Role of Physical Activity in Bone-Muscle Crosstalk: Biological Aspects and Clinical Implications.

Bone and muscle tissues influence each other through the integration of mechanical and biochemical signals, giving rise to bone-muscle crosstalk. They are also known to secrete osteokines, myokines, and cytokines into the circulation, influencing the biological and pathological activities in local and distant organs and cells. In this regard, even osteoporosis and sarcopenia, which were initially thought to be two independent diseases, have recently been defined under the term "osteosarcopenia", to indicate a synergistic condition of low bone mass with muscle atrophy and hypofunction. Undoubtedly, osteosarcopenia is a major public health concern, being associated with high rates of morbidity and mortality. The best current defence against osteosarcopenia is prevention based on a healthy lifestyle and regular exercise. The most appropriate type, intensity, duration, and frequency of exercise to positively influence osteosarcopenia are not yet known. However, combined programmes of progressive resistance exercises, weight-bearing impact exercises, and challenging balance/mobility activities currently appear to be the most effective in optimising musculoskeletal health and function. Based on this evidence, the aim of our review was to summarize the current knowledge about the role of exercise in bone-muscle crosstalk, highlighting how it may represent an effective alternative strategy to prevent and/or counteract the onset of osteosarcopenia.

SEAI: Social Emotional Artificial Intelligence Based on Damasio's Theory of Mind.

A socially intelligent robot must be capable to extract meaningful information in real time from the social environment and react accordingly with coherent human-like behavior. Moreover, it should be able to internalize this information, to reason on it at a higher level, build its own opinions independently, and then automatically bias the decision-making according to its unique experience. In the last decades, neuroscience research highlighted the link between the evolution of such complex behavior and the evolution of a certain level of consciousness, which cannot leave out of a body that feels emotions as discriminants and prompters. In order to develop cognitive systems for social robotics with greater human-likeliness, we used an "understanding by building" approach to model and implement a well-known theory of mind in the form of an artificial intelligence, and we tested it on a sophisticated robotic platform. The name of the presented system is SEAI (Social Emotional Artificial Intelligence), a cognitive system specifically conceived for social and emotional robots. It is designed as a bio-inspired, highly modular, hybrid system with emotion modeling and high-level reasoning capabilities. It follows the deliberative/reactive paradigm where a knowledge-based expert system is aimed at dealing with the high-level symbolic reasoning, while a more conventional reactive paradigm is deputed to the low-level processing and control. The SEAI system is also enriched by a model that simulates the Damasio's theory of consciousness and the theory of Somatic Markers. After a review of similar bio-inspired cognitive systems, we present the scientific foundations and their computational formalization at the basis of the SEAI framework. Then, a deeper technical description of the architecture is disclosed underlining the numerous parallelisms with the human cognitive system. Finally, the influence of artificial emotions and feelings, and their link with the robot's beliefs and decisions have been tested in a physical humanoid involved in Human-Robot Interaction (HRI).

Electrically tunable soft solid lens inspired by reptile and bird accommodation.

Electrically tunable lenses are conceived as deformable adaptive optical components able to change focus without motor-controlled translations of stiff lenses. In order to achieve large tuning ranges, large deformations are needed. This requires new technologies for the actuation of highly stretchable lenses. This paper presents a configuration to obtain compact tunable lenses entirely made of soft solid matter (elastomers). This was achieved by combining the advantages of dielectric elastomer actuation (DEA) with a design inspired by the accommodation of reptiles and birds. An annular DEA was used to radially deform a central solid-body lens. Using an acrylic elastomer membrane, a silicone lens and a simple fabrication method, we assembled a tunable lens capable of focal length variations up to 55%, driven by an actuator four times larger than the lens. As compared to DEA-based liquid lenses, the novel architecture halves the required driving voltages, simplifies the fabrication process and allows for a higher versatility in design. These new lenses might find application in systems requiring large variations of focus with low power consumption, silent operation, low weight, shock tolerance, minimized axial encumbrance and minimized changes of performance against vibrations and variations in temperature.

