A Novel Capacitive Measurement Device for Longitudinal Monitoring of Bone Fracture Healing.

The healing process of surgically-stabilised long bone fractures depends on two main factors: (a) the assessment of implant stability, and (b) the knowledge of bone callus stiffness. Currently, X-rays are the main diagnostic tool used for the assessment of bone fractures. However, they are considered unsafe, and the interpretation of the clinical results is highly subjective, depending on the clinician's experience. Hence, there is the need for objective, non-invasive and repeatable methods to allow a longitudinal assessment of implant stability and bone callus stiffness. In this work, we propose a compact and scalable system, based on capacitive sensor technology, able to measure, quantitatively, the relative pins displacements in bone fractures treated with external fixators. The measurement device proved to be easily integrable with the external fixator pins. Smart arrangements of the sensor units were exploited to discriminate relative movements of the external pins in the 3D space with a resolution of 0.5 mm and 0.5°. The proposed capacitive technology was able to detect all of the expected movements of the external pins in the 3D space, providing information on implant stability and bone callus stiffness.

Endoluminal Motion Recognition of a Magnetically-Guided Capsule Endoscope Based on Capsule-Tissue Interaction Force.

A magnetically-guided capsule endoscope, embedding flexible force sensors, is designed to measure the capsule-tissue interaction force. The flexible force sensor is composed of eight force-sensitive elements surrounding the internal permanent magnet (IPM). The control of interaction force acting on the intestinal wall can reduce patient's discomfort and maintain the magnetic coupling between the external permanent magnet (EPM) and the IPM during capsule navigation. A flexible force sensor can achieve this control. In particular, by analyzing the signals of the force sensitive elements, we propose a method to recognize the status of the motion of the magnetic capsule, and provide corresponding formulas to evaluate whether the magnetic capsule follows the motion of the external driving magnet. Accuracy of the motion recognition in Ex Vivo tests reached 94% when the EPM was translated along the longitudinal axis. In addition, a method is proposed to realign the EPM and the IPM before the loss of their magnetic coupling. Its translational error, rotational error, and runtime are 7.04 ± 0.71 mm, 3.13 ± 0.47∘, and 11.4 ± 0.39 s, respectively. Finally, a control strategy is proposed to prevent the magnetic capsule endoscope from losing control during the magnetically-guided capsule colonoscopy.

Template-based fabrication of spatially organized 3D bioactive constructs using magnetic low-concentration gelation methacrylate (GelMA) microfibers.

Low concentrations of gelatin methacrylate (GelMA) microfibers are more favorable for cellular activity compared with high concentrations. However, applying low-concentration GelMA microfibers as building blocks for higher-order cellular assembly remains challenging owing to their poor mechanical properties. Herein, we report a new template-based method to solve this problem. GelMA microfibers (5%, w/v) containing magnetic nanoparticles were synthesized by a microfluidic spinning method. A 9 × 9 micropillar array surrounded by a magnetic substrate was constructed to form 8 × 8 microgaps arranged in a crisscross pattern as a magnetic template. In DMEM solution, magnetic attraction facilitated efficient arrangement of the microfibers according to the template with micron assembly accuracy, with a microgrid-like construct (microGC) generated after removing all micropillars. MicroGCs were shown to effectively support the activities of surface seeded or encapsulated cells and be flexibly constructed with various organized spatial patterns. Owing to the low mechanical property requirements of assembled microfibers and the easy-to-implement operation, the proposed method provides a versatile pathway for the assembly of various microfluidic spun microfibers. Furthermore, the resulting 3D microgrid-like cellular constructs with organized spatiotemporal composition offer a convenient platform for the study of tissue engineering.

Photometric Stereo-Based Depth Map Reconstruction for Monocular Capsule Endoscopy.

The capsule endoscopy robot can only use monocular vision due to the dimensional limit. To improve the depth perception of the monocular capsule endoscopy robot, this paper proposes a photometric stereo-based depth map reconstruction method. First, based on the characteristics of the capsule endoscopy robot system, a photometric stereo framework is established. Then, by combining the specular property and Lambertian property of the object surface, the depth of the specular highlight point is estimated, and the depth map of the whole object surface is reconstructed by a forward upwind scheme. To evaluate the precision of the depth estimation of the specular highlight region and the depth map reconstruction of the object surface, simulations and experiments are implemented with synthetic images and pig colon tissue, respectively. The results of the simulations and experiments show that the proposed method provides good precision for depth map reconstruction in monocular capsule endoscopy.

