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

Recognition physical activities with optimal number of wearable sensors using data mining algorithms and deep belief network

© 2017 IEEE. Daily physical activities monitoring is benefiting the health care field in several ways, in particular with the development of the wearable sensors. This paper adopts effective ways to calculate the optimal number of the necessary sensors and to build a reliable and a high accuracy monitoring system. Three data mining algorithms, namely Decision Tree, Random Forest and PART Algorithm, have been applied for the sensors selection process. Furthermore, the deep belief network (DBN) has been investigated to recognise 33 physical activities effectively. The results indicated that the proposed method is reliable with an overall accuracy of 96.52% and the number of sensors is minimised from nine to six sensors

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Distributed Computing and Monitoring Technologies for Older Patients

This book summarizes various approaches for the automatic detection of health threats to older patients at home living alone. The text begins by briefly describing those who would most benefit from healthcare supervision. The book then summarizes possible scenarios for monitoring an older patient at home, deriving the common functional requirements for monitoring technology. Next, the work identifies the state of the art of technological monitoring approaches that are practically applicable to geriatric patients. A survey is presented on a range of such interdisciplinary fields as smart homes, telemonitoring, ambient intelligence, ambient assisted living, gerontechnology, and aging-in-place technology. The book discusses relevant experimental studies, highlighting the application of sensor fusion, signal processing and machine learning techniques. Finally, the text discusses future challenges, offering a number of suggestions for further research directions

Physical and digital architecture for collection and analysis of imparted accelerations on Zip Line attractions

The accelerations experienced by riders of Zip Line attractions is an underexplored area of public safety assurance. These amusement devices require complex processes to collect and analyze acceleration data. Highly versatile and effective rider-worn and ride-carried devices are necessary to collect acceleration and velocity data without affecting the integrity of the ride. This paper introduces the use of a sensor device for collecting Zip Line acceleration data in the form of a Trailing Trolley. This architecture extends the work of Sicat et. al.’s which proposed the use of a Sensor Vest and Headwear to collect linear and rotational accelerations of a Zip Line rider. We investigate the logistics of combining the two sensor platforms and formulate a procedure to post-process and analyze the data. Techniques to extract, filter, and process the accelerations recorded is discussed and the potential for the synthesis of positioning linear and rotational data is described. Additional testing of data collection and analysis is necessary to prove the viability of these techniques and apparatuses as potential parts of a standardized test method for measuring rider experienced g-forces on Zip Lines

Kompensation positionsbezogener Artefakte in Aktivitätserkennung

This thesis investigates, how placement variations of electronic devices influence the possibility of using sensors integrated in those devices for context recognition. The vast majority of context recognition research assumes well defined, fixed sen- sor locations. Although this might be acceptable for some application domains (e.g. in an industrial setting), users, in general, will have a hard time coping with these limitations. If one needs to remember to carry dedicated sensors and to adjust their orientation from time to time, the activity recognition system is more distracting than helpful. How can we deal with device location and orientation changes to make context sensing mainstream? This thesis presents a systematic evaluation of device placement effects in context recognition. We first deal with detecting if a device is carried on the body or placed somewhere in the environ- ment. If the device is placed on the body, it is useful to know on which body part. We also address how to deal with sensors changing their position and their orientation during use. For each of these topics some highlights are given in the following. Regarding environmental placement, we introduce an active sampling ap- proach to infer symbolic object location. This approach requires only simple sensors (acceleration, sound) and no infrastructure setup. The method works for specific placements such as "on the couch", "in the desk drawer" as well as for general location classes, such as "closed wood compartment" or "open iron sur- face". In the experimental evaluation we reach a recognition accuracy of 90% and above over a total of over 1200 measurements from 35 specific locations (taken from 3 different rooms) and 12 abstract location classes. To derive the coarse device placement on the body, we present a method solely based on rotation and acceleration signals from the device. It works independent of the device orientation. The on-body placement recognition rate is around 80% over 4 min. of unconstrained motion data for the worst scenario and up to 90% over a 2 min. interval for the best scenario. We use over 30 hours of motion data for the analysis. Two special issues of device placement are orientation and displacement. This thesis proposes a set of heuristics that significantly increase the robustness of motion sensor-based activity recognition with respect to sen- sor displacement. We show how, within certain limits and with modest quality degradation, motion sensor-based activity recognition can be implemented in a displacement tolerant way. We evaluate our heuristics first on a set of synthetic lower arm motions which are well suited to illustrate the strengths and limits of our approach, then on an extended modes of locomotion problem (sensors on the upper leg) and finally on a set of exercises performed on various gym machines (sensors placed on the lower arm). In this example our heuristic raises the dis- placed recognition rate from 24% for a displaced accelerometer, which had 96% recognition when not displaced, to 82%

Telemonitoring systems for respiratory patients: technological aspects.

Abstract This review introduces the reader to the available technologies in the field of telemonitoring, with focus on respiratory patients. In the materials and methods section, a general structure of telemonitoring systems for respiratory patients is presented and the sensors of interest are illustrated, i.e., respiratory monitors (wearable and non-wearable), activity trackers, pulse oximeters, environmental monitors and other sensors of physiological variables. Afterwards, the most common communication protocols are briefly introduced. In the results section, selected clinical studies that prove the significance of the presented parameters in chronic respiratory diseases are presented. This is followed by a discussion on the main current issues in telemedicine, in particular legal aspects, data privacy and benefits both in economic and health terms