On Simultaneous Localization and Mapping inside the Human Body (Body-SLAM)

Wireless capsule endoscopy (WCE) offers a patient-friendly, non-invasive and painless investigation of the entire small intestine, where other conventional wired endoscopic instruments can barely reach. As a critical component of the capsule endoscopic examination, physicians need to know the precise position of the endoscopic capsule in order to identify the position of intestinal disease after it is detected by the video source. To define the position of the endoscopic capsule, we need to have a map of inside the human body. However, since the shape of the small intestine is extremely complex and the RF signal propagates differently in the non-homogeneous body tissues, accurate mapping and localization inside small intestine is very challenging. In this dissertation, we present an in-body simultaneous localization and mapping technique (Body-SLAM) to enhance the positioning accuracy of the WCE inside the small intestine and reconstruct the trajectory the capsule has traveled. In this way, the positions of the intestinal diseases can be accurately located on the map of inside human body, therefore, facilitates the following up therapeutic operations. The proposed approach takes advantage of data fusion from two sources that come with the WCE: image sequences captured by the WCE\u27s embedded camera and the RF signal emitted by the capsule. This approach estimates the speed and orientation of the endoscopic capsule by analyzing displacements of feature points between consecutive images. Then, it integrates this motion information with the RF measurements by employing a Kalman filter to smooth the localization results and generate the route that the WCE has traveled. The performance of the proposed motion tracking algorithm is validated using empirical data from the patients and this motion model is later imported into a virtual testbed to test the performance of the alternative Body-SLAM algorithms. Experimental results show that the proposed Body-SLAM technique is able to provide accurate tracking of the WCE with average error of less than 2.3cm

Towards Clustering of Mobile and Smartwatch Accelerometer Data for Physical Activity Recognition

Mobile and wearable devices now have a greater capability of sensing human activity ubiquitously and unobtrusively through advancements in miniaturization and sensing abilities. However, outstanding issues remain around the energy restrictions of these devices when processing large sets of data. This paper presents our approach that uses feature selection to refine the clustering of accelerometer data to detect physical activity. This also has a positive effect on the computational burden that is associated with processing large sets of data, as energy efficiency and resource use is decreased because less data is processed by the clustering algorithms. Raw accelerometer data, obtained from smartphones and smartwatches, have been preprocessed to extract both time and frequency domain features. Principle component analysis feature selection (PCAFS) and correlation feature selection (CFS) have been used to remove redundant features. The reduced feature sets have then been evaluated against three widely used clustering algorithms, including hierarchical clustering analysis (HCA), k-means, and density-based spatial clustering of applications with noise (DBSCAN). Using the reduced feature sets resulted in improved separability, reduced uncertainty, and improved efficiency compared with the baseline, which utilized all features. Overall, the CFS approach in conjunction with HCA produced higher Dunn Index results of 9.7001 for the phone and 5.1438 for the watch features, which is an improvement over the baseline. The results of this comparative study of feature selection and clustering, with the specific algorithms used, has not been performed previously and provides an optimistic and usable approach to recognize activities using either a smartphone or smartwatch

Automatic Information Exchange in the Early Rescue Chain Using the International Standard Accident Number (ISAN)

Thus far, emergency calls are answered by human operators who interview the calling person in order to obtain all relevant information. In the near future-based on the Internet of (Medical) Things (IoT, IoMT)-accidents, emergencies, or adverse health events will be reported automatically by smart homes, smart vehicles, or smart wearables, without any human in the loop. Several parties are involved in this communication: the alerting system, the rescue service (responding system), and the emergency department in the hospital (curing system). In many countries, these parties use isolated information and communication technology (ICT) systems. Previously, the International Standard Accident Number (ISAN) has been proposed to securely link the data in these systems. In this work, we propose an ISAN-based communication platform that allows semantically interoperable information exchange. Our aims are threefold: (i) to enable data exchange between the isolated systems, (ii) to avoid data misinterpretation, and (iii) to integrate additional data sources. The suggested platform is composed of an alerting, responding, and curing system manager, a workflow manager, and a communication manager. First, the ICT systems of all parties in the early rescue chain register with their according system manager, which tracks the keep-alive. In case of emergency, the alerting system sends an ISAN to the platform. The responsible rescue services and hospitals are determined and interconnected for platform-based communication. Next to the conceptual design of the platform, we evaluate a proof-of-concept implementation according to (1) the registration, (2) channel establishment, (3) data encryption, (4) event alert, and (5) information exchange. Our concept meets the requirements for scalability, error handling, and information security. In the future, it will be used to implement a virtual accident registry

