The role of the peripheral and central adrenergic system in the construction of the subjective emotional experience of panic.

RATIONALE: Although the study of emotions can look back to over 100 years of research, it is unclear which information the brain uses to construct the subjective experience of an emotion. OBJECTIVE: In the current study, we assess the role of the peripheral and central adrenergic system in this respect. METHODS: Healthy volunteers underwent a double inhalation of 35% CO2, which is a well-validated procedure to induce an intense emotion, namely panic. In a randomized, cross-over design, 34 participants received either a ß1-blocker acting selectively in the peripheral nervous system (atenolol), a ß1-blocker acting in the peripheral and central nervous system (metoprolol), or a placebo before the CO2 inhalation. RESULTS: Heart rate and systolic blood pressure were reduced in both ß-blocker conditions compared to placebo, showing effective inhibition of the adrenergic tone. Nevertheless, the subjective experience of the induced panic was the same in all conditions, as measured by self-reported fear, discomfort, and panic symptom ratings. CONCLUSIONS: These results indicate that information from the peripheral and central adrenergic system does not play a major role in the construction of the subjective emotion.

Motion Sensor-Based Detection of Outlier Days Supporting Continuous Health Assessment for Single Older Adults.

The aging population has resulted in interest in remote monitoring of elderly individuals' health and well being. This paper describes a simple unsupervised monitoring system that can automatically detect if an elderly individual's pattern of presence deviates substantially from the recent past. The proposed system uses a small set of low-cost motion sensors and analyzes the produced data to establish an individual's typical presence pattern. Then, the algorithm uses a distance function to determine whether the individual's observed presence for each day significantly deviates from their typical pattern. Empirically, the algorithm is validated on both synthetic data and data collected by installing our system in the residences of three older individuals. In the real-world setting, the system detected, respectively, five, four, and one deviating days in the three locations. The deviating days detected by the system could result from a health issue that requires attention. The information from the system can aid caregivers in assessing the subject's health status and allows for a targeted intervention. Although the system can be refined, we show that otherwise hidden but relevant events (e.g., fall incident and irregular sleep patterns) are detected and reported to the caregiver.

Physiological and self-reported arousal in virtual reality versus face-to-face emotional activation and cognitive restructuring in university students: A crossover experimental study using wearable monitoring.

BACKGROUND: Arousal may be important for learning to restructure ones' negative cognitions, a core technique in depression treatment. In virtual reality (VR), situations may be experienced more vividly than, e.g., in an imaginative approach, potentially aiding the emotional activation of negative cognitions. However, it is unclear whether such activation and subsequent cognitive restructuring in VR elicits more physiological, e.g. changes in skin conductance (SC), heart rate (HR), and self-reported arousal. METHOD: In a cross-over experiment, 41 healthy students experienced two sets, one in VR, one face-to-face (F2F), of three situations aimed at activating negative cognitions. Order of the sets and mode of delivery were randomised. A wristband wearable monitored SC and HR; self-reported arousal was registered verbally. RESULTS: Repeated measures analyses of variance revealed significantly more SC peaks per minute, F (1, 40) = 13.89, p = .001, higher mean SC, F (1,40) = 7.47, p = .001, and higher mean HR, F (1, 40) = 75.84, p < .001 in VR compared to F2F. No differences emerged on the paired-samples t-test for self-reported arousal, t (40) = -1.35, p = .18. DISCUSSION: To the best of our knowledge, this is the first study indicating that emotional activation and subsequent cognitive restructuring in VR can lead to significantly more physiological arousal compared to an imaginative approach. These findings need to be replicated before they can be extended to patient populations.

Use of wearable technology to quantify fall risk in psychogeriatric environments: a feasability study.

Fall incidents with elderly suffering from psychological pathologies, in combination with a comorbidity of clinical problems are highly prevalent. In our research setting, the psychiatric hospital OPZ in Geel, Belgium, 1790 fall incidents were recorded with 283 patients since 2013. The nature of the patients' profiles makes a valid, objective fall risk assessment very difficult; for them, instructions to perform the tests are difficult to understand and execute. Therefore, the currently used instruments are not suited for this complex situation. In this study we propose an alternative system for the assessment of fall risk for patients of a psychogeriatric ward. We also study the essential precautions needed for acceptance of wearables in this complex setting.We collected individual daily mean gait speeds of 17 patients at a psychogeriatric ward over a period of five months. We show that it is possible, using wearable technology, to measure individual gait speed. We also show that it is possible to have the wearable technology accepted by the target group. The results obtained so far are promising to use automatical gait measurement to correlate to the currently used risk assessment tests and to eventually replace these tests.

Improving the accuracy of existing camera based fall detection algorithms through late fusion.

Fall incidents remain an important health hazard for older adults. Fall detection systems can reduce the consequences of a fall incident by insuring that timely aid is given. Currently fall detection algorithms however suffer a reduction in accuracy when introduced in real-life situations. In this paper a late fusion technique is proposed that will improve the accuracy of existing fall detection systems. It combines the confidence levels of different single camera fall detection systems. Four different aggregation methods are compared to each other based on the Area Under the Curve (AUC) of precision-recall curves. Calculating the median of the confidence levels of five cameras an increase of 218% in the AUC of the precision-recall-curves is achieved compared to the AUC of the single camera fall detector. These results show that significant improvements can be made to the accuracy of single camera fall detectors in a relatively easy way.

Scalable Semi-Automatic Annotation for Multi-Camera Person Tracking.