Visual-Based Defect Detection and Classification Approaches for Industrial Applications-A SURVEY.

This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.

Actigraph assessment for measuring upper limb activity in unilateral cerebral palsy.

BACKGROUND: Detecting differences in upper limb use in children with unilateral cerebral palsy (UCP) is challenging and highly dependent on examiner experience. The recent introduction of technologies in the clinical environment, and in particular the use of wearable sensors, can provide quantitative measurement to overcome this issue. This study aims to evaluate ActiGraph GT3X+ as a tool for measuring asymmetry in the use of the two upper limbs (ULs) during the assessment with a standardized clinical tool, the Assisting Hand Assessment (AHA) in UCP patients aged 3-25 years compared to age-matched typically developing (TD) subjects. METHODS: Fifty children with UCP and 50 TD subjects were assessed with AHA while wearing ActiGraphs GT3X+ on both wrists. The mean activity of each hand (dominant and non-dominant, MADH and MANDH, respectively) and the asymmetry index (AI) were calculated. Two linear mixed model analyses were carried out to evaluate how dependent actigraphic variables (i.e. MANDH and AI) varied by group (TD vs UCP) and among levels of manual ability based on Manual Ability Classification System (MACS). In both models age, sex, side of hemiplegia, presence/absence of mirror movements were specified as random effects. RESULTS: The MANDH was significantly lower in UCP compared to TD, while the AI was significantly higher in UCP compared to TD. Moreover, in UCP group there were significant differences related to MACS levels, both for MANDH and AI. None of the random variables (i.e. age, sex, side, presence/absence of mirror movements) showed significant interaction with MANDH and AI. CONCLUSIONS: These results confirm that actigraphy could provide, in a standardized setting, a quantitative description of differences between upper limbs activity. TRIAL REGISTRATION: ClincalTrials.gov, NCT03054441 . Registered 15 February 2017.

Correction: Fiorini, L., et al., Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data. Sensors 2017, 17, 1034.

A correction is presented to correct the section headings of Sections 5.1, 5.2, and 5.3 in[Sensors, 2017, 17, 1034].

Robotic Services Acceptance in Smart Environments With Older Adults: User Satisfaction and Acceptability Study.

BACKGROUND: In Europe, the population of older people is increasing rapidly. Many older people prefer to remain in their homes but living alone could be a risk for their safety. In this context, robotics and other emerging technologies are increasingly proposed as potential solutions to this societal concern. However, one-third of all assistive technologies are abandoned within one year of use because the end users do not accept them. OBJECTIVE: The aim of this study is to investigate the acceptance of the Robot-Era system, which provides robotic services to permit older people to remain in their homes. METHODS: Six robotic services were tested by 35 older users. The experiments were conducted in three different environments: private home, condominium, and outdoor sites. The appearance questionnaire was developed to collect the users' first impressions about the Robot-Era system, whereas the acceptance was evaluated through a questionnaire developed ad hoc for Robot-Era. RESULTS: A total of 45 older users were recruited. The people were grouped in two samples of 35 participants, according to their availability. Participants had a positive impression of Robot-Era robots, as reflected by the mean score of 73.04 (SD 11.80) for DORO's (domestic robot) appearance, 76.85 (SD 12.01) for CORO (condominium robot), and 75.93 (SD 11.67) for ORO (outdoor robot). Men gave ORO's appearance an overall score higher than women (P=.02). Moreover, participants younger than 75 years understood more readily the functionalities of Robot-Era robots compared to older people (P=.007 for DORO, P=.001 for CORO, and P=.046 for ORO). For the ad hoc questionnaire, the mean overall score was higher than 80 out of 100 points for all Robot-Era services. Older persons with a high educational level gave Robot-Era services a higher score than those with a low level of education (shopping: P=.04; garbage: P=.047; reminding: P=.04; indoor walking support: P=.006; outdoor walking support: P=.03). A higher score was given by male older adults for shopping (P=.02), indoor walking support (P=.02), and outdoor walking support (P=.03). CONCLUSIONS: Based on the feedback given by the end users, the Robot-Era system has the potential to be developed as a socially acceptable and believable provider of robotic services to facilitate older people to live independently in their homes.

Smartphone-Based Food Diagnostic Technologies: A Review.

