Office buildings are key energy consumers and thus require attention to achieve efficient operation. While individual office spaces are dynamically used, current building automation does not receive information on utilisation that could be used to adaptively adjust energy consumption. In this work, we propose an approach to estimate people count per office space using distributed strategically placed PIR sensors and algorithms that can process the distributed sensor information.We detail our sensing node and evaluate its performance in an office installation. A sensor model was subsequently used in a floor-wide simulation of realistic occupant behaviours to investigate two algorithms to estimate people count per office space. The occupant behaviour simulations confirmed that our estimation algorithms can accurately predict people count in different office use scenarios. The errors introduced by the PIR masking time after a detection can be partially compensated when using distributed sensor information. Our approach can be used for dynamic, occupancy-dependent lighting, climate, and appliances control of office spaces.

M ost of our senses, vital signs, and actions involve the head, making the human skull one of the most interesting body locations for the simultaneous sensing and interaction of assistance applications. Although hearing aids and mobile headsets have become widely accepted as headworn devices, users in public spaces often consider novel head-attached sensors and devices to be uncomfortable, irritating, or stigmatizing. Nevertheless, numerous wearable computing studies have shown that headworn sensors and adequate signal processing could reveal cognition-related behavior and essential vital parameters (such as heartbeat and breathing rates). Behavior and vital data is the key component for many assistance applications in daily life, from those that offer memory augmentation to those advising chronically ill patients. Assistance applications thus require continuous measurements and context estimation.Today's regular eyeglasses could fill the gap between sensing and assistance in daily life. Most eyeglasses are publicly accepted accessories, often worn continuously throughout the day, rendering them an ideal platform for additional assistive functions. In contrast to Google Glass and early wearable system approaches that just attached devices to standard glasses, we envision exploiting smart eyeglasses for assistance applications through fully integrated sensing, processing, and interaction functions in the regular spectacles design, thus maximizing unobtrusiveness. We also envision embedded software apps that let wearers dynamically alter assistance functionality depending on momentary needs. With a rich set of software apps that can be selectively run on smart eyeglasses, one pair of smart eyeglasses could serve various purposes in daily life and provide novel assistance applications in continuous monitoring, augmentation, and therapy, beyond what smartphones and smart watches can achieve today. They would offer much more than short-term interaction and quick information access.Although the first steps toward commercial smart eyeglasses are currently being made (with J!NS MEME; www.jins-jp.com/jinsmeme/en), scientific analyses and studies of variable assistive functions in smart eyeglasses are lacking. Consequently, opportunities and requirements for smart eyeglasses and assistance applications are unclear. Following from commercial examples, smart eyeglasses seem to be primarily useful in sensing and processing tasks, given that eyeglasses provide access to unique sensor locations near the head. Here, we present an architecture for integrating technology into traditional eyeglass designs, discuss ergonomic design requirements, and derive recommendations for further smart eyeglasses developments. In three case studies, we observed the potential of using smart eyeglasses for assistive functions. Software apps running on smart eyeglasses offer unobtrusive universal assistance systems that can support wearers in their daily lives. The authors describe a blueprint of the embedded architecture of smart eyeglasses and di...

Screen use can influence the circadian phase and cause eye strain. Smart eyeglasses with an integrated color light sensor can detect screen use. We present a screen use detection approach based on a light sensor embedded into the bridge of smart eyeglasses. By calculating the light intensity at the user's eyes for different screens and content types, we found only computer screens to have a significant impact on the circadian phase. Our screen use detection is based on ratios between color channels and used a linear support vector machine to detect screen use. We validated our detection approach in three studies. A test bench was built to detect screen use under different ambient light sources and intensities in a controlled environment. In a lab study, we evaluated recognition performance for different ambient light intensities. By using participant-independent models, we achieved an ROC AUC above 0.9 for ambient light intensities below 200 lx. In a study of typical ADLs, screen use was detected with an average ROC AUC of 0.83 assuming screen use for 30% of the time.

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Age-associated organ failure and degenerative diseases have a major impact on human health. Cardiovascular dysfunction has an increasing prevalence with age and is one of the leading causes of death. In contrast to humans, zebrafish have extraordinary regeneration capacities of complex organs including the heart. In addition, zebrafish has recently become a model organism in research on aging. Here, we have compared the ventricular transcriptome as well as the regenerative capacity after cryoinjury of old and young zebrafish hearts. We identified the immune system as activated in old ventricles and found muscle organization to deteriorate upon aging. Our data show an accumulation of immune cells, mostly macrophages, in the old zebrafish ventricle. Those immune cells not only increased in numbers but also showed morphological and behavioral changes with age. Our data further suggest that the regenerative response to cardiac injury is generally impaired and much more variable in old fish. Collagen in the wound area was already significantly enriched in old fish at 7 days post injury. Taken together, these data indicate an ‘inflammaging’-like process in the zebrafish heart and suggest a change in regenerative response in the old.

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We investigated how regular eyeglasses could be extended with multi-modal sensing and processing functions to support context-awareness applications. Our aim was to leverage eyeglasses as a platform for acquiring and processing context information according to the wearer's needs. The WISEglass architecture consists of inertial motion, environmental light, and pulse sensors, processing and wireless data transmission functionality, besides a rechargeable battery. We implemented prototypes of WISEglass and evaluated them in three application scenarios: daily activity recognition, screen-use detection, and heart rate estimation. We conducted a daily activity study with nine participants, each wearing WISEglass and recording for one day. When evaluating daily activity recognition, we obtained 77 % average accuracy for continuous recognition using Gaussian Mixture Models and classifier reject to ignore null class data. Using the light sensor for detecting screen-use, yielded 80 % accuracy. Against a chest-worn ECG reference, our heart rate estimation showed an difference below 10 beats for stationary activities across the full recording day. We concluded that smart eyeglasses provide information from a single measurement spot that is relevant in various context recognition applications.

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