Multiobjective design of wearable sensor systems for electrocardiogram monitoring

Wearable sensor systems will soon become part of the available medical tools for remote and long term physiological monitoring. However, the set of variables involved in the performance of these systems are usually antagonistic, and therefore the design of usable wearable systems in real clinical applications entails a number of challenges that have to be addressed first. This paper describes a method to optimise the design of these systems for the specific application of cardiac monitoring. The method proposed is based on the selection of a subset of 5 design variables, sensor contact, location, and rotation, signal correlation, and patient comfort, and 2 objective functions, functionality and wearability. These variables are optimised using linear and nonlinear models to maximise those objective functions simultaneously. The methodology described and the results achieved demonstrate that it is possible to find an optimal solution and therefore overcome most of the design barriers that prevent wearable sensor systems from being used in normal clinical practice.This work was supported by the Departamento Administrativo de Ciencia, Tecnologia e Innovacion, COLCIENCIAS, Republic of Colombia, under Grant nos. 511 and 523.Martinez-Tabares, FJ.; Costa-Salas, YJ.; Cuesta Frau, D.; Castellanos-Dominguez, G. (2016). Multiobjective design of wearable sensor systems for electrocardiogram monitoring. Journal of Sensors. (2418065):1-15. https://doi.org/10.1155/2016/2418065115241806

Sensing with Earables: A Systematic Literature Review and Taxonomy of Phenomena

Earables have emerged as a unique platform for ubiquitous computing by augmenting ear-worn devices with state-of-the-art sensing. This new platform has spurred a wealth of new research exploring what can be detected on a wearable, small form factor. As a sensing platform, the ears are less susceptible to motion artifacts and are located in close proximity to a number of important anatomical structures including the brain, blood vessels, and facial muscles which reveal a wealth of information. They can be easily reached by the hands and the ear canal itself is affected by mouth, face, and head movements. We have conducted a systematic literature review of 271 earable publications from the ACM and IEEE libraries. These were synthesized into an open-ended taxonomy of 47 different phenomena that can be sensed in, on, or around the ear. Through analysis, we identify 13 fundamental phenomena from which all other phenomena can be derived, and discuss the different sensors and sensing principles used to detect them. We comprehensively review the phenomena in four main areas of (i) physiological monitoring and health, (ii) movement and activity, (iii) interaction, and (iv) authentication and identification. This breadth highlights the potential that earables have to offer as a ubiquitous, general-purpose platform

Sensing with Earables: A Systematic Literature Review and Taxonomy of Phenomena

Earables have emerged as a unique platform for ubiquitous computing by augmenting ear-worn devices with state-of-the-art sensing. This new platform has spurred a wealth of new research exploring what can be detected on a wearable, small form factor. As a sensing platform, the ears are less susceptible to motion artifacts and are located in close proximity to a number of important anatomical structures including the brain, blood vessels, and facial muscles which reveal a wealth of information. They can be easily reached by the hands and the ear canal itself is affected by mouth, face, and head movements. We have conducted a systematic literature review of 271 earable publications from the ACM and IEEE libraries. These were synthesized into an open-ended taxonomy of 47 different phenomena that can be sensed in, on, or around the ear. Through analysis, we identify 13 fundamental phenomena from which all other phenomena can be derived, and discuss the different sensors and sensing principles used to detect them. We comprehensively review the phenomena in four main areas of (i) physiological monitoring and health, (ii) movement and activity, (iii) interaction, and (iv) authentication and identification. This breadth highlights the potential that earables have to offer as a ubiquitous, general-purpose platform

Dealing with the effects of sensor displacement in wearable activity recognition

Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined sensor placement. Also, sensors may move from their original location to a different one, due to a loose attachment. Activity recognition systems trained on activity patterns characteristic of a given sensor deployment may likely fail due to sensor displacements. In this work, we innovatively explore the effects of sensor displacement induced by both the intentional misplacement of sensors and self-placement by the user. The effects of sensor displacement are analyzed for standard activity recognition techniques, as well as for an alternate robust sensor fusion method proposed in a previous work. While classical recognition models show little tolerance to sensor displacement, the proposed method is proven to have notable capabilities to assimilate the changes introduced in the sensor position due to self-placement and provides considerable improvements for large misplacements.This work was supported by the High Performance Computing (HPC)-Europa2 project funded by the European Commission-DG Research in the Seventh Framework Programme under grant agreement No. 228398 and by the EU Marie Curie Network iCareNet under grant No. 264738. This work was also supported by the Spanish Comision Interministerial de Ciencia y Tecnologia (CICYT) Project SAF2010-20558, Junta de Andalucia Project P09-TIC-175476 and the FPU Spanish grant, AP2009-2244

Multi-modal on-body sensing of human activities

Increased usage and integration of state-of-the-art information technology in our everyday work life aims at increasing the working efficiency. Due to unhandy human-computer-interaction methods this progress does not always result in increased efficiency, for mobile workers in particular. Activity recognition based contextual computing attempts to balance this interaction deficiency. This work investigates wearable, on-body sensing techniques on their applicability in the field of human activity recognition. More precisely we are interested in the spotting and recognition of so-called manipulative hand gestures. In particular the thesis focuses on the question whether the widely used motion sensing based approach can be enhanced through additional information sources. The set of gestures a person usually performs on a specific place is limited -- in the contemplated production and maintenance scenarios in particular. As a consequence this thesis investigates whether the knowledge about the user's hand location provides essential hints for the activity recognition process. In addition, manipulative hand gestures -- due to their object manipulating character -- typically start in the moment the user's hand reaches a specific place, e.g. a specific part of a machinery. And the gestures most likely stop in the moment the hand leaves the position again. Hence this thesis investigates whether hand location can help solving the spotting problem. Moreover, as user-independence is still a major challenge in activity recognition, this thesis investigates location context as a possible key component in a user-independent recognition system. We test a Kalman filter based method to blend absolute position readings with orientation readings based on inertial measurements. A filter structure is suggested which allows up-sampling of slow absolute position readings, and thus introduces higher dynamics to the position estimations. In such a way the position measurement series is made aware of wrist motions in addition to the wrist position. We suggest location based gesture spotting and recognition approaches. Various methods to model the location classes used in the spotting and recognition stages as well as different location distance measures are suggested and evaluated. In addition a rather novel sensing approach in the field of human activity recognition is studied. This aims at compensating drawbacks of the mere motion sensing based approach. To this end we develop a wearable hardware architecture for lower arm muscular activity measurements. The sensing hardware based on force sensing resistors is designed to have a high dynamic range. In contrast to preliminary attempts the proposed new design makes hardware calibration unnecessary. Finally we suggest a modular and multi-modal recognition system; modular with respect to sensors, algorithms, and gesture classes. This means that adding or removing a sensor modality or an additional algorithm has little impact on the rest of the recognition system. Sensors and algorithms used for spotting and recognition can be selected and fine-tuned separately for each single activity. New activities can be added without impact on the recognition rates of the other activities

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Group Activity Recognition Using Wearable Sensing Devices

Understanding behavior of groups in real time can help prevent tragedy in crowd emergencies. Wearable devices allow sensing of human behavior, but the infrastructure required to communicate data is often the first casualty in emergency situations. Peer-to-peer (P2P) methods for recognizing group behavior are necessary, but the behavior of the group cannot be observed at any single location. The contribution is the methods required for recognition of group behavior using only wearable devices