Wearable Textile Platform for Assessing Stroke Patient Treatment in Daily Life Conditions.

Monitoring physical activities during post-stroke rehabilitation in daily life may help physicians to optimize and tailor the training program for patients. The European research project INTERACTION (FP7-ICT-2011-7-287351) evaluated motor capabilities in stroke patients during the recovery treatment period. We developed wearable sensing platform based on the sensor fusion among inertial, knitted piezoresistive sensors and textile EMG electrodes. The device was conceived in modular form and consists of a separate shirt, trousers, glove, and shoe. Thanks to the novel fusion approach it has been possible to develop a model for the shoulder taking into account the scapulo-thoracic joint of the scapular girdle, considerably improving the estimation of the hand position in reaching activities. In order to minimize the sensor set used to monitor gait, a single inertial sensor fused with a textile goniometer proved to reconstruct the orientation of all the body segments of the leg. Finally, the sensing glove, endowed with three textile goniometers and three force sensors showed good capabilities in the reconstruction of grasping activities and evaluating the interaction of the hand with the environment, according to the project specifications. This paper reports on the design and the technical evaluation of the performance of the sensing platform, tested on healthy subjects.

A bi-articular model for scapular-humeral rhythm reconstruction through data from wearable sensors.

BACKGROUND: Patient-specific performance assessment of arm movements in daily life activities is fundamental for neurological rehabilitation therapy. In most applications, the shoulder movement is simplified through a socket-ball joint, neglecting the movement of the scapular-thoracic complex. This may lead to significant errors. We propose an innovative bi-articular model of the human shoulder for estimating the position of the hand in relation to the sternum. The model takes into account both the scapular-toracic and gleno-humeral movements and their ratio governed by the scapular-humeral rhythm, fusing the information of inertial and textile-based strain sensors. METHOD: To feed the reconstruction algorithm based on the bi-articular model, an ad-hoc sensing shirt was developed. The shirt was equipped with two inertial measurement units (IMUs) and an integrated textile strain sensor. We built the bi-articular model starting from the data obtained in two planar movements (arm abduction and flexion in the sagittal plane) and analysing the error between the reference data - measured through an optical reference system - and the socket-ball approximation of the shoulder. The 3D model was developed by extending the behaviour of the kinematic chain revealed in the planar trajectories through a parameter identification that takes into account the body structure of the subject. RESULT: The bi-articular model was evaluated in five subjects in comparison with the optical reference system. The errors were computed in terms of distance between the reference position of the trochlea (end-effector) and the correspondent model estimation. The introduced method remarkably improved the estimation of the position of the trochlea (and consequently the estimation of the hand position during reaching activities) reducing position errors from 11.5 cm to 1.8 cm. CONCLUSION: Thanks to the developed bi-articular model, we demonstrated a reliable estimation of the upper arm kinematics with a minimal sensing system suitable for daily life monitoring of recovery.

Wearable Goniometer and Accelerometer Sensory Fusion for Knee Joint Angle Measurement in Daily Life.

Human motion analysis is crucial for a wide range of applications and disciplines. The development and validation of low cost and unobtrusive sensing systems for ambulatory motion detection is still an open issue. Inertial measurement systems and e-textile sensors are emerging as potential technologies for daily life situations. We developed and conducted a preliminary evaluation of an innovative sensing concept that combines e-textiles and tri-axial accelerometers for ambulatory human motion analysis. Our sensory fusion method is based on a Kalman filter technique and combines the outputs of textile electrogoniometers and accelerometers without making any assumptions regarding the initial accelerometer position and orientation. We used our technique to measure the flexion-extension angle of the knee in different motion tasks (monopodalic flexions and walking at different velocities). The estimation technique was benchmarked against a commercial measurement system based on inertial measurement units and performed reliably for all of the various tasks (mean and standard deviation of the root mean square error of 1:96 and 0:96, respectively). In addition, the method showed a notable improvement in angular estimation compared to the estimation derived by the textile goniometer and accelerometer considered separately. In future work, we will extend this method to more complex and multi-degree of freedom joints.

