Characterization of Volatile and Particulate Emissions from Desktop 3D Printers.

The rapid expansion of 3D printing technologies has led to increased utilization in various industries and has also become pervasive in the home environment. Although the benefits are well acknowledged, concerns have arisen regarding potential health and safety hazards associated with emissions of volatile organic compounds (VOCs) and particulates during the 3D printing process. The home environment is particularly hazardous given the lack of health and safety awareness of the typical home user. This study aims to assess the safety aspects of 3D printing of PLA and ABS filaments by investigating emissions of VOCs and particulates, characterizing their chemical and physical profiles, and evaluating potential health risks. Gas chromatography-mass spectrometry (GC-MS) was employed to profile VOC emissions, while a particle analyzer (WIBS) was used to quantify and characterize particulate emissions. Our research highlights that 3D printing processes release a wide range of VOCs, including straight and branched alkanes, benzenes, and aldehydes. Emission profiles depend on filament type but also, importantly, the brand of filament. The size, shape, and fluorescent characteristics of particle emissions were characterized for PLA-based printing emissions and found to vary depending on the filament employed. This is the first 3D printing study employing WIBS for particulate characterization, and distinct sizes and shape profiles that differ from other ambient WIBS studies were observed. The findings emphasize the importance of implementing safety measures in all 3D printing environments, including the home, such as improved ventilation, thermoplastic material, and brand selection. Additionally, our research highlights the need for further regulatory guidelines to ensure the safe use of 3D printing technologies, particularly in the home setting.

Periodicity Intensity of the 24 h Circadian Rhythm in Newborn Calves Show Indicators of Herd Welfare.

Circadian rhythms are a process of the sleep-wake cycle that regulates the physical, mental and behavioural changes in all living beings with a period of roughly 24 h. Wearable accelerometers are typically used in livestock applications to record animal movement from which we can estimate the activity type. Here, we use the overall movement recorded by accelerometers worn on the necks of newborn calves for a period of 8 weeks. From the movement data, we calculate 24 h periodicity intensities corresponding to circadian rhythms, from a 7-day window that slides through up to 8-weeks of data logging. The strength or intensity of the 24 h periodicity is computed at intervals as the calves become older, which is an indicator of individual calf welfare. We observe that the intensities of these 24 h periodicities for individual calves, derived from movement data, increase and decrease synchronously in a herd of 19 calves. Our results show that external factors affecting the welfare of the herd can be observed by processing and visualising movement data in this way and our method reveals insights that are not observable from movement data alone.

Tracking skin colour and wrinkle changes during cosmetic product trials using smartphone images.

BACKGROUND: To explore how the efficacy of product trials for skin cosmetics can be improved through the use of consumer-level images taken by volunteers using a conventional smartphone. MATERIALS AND METHODS: 12 women aged 30-60 years participated in a product trial and had close-up images of the cheek and temple regions of their faces taken with a high-resolution Antera 3D CS camera at the start and end of a 4-week period. Additionally, they each had "selfies" of the same regions of their faces taken regularly throughout the trial period. Automatic image analysis to identify changes in skin colour used three kinds of colour normalisation and analysis for wrinkle composition identified edges and calculated their magnitude. RESULTS: Images taken at the start and end of the trial acted as baseline ground truth for normalisation of smartphone images and showed large changes in both colour and wrinkle magnitude during the trial for many volunteers. CONCLUSIONS: Results demonstrate that regular use of selfie smartphone images within trial periods can add value to interpretation of the efficacy of the trial.

Monitoring of Particulate Matter Emissions from 3D Printing Activity in the Home Setting.

Consumer-level 3D printers are becoming increasingly prevalent in home settings. However, research shows that printing with these desktop 3D printers can impact indoor air quality (IAQ). This study examined particulate matter (PM) emissions generated by 3D printers in an indoor domestic setting. Print filament type, brand, and color were investigated and shown to all have significant impacts on the PM emission profiles over time. For example, emission rates were observed to vary by up to 150-fold, depending on the brand of a specific filament being used. Various printer settings (e.g., fan speed, infill density, extruder temperature) were also investigated. This study identifies that high levels of PM are triggered by the filament heating process and that accessible, user-controlled print settings can be used to modulate the PM emission from the 3D printing process. Considering these findings, a low-cost home IAQ sensor was evaluated as a potential means to enable a home user to monitor PM emissions from their 3D printing activities. This sensing approach was demonstrated to detect the timepoint where the onset of PM emission from a 3D print occurs. Therefore, these low-cost sensors could serve to inform the user when PM levels in the home become elevated significantly on account of this activity and furthermore, can indicate the time at which PM levels return to baseline after the printing process and/or after adding ventilation. By deploying such sensors at home, domestic users of 3D printers can assess the impact of filament type, color, and brand that they utilize on PM emissions, as well as be informed of how their selected print settings can impact their PM exposure levels.

