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

Local Positioning Systems in (Game) Sports

Position data of players and athletes are widely used in sports performance analysis for measuring the amounts of physical activities as well as for tactical assessments in game sports. However, positioning sensing systems are applied in sports as tools to gain objective information of sports behavior rather than as components of intelligent spaces (IS). The paper outlines the idea of IS for the sports context with special focus to game sports and how intelligent sports feedback systems can benefit from IS. Henceforth, the most common location sensing techniques used in sports and their practical application are reviewed, as location is among the most important enabling techniques for IS. Furthermore, the article exemplifies the idea of IS in sports on two applications

Using FSR

based muscule activity monitoring to recognize manipulative arm gesture

Event-Based Activity Tracking in Work

Wearable computers aim to empower people by providing relevant information at appropriate time. This context-based information delivery helps to perform intricate, tedious or critical tasks and improves productivity, decreases error rates, and thus results in a reduction of labor cost