A new generation of mobile sensing approaches offers significant advantages over traditional platforms in terms of test speed, control, low cost, ease-of-operation, and data management, and requires minimal equipment and user involvement. The marriage of novel sensing technologies with cellphones enables the development of powerful lab-on-smartphone platforms for many important applications including medical diagnosis, environmental monitoring, and food safety analysis. This paper reviews the recent advancements and developments in the field of smartphone-based food diagnostic technologies, with an emphasis on custom modules to enhance smartphone sensing capabilities. These devices typically comprise multiple components such as detectors, sample processors, disposable chips, batteries and software, which are integrated with a commercial smartphone. One of the most important aspects of developing these systems is the integration of these components onto a compact and lightweight platform that requires minimal power. To date, researchers have demonstrated several promising approaches employing various sensing techniques and device configurations. We aim to provide a systematic classification according to the detection strategy, providing a critical discussion of strengths and weaknesses. We have also extended the analysis to the food scanning devices that are increasingly populating the Internet of Things (IoT) market, demonstrating how this field is indeed promising, as the research outputs are quickly capitalized on new start-up companies.

A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data.

Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM), trained with Sequential Minimal Optimization (SMO). The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60) and the Telecommunication Systems Team (TST) Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames) and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context.

Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data.

The goal of this study is to address two major issues that undermine the large scale deployment of smart home sensing solutions in people's homes. These include the costs associated with having to install and maintain a large number of sensors, and the pragmatics of annotating numerous sensor data streams for activity classification. Our aim was therefore to propose a method to describe individual users' behavioural patterns starting from unannotated data analysis of a minimal number of sensors and a "blind" approach for activity recognition. The methodology included processing and analysing sensor data from 17 older adults living in community-based housing to extract activity information at different times of the day. The findings illustrate that 55 days of sensor data from a sensor configuration comprising three sensors, and extracting appropriate features including a "busyness" measure, are adequate to build robust models which can be used for clustering individuals based on their behaviour patterns with a high degree of accuracy (>85%). The obtained clusters can be used to describe individual behaviour over different times of the day. This approach suggests a scalable solution to support optimising the personalisation of care by utilising low-cost sensing and analysis. This approach could be used to track a person's needs over time and fine-tune their care plan on an ongoing basis in a cost-effective manner.

'HighChest': An Augmented Freezer Designed for Smart Food Management and Promotion of Eco-Efficient Behaviour.

This paper introduces HighChest, an innovative smart freezer designed to promote energy efficient behavior and the responsible use of food. Introducing a novel human-machine interface (HMI) design developed through assessment phases and a user involvement stage, HighChest is state of the art, featuring smart services that exploit embedded sensors and Internet of things functionalities, which enhance the local capabilities of the appliance. The industrial design thinking approach followed for the advanced HMI is intended to maximize the social impact of the food management service, enhancing both the user experience of the product and the user's willingness to adopt eco- and energy-friendly behaviors. The sensor equipment realizes automatic recognition of food by learning from the users, as well as automatic localization inside the deposit space. Moreover, it provides monitoring of the appliance's usage, avoiding temperature and humidity issues related to improper use. Experimental tests were conducted to evaluate the localization system, and the results showed 100% accuracy for weights greater or equal to 0.5 kg. Drifts due to the lid opening and prolonged usage time were also measured, to implement automatic reset corrections.

Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets.

Recognition of activities of daily living plays an important role in monitoring elderly people and helping caregivers in controlling and detecting changes in daily behaviors. Thanks to the miniaturization and low cost of Microelectromechanical systems (MEMs), in particular of Inertial Measurement Units, in recent years body-worn activity recognition has gained popularity. In this context, the proposed work aims to recognize nine different gestures involved in daily activities using hand and wrist wearable sensors. Additionally, the analysis was carried out also considering different combinations of wearable sensors, in order to find the best combination in terms of unobtrusiveness and recognition accuracy. In order to achieve the proposed goals, an extensive experimentation was performed in a realistic environment. Twenty users were asked to perform the selected gestures and then the data were off-line analyzed to extract significant features. In order to corroborate the analysis, the classification problem was treated using two different and commonly used supervised machine learning techniques, namely Decision Tree and Support Vector Machine, analyzing both personal model and Leave-One-Subject-Out cross validation. The results obtained from this analysis show that the proposed system is able to recognize the proposed gestures with an accuracy of 89.01% in the Leave-One-Subject-Out cross validation and are therefore promising for further investigation in real life scenarios.

ESPRESSO: A novel device for laser-assisted surgery of the anterior eye segment.