A review of contemporary work on the ethics of ambient assisted living technologies for people with dementia.

Ambient assisted living (AAL) technologies can provide assistance and support to persons with dementia. They might allow them the possibility of living at home for longer whilst maintaining their comfort and security as well as offering a way towards reducing the huge economic and personal costs forecast as the incidence of dementia increases worldwide over coming decades. However, the development, introduction and use of AAL technologies also trigger serious ethical issues. This paper is a systematic literature review of the on-going scholarly debate about these issues. More specifically, we look at the ethical issues involved in research and development, clinical experimentation, and clinical application of AAL technologies for people with dementia and related stakeholders. In the discussion we focus on: (1) the value of the goals of AAL technologies, (2) the special vulnerability of persons with dementia in their private homes, (3) the complex question of informed consent for the usage of AAL technologies.

Challenges and opportunities of lifelog technologies: a literature review and critical analysis.

In a lifelog, data from various sources are combined to form a record from which one can retrieve information about oneself and the environment in which one is situated. It could be considered similar to an automated biography. Lifelog technology is still at an early stage of development. However, the history of lifelogs so far shows a clear academic, corporate and governmental interest. Therefore, a thorough inquiry into the ethical aspects of lifelogs could prove beneficial to the responsible development of this field. This article maps the main ethically relevant challenges and opportunities associated with the further development of lifelog technologies as discussed in the scholarly literature. By identifying challenges and opportunities in the current debate, we were able to identify other challenges and opportunities left unmentioned. Some of these challenges are partly explained by a blind spot in the current debate. Whilst the current debate focuses mainly on lifelogs held by individuals, lifelogs held by governmental institutions and corporations pose idiosyncratic ethical concerns as well. We have provided a brief taxonomy of lifelog technology to show the variety in uses for lifelogs. In addition, we provided a general approach to alleviate the ethical challenges identified in the critical analysis.

A dataset of text prompts, videos and video quality metrics from generative text-to-video AI models.

Evaluating the quality of videos which have been automatically generated from text-to-video (T2V) models is important if the models are to produce plausible outputs that convince a viewer of their authenticity. This paper presents a dataset of 201 text prompts used to automatically generate 1,005 videos using 5 very recent T2V models namely Tune-a-Video, VideoFusion, Text-To-Video Synthesis, Text2Video-Zero and Aphantasia. The prompts are divided into short, medium and longer lengths. We also include the results of some commonly used metrics used to automatically evaluate the quality of those generated videos. These include each video's naturalness, the text similarity between the original prompt and an automatically generated text caption for the video, and the inception score which measures how realistic is each generated video. Each of the 1,005 generated videos was manually rated by 24 different annotators for alignment between the videos and their original prompts, as well as for the perception and overall quality of the video. The data also includes the Mean Opinion Scores (MOS) for alignment between the generated videos and the original prompts. The dataset of T2V prompts, videos and assessments can be reused by those building or refining text-to-video generation models to compare the accuracy, quality and naturalness of their new models against existing ones.

Teaching Health Literacy and Digital Literacy Together at University Level: The FLOURISH Module.

Many universities have wellness programs to promote overall health and well-being. Using students' own personal data as part of improving their own wellness would seem to be a natural fit given that most university students are already data and information literate. In this work, we aim to show how the interplay between health literacy and data literacy can be used and taught together. The method we use is the development and delivery of the FLOURISH module, an accredited, online-only but extra-curricular course that delivers practical tips in the areas that impact students' everyday wellness including sleep, nutrition, work habits, procrastination, relationships with others, physical activity, positive psychology, critical thinking, and more. For most of these topics, students gather personal data related to the topic and submit an analysis of their data for assessment thus demonstrating how students can use their personal data for their benefit. More than 350 students have taken the module and an analysis of the use of online resources, as well as feedback on the module experience, are presented. The contributions of this article are to further endorse the need for health literacy and digital literacy for students, and we demonstrate that these can be taught together making each literacy more appealing to the digital natives of Generation Z who make up the majority of students. The implications for public health research and practice are that two student literacies, health and digital, are not independent and for our students, they should be taught together.

