking task and a pouring task comprising multiple subtasks. For each device, two modes of operation were studied: synchronous mode (the user received a visual cue indicating the sub-tasks to be performed at each time) and asynchronous mode (the user started and finished each of the sub-tasks independently). Fluent control was assumed when the time for successful initializations ranged below 3 s and a reliable control in case it remained below 5 s. NASA-TLX questionnaire was used to evaluate the task workload. For the trials involving the use of the exoskeleton, a custom Likert-Scale questionnaire was used to evaluate the user's experience in terms of perceived comfort, safety, and reliability. Results All participants were able to control both systems fluently and reliably. However, results suggest better performances of the exoskeleton over the external manipulator (75% successful initializations remain below 3 s in case of the exoskeleton and bellow 5s in case of the external manipulator). Conclusions Although the results of our study in terms of fluency and reliability of EEG control suggest better performances of the exoskeleton over the external manipulator, such results cannot be considered conclusive, due to the heterogeneity of the population under test and the relatively limited number of participants.This study was funded by the European Commission under the project AIDE (G.A. no: 645322), Spanish Ministry of Science and Innovation, through the projects PID2019-108310RB-I00 and PLEC2022-009424 and by the Ministry of Universities and European Union, "fnanced by European Union-Next Generation EU" through Margarita Salas grant for the training of young doctors.Catalán, JM.; Trigili, E.; Nann, M.; Blanco-Ivorra, A.; Lauretti, C.; Cordella, F.; Ivorra, E.... (2023). Hybrid brain/neural interface and autonomous vision-guided whole-arm exoskeleton control to perform activities of daily living (ADLs). Journal of NeuroEngineering and Rehabilitation. 20(1):1-16. https://doi.org/10.1186/s12984-023-01185-w11620

Apnea detection in newborns using an abdominal IMU

[Resumen] En los últimos años, ha habido un aumento en los casos de colapso postnatal repentino en lactantes aparentemente sanos durante las primeras 24 horas de vida. Actualmente, los sistemas de monitoreo existentes no son capaces de detectar situaciones de apnea. En este trabajo, se propone un sistema que incluye un dispositivo electrónico para monitorear el movimiento abdominal generado por la respiración en recién nacidos y, una interfaz de usuario para el seguimiento de información. Se llevaron a cabo pruebas experimentales para validar el funcionamiento del sistema, tanto en posición supina como prona, así como en situaciones que involucraron el contacto entre la madre y el recién nacido. En general, los resultados obtenidos respaldan la viabilidad de utilizar dispositivos electrónicos de contacto directo como una alternativa prometedora para mejorar la detección de la respiración y la identificación de situaciones de apnea.[Abstract] In recent years, there has been an increase in cases of sudden postnatal collapse in apparently healthy newborns within the first 24 hours of life. Currently, existing monitoring systems are unable to detect apnea situations. In this study, a system is proposed that includes an electronic device to monitor the abdominal movement generated by respiration in newborns, along with a user interface for information tracking. Experimental tests were conducted to validate the system’s performance in both supine and prone positions, as well as in situations involving mother-infant contact. Overall, the results support the feasibility of using direct contact electronic devices as a promising alternative to improve the detection of respiration and identify apnea situations.Ministerio de Ciencia e Innovación; PID2019-111023RB-C3

