Color-Tunable Organic Light-Emitting Diodes with Single Pt (O

Color-tunable organic light-emitting diodes (CT-OLEDs) have a large color-tuning range, high efficiency and operational stability at practical luminance, making them ideal for human-machine interactive terminals of wearable biomedical devices. However, the device operational lifetime of CT-OLEDs is currently far from reaching practical requirements. To address this problem, a tetradentate Pt(II) complex named tetra-Pt-dbf, which can emit efficiently in both monomer and aggregation states, is designed. This emitter has high Td of 508 °C and large intermolecular bonding energy of -52.0 kcal mol⁻1, which improve its thermal/chemical stability. This unique single-emitter CT-OLED essentially avoids the "color-aging issue" and achieves a large color-tuning span (red to yellowish green) and a high external quantum efficiency （EQE） of ≈30% at 1000 cd m-2 as well as an EQE of above 25% at 10000 cd m-2. A superior LT90 operational lifetime of 520,536 h at a functional luminance of 100 cd m-2, which is over 20 times longer than the state-of-the-art CT-OLEDs, is estimated. To demonstrate the potential application of such OLEDs in wearable biomedical devices, a simple electromyography (EMG)-visualization system is fabricated using the CT-OLEDs.

Investigating socially assistive systems from system design and evaluation: a systematic review.

Purpose The development of assistive technologies that support people in social interactions has attracted increased attention in HCI. This paper presents a systematic review of studies of Socially Assistive Systems targeted at older adults and people with disabilities. The purpose is threefold: (1) Characterizing related assistive systems with a special focus on the system design, primarily including HCI technologies used and user-involvement approach taken; (2) Examining their ways of system evaluation; (3) Reflecting on insights for future design research. Methods A systematic literature search was conducted using the keywords "social interactions" and "assistive technologies" within the following databases: Scopus, Web of Science, ACM, Science Direct, PubMed, and IEEE Xplore. Results Sixty-five papers met the inclusion criteria and were further analyzed. Our results showed that there were 11 types of HCI technologies that supported social interactions for target users. The most common was cognitive and meaning understanding technologies, often applied with wearable devices for compensating users' sensory loss; 33.85% of studies involved end-users and stakeholders in the design phase; Four types of evaluation methods were identified. The majority of studies adopted laboratory experiments to measure user-system interaction and system validation. Proxy users were used in system evaluation, especially in initial experiments; 42.46% of evaluations were conducted in field settings, primarily including the participants' own homes and institutions. Conclusion We contribute an overview of Socially Assistive Systems that support social interactions for older adults and people with disabilities, as well as illustrate emerging technologies and research opportunities for future work.

A Review of Data Gathering Methods for Evaluating Socially Assistive Systems.

Social interactions significantly impact the quality of life for people with special needs (e.g., older adults with dementia and children with autism). They may suffer loneliness and social isolation more often than people without disabilities. There is a growing demand for technologies to satisfy the social needs of such user groups. However, evaluating these systems can be challenging due to the extra difficulty of gathering data from people with special needs (e.g., communication barriers involving older adults with dementia and children with autism). Thus, in this systematic review, we focus on studying data gathering methods for evaluating socially assistive systems (SAS). Six academic databases (i.e., Scopus, Web of Science, ACM, Science Direct, PubMed, and IEEE Xplore) were searched, covering articles published from January 2000 to July 2021. A total of 65 articles met the inclusion criteria for this systematic review. The results showed that existing SASs most often targeted people with visual impairments, older adults, and children with autism. For instance, a common type of SASs aimed to help blind people perceive social signals (e.g., facial expressions). SASs were most commonly assessed with interviews, questionnaires, and observation data. Around half of the interview studies only involved target users, while the other half also included secondary users or stakeholders. Questionnaires were mostly used with older adults and people with visual impairments to measure their social interaction, emotional state, and system usability. A great majority of observational studies were carried out with users in special age groups, especially older adults and children with autism. We thereby contribute an overview of how different data gathering methods were used with various target users of SASs. Relevant insights are extracted to inform future development and research.

An Unobtrusive Stress Recognition System for the Smart Office.

This paper presents a novel approach to monitor office workers' behavioral patterns and heart rate variability. We integrated an EMFi sensor into a chair to measure the pressure changes caused by a user's body movements and heartbeat. Then, we employed machine learning methods to develop a classification model through which different work behaviors (body moving, typing, talking and browsing) could be recognized from the sensor data. Subsequently, we developed a BCG processing method to process the data recognized as `browsing' and further calculate heart rate variability. The results show that the developed model achieved classification accuracies of up to 91% and the HRV could be calculated effectively with an average error of 5.77ms. By combining these behavioral and physiological measures, the proposed approach portrays work-related stress in a more comprehensive manner and could contribute an unobtrusive early stress detection system for future smart offices.