A Testbed for Design and Performance Evaluation of Visual Localization Technique inside the Small Intestine

Wireless video capsule endoscopy (VCE) plays an increasingly important role in assisting clinical diagnoses of gastrointestinal (GI) diseases. It provides a non-invasive way to examine the entire small intestine, where other conventional endoscopic instruments can barely reach. Existing examination systems for the VCE cannot track the location of a endoscopic capsule, which prevents the physician from identifying the exact location of the diseases. During the eight hour examination time, the video capsule continuously keeps taking images at a frame rate up to six frame per sec, so it is possible to extract the motion information from the content of the image sequence. Many attempts have been made to develop computer vision algorithms to detect the motion of the capsule based on the small changes in the consecutive video frames and then trace the location of the capsule. However, validation of those algorithms has become a challenging topic because conducting experiments on the human body is extremely difficult due to individual differences and legal issues. In this thesis, two validation approaches for motion tracking of the VCE are presented in detail respectively. One approach is to build a physical testbed with a plastic pipe and an endoscopy camera; the other is to build a virtual testbed by creating a three-dimensional virtual small intestine model and simulating the motion of the capsule. Based on the virtual testbed, a physiological factor, intestinal contraction, has been studied in terms of its influence on visual based localization algorithm and a geometric model for measuring the amount of contraction is proposed and validated via the virtual testbed. Empirical results have made contributions in support of the performance evaluation of other research on the visual based localization algorithm of VCE

Green communication for tracking heart rate with smartbands

The trend of using wearables for healthcare is steeply increasing nowadays, and, consequently, in the market, there are several gadgets that measure several body features. In addition, the mixed use between smartphones and wearables has motivated research like the current one. The main goal of this work is to reduce the amount of times that a certain smartband (SB) measures the heart rate (HR) in order to save energy in communications without significantly reducing the utility of the application. This work has used an SB Sony 2 for measuring heart rate, Fit API for storing data and Android for managing data. The current approach has been assessed with data from HR sensors collected for more than three months. Once all HR measures were collected, then the current approach detected hourly ranges whose heart rate were higher than normal. The hourly ranges allowed for estimating the time periods of weeks that the user could be at potential risk for measuring frequently in these (60 times per hour) ranges. Out of these ranges, the measurement frequency was lower (six times per hour). If SB measures an unusual heart rate, the app warns the user so they are aware of the risk and can act accordingly. We analyzed two cases and we conclude that energy consumption was reduced in 83.57% in communications when using training of several weeks. In addition, a prediction per day was made using data of 20 users. On average, tests obtained 63.04% of accuracy in this experimentation using the training over the data of one day for each user

Digitizing the chemical senses: possibilities & pitfalls

Many people are understandably excited by the suggestion that the chemical senses can be digitized; be it to deliver ambient fragrances (e.g., in virtual reality or health-related applications), or else to transmit flavour experiences via the internet. However, to date, progress in this area has been surprisingly slow. Furthermore, the majority of the attempts at successful commercialization have failed, often in the face of consumer ambivalence over the perceived benefits/utility. In this review, with the focus squarely on the domain of Human-Computer Interaction (HCI), we summarize the state-of-the-art in the area. We highlight the key possibilities and pitfalls as far as stimulating the so-called ‘lower’ senses of taste, smell, and the trigeminal system are concerned. Ultimately, we suggest that mixed reality solutions are currently the most plausible as far as delivering (or rather modulating) flavour experiences digitally is concerned. The key problems with digital fragrance delivery are related to attention and attribution. People often fail to detect fragrances when they are concentrating on something else; And even when they detect that their chemical senses have been stimulated, there is always a danger that they attribute their experience (e.g., pleasure) to one of the other senses – this is what we call ‘the fundamental attribution error’. We conclude with an outlook on digitizing the chemical senses and summarize a set of open-ended questions that the HCI community has to address in future explorations of smell and taste as interaction modalities

An analytical comparison of datasets of Real-World and simulated falls intended for the evaluation of wearable fall alerting systems

