SVILUPPO DI UN SISTEMA INDOSSABILE DI MONITORAGGIO DI POSTURA E MOVIMENTO UMANO BASATO SU TECNOLOGIA BLUETOOTH

Oggetto di questa tesi è la progettazione e realizzazione di un sistema indossabile, che con l’utilizzo di accelerometri e tecnologie wireless, sia capace di monitorare il movimento e la postura di una persona. Il nucleo del presente lavoro di tesi è un dispositivo elettronico wireless, microcontrollato, di dimensioni contenute, trasportabile dall’utente, che preleva i segnali digitali modulati PWM da una coppia di accelerometri, posta direttamente sul corpo del soggetto. I dati acquisiti vengono trasmessi e registrati in tempo reale ma anche elaborati al fine di estrarne parametri significativi sulla postura del soggetto sotto esame

Touch Position Detection in Electrical Tomography Tactile Sensors Through Quadratic Classifier

Traditional electrical tomography tactile sensors consider the usage of the system’s finite element model. This approach brings disadvantages that jeopardise their applicability aspect and wide use. To address this limitation, the main thrust of this work is to present a method for touch position identification for an electrical tomography flexible tactile sensor. This is done by using a supervised machine learning algorithm for performing classification, namely quadratic discriminant analysis. This approach provides accurate contact location identification, increasing the detection speed and the sensor versatility when compared to traditional electrical tomography approaches. Results obtained show classification accuracy rates of up to 91.6% on unseen test data and an average euclidean error ranging from 1 to 10 mm depending on the contact location over the sensor. The sensor is then applied in real case scenarios to show its efficiency. These outcomes are encouraging since they promote the future practical usage of flexible and low-cost sensors

ePhysio: A Wearables-Enabled Platform for the Remote Management of Musculoskeletal Diseases

Technology advancements in wireless communication and embedded computing are fostering their evolution from standalone elements to smart objects seamlessly integrated in the broader context of the Internet of Things. In this context, wearable sensors represent the building block for new cyber-physical social systems, which aim at improving the well-being of people by monitoring and measuring their activities and provide an immediate feedback to the users. In this paper, we introduce ePhysio, a large-scale and flexible platform for sensor-assisted physiotherapy and remote management of musculoskeletal diseases. The system leverages networking and computing tools to provide real-time and ubiquitous monitoring of patients. We propose three use cases which differ in scale and context and are characterized by different human interactions: single-user therapy, indoor group therapy, and on-field therapy. For each use case, we identify the social interactions, e.g., between the patient and the physician and between different users and the performance requirements in terms of monitoring frequency, communication, and computation. We then propose three related deployments, highlighting the technologies that can be applied in a real system. Finally, we describe a proof-of-concept implementation, which demonstrates the feasibility of the proposed solution

A quantitative evaluation of drive pattern selection for optimizing EIT-based stretchable sensors

Electrical Impedance Tomography (EIT) is a medical imaging technique that has been recently used to realize stretchable pressure sensors. In this method, voltage measurements are taken at electrodes placed at the boundary of the sensor and are used to reconstruct an image of the applied touch pressure points. The drawback in EIT-based sensors however, is their low spatial resolution due to the ill-posed nature of the EIT reconstruction. In this paper, we show our performance evaluation of different EIT drive patterns, specifically strategies for electrode selection when performing current injection and voltage measurements. We compare voltage data with Signal to Noise Ratio (SNR) and Boundary Voltage Changes (BVC), and study image quality with Size Error (SE), Position Error (PE) and Ringing (RNG) parameters, in the case of one-point and two-point simultaneous contact locations. The study shows that, in order to improve the performance of EIT based sensors, the electrode selection strategies should dynamically change correspondingly to the location of the input stimuli. In fact, the selection of a drive pattern over another can improve the target size detection and position accuracy up to 4.7% and 18% respectively

Moving Auto-Correlation Window Approach for Heart Rate Estimation in Ballistocardiography Extracted by Mattress-Integrated Accelerometers