Smartphone Application for the Analysis of Prosodic Features in Running Speech with a Focus on Bipolar Disorders: System Performance Evaluation and Case Study.

Bipolar disorder is one of the most common mood disorders characterized by large and invalidating mood swings. Several projects focus on the development of decision support systems that monitor and advise patients, as well as clinicians. Voice monitoring and speech signal analysis can be exploited to reach this goal. In this study, an Android application was designed for analyzing running speech using a smartphone device. The application can record audio samples and estimate speech fundamental frequency, F0, and its changes. F0-related features are estimated locally on the smartphone, with some advantages with respect to remote processing approaches in terms of privacy protection and reduced upload costs. The raw features can be sent to a central server and further processed. The quality of the audio recordings, algorithm reliability and performance of the overall system were evaluated in terms of voiced segment detection and features estimation. The results demonstrate that mean F0 from each voiced segment can be reliably estimated, thus describing prosodic features across the speech sample. Instead, features related to F0 variability within each voiced segment performed poorly. A case study performed on a bipolar patient is presented.

Can a Humanoid Face be Expressive? A Psychophysiological Investigation.

Non-verbal signals expressed through body language play a crucial role in multi-modal human communication during social relations. Indeed, in all cultures, facial expressions are the most universal and direct signs to express innate emotional cues. A human face conveys important information in social interactions and helps us to better understand our social partners and establish empathic links. Latest researches show that humanoid and social robots are becoming increasingly similar to humans, both esthetically and expressively. However, their visual expressiveness is a crucial issue that must be improved to make these robots more realistic and intuitively perceivable by humans as not different from them. This study concerns the capability of a humanoid robot to exhibit emotions through facial expressions. More specifically, emotional signs performed by a humanoid robot have been compared with corresponding human facial expressions in terms of recognition rate and response time. The set of stimuli included standardized human expressions taken from an Ekman-based database and the same facial expressions performed by the robot. Furthermore, participants' psychophysiological responses have been explored to investigate whether there could be differences induced by interpreting robot or human emotional stimuli. Preliminary results show a trend to better recognize expressions performed by the robot than 2D photos or 3D models. Moreover, no significant differences in the subjects' psychophysiological state have been found during the discrimination of facial expressions performed by the robot in comparison with the same task performed with 2D photos and 3D models.

Inference of human affective states from psychophysiological measurements extracted under ecologically valid conditions.

Compared to standard laboratory protocols, the measurement of psychophysiological signals in real world experiments poses technical and methodological challenges due to external factors that cannot be directly controlled. To address this problem, we propose a hybrid approach based on an immersive and human accessible space called the eXperience Induction Machine (XIM), that incorporates the advantages of a laboratory within a life-like setting. The XIM integrates unobtrusive wearable sensors for the acquisition of psychophysiological signals suitable for ambulatory emotion research. In this paper, we present results from two different studies conducted to validate the XIM as a general-purpose sensing infrastructure for the study of human affective states under ecologically valid conditions. In the first investigation, we recorded and classified signals from subjects exposed to pictorial stimuli corresponding to a range of arousal levels, while they were free to walk and gesticulate. In the second study, we designed an experiment that follows the classical conditioning paradigm, a well-known procedure in the behavioral sciences, with the additional feature that participants were free to move in the physical space, as opposed to similar studies measuring physiological signals in constrained laboratory settings. Our results indicate that, by using our sensing infrastructure, it is indeed possible to infer human event-elicited affective states through measurements of psychophysiological signals under ecological conditions.

Wearable wireless tactile display for virtual interactions with soft bodies.

We describe here a wearable, wireless, compact, and lightweight tactile display, able to mechanically stimulate the fingertip of users, so as to simulate contact with soft bodies in virtual environments. The device was based on dielectric elastomer actuators, as high-performance electromechanically active polymers. The actuator was arranged at the user's fingertip, integrated within a plastic case, which also hosted a compact high-voltage circuitry. A custom-made wireless control unit was arranged on the forearm and connected to the display via low-voltage leads. We present the structure of the device and a characterization of it, in terms of electromechanical response and stress relaxation. Furthermore, we present results of a psychophysical test aimed at assessing the ability of the system to generate different levels of force that can be perceived by users.