Automatic fall detection is one of the most promising applications of wearables in the field of mobile health. The characterization of the effectiveness of wearable fall detectors is hampered by the inherent difficulty of testing these devices with real-world falls. In fact, practically all the proposals in the literature assess the detection algorithms with ‘scripted’ falls that are simulated in a controlled laboratory environment by a group of volunteers (normally young and healthy participants). Aiming at appraising the adequacy of this method, this work systematically compares the statistical characteristics of the acceleration signals from two databases with real falls and those computed from the simulated falls provided by 18 well-known repositories commonly employed by the related works. The results show noteworthy differences between the dynamics of emulated and real-life falls, which undermines the testing procedures followed to date and forces to rethink the strategies for evaluating wearable fall detectors.Funding for open access charge: Universidad de Málaga / CBUA. This research was funded by FEDER Funds (under grant UMA18-FEDERJA-022), Andalusian Regional Government (-Junta de Andalucía- grant PAIDI P18-RT-1652) and Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech

Performance assessment of mobility solutions for IPv6-based healthcare wireless sensor networks

This thesis focuses on the study of mobile wireless sensor networks applied to healthcare scenarios. The promotion of better quality-of-life for hospitalized patients is addressed in this research work with a solution that can help these patients to keep their mobility (if possible). The solution proposed allows remote monitoring and control of patients’ health in real-time and without interruptions. Small sensor nodes able to collect and send wirelessly the health parameters allow for the control of the patients' health condition. A network infrastructure, composed by several access points, allows the connection of the sensor nodes (carried by the patients) to remote healthcare providers. To ensure continuous access to sensor nodes special attention should be dedicated to manage the transition of these sensor nodes between different access points’ coverage areas. The process of changing an access point attachment of a sensor node is called handover. In that context, this thesis proposes a new handover mechanism that can ensure continuous connection to mobile sensor nodes in a healthcare wireless sensor network. Due to the limitations of sensor nodes’ resources, namely available energy (these sensor nodes are typically powered by small batteries), the proposed mechanism pays a special attention in the optimization of energy consumption. To achieve this optimization, part of this work is dedicated to the construction of a small sensor node. The handover mechanism proposed in this work is called Hand4MAC (handover mechanism for MAC layer). This mechanism is compared with other mechanisms commonly used in handover management. The Hand4MAC mechanism is deployed and validated through by simulation and in a real testbed. The scenarios used for the validation reproduces a hospital ward. The performance evaluation is focused in the percentage of time that senor nodes are accessible to the network while traveling across several access points’ coverage areas and the energy expenditures in handover processes. The experiments performed take into account various parameters that are the following: number of sent messages, number of received messages, multicast message usage, energy consumption, number of sensor nodes present in the scenario, velocity of sensor nodes, and time-to-live value. In both simulation and real testbed, the Hand4MAC mechanism is shown to perform better than all the other handover mechanisms tested. In this comparison it was only considered the most promising handover mechanisms proposed in the literature.Fundação para a Ciência e a Tecnologia (FCT

Usable Security for Wireless Body-Area Networks

We expect wireless body-area networks of pervasive wearable devices will enable in situ health monitoring, personal assistance, entertainment personalization, and home automation. As these devices become ubiquitous, we also expect them to interoperate. That is, instead of closed, end-to-end body-worn sensing systems, we envision standardized sensors that wirelessly communicate their data to a device many people already carry today, the smart phone. However, this ubiquity of wireless sensors combined with the characteristics they sense present many security and privacy problems. In this thesis we describe solutions to two of these problems. First, we evaluate the use of bioimpedance for recognizing who is wearing these wireless sensors and show that bioimpedance is a feasible biometric. Second, we investigate the use of accelerometers for verifying whether two of these wireless sensors are on the same person and show that our method is successful as distinguishing between sensors on the same body and on different bodies. We stress that any solution to these problems must be usable, meaning the user should not have to do anything but attach the sensor to their body and have them just work. These methods solve interesting problems in their own right, but it is the combination of these methods that shows their true power. Combined together they allow a network of wireless sensors to cooperate and determine whom they are sensing even though only one of the wireless sensors might be able to determine this fact. If all the wireless sensors know they are on the same body as each other and one of them knows which person it is on, then they can each exploit the transitive relationship to know that they must all be on that person’s body. We show how these methods can work together in a prototype system. This ability to operate unobtrusively, collecting in situ data and labeling it properly without interrupting the wearer’s activities of daily life, will be vital to the success of these wireless sensors