MOCA: A Low-Power, Low-Cost Motion Capture System Based on Integrated Accelerometers

Human-computer interaction (HCI) and virtual reality applications pose the challenge of enabling real-time interfaces for natural interaction. Gesture recognition based on body-mounted accelerometers has been proposed as a viable solution to translate patterns of movements that are associated with user commands, thus substituting point-and-click methods or other cumbersome input devices. On the other hand, cost and power constraints make the implementation of a natural and efficient interface suitable for consumer applications a critical task. Even though several gesture recognition solutions exist, their use in HCI context has been poorly characterized. For this reason, in this paper, we consider a low-cost/low-power wearable motion tracking system based on integrated accelerometers called motion capture with accelerometers (MOCA) that we evaluated for navigation in virtual spaces. Recognition is based on a geometric algorithm that enables efficient and robust detection of rotational movements. Our objective is to demonstrate that such a low-cost and a low-power implementation is suitable for HCI applications. To this purpose, we characterized the system from both a quantitative point of view and a qualitative point of view. First, we performed static and dynamic assessment of movement recognition accuracy. Second, we evaluated the effectiveness of user experience using a 3D game application as a test bed

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

Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers

The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis of human motion and its automatic classification are the main computational tasks to be pursued. In this paper, we discuss how human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose. In particular, we motivate our current interest for classifiers based on Hidden Markov Models (HMMs). An example is illustrated and discussed by analysing a dataset of accelerometer time series

A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation

BACKGROUND: Recent technological advances in integrated circuits, wireless communications, and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices. A number of these devices can be integrated into a Wireless Body Area Network (WBAN), a new enabling technology for health monitoring. METHODS: Using off-the-shelf wireless sensors we designed a prototype WBAN which features a standard ZigBee compliant radio and a common set of physiological, kinetic, and environmental sensors. RESULTS: We introduce a multi-tier telemedicine system and describe how we optimized our prototype WBAN implementation for computer-assisted physical rehabilitation applications and ambulatory monitoring. The system performs real-time analysis of sensors' data, provides guidance and feedback to the user, and can generate warnings based on the user's state, level of activity, and environmental conditions. In addition, all recorded information can be transferred to medical servers via the Internet and seamlessly integrated into the user's electronic medical record and research databases. CONCLUSION: WBANs promise inexpensive, unobtrusive, and unsupervised ambulatory monitoring during normal daily activities for prolonged periods of time. To make this technology ubiquitous and affordable, a number of challenging issues should be resolved, such as system design, configuration and customization, seamless integration, standardization, further utilization of common off-the-shelf components, security and privacy, and social issues

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

Context Aware Middleware Architectures: Survey and Challenges

Abstract: Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work

Review of technology‐supported multimodal solutions for people with dementia

Funding Information: This research was partially funded by FAITH project (H2020?SC1?DTH?2019?875358), CARELINK project (AAL?CALL?2016?049), and Funda??o para a Ci?ncia e Tecnologia through the program UIDB/00066/2020 (CTS?Center of Technology and Systems).Acknowledgments: The authors acknowledge the European Commission for its support and partial funding; the partners of the research project FAITH project (H2020?SC1?DTH?2019?875358); and CARELINK, AAL?CALL?2016?049 funded by AAL JP and co?funded by the European Commission and National Funding Authorities of Ireland, Belgium, Portugal, and Switzerland. Partial support also comes from Funda??o para a Ci?ncia e Tecnologia through the program UIDB/00066/2020 (CTS?Center of Technology and Systems). Funding Information: Acknowledgments: The authors acknowledge the European Commission for its support and partial funding; the partners of the research project FAITH project (H2020‐SC1‐DTH‐2019‐875358); and CARELINK, AAL‐CALL‐2016‐049 funded by AAL JP and co‐funded by the European Commission and National Funding Authorities of Ireland, Belgium, Portugal, and Switzerland. Partial support also comes from Fundação para a Ciência e Tecnologia through the program UIDB/00066/2020 (CTS—Center of Technology and Systems). Funding Information: Funding: This research was partially funded by FAITH project (H2020‐SC1‐DTH‐2019‐875358), CARELINK project (AAL‐CALL‐2016‐049), and Fundação para a Ciência e Tecnologia through the program UIDB/00066/2020 (CTS—Center of Technology and Systems). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.The number of people living with dementia in the world is rising at an unprecedented rate, and no country will be spared. Furthermore, neither decisive treatment nor effective medicines have yet become effective. One potential alternative to this emerging challenge is utilizing supportive technologies and services that not only assist people with dementia to do their daily activities safely and independently, but also reduce the overwhelming pressure on their caregivers. Thus, for this study, a systematic literature review is conducted in an attempt to gain an overview of the latest findings in this field of study and to address some commercially available supportive technologies and services that have potential application for people living with dementia. To this end, 30 potential supportive technologies and 15 active supportive services are identified from the literature and related websites. The technologies and services are classified into different classes and subclasses (according to their functionalities, capabilities, and features) aiming to facilitate their understanding and evaluation. The results of this work are aimed as a base for designing, integrating, developing, adapting, and customizing potential multimodal solutions for the specific needs of vulnerable people of our societies, such as those who suffer from different degrees of dementia.publishersversionpublishe

Error Estimation for the Linearized Auto-Localization Algorithm

The Linearized Auto-Localization (LAL) algorithm estimates the position of beacon nodes in Local Positioning Systems (LPSs), using only the distance measurements to a mobile node whose position is also unknown. The LAL algorithm calculates the inter-beacon distances, used for the estimation of the beacons’ positions, from the linearized trilateration equations. In this paper we propose a method to estimate the propagation of the errors of the inter-beacon distances obtained with the LAL algorithm, based on a first order Taylor approximation of the equations. Since the method depends on such approximation, a confidence parameter τ is defined to measure the reliability of the estimated error. Field evaluations showed that by applying this information to an improved weighted-based auto-localization algorithm (WLAL), the standard deviation of the inter-beacon distances can be improved by more than 30% on average with respect to the original LAL method