Continuous heart monitoring is essential for early detection and diagnosis of cardiovascular diseases, which are key factors for the evaluation of health status in the general population. Therefore, in the future, it will be increasingly important to develop unobtrusive and transparent cardiac monitoring technologies for the population. The possible approaches are the development of wearable technologies or the integration of sensors in daily-life objects. We developed a smart bed for monitoring cardiorespiratory functions during the night or in the case of continuous monitoring of bedridden patients. The mattress includes three accelerometers for the estimation of the ballistocardiogram (BCG). BCG signal is generated due to the vibrational activity of the body in response to the cardiac ejection of blood. BCG is a promising technique but is usually replaced by electrocardiogram due to the difficulty involved in detecting and processing the BCG signals. In this work, we describe a new algorithm for heart parameter extraction from the BCG signal, based on a moving auto-correlation sliding-window. We tested our method on a group of volunteers with the simultaneous co-registration of electrocardiogram (ECG) using a single-lead configuration. Comparisons with ECG reference signals indicated that the algorithm performed satisfactorily. The results presented demonstrate that valuable cardiac information can be obtained from the BCG signal extracted by low cost sensors integrated in the mattress. Thus, a continuous unobtrusive heart-monitoring through a smart bed is now feasible

A Synergy-Based Optimally Designed Sensing Glove for Functional Grasp Recognition

Achieving accurate and reliable kinematic hand pose reconstructions represents a challenging task. The main reason for this is the complexity of hand biomechanics, where several degrees of freedom are distributed along a continuous deformable structure. Wearable sensing can represent a viable solution to tackle this issue, since it enables a more natural kinematic monitoring. However, the intrinsic accuracy (as well as the number of sensing elements) of wearable hand pose reconstruction (HPR) systems can be severely limited by ergonomics and cost considerations. In this paper, we combined the theoretical foundations of the optimal design of HPR devices based on hand synergy information, i.e., the inter-joint covariation patterns, with textile goniometers based on knitted piezoresistive fabrics (KPF) technology, to develop, for the first time, an optimally-designed under-sensed glove for measuring hand kinematics. We used only five sensors optimally placed on the hand and completed hand pose reconstruction (described according to a kinematic model with 19 degrees of freedom) leveraging upon synergistic information. The reconstructions we obtained from five different subjects were used to implement an unsupervised method for the recognition of eight functional grasps, showing a high degree of accuracy and robustness

A Multi-Modal Sensing Glove for Human Manual-Interaction Studies

We present an integrated sensing glove that combines two of the most visionary wearable sensing technologies to provide both hand posture sensing and tactile pressure sensing in a unique, lightweight, and stretchable device. Namely, hand posture reconstruction employs Knitted Piezoresistive Fabrics that allows us to measure bending. From only five of these sensors (one for each finger) the full hand pose of a 19 degrees of freedom (DOF) hand model is reconstructed leveraging optimal sensor placement and estimation techniques. To this end, we exploit a-priori information of synergistic coordination patterns in grasping tasks. Tactile sensing employs a piezoresistive fabric allowing us to measure normal forces in more than 50 taxels spread over the palmar surface of the glove. We describe both sensing technologies, report on the software integration of both modalities, and describe a preliminary evaluation experiment analyzing hand postures and force patterns during grasping. Results of the reconstruction are promising and encourage us to push further our approach with potential applications in neuroscience, virtual reality, robotics and tele-operation

Palpreast - A New Wearable Device for Breast Self-Examination

Breast cancer is the most commonly diagnosed cancer in women worldwide. Although targeted screening programs using mammography have facilitated earlier detection and improved treatment has resulted in a significant reduction in mortality, some negative aspects related to cost, the availability of trained staff, the duration of the procedure, and its non-generalizability to all women must be taken into consideration. Breast palpation is a simple non-invasive procedure that can be performed by lay individuals for detecting possible malignant nodules in the breast. It is a simple test, based on the haptic perception of different stiffness between healthy and abnormal tissues. According to a survey we carried out, despite being safe and simple, breast self-examination is not carried by women because they are not confident of their ability to detect a lump. In this study, a non-invasive wearable device designed to mimic the process of breast self-examination using pressure sensing textiles and thus increase the confidence and self-awareness of women is proposed. Combined with other screening methods, the device can increase the odds of early detection for better prognosis. Here, we present the physical implementation of the device and a finite element analysis of the mechanics underlying its working principle. Characterization of the device using models of large and medium breast phantoms with rigid inclusions demonstrates that it can detect nodules in much the same way as does the human hand during breast self-examination

A Multimodal Perception Framework for Users Emotional State Assessment in Social Robotics

In this work, we present an unobtrusive and non-invasive perception framework based on the synergy between two main acquisition systems: the Touch-Me Pad, consisting of two electronic patches for physiological signal extraction and processing; and the Scene Analyzer, a visual-auditory perception system specifically designed for the detection of social and emotional cues. It will be explained how the information extracted by this specific kind of framework is particularly suitable for social robotics applications and how the system has been conceived in order to be used in human-robot interaction scenarios

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