Calculating the matrix profile from noisy data.

The matrix profile (MP) is a data structure computed from a time series which encodes the data required to locate motifs and discords, corresponding to recurring patterns and outliers respectively. When the time series contains noisy data then the conventional approach is to pre-filter it in order to remove noise but this cannot apply in unsupervised settings where patterns and outliers are not annotated. The resilience of the algorithm used to generate the MP when faced with noisy data remains unknown. We measure the similarities between the MP from original time series data with MPs generated from the same data with noisy data added under a range of parameter settings including adding duplicates and adding irrelevant data. We use three real world data sets drawn from diverse domains for these experiments Based on dissimilarities between the MPs, our results suggest that MP generation is resilient to a small amount of noise being introduced into the data but as the amount of noise increases this reslience disappears.

Automatically detecting activities of daily living from in-home sensors as indicators of routine behaviour in an older population.

Objective: The NEX project has developed an integrated Internet of Things (IoT) system coupled with data analytics to offer unobtrusive health and wellness monitoring supporting older adults living independently at home. Monitoring involves visualising a set of automatically detected activities of daily living (ADLs) for each participant. ADL detection allows the incorporation of additional participants whose ADLs are detected without system re-training. Methods: Following a user needs and requirements study involving 426 participants, a pilot trial and a friendly trial of the deployment, an action research cycle (ARC) trial was completed. This involved 23 participants over a 10-week period each with ∼20 IoT sensors in their homes. During the ARC trial, participants took part in two data-informed briefings which presented visualisations of their own in-home activities. The briefings also gathered training data on the accuracy of detected activities. Association rule mining was used on the combination of data from sensors and participant feedback to improve the automatic ADL detection. Results: Association rule mining was used to detect a range of ADLs for each participant independently of others and then used to detect ADLs across participants using a single set of rules for each ADL. This allows additional participants to be added without the necessity of them providing training data. Conclusions: Additional participants can be added to the NEX system without the necessity to re-train the system for automatic detection of their ADLs.

Assessing the physiological effect of non-driving-related task performance and task modality in conditionally automated driving systems: A systematic review and meta-analysis.

In conditionally automated driving, the driver is free to disengage from controlling the vehicle, but they are expected to resume driving in response to certain situations or events that the system is not equipped to respond to. As the level of vehicle automation increases, drivers often engage in non-driving-related tasks (NDRTs), defined as any secondary task unrelated to the primary task of driving. This engagement can have a detrimental effect on the driver's situation awareness and attentional resources. NDRTs with resource demands that overlap with the driving task, such as visual or manual tasks, may be particularly deleterious. Therefore, monitoring the driver's state is an important safety feature for conditionally automated vehicles, and physiological measures constitute a promising means of doing this. The present systematic review and meta-analysis synthesises findings from 32 studies concerning the effect of NDRTs on drivers' physiological responses, in addition to the effect of NDRTs with a visual or a manual modality. Evidence was found that NDRT engagement led to higher physiological arousal, indicated by increased heart rate, electrodermal activity and a decrease in heart rate variability. There was mixed evidence for an effect of both visual and manual NDRT modalities on all physiological measures. Understanding the relationship between task performance and arousal during automated driving is of critical importance to the development of driver monitoring systems and improving the safety of this technology.

Assessing the physiological effect of non-driving-related task performance in conditionally automated driving systems: A systematic review and meta-analysis protocol.

Background: Level 3 automated driving systems involve the continuous performance of the driving task by artificial intelligence within set environmental conditions, such as a straight highway. The driver's role in Level 3 is to resume responsibility of the driving task in response to any departure from these conditions. As automation increases, a driver's attention may divert towards non-driving-related tasks (NDRTs), making transitions of control between the system and user more challenging. Safety features such as physiological monitoring thus become important with increasing vehicle automation. However, to date there has been no attempt to synthesise the evidence for the effect of NDRT engagement on drivers' physiological responses in Level 3 automation. Methods: A comprehensive search of the electronic databases MEDLINE, EMBASE, Web of Science, PsycINFO, and IEEE Explore will be conducted. Empirical studies assessing the effect of NDRT engagement on at least one physiological parameter during Level 3 automation, in comparison with a control group or baseline condition will be included. Screening will take place in two stages, and the process will be outlined within a PRISMA flow diagram. Relevant physiological data will be extracted from studies and analysed using a series of meta-analyses by outcome. A risk of bias assessment will also be completed on the sample. Conclusion: This review will be the first to appraise the evidence for the physiological effect of NDRT engagement during Level 3 automation, and will have implications for future empirical research and the development of driver state monitoring systems.

