Anonymous activity recognition in an office environment (office tasks dataset): [research data]

This datasets contains measurements of pressure mats generated by users performing tasks in a faked office environment. Annotation describe the real activities performed by the users. One, two or three persons enter an office room and two tasks to perform: get some coffee, and print a paper. The coffee machine requires water and ground coffee, paper is required for printing. These resources may already be at the coffee machine or the printer, or must be obtained. There are six different distinct places: door, printer, coffee machine, paper stack, water tap, coffee jar. Additionally, the printer may be jammed and needs to be repaired prior to printing. The dataset contains measurements of pressure mats at the six locations as well as annotations of the real action sequence. Four recordings have been performed with a single user, one recording for two users and one recording for three users

Detecting high-level team intentions (Three person meeting dataset): [research data]

The dataset contains 20 recordings of scripted meetings and an annotation of the performed activities. Three persons (A, B and C) attend the meeting. Each member holds a presentation, and the meeting concludes with a common discussion. This dataset contains the individual team members positions as recorded by an indoor positioning systems

05181 Abstracts Collection -- Mobile Computing and Ambient Intelligence: The Challenge of Multimedia

From 01.05.05 to 04.05.05, the Dagstuhl Seminar 05181 ``Mobile Computing and Ambient Intelligence: The Challenge of Multimedia\u27\u27was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Discovering Behavioral Predispositions in Data to Improve Human Activity Recognition

The automatic, sensor-based assessment of challenging behavior of persons with dementia is an important task to support the selection of interventions. However, predicting behaviors like apathy and agitation is challenging due to the large inter- and intra-patient variability. Goal of this paper is to improve the recognition performance by making use of the observation that patients tend to show specific behaviors at certain times of the day or week. We propose to identify such segments of similar behavior via clustering the distributions of annotations of the time segments. All time segments within a cluster then consist of similar behaviors and thus indicate a behavioral predisposition (BPD). We utilize BPDs by training a classifier for each BPD. Empirically, we demonstrate that when the BPD per time segment is known, activity recognition performance can be substantially improved.Comment: Submitted to iWOAR 2022 - 7th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligenc

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

What's cooking and why? Behaviour recognition during unscripted cooking tasks for health monitoring

Nutrition related health conditions can seriously decrease quality of life; a system able to monitor the kitchen activities and eating behaviour of patients could provide clinicians with important indicators for improving a patient’s condition. To achieve this, the system has to reason about the person’s actions and goals. To address this challenge, we present a behaviour recognition approach that relies on symbolic behaviour repre- sentation and probabilistic reasoning to recognise the person’s actions, the type of meal being prepared and its potential impact on a patient’s health. We test our approach on a cooking dataset containing unscripted kitchen activities recorded with various sensors in a real kitchen. The results show that the approach is able to recognise the sequence of executed actions and the prepared meal, to determine whether it is healthy, and to reason about the possibility of depression based on the type of meal

Compensation effects in GaN:Mg probed by Raman spectroscopy and photoluminescence measurements

This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in J. Appl. Phys. 113, 103504 (2013) and may be found at https://doi.org/10.1063/1.4794094.Compensation effects in metal organic chemical vapour deposition grown GaN doped with magnesium are investigated with Raman spectroscopy and photoluminescence measurements. Examining the strain sensitive E2(high) mode, an increasing compressive strain is revealed for samples with Mg-concentrations lower than 7 × 1018 cm−3. For higher Mg-concentrations, this strain is monotonically reduced. This relaxation is accompanied by a sudden decrease in crystal quality. Luminescence measurements reveal a well defined near band edge luminescence with free, donor bound, and acceptor bound excitons as well as a characteristic donor acceptor pair (DAP) luminescence. Following recent results, three acceptor bound excitons and donor acceptor pairs are identified. Along with the change of the strain, a strong modification in the luminescence of the dominating acceptor bound exciton and DAP luminescence is observed. The results from Raman spectroscopy and luminescence measurements are interpreted as fingerprints of compensation effects in GaN:Mg leading to the conclusion that compensation due to defect incorporation triggered by Mg-doping already affects the crystal properties at doping levels of around 7 × 1018 cm−3. Thereby, the generation of nitrogen vacancies is introduced as the driving force for the change of the strain state and the near band edge luminescence.DFG, 43659573, SFB 787: Halbleiter - Nanophotonik: Materialien, Modelle, Bauelement