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%

Recognizing Human Activities Userindependently on Smartphones Based on Accelerometer Data

Real-time human activity recognition on a mobile phone is presented in this article. Unlike in most other studies, not only the data were collected using the accelerometers of a smartphone, but also models were implemented to the phone and the whole classification process (preprocessing, feature extraction and classification) was done on the device. The system is trained using phone orientation independent features to recognize five everyday activities: walking, running, cycling, driving a car and sitting/standing while the phone is in the pocket of the subject's trousers. Two classifiers were compared, knn (k nearest neighbors) and QDA (quadratic discriminant analysis). The models for real-time activity recognition were trained offline using a data set collected from eight subjects and these offline results were compared to real-time recognition rates, which are obtained by implementing models to mobile activity recognition application which currently supports two operating systems: Symbian^3 and Android. The results show that the presented method is light and, therefore, suitable for be used in real-time recognition. In addition, the recognition rates on the smartphones were encouraging, in fact, the recognition accuracies obtained are approximately as high as offline recognition rates. Also, the results show that the method presented is not an operating system dependent

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Visual Localisation of Mobile Devices in an Indoor Environment under Network Delay Conditions

Current progresses in home automation and service robotic environment have highlighted the need to develop interoperability mechanisms that allow a standard communication between the two systems. During the development of the DHCompliant protocol, the problem of locating mobile devices in an indoor environment has been investigated. The communication of the device with the location service has been carried out to study the time delay that web services offer in front of the sockets. The importance of obtaining data from real-time location systems portends that a basic tool for interoperability, such as web services, can be ineffective in this scenario because of the delays added in the invocation of services. This paper is focused on introducing a web service to resolve a coordinates request without any significant delay in comparison with the sockets

Characterization of a Multi-User Indoor Positioning System Based on Low Cost Depth Vision (Kinect) for Monitoring Human Activity in a Smart Home

An increasing number of systems use indoor positioning for many scenarios such as asset tracking, health care, games, manufacturing, logistics, shopping, and security. Many technologies are available and the use of depth cameras is becoming more and more attractive as this kind of device becomes affordable and easy to handle. This paper contributes to the effort of creating an indoor positioning system based on low cost depth cameras (Kinect). A method is proposed to optimize the calibration of the depth cameras, to describe the multi-camera data fusion and to specify a global positioning projection to maintain the compatibility with outdoor positioning systems. The monitoring of the people trajectories at home is intended for the early detection of a shift in daily activities which highlights disabilities and loss of autonomy. This system is meant to improve homecare health management at home for a better end of life at a sustainable cost for the community

Human-activity-centered measurement system:challenges from laboratory to the real environment in assistive gait wearable robotics

Assistive gait wearable robots (AGWR) have shown a great advancement in developing intelligent devices to assist human in their activities of daily living (ADLs). The rapid technological advancement in sensory technology, actuators, materials and computational intelligence has sped up this development process towards more practical and smart AGWR. However, most assistive gait wearable robots are still confined to be controlled, assessed indoor and within laboratory environments, limiting any potential to provide a real assistance and rehabilitation required to humans in the real environments. The gait assessment parameters play an important role not only in evaluating the patient progress and assistive device performance but also in controlling smart self-adaptable AGWR in real-time. The self-adaptable wearable robots must interactively conform to the changing environments and between users to provide optimal functionality and comfort. This paper discusses the performance parameters, such as comfortability, safety, adaptability, and energy consumption, which are required for the development of an intelligent AGWR for outdoor environments. The challenges to measuring the parameters using current systems for data collection and analysis using vision capture and wearable sensors are presented and discussed