For Every Thing There is a Season and a Time : The Construction of a Humanoid. A Tribute to Vincenzo Tagliasco.

"There's a time to be born, and a time to die; a time to break down, and a time to build up; a time to weep, and a time to laugh; a time to keep silence, and a time to speak…" (Ecclesiastes 3, 2-7). There was a time when automata were designed like clocks. Androids will have the time of their creators, the state of the art in technology, a wealth of experience to draw from, as well as the capacity to carry out actions as being endowed with meaning. The machine will undergo a long period of nurturing, from which it will learn to shape some sort of identity.

Exploiting wearable goniometer technology for motion sensing gloves.

This paper presents an innovative wearable kinesthetic glove realized with knitted piezoresistive fabric (KPF) sensor technology. The glove is conceived to capture hand movement and gesture by using KPF in a double-layer configuration working as angular sensors (electrogoniometers). The sensing glove prototype is endowed by three KPF goniometers, used to track flexion and extension movement of metacarpophalangeal joint of thumb, index, and middle fingers. The glove is devoted to the continuous monitoring of patients during their daily-life activities, in particular for stroke survivors during their rehabilitation. The prototype performances have been evaluated in comparison with an optical tracking system considered as a gold standard both for relieving static and dynamic posture and gesture of the hand. The introduced prototype has shown very interesting figures of merit. The angular error, evaluated through the standard Bland Altman analysis, has been estimated in ±3° which is slightly less accurate than commercial electrogoniometers. Moreover, a new conceptual prototype design, preliminary evaluated within this study, is presented and discussed in order to solve actual limitations in terms of number and type of sensor connections, avoiding mechanical constraints given by metallic inextensible wires and improving user comfort.

A decision support system for real-time stress detection during virtual reality exposure.

Virtual Reality (VR) is increasingly being used in combination with psycho-physiological measures to improve assessment of distress in mental health research and therapy. However, the analysis and interpretation of multiple physiological measures is time consuming and requires specific skills, which are not available to most clinicians. To address this issue, we designed and developed a Decision Support System (DSS) for automatic classification of stress levels during exposure to VR environments. The DSS integrates different biosensor data (ECG, breathing rate, EEG) and behavioral data (body gestures correlated with stress), following a training process in which self-rated and clinical-rated stress levels are used as ground truth. Detected stress events for each VR session are reported to the therapist as an aggregated value (ranging from 0 to 1) and graphically displayed on a diagram accessible by the therapist through a web-based interface.

New generation of wearable goniometers for motion capture systems.

BACKGROUND: Monitoring joint angles through wearable systems enables human posture and gesture to be reconstructed as a support for physical rehabilitation both in clinics and at the patient's home. A new generation of wearable goniometers based on knitted piezoresistive fabric (KPF) technology is presented. METHODS: KPF single-and double-layer devices were designed and characterized under stretching and bending to work as strain sensors and goniometers. The theoretical working principle and the derived electromechanical model, previously proved for carbon elastomer sensors, were generalized to KPF. The devices were used to correlate angles and piezoresistive fabric behaviour, to highlight the differences in terms of performance between the single layer and the double layer sensors. A fast calibration procedure is also proposed. RESULTS: The proposed device was tested both in static and dynamic conditions in comparison with standard electrogoniometers and inertial measurement units respectively. KPF goniometer capabilities in angle detection were experimentally proved and a discussion of the device measurement errors of is provided. The paper concludes with an analysis of sensor accuracy and hysteresis reduction in particular configurations. CONCLUSIONS: Double layer KPF goniometers showed a promising performance in terms of angle measurements both in quasi-static and dynamic working mode for velocities typical of human movement. A further approach consisting of a combination of multiple sensors to increase accuracy via sensor fusion technique has been presented.

