Concept of a new Medical Data-Driven Health Care Model based on Remote Patient Monitoring.

This paper introduces a health care model for a physician supervised remote monitoring process of patient's vital signs. The model is discussed from a process view, a medical view and a technical view. Subsequently, different scenarios for patients at home with and without outpatient care, and in a nursing home were compared. Parts of this model have been implemented and evaluated as a proof of concept.Clinical Relevance- Remote patient monitoring has the potential to relieve general practitioners in their work and help them to improve prevention and treatment of their patients. The prevention aspect in particular can contribute to a general reduction in the burden on the entire health care system.

Toward More Robust Hand Gesture Recognition on EIT Data.

Striving for more robust and natural control of multi-fingered hand prostheses, we are studying electrical impedance tomography (EIT) as a method to monitor residual muscle activations. Previous work has shown promising results for hand gesture recognition, but also lacks generalization across multiple sessions and users. Thus, the present paper aims for a detailed analysis of an existing EIT dataset acquired with a 16-electrode wrist band as a prerequisite for further improvements of machine learning results on this type of signal. The performed t-SNE analysis confirms a much stronger inter-session and inter-user variance compared to the expected in-class variance. Additionally, we observe a strong drift of signals within a session. To handle these challenging problems, we propose new machine learning architectures based on deep learning, which allow to separate undesired from desired variation and thus significantly improve the classification accuracy. With these new architectures we increased cross-session classification accuracy on 12 gestures from 19.55 to 30.45%. Based on a fundamental data analysis we developed three calibration methods and thus were able to further increase cross-session classification accuracy to 39.01, 55.37, and 56.34%, respectively.

Approach bias retraining through virtual reality in smokers willing to quit smoking: A randomized-controlled study.

Automatic approach biases toward smoking-related cues have been implicated in the development and maintenance of addictive behaviors. Studies aiming at modifying such biases have shown promise in changing maladaptive approach tendencies for smoking cues and reducing smoking behavior. However, training effects tend to be small and partly inconsistent. The present randomized-controlled trial incorporated virtual reality (VR) technology into Approach Bias Modification (ABM) to improve efficacy. One-hundred-eight smokers attended behavioral counseling for smoking cessation and were thereafter randomized to receive VR-ABM or VR-control training. During VR-ABM, participants trained to implicitly avoid smoking-related objects and to approach alternative objects, while no such contingency existed in the VR-control condition. Trainings were administered in six sessions within a two-week period. Assessments were conducted at baseline, post-intervention (three weeks after baseline), and at follow-up (seven weeks after baseline). VR-ABM did not change approach biases, nor other cognitive biases, but it was superior in reducing daily smoking. However, this effect was limited to the two-week training period. Both groups improved in other smoking- and health-related variables across time. Future work should continue to investigate working mechanisms of ABM, in particular crucial training ingredients. VR could prove valuable for public health as the potential of VR-based treatments is large and not fully explored.

Promoting smoking abstinence in smokers willing to quit smoking through virtual reality-approach bias retraining: a study protocol for a randomized controlled trial.

BACKGROUND: Automatic processes to approach smoking-related cues have been repeatedly linked to smoking status, intensity of smoking, and cigarette craving. Moreover, recent findings suggest that targeting those tendencies directly by means of approach bias modification (ABM) has merit in changing maladaptive approach tendencies for drug cues and reducing drug consumption. However, training effects tend to be small. Embedding the training into virtual reality (VR) technology could be a promising way to improve training efficacy. The present protocol describes a randomized controlled trial that aims to assess the efficacy of a newly developed VR-ABM as a means of reducing smoking-related approach biases or nicotine consumption in smokers seeking abstinence. METHODS: One hundred daily smokers who are motivated to quit smoking will be recruited into the randomized controlled trial. All participants will attend a brief smoking cessation intervention (TAU) and will be randomly assigned either to the experimental (VR-avoidance training) or the placebo-control group (VR-placebo training). During the VR-avoidance training, participants are implicitly instructed to make an avoidance movement in response to smoking-related objects (e.g., cigarettes) and an approach movement in response to alternative objects (e.g., healthy food). During the VR-placebo training, no such contingency between arm movement and item content exists. Trainings are administered in six sessions within two weeks. Training effects on automatic approach tendencies and smoking behavior are measured immediately after training and at a 7-week follow-up. DISCUSSION: Embedding the training into virtual reality (VR) technology could be a promising new way to improve ecological validity, realism, and immersion and thereby increase ABM training effects. The results of this study can inform future research in the optimization and advancement of treatment for addiction. TRIAL REGISTRATION: Registered with Current Controlled Trials: study ID ISRCTN16006023. Registered on 28 March 2019.

Retraining automatic action tendencies for smoking using mobile phone-based approach-avoidance bias training: A study protocol for a randomized controlled study.