INTRODUCTION: Merging robotics with laser eye surgery could enhance precision, repeatability and automation. During some eye laser procedures the patient is awake, thus eye stabilization is desired to avoid movements that could affect the treatment. MATERIAL AND METHODS: The ESPRESSO platform has a two-stage actuation system to position a stabilization tool on the eye, a proximity sensing unit to monitor the stabilization tool position, and a sensing unit to monitor the pressure exerted on the eye. The platform is tested in-vitro and ex-vivo with clinicians. A maximum pressure to be exerted on the eye is defined with expert ophthalmic surgeons to be 22 mmHg: physiological intraocular pressure (IOP) range is 10-21 mmHg. This pressure corresponds to a force of 0.3 N. RESULTS: The necessary contact force to have eye fixation (according to the clinicians' feedback) is evaluated: maximum values resulted always below 0.3 N. A maximum IOP increase of 4.67 mmHg is observed, that is a slight variation with respect to the performance of other platforms (IOP elevations up to 328 mmHg). CONCLUSION: Design and initial assessment of the platform is presented. Eye stabilization is performed without exceeding the critical contact force value and causing large/sudden IOP increases.

Untethered magnetic millirobot for targeted drug delivery.

This paper reports the design and development of a novel millimeter-sized robotic system for targeted therapy. The proposed medical robot is conceived to perform therapy in relatively small diameter body canals (spine, urinary system, ovary, etc.), and to release several kinds of therapeutics, depending on the pathology to be treated. The robot is a nearly-buoyant bi-component system consisting of a carrier, in which the therapeutic agent is embedded, and a piston. The piston, by exploiting magnetic effects, docks with the carrier and compresses a drug-loaded hydrogel, thus activating the release mechanism. External magnetic fields are exploited to propel the robot towards the target region, while intermagnetic forces are exploited to trigger drug release. After designing and fabricating the robot, the system has been tested in vitro with an anticancer drug (doxorubicin) embedded in the carrier. The efficiency of the drug release mechanism has been demonstrated by both quantifying the amount of drug released and by assessing the efficacy of this therapeutic procedure on human bladder cancer cells.

A new system for quantitative evaluation of infant gaze capabilities in a wide visual field.

BACKGROUND: The visual assessment of infants poses specific challenges: many techniques that are used on adults are based on the patient's response, and are not suitable for infants. Significant advances in the eye-tracking have made this assessment of infant visual capabilities easier, however, eye-tracking still requires the subject's collaboration, in most cases and thus limiting the application in infant research. Moreover, there is a lack of transferability to clinical practice, and thus it emerges the need for a new tool to measure the paradigms and explore the most common visual competences in a wide visual field. This work presents the design, development and preliminary testing of a new system for measuring infant's gaze in the wide visual field called CareToy C: CareToy for Clinics. METHODS: The system is based on a commercial eye tracker (SmartEye) with six cameras running at 60 Hz, suitable for measuring an infant's gaze. In order to stimulate the infant visually and audibly, a mechanical structure has been designed to support five speakers and five screens at a specific distance (60 cm) and angle: one in the centre, two on the right-hand side and two on the left (at 30° and 60° respectively). Different tasks have been designed in order to evaluate the system capability to assess the infant's gaze movements during different conditions (such as gap, overlap or audio-visual paradigms). Nine healthy infants aged 4-10 months were assessed as they performed the visual tasks at random. RESULTS: We developed a system able to measure infant's gaze in a wide visual field covering a total visual range of ±60° from the centre with an intermediate evaluation at ±30°. Moreover, the same system, thanks to different integrated software, was able to provide different visual paradigms (as gap, overlap and audio-visual) assessing and comparing different visual and multisensory sub-competencies. The proposed system endowed the integration of a commercial eye-tracker into a purposive setup in a smart and innovative way. CONCLUSIONS: The proposed system is suitable for measuring and evaluating infant's gaze capabilities in a wide visual field, in order to provide quantitative data that can enrich the clinical assessment.

A miniaturized robotic platform for natural orifice transluminal endoscopic surgery: in vivo validation.