Grasping algorithms for anthropomorphic robotic hands inspired to human behavior

Biologically inspired robotic systems are becoming increasingly popular, especially in the field of medical robotics, in which building robotic devices able to replicate the human behavior guarantees obtaining motor recovery, functional substitution or human-robot interaction as human-like as possible. It is widely recognized that robotic rehabilitation devices improve the performance of the rehabilitation therapy performed by a human therapist in terms of action repetition and accurate tracking of the desired trajectory. Taking advantage from the plasticity of the neuro-muscular system, a human-inspired robotic rehabilitation therapy helps patients to re-learn movements. In the field of upper limb prosthetics, since the aim of a prosthetic hand is to replace a human hand, the robotic device has to be not only functional, but also as similar as possible to the human one both from the morphological point of view and as regards movement naturalness. On the other hand, since grasping is one of the human skills that robotic researchers mostly attempt at imitating, in the development of new robotic hands, the inspiration to the human hand behavior is increasing. From the analysis of the grasping action performed by human beings and from the study of the anatomy of the human hand and of its behavior during grasping, it is possible to obtain useful information for developing human-like grasping algorithms so as to acquire a better knowledge of the hand kinematics in order to design new human-like robotic hands and new rehabilitation devices. The definition of the kinematic structure of the hand and of the fingers is, in fact, the basis for designing new dexterous robotic hands and devices devoted to interact with the human hand (such as rehabilitation devices). Therefore this work is focused on the study of the hand kinematics, providing the basis for a further study regarding the hand dynamics. All the experiments done are in fact adaptable for a future study of the hand dynamics. In assistive robotics, as well as in the field of hand prostheses, the ability of performing smooth movements and obtaining a stable grasp is essential. Therefore, one of the aims of this thesis is to develop a bio-inspired approach for posture prediction and finger trajectory planning with a robotic hand. In order to do that, the human grasping action has been deeply analyzed. It has been decomposed in three main phases: reaching, pre-shaping and grasping. In order to reduce the complexity of planning dexterous hand grasps, it is useful to find the best hand preshape: therefore, this work is focused on this grasping phase. An accurate analysis of anatomy, surgery and rehabilitation literature has been done. In order to confirm the literature results and to cope with the lack of information, e.g. about thumb behavior, different methods for acquiring information about the human hand behavior have been used. Some important features about grasping have been collected from the analysis of the data obtained from two different devices for movement analysis: the Vicon system and a sensorized glove (the CyberGlove). The hand joint behavior during the grasping action has been analyzed asking different subjects to realize four different grasping tasks. The selected tasks guarantee that the subjects pose the hand in the most commonly used configurations. The experiments were performed asking subjects to wear the CyberGlove or attaching on their hands markers visible by the Vicon cameras. The obtained data have been analyzed using different hand kinematic human-inspired models. In order to overcome the drawbacks of the motion analysis devices listed before (as the not completely natural movements performed wearing a data glove, the impossibility to use the CyberGlove from people of different hand sizes and the high cost of the Vicon system), and to obtain information about the hand movements, the Kinect motion sensing device has also been used. For determining the finger joint positions and trajectories during hand movements, a finger tracking algorithm for the Kinect camera has been implemented. Blue markers have been placed on the hand joints following the same configuration used in the experiments performed with the Vicon cameras. A coloured blob detection algorithm and a multiple object tracking algorithm based on particle filters and extended Kalman filter has been implemented. When observing the human grasping behavior, thanks to the input devices listed before, it has been possible to notice some common characteristics among different subjects. The literature results about the dependence of grasping shape on object properties and grip types have been confirmed. The relationship between hand joints for each subject and among different subject has been investigated. One of the obtained results has been finding a constant value of the hand aperture angle (the angle between thumb and index finger). Also the curvature of the fingers is constant among different subjects (related to hand dimensions). Therefore, on the basis of neurological studies and of the analysis of the obtained data, a bio-inspired algorithm for predicting the power-grip posture and planning the finger trajectory of a robotic hand has been developed. The method estimates the best joint hand configuration during diagonal and transverse volar grasp minimizing a purposely defined objective function given by the sum of the joint distances from the object center of rotation (COR). The developed grasping algorithm calculates the position of the fingers for grasping, finding the best hand configuration that ensures a stable human-like grasp. The implementation of the algorithm on a real robotic platform has validated its effectiveness. From the above discussion, it is clear that the aim of this work is to find a way of exploiting the knowledge about a natural system, namely the human hand, in order to design a robotic system. After investigating and understanding in depth the human grasping action, the obtained results have multiple applications such as: overcoming the structural lack of the actual robotic hands (for instance, the non opposable thumb); developing new interfaces for rehabilitation (the finger tracking algorithm developed for the Kinect motion sensing device could be a new rehabilitation interface with potential application in the rehabilitation field); developing bio-inspired approaches for posture prediction and finger trajectory planning in order to perform a stable human-like grasp with a robotic hand