A neural network approach to smarter sensor networks for water quality monitoring.

Environmental monitoring is evolving towards large-scale and low-cost sensor networks operating reliability and autonomously over extended periods of time. Sophisticated analytical instrumentation such as chemo-bio sensors present inherent limitations because of the number of samples that they can take. In order to maximize their deployment lifetime, we propose the coordination of multiple heterogeneous information sources. We use rainfall radar images and information from a water depth sensor as input to a neural network (NN) to dictate the sampling frequency of a phosphate analyzer at the River Lee in Cork, Ireland. This approach shows varied performance for different times of the year but overall produces output that is very satisfactory for the application context in question. Our study demonstrates that even with limited training data, a system for controlling the sampling rate of the nutrient sensor can be set up and can improve the efficiency of the more sophisticated nodes of the sensor network.

Privacy-aware sharing and collaborative analysis of personal wellness data: Process model, domain ontology, software system and user trial.

Personal wellness data collected using wearable devices is a valuable resource, potentially containing knowledge that goes beyond what the device and its the associated software application can tell the user. However, extracting such knowledge from the data requires expertise that an average user cannot be expected to have. To overcome this problem, the data owner could collaborate with a data analysis expert; for such a collaboration to succeed, the collaborators need to be able to find one another, communicate with one another and share datasets and analysis results with one another. In this paper we presents a process model for such collaborations, a domain ontology and software system developed to support the process, and the results of a user trial demonstrating collaborative analysis of sleep data. Unlike existing collaborative data analytics tools, the process and software have been specifically designed with the non-expert data owner in mind, enabling them to control their data and protect their privacy by selecting the data to be shared on a case-by-case basis. Theoretical analysis and empirical results suggest that the process and its implementation are valid as a proof of concept.

Development of an Internet of Things Technology Platform (the NEX System) to Support Older Adults to Live Independently: Protocol for a Development and Usability Study.

BACKGROUND: In a rapidly aging population, new and efficient ways of providing health and social support to older adults are required that not only preserve independence but also maintain quality of life and safety. OBJECTIVE: The NEX project aims to develop an integrated Internet of Things system coupled with artificial intelligence to offer unobtrusive health and wellness monitoring to support older adults living independently in their home environment. The primary objective of this study is to develop and evaluate the technical performance and user acceptability of the NEX system. The secondary objective is to apply machine learning algorithms to the data collected via the NEX system to identify and eventually predict changes in the routines of older adults in their own home environment. METHODS: The NEX project commenced in December 2019 and is expected to be completed by August 2022. Mixed methods research (web-based surveys and focus groups) was conducted with 426 participants, including older adults (aged ≥60 years), family caregivers, health care professionals, and home care workers, to inform the development of the NEX system (phase 1). The primary outcome will be evaluated in 2 successive trials (the Friendly trial [phase 2] and the Action Research Cycle trial [phase 3]). The secondary objective will be explored in the Action Research Cycle trial (phase 3). For the Friendly trial, 7 older adult participants aged ≥60 years and living alone in their own homes for a 10-week period were enrolled. A total of 30 older adult participants aged ≥60 years and living alone in their own homes will be recruited for a 10-week data collection period (phase 3). RESULTS: Phase 1 of the project (n=426) was completed in December 2020, and phase 2 (n=7 participants for a 10-week pilot study) was completed in September 2021. The expected completion date for the third project phase (30 participants for the 10-week usability study) is June 2022. CONCLUSIONS: The NEX project has considered the specific everyday needs of older adults and other stakeholders, which have contributed to the design of the integrated system. The innovation of the NEX system lies in the use of Internet of Things technologies and artificial intelligence to identify and predict changes in the routines of older adults. The findings of this project will contribute to the eHealth research agenda, focusing on the improvement of health care provision and patient support in home and community environments. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35277.

Automatically assisting human memory: a SenseCam browser.