This paper proposes a generic methodology for the semi-automatic generation of reliable position annotations for evaluating multi-camera people-trackers on large video data sets. Most of the annotation data are automatically computed, by estimating a consensus tracking result from multiple existing trackers and people detectors and classifying it as either reliable or not. A small subset of the data, composed of tracks with insufficient reliability, is verified by a human using a simple binary decision task, a process faster than marking the correct person position. The proposed framework is generic and can handle additional trackers. We present results on a data set of $sim 6$ h captured by 4 cameras, featuring a person in a holiday flat, performing activities such as walking, cooking, eating, cleaning, and watching TV. When aiming for a tracking accuracy of 60 cm, 80% of all video frames are automatically annotated. The annotations for the remaining 20% of the frames were added after human verification of an automatically selected subset of data. This involved $sim 2.4$ h of manual labor. According to a subsequent comprehensive visual inspection to judge the annotation procedure, we found 99% of the automatically annotated frames to be correct. We provide guidelines on how to apply the proposed methodology to new data sets. We also provide an exploratory study for the multi-target case, applied on the existing and new benchmark video sequences.

Bridging the gap between real-life data and simulated data by providing a highly realistic fall dataset for evaluating camera-based fall detection algorithms.

Fall incidents are an important health hazard for older adults. Automatic fall detection systems can reduce the consequences of a fall incident by assuring that timely aid is given. The development of these systems is therefore getting a lot of research attention. Real-life data which can help evaluate the results of this research is however sparse. Moreover, research groups that have this type of data are not at liberty to share it. Most research groups thus use simulated datasets. These simulation datasets, however, often do not incorporate the challenges the fall detection system will face when implemented in real-life. In this Letter, a more realistic simulation dataset is presented to fill this gap between real-life data and currently available datasets. It was recorded while re-enacting real-life falls recorded during previous studies. It incorporates the challenges faced by fall detection algorithms in real life. A fall detection algorithm from Debard et al. was evaluated on this dataset. This evaluation showed that the dataset possesses extra challenges compared with other publicly available datasets. In this Letter, the dataset is discussed as well as the results of this preliminary evaluation of the fall detection algorithm. The dataset can be downloaded from www.kuleuven.be/advise/datasets.

Scalable Semi-Automatic Annotation for Multi-Camera Person Tracking.

This paper proposes a generic methodology for semi-automatic generation of reliable position annotations for evaluating multi-camera people-trackers on large video datasets. Most of the annotation data is computed automatically, by estimating a consensus tracking result from multiple existing trackers and people detectors and classifying it as either reliable or not. A small subset of the data, composed of tracks with insufficient reliability is verified by a human using a simple binary decision task, a process faster than marking the correct person position. The proposed framework is generic and can handle additional trackers. We present results on a dataset of approximately 6 hours captured by 4 cameras, featuring a person in a holiday flat, performing activities such as walking, cooking, eating, cleaning, and watching TV. When aiming for a tracking accuracy of 60cm, 80% of all video frames are automatically annotated. The annotations for the remaining 20% of the frames were added after human verification of an automatically selected subset of data. This involved about 2.4 hours of manual labour. According to a subsequent comprehensive visual inspection to judge the annotation procedure, we found 99% of the automatically annotated frames to be correct. We provide guidelines on how to apply the proposed methodology to new datasets. We also provide an exploratory study for the multi-target case, applied on existing and new benchmark video sequences.

Camera-based fall detection using a particle filter.

More than thirty percent of persons over 65 years fall at least once a year and are often not able to get up again. The lack of timely aid after such a fall incident can lead to severe complications. This timely aid can however be assured by a camera-based fall detection system triggering an alarm when a fall occurs. Most algorithms described in literature use the biggest object detected using background subtraction to extract the fall features. In this paper we compare the performance of our state-of-the-art fall detection algorithm when using only background subtraction, when using a particle filter to track the person and a hybrid method in which the particle filter is only used to enhance the background subtraction and not for the feature extraction. We tested this using our simulation data set containing reenactments of real-life falls. This comparison shows that this hybrid method significantly increases the sensitivity and robustness of the fall detection algorithm resulting in a sensitivity of 76.1% and a PPV of 41.2%.

Fall incidents unraveled: a series of 26 video-based real-life fall events in three frail older persons.

BACKGROUND: For prevention and detection of falls, it is essential to unravel the way in which older people fall. This study aims to provide a description of video-based real-life fall events and to examine real-life falls using the classification system by Noury and colleagues, which divides a fall into four phases (the prefall, critical, postfall and recovery phase). METHODS: Observational study of three older persons at high risk for falls, residing in assisted living or residential care facilities: a camera system was installed in each participant's room covering all areas, using a centralized PC platform in combination with standard Internet Protocol (IP) cameras. After a fall, two independent researchers analyzed recorded images using the camera position with the clearest viewpoint. RESULTS: A total of 30 falls occurred of which 26 were recorded on camera over 17 months. Most falls happened in the morning or evening (62%), when no other persons were present (88%). Participants mainly fell backward (initial fall direction and landing configuration) on the pelvis or torso and none could get up unaided. In cases where a call alarm was used (54%), an average of 70 seconds (SD=64; range 15-224) was needed to call for help. Staff responded to the call after an average of eight minutes (SD=8.4; range 2-33). Mean time on the ground was 28 minutes (SD=25.4; range 2-59) without using a call alarm compared to 11 minutes (SD=9.2; range 3-38) when using a call alarm (p=0.445).The real life falls were comparable with the prefall and recovery phase of Noury's classification system. The critical phase, however, showed a prolonged duration in all falls. We suggest distinguishing two separate phases: a prolonged loss of balance phase and the actual descending phase after failure to recover balance, resulting in the impact of the body on the ground. In contrast to the theoretical description, the postfall phase was not typically characterized by inactivity; this depended on the individual. CONCLUSIONS: This study contributes to a better understanding of the fall process in private areas of assisted living and residential care settings in older persons at high risk for falls.