Examination of Wireless Power Transfer Combined With the Utilization of Distance Detection

Wireless power transfer using magnetic resonant coupling is expected to be widely used in the charging of an electric vehicle and in the use of home electric appliances. Wireless power transfer requires a high efficiency and power transfer over a long distance. However, the efficiency is reduced by the increase of the transmission distance and the change of the impedance of the receiving load. Therefore, a distance sensor using magnetic resonant coupling is proposed. In this paper, we propose a method of power transfer and distance detection using one couple of coils in real time. We also propose to use different frequencies in the power transfer and the distance detection, which are superposed and separated. Using the proposed approach, we demonstrated that the power transfer and the distance detection can be performed without relying on the change of the transmission distance.ArticleIEEE TRANSACTIONS ON MAGNETICS. 50(11):4005304 (2014)journal articl

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%

Adapting magnetic resonant coupling based relative positioning technology for wearable activitiy recogniton

We demonstrate how modulated magnetic field technology that is well established in high precision, stationary motion tracking systems can be adapted to wearable activity recog-nition. To this end we describe the design and implementa-tion of a cheap (components cost about 20 Euro for the trans-mitter and 15 Euro for the receiver), low power (17mA for the transmitter and 40mA for the receiver), and easily wear-able (the main size constraint are the coils which are about 25mm3) system for tracking the relative position and orienta-tion of body parts. We evaluate our system on two recognition tasks. On a set of 6 subtle nutrition related gestures it achieves 99.25 % recognition rate compared to 94.1 % for a XSens in-ertial device ( operated calibrated, euler angle mode). On the recognition of 8 Tai Chi moves it reaches 94 % compared to 86 % of an accelerometer. Combining our sensor with the ac-celerometer leads to 100 % correct recognition (as compared to 90 % when combining the accelerometer with a gyro).

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

Advancement in Dietary Assessment and Self-Monitoring Using Technology

Although methods to assess or self-monitor intake may be considered similar, the intended function of each is quite distinct. For the assessment of dietary intake, methods aim to measure food and nutrient intake and/or to derive dietary patterns for determining diet-disease relationships, population surveillance or the effectiveness of interventions. In comparison, dietary self-monitoring primarily aims to create awareness of and reinforce individual eating behaviours, in addition to tracking foods consumed. Advancements in the capabilities of technologies, such as smartphones and wearable devices, have enhanced the collection, analysis and interpretation of dietary intake data in both contexts. This Special Issue invites submissions on the use of novel technology-based approaches for the assessment of food and/or nutrient intake and for self-monitoring eating behaviours. Submissions may document any part of the development and evaluation of the technology-based approaches. Examples may include: web adaption of existing dietary assessment or self-monitoring tools (e.g., food frequency questionnaires, screeners) image-based or image-assisted methods mobile/smartphone applications for capturing intake for assessment or self-monitoring wearable cameras to record dietary intake or eating behaviours body sensors to measure eating behaviours and/or dietary intake use of technology-based methods to complement aspects of traditional dietary assessment or self-monitoring, such as portion size estimation