SenseCams have many potential applications as tools for lifelogging, including the possibility of use as a memory rehabilitation tool. Given that a SenseCam can log hundreds of thousands of images per year, it is critical that these be presented to the viewer in a manner that supports the aims of memory rehabilitation. In this article we report a software browser constructed with the aim of using the characteristics of memory to organise SenseCam images into a form that makes the wealth of information stored on SenseCam more accessible. To enable a large amount of visual information to be easily and quickly assimilated by a user, we apply a series of automatic content analysis techniques to structure the images into "events", suggest their relative importance, and select representative images for each. This minimises effort when browsing and searching. We provide anecdotes on use of such a system and emphasise the need for SenseCam images to be meaningfully sorted using such a browser.

Analysis of the 5 iron golf swing when hitting for maximum distance.

Most previous research on golf swing mechanics has focused on the driver club. The aim of this study was to identify the kinematic factors that contribute to greater hitting distance when using the 5 iron club. Three-dimensional marker coordinate data were collected (250 Hz) to calculate joint kinematics at eight key swing events, while a swing analyser measured club swing and ball launch characteristics. Thirty male participants were assigned to one of two groups, based on their ball launch speed (high: 52.9 ± 2.1 m · s(-1); low: 39.9 ± 5.2 m · s(-1)). Statistical analyses were used to identify variables that differed significantly between the two groups. Results showed significant differences were evident between the two groups for club face impact point and a number of joint angles and angular velocities, with greater shoulder flexion and less left shoulder internal rotation in the backswing, greater extension angular velocity in both shoulders at early downswing, greater left shoulder adduction angular velocity at ball contact, greater hip joint movement and X Factor angle during the downswing, and greater left elbow extension early in the downswing appearing to contribute to greater hitting distance with the 5 iron club.

Remote real-time monitoring of subsurface landfill gas migration.

The cost of monitoring greenhouse gas emissions from landfill sites is of major concern for regulatory authorities. The current monitoring procedure is recognised as labour intensive, requiring agency inspectors to physically travel to perimeter borehole wells in rough terrain and manually measure gas concentration levels with expensive hand-held instrumentation. In this article we present a cost-effective and efficient system for remotely monitoring landfill subsurface migration of methane and carbon dioxide concentration levels. Based purely on an autonomous sensing architecture, the proposed sensing platform was capable of performing complex analytical measurements in situ and successfully communicating the data remotely to a cloud database. A web tool was developed to present the sensed data to relevant stakeholders. We report our experiences in deploying such an approach in the field over a period of approximately 16 months.

Visualising household air pollution: Colorimetric sensor arrays for monitoring volatile organic compounds indoors.

Indoor air quality monitoring as it relates to the domestic setting is an integral part of human exposure monitoring and health risk assessment. Hence there is a great need for easy to use, fast and economical indoor air quality sensors to monitor the volatile organic compound composition of the air which is known to be significantly perturbed by the various source emissions from activities in the home. To meet this need, paper-based colorimetric sensor arrays were deployed as volatile organic compound detectors in a field study aiming to understand which activities elicit responses from these sensor arrays in household settings. The sensor array itself is composed of pH indicators and aniline dyes that enable molecular recognition of carboxylic acids, amines and carbonyl-containing compounds. The sensor arrays were initially deployed in different rooms in a single household having different occupant activity types and levels. Sensor responses were shown to differ for different room settings on the basis of occupancy levels and the nature of the room emission sources. Sensor responses relating to specific activities such as cooking, cleaning, office work, etc were noted in the temporal response. Subsequently, the colorimetric sensor arrays were deployed in a broader study across 9 different households and, using multivariate analysis, the sensor responses were shown to correlate strongly with household occupant activity and year of house build. Overall, this study demonstrates the significant potential for this type of simple approach to indoor air pollution monitoring in residential environments.

An annotated video dataset for computing video memorability.

Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1275 users manually annotated each video multiple times to indicate both long-term and short-term memorability of the videos. The annotations were gathered as part of an online memory game and measured a participant's ability to recall having seen the video previously when shown a collection of videos. The recognition tasks were performed on videos seen within the previous few minutes for short-term memorability and within the previous 24 to 72 hours for long-term memorability. Data includes the reaction times for each recognition of each video. Associated with each video are text descriptions (captions) as well as a collection of image-level features applied to 3 frames extracted from each video (start, middle and end). Video-level features are also provided. The dataset was used in the Video Memorability task as part of the MediaEval benchmark in 2020.