BACKGROUND: Natural orifice transluminal endoscopic surgery (NOTES) involves accessing the abdominal cavity via one of the body natural orifices for enabling minimally invasive surgical procedures. However, the constraints imposed by the access modality and the limited available technology make NOTES very challenging for surgeons. Tools redesign and introduction of novel surgical instruments are imperative in order to make NOTES operative in a real surgical scenario, reproducible and reliable. Robotic technology has major potential to overcome current limitations. METHODS: The robotic platform described here consists of a magnetic anchoring frame equipped with dedicated docking/undocking mechanisms to house up to three modular robots for surgical interventions. The magnetic anchoring frame guarantees the required stability for surgical tasks execution, whilst dedicated modular robots provide the platform with adequate vision, stability and manipulation capabilities. RESULTS: Platform potentialities were demonstrated in a porcine model. Assessment was organized into two consecutive experimental steps, with a hybrid testing modality. First, platform deployment, anchoring and assembly through transoral-transgastric access were demonstrated in order to assess protocol feasibility and guarantee the safe achievement of the following experimental session. Second, transabdominal deployment, anchoring, assembly and robotic module actuation were carried out. CONCLUSIONS: This study has demonstrated the feasibility of inserting an endoluminal robotic platform composed of an anchoring frame and modular robotic units into a porcine model through a natural orifice. Once inserted into the peritoneal cavity, the platform provides proper visualization from multiple orientations. For the first time, a platform with interchangeable modules has been deployed and its components have been connected, demonstrating in vivo the feasibility of intra-abdominal assembly. Furthermore, increased dexterity employing different robotic units will enhance future system capabilities.

MEMS sensor technologies for human centred applications in healthcare, physical activities, safety and environmental sensing: a review on research activities in Italy.

Over the past few decades the increased level of public awareness concerning healthcare, physical activities, safety and environmental sensing has created an emerging need for smart sensor technologies and monitoring devices able to sense, classify, and provide feedbacks to users' health status and physical activities, as well as to evaluate environmental and safety conditions in a pervasive, accurate and reliable fashion. Monitoring and precisely quantifying users' physical activity with inertial measurement unit-based devices, for instance, has also proven to be important in health management of patients affected by chronic diseases, e.g., Parkinson's disease, many of which are becoming highly prevalent in Italy and in the Western world. This review paper will focus on MEMS sensor technologies developed in Italy in the last three years describing research achievements for healthcare and physical activity, safety and environmental sensing, in addition to smart systems integration. Innovative and smart integrated solutions for sensing devices, pursued and implemented in Italian research centres, will be highlighted, together with specific applications of such technologies. Finally, the paper will depict the future perspective of sensor technologies and corresponding exploitation opportunities, again with a specific focus on Italy.

An active one-lobe pulmonary simulator with compliance control for medical training in neonatal mechanical ventilation.

Mechanical ventilation is a current support therapy for newborns affected by respiratory diseases. However, several side effects have been observed after treatment, making it mandatory for physicians to determine more suitable approaches. High fidelity simulation is an efficient educational technique that recreates clinical experience. The aim of the present study is the design of an innovative and versatile neonatal respiratory simulator which could be useful in training courses for physicians and nurses as for mechanical ventilation. A single chamber prototype, reproducing a pulmonary lobe both in size and function, was designed and assembled. Volume and pressure within the chamber can be tuned by the operator through the device control system, in order to simulate both spontaneous and assisted breathing. An innovative software-based simulator for training neonatologists and nurses within the continuing medical education program on respiratory disease management was validated. Following the clinical needs, three friendly graphic user interfaces were implemented for simulating three different clinical scenarios (spontaneous breathing, controlled breathing and triggered/assisted ventilation modalities) thus providing physicians with an active experience. The proposed pulmonary simulator has the potential to be included in the range of computer-driven technologies used in medical training, adding novel functions and improving simulation results.

Biomechanics-machine learning system for surgical gesture analysis and development of technologies for minimal access surgery.

BACKGROUND: The uptake of minimal access surgery (MAS) has by virtue of its clinical benefits become widespread across the surgical specialties. However, despite its advantages in reducing traumatic insult to the patient, it imposes significant ergonomic restriction on the operating surgeons who require training for the safe execution. Recent progress in manipulator technologies (robotic or mechanical) have certainly reduced the level of difficulty, however it requires information for a complete gesture analysis of surgical performance. This article reports on the development and evaluation of such a system capable of full biomechanical and machine learning. METHODS: The system for gesture analysis comprises 5 principal modules, which permit synchronous acquisition of multimodal surgical gesture signals from different sources and settings. The acquired signals are used to perform a biomechanical analysis for investigation of kinematics, dynamics, and muscle parameters of surgical gestures and a machine learning model for segmentation and recognition of principal phases of surgical gesture. RESULTS: The biomechanical system is able to estimate the level of expertise of subjects and the ergonomics in using different instruments. The machine learning approach is able to ascertain the level of expertise of subjects and has the potential for automatic recognition of surgical gesture for surgeon-robot interactions. CONCLUSIONS: Preliminary tests have confirmed the efficacy of the system for surgical gesture analysis, providing an objective evaluation of progress during training of surgeons in their acquisition of proficiency in MAS approach and highlighting useful information for the design and evaluation of master-slave manipulator systems.