Enabling variable-stiffness hand rehabilitation orthoses with dielectric elastomer transducers.

Patients affected by motor disorders of the hand and having residual voluntary movements of fingers or wrist can benefit from self-rehabilitation exercises performed with so-called dynamic hand splints. These systems consist of orthoses equipped with elastic cords or springs, which either provide a sustained stretch or resist voluntary movements of fingers or wrist. These simple systems are limited by the impossibility of modulating the mechanical stiffness. This limitation does not allow for customizations and real-time control of the training exercise, which would improve the rehabilitation efficacy. To overcome this limitation, 'active' orthoses equipped with devices that allow for electrical control of the mechanical stiffness are needed. Here, we report on a solution that relies on compact and light-weight electroactive elastic transducers that replace the passive elastic components. We developed a variable-stiffness transducer made of dielectric elastomers, as the most performing types of electromechanically active polymers. The transducer was manufactured with a silicone film and tested with a purposely-developed stiffness control strategy that allowed for electrical modulations of the force-elongation response. Results showed that the proposed new technology is a promising and viable solution to develop electrically controllable dynamic hand orthoses for hand rehabilitation.

Novel Spiking Neuron-Astrocyte Networks based on nonlinear transistor-like models of tripartite synapses.

In this paper a novel and efficient computational implementation of a Spiking Neuron-Astrocyte Network (SNAN) is reported. Neurons are modeled according to the Izhikevich formulation and the neuron-astrocyte interactions are intended as tripartite synapsis and modeled with the previously proposed nonlinear transistor-like model. Concerning the learning rules, the original spike-timing dependent plasticity is used for the neural part of the SNAN whereas an ad-hoc rule is proposed for the astrocyte part. SNAN performances are compared with a standard spiking neural network (SNN) and evaluated using the polychronization concept, i.e., number of co-existing groups that spontaneously generate patterns of polychronous activity. The astrocyte-neuron ratio is the biologically inspired value of 1.5. The proposed SNAN shows higher number of polychronous groups than SNN, remarkably achieved for the whole duration of simulation (24 hours).

Motor imagery effectiveness for mirror reversed movements.

Physical practice is known to enhance motor adaptation skills, which refer to the individual ability to compensate for environmental changes. So far, it is still unknown whether a similar effect can be observed following motor imagery (MI). Thirty-nine participants were tested during a joystick tracking task under both normal and mirror conditions (i.e., the inductive direction of the joystick was reversed), before and after a physical practice or MI training phase. Eye movements and electromyographic activity were recorded during MI. Motor performance was also evaluated after a 6 h interval during daytime. As compared to the control group, the results revealed that both MI and physical practice improved motor performance in the mirror condition, during the post-training test. Furthermore, the time to complete the task was further reduced after 6 hours, both in the normal and mirror conditions. These results demonstrate the effectiveness of MI for learning mirror-reversed movements, and for the consolidation process that follows motor adaptation.

SoC CMOS UWB Pulse Radar Sensor for Contactless Respiratory Rate Monitoring.

An ultra wideband (UWB) system-on-chip radar sensor for respiratory rate monitoring has been realized in 90 nm CMOS technology and characterized experimentally. The radar testchip has been applied to the contactless detection of the respiration activity of adult and baby. The field operational tests demonstrate that the UWB radar sensor detects the respiratory rate of person under test (adult and baby) associated with sub-centimeter chest movements, allowing the continuous-time non-invasive monitoring of hospital patients and other people at risk of obstructive apneas such as babies in cot beds, or other respiratory diseases.

Electroactive polymer patches for wearable haptic interfaces.

Fully wearable and unobtrusive sensing will enable the possibility of monitoring people anywhere and anytime, for healthcare, well-being, protection and safety. Many research groups have exploited textiles as the ideal platform for pervasive monitoring. This paper reports advances in electroactive polymer technology oriented to mechanical sensing and actuation within textile interfaces. The preliminary development of a textile-based glove in which electroactive polymers act as force/position sensors and haptic feedback actuators is presented.