BACKGROUND: Automatic tendencies to approach drug-related cues have been linked to the development and maintainance of harmful drug-taking behavior. Recent studies have demonstrated that these automatic approach tendencies can be targeted directly by means of cognitive bias modification (CBM). Moreover, changing those approach tendencies may enhance treatment outcomes. However, training and therapy effects tend to be rather small and adherence to the training might be impaired by time-consuming multiple laboratory training sessions. Here, we present a protocol for a randomized controlled design to improve CBM training efficiency and facilitate access to the training by providing mobile-phone-based training sessions at home to current smokers motivated to quit smoking. METHODS: Participants (n = 100) are current smokers who smoke at least six cigarettes per day for at least 6 months and are willing to quit smoking. All participants attend a brief behavioral smoking cessation intervention (TAU) and are randomly assigned either to an experimental (TAU + training) or a control group. Participants in the experimental condition are given access to a training application (app) aimed at retraining automatic approach biases for smoking cues. Participants are instructed to perform the app training outside the laboratory context on a daily basis for 14 consecutive days. Participants in the control group do not receive the training. Primary outcome measures are changes in smoking-related approach biases and reductions in daily nicotine consumption as assessed at baseline, post-training and at 6-week follow up. Secondary outcome measures include approach biases for alternative stimuli or smoking stimuli to which participants were not exposed during training, attentional and association biases, biochemical outcomes, and self-reported smoking behavior, also measured at three different time points (baseline, post-training, and follow up). After completion of the study, smokers in the control condition will receive access to the training app. DISCUSSION: This randomized controlled trial is the first to test the effectiveness of an app-based CBM intervention as an adjunct to a brief smoking cessation intervention in smokers motivated to quit smoking. The results of this study can inform future research in the optimization and advancement of CBM treatment for addiction. TRIAL REGISTRATION: Current Controlled Trials, ISRCTN15690771. Registered on 20 November 2018.

A wearable EIT system for detection of muscular activity in the extremities.

Electrical Impedance Tomography (EIT) is a method for measuring physiological states and processes that can be used as an imaging method for muscular activities. In addition to the medical evaluation of the EIT data of the lung, this technology can be used to make a statement about muscular activity in the extremities. This paper presents a developed, mobile EIT system that can be used with an electrode bracelet on the arm. In a rst study, the EIT data for different hand gestures were evaluated.

A home automation based environmental sound alert for people experiencing hearing loss.

Different assistive technologies are available for deaf people (i.e. deaf, deafened, and hard of hearing). Besides the well-known hearing aid, devices for detection of sound events that occur at home or at work (e.g. doorbell, telephone) are available. Despite the technological progress in the last years and resulting new possibilities, the basic functions and concepts of such devices have not changed. The user still needs special assistive technology that is bound to the home or work environment. In this contribution a new concept for awareness of events in buildings is presented. In contrast to state-of-the-art assistive devices, it makes use of modern Information and Communication and home automation technology, and thus offers the prospect of cheap implementation and higher comfort for the user. In this concept events are indicated by notifications that are send over a Bluetooth Low Energy mesh network from a source to the user. The notifications are received by the user's smartwatch and the event is indicated by vibration and an icon representing its source.

AUDIS wear: a smartwatch based assistive device for ubiquitous awareness of environmental sounds.

A multitude of assistive devices is available for deaf people (i.e. deaf, deafened, and hard of hearing). Besides hearing and communication aids, devices to access environmental sounds are available commercially. But the devices have two major drawbacks: 1. they are targeted at indoor environments (e.g. home or work), and 2. only specific events are supported (e.g. the doorbell or telephone). Recent research shows that important sounds can occur in all contexts and that the interests in sounds are diverse. These drawbacks can be tackled by using modern information and communication technology that enables the development of new and improved assistive devices. The smartwatch, a new computing platform in the form of a wristwatch, offers new potential for assistive technology. Its design promises a perfect integration into various different social contexts and thus blends perfectly into the user's life. Based on a smartwatch and algorithms from pattern recognition, a prototype for awareness of environmental sounds is presented here. It observes the acoustic environment of the user and detects environmental sounds. A vibration is triggered when a sound is detected and the type of sound is shown on the display. The design of the prototype was discussed with deaf people in semi-structured interviews, leading to a set of implications for the design of such a device.

An assistive technology for hearing-impaired persons: analysis, requirements and architecture.

In this contribution, a concept of an assistive technology for hearing-impaired and deaf persons is presented. The concept applies pattern recognition algorithms and makes use of modern communication technology to analyze the acoustic environment around a user, identify critical acoustic signatures and give an alert to the user when an event of interest happened. A detailed analysis of the needs of deaf and hearing-impaired people has been performed. Requirements for an adequate assisting device have been derived from the results of the analysis, and have been turned into an architecture for its implementation that will be presented in this article. The presented concept is the basis for an assistive system which is now under development at the Institute of Microsystem Engineering at the University of Siegen.