Monitoring of human activities is an essential capability of many smart systems. In recent years much progress has been achieved. One of the key remaining challenges is the availability of labeled training data, in particular taking into account the degree of variability in human activities. A possible solution is to leverage large scale online data repositories. This has been previously attempted with image and sound data, as both microphones and cameras are widely used sensing modalities. In this paper, we describe a first step towards the use of online, text-based activity descriptions to support general sensor-based activity recognition systems. The idea is to extract semantic information from online texts about the way complex activities are composed of simple ones that have to be performed (e.g. a manual for assembling a furniture piece) and use such a semantic description in conjunction with sensor based, statistical classifiers of basic actions to recognize the complex activities and compose them into semantic trees. Extraction of domain relevant information evaluated in 11 different text-based manuals from different domains reached an average recall of 77%, and precision of 88%. Actual structural error-rate in the construction of respective trees was around 1%.

We present a study comparing the effect of real-time wearable feedback with traditional training methods for cardiopulmonary resuscitation (CPR). The aim is to ensure that the students can deliver CPR with the right compression speed and depth. On the wearable side, we test two systems: one based on a combination of visual feedback and tactile information on a smart-watch and one based on visual feedback and audio information on a Google Glass. In a trial with 50 subjects (23 trainee nurses and 27 novices,) we compare those modalities to standard human teaching that is used in nurse training. While a single traditional teaching session tends to improve only the percentage of correct depth, it has less effect on the percentage of effective CPR (depth and speed correct at the same time). By contrast, in a training session with the wearable feedback device, the average percentage of time when CPR is effective improves by up to almost 25%.

Today's health care is difficult to imagine without the possibility to objectively measure various physiological parameters related to patients' symptoms (from temperature through blood pressure to complex tomographic procedures). Psychiatric care remains a notable exception that heavily relies on patient interviews and self-assessment. This is due to the fact that mental illnesses manifest themselves mainly in the way patients behave throughout their daily life and, until recently there were no "behavior measurement devices." This is now changing with the progress in wearable activity recognition and sensor enabled smartphones. In this paper, we introduce a system, which, based on smartphone-sensing is able to recognize depressive and manic states and detect state changes of patients suffering from bipolar disorder. Drawing upon a real-life dataset of ten patients, recorded over a time period of 12 weeks (in total over 800 days of data tracing 17 state changes) by four different sensing modalities, we could extract features corresponding to all disease-relevant aspects in behavior. Using these features, we gain recognition accuracies of 76% by fusing all sensor modalities and state change detection precision and recall of over 97%. This paper furthermore outlines the applicability of this system in the physician-patient relations in order to facilitate the life and treatment of bipolar patients.

Mobile computing is changing the landscape of clinical monitoring and self-monitoring. One of the major impacts will be in healthcare, where increase in number of sensing modalities is providing more and more information on the state of overall wellbeing, behaviour and health. There are numerous applications of mobile computing that range from wellbeing applications, such as physical fitness, stress or burnout up to applications that target mental disorders including bipolar disorder. Use of information provided by mobile computing devices can track the state of the subjects and also allow for experience sampling in order to gather subjective information. This paper reports on the results obtained from a medical trial with monitoring of bipolar disorder patients and how the episodes of the diseases correlate to the analysis of the data sampled from mobile phone acting as a monitoring device.

The paper describes a year-long experiment dedicated to the development, deployment and evaluation of a system for coarse assessment of the state of dementia patients in a real world nursing home. The system is based on the analysis of users' motion patterns between broadly defined, semantically meaningful areas of the living space. On a data set recorded from six inhabitants in different stages of dementia during a year (a total of about 900 individual daily traces organized in groups of 14 days) a recognition accuracy of 92% is demonstrated for a two state problem (positive, negative)with 3 subjects having perfect (100%) recognition. For the 3 state problem (positive, normal, negative) we get 80% with one case of perfect recognition. We present an elaborate evaluation of the methods' performance and describe relevant practical issues involved in the design and deployment in the nursing home.

Currently, conventional X-ray and CT images as well as invasive methods performed during the surgical intervention are used to judge the local quality of a fractured proximal femur. However, these approaches are either dependent on the surgeon's experience or cannot assist diagnostic and planning tasks preoperatively. Therefore, in this work a method for the individual analysis of local bone quality in the proximal femur based on model-based analysis of CT- and X-ray images of femur specimen will be proposed. A combined representation of shape and spatial intensity distribution of an object and different statistical approaches for dimensionality reduction are used to create a statistical appearance model in order to assess the local bone quality in CT and X-ray images. The developed algorithms are tested and evaluated on 28 femur specimen. It will be shown that the tools and algorithms presented herein are highly adequate to automatically and objectively predict bone mineral density values as well as a biomechanical parameter of the bone that can be measured intraoperatively.