bluuebunny/crossref_metadata_2025 huggingface dataset:
Serious adverse event rates and reoperation after arthroscopic shoulder surgery: population based cohort study
Authors: Rees Jonathan L , Craig Richard , Nagra Navraj , Baldwin Mathew , Lane Jennifer C E , Price Andrew , Beard David J , Abram Simon , Judge Andrew , Prieto-Alhambra Daniel , Furniss Dominic , Carr Andrew J
Published: July 2022

Objective

To provide clinicians and patients with accurate risk estimates of serious adverse events after common elective shoulder arthroscopic procedures, including reoperation within one year.

Design

Population based cohort study.

Setting

Hospital Episode Statistics for NHS England, including civil registration mortality data from the Office for National Statistics.

Participants

288 250 arthroscopic shoulder procedures performed in 261 248 patients aged ≥16 years between 1 April 2009 and 31 March 2017. Elective procedures were grouped into subacromial decompression, rotator cuff repair, acromioclavicular joint excision, glenohumeral stabilisation, and frozen shoulder release.

Main outcome measures

The primary outcomes were rates of serious adverse events (mortality, pulmonary embolism, pneumonia, myocardial infarction, acute kidney injury, stroke, and urinary tract infection) requiring inpatient care within 90 days post-surgery. Secondary outcomes were specific adverse event rates at 90 days, and reoperations (including for deep infection) within one year.

Results

The overall rate of complications within 90 days after arthroscopic shoulder surgery (including reoperation) was low at 1.2% (95% confidence interval 1.2% to 1.3%), with one in 81 patients at risk, and varied according to type of procedure, from 0.6% (0.5% to 0.8%) for glenohumeral stabilisation to 1.7% (1.5% to 1.8%) for frozen shoulder release. After adjustment for age, comorbidities, and sex, no effect of procedure type was observed. Pneumonia was the most common adverse event (0.3%, 0.3% to 0.4%), with one in 303 patients at risk. Pulmonary embolic events were rare, at 0.1% (0.1% to 0.1%), with one in 1428 patients at risk. At one year, the overall rate for reoperation was 3.8% (3.8% to 3.9%), with one in 26 patients at risk, ranging from 2.7% (2.5% to 3.0%) for glenohumeral stabilisation to 5.7% (5.4% to 6.1%) for frozen shoulder release. The overall rate of further surgery for deep infection was low, at 0.1% (0.1% to 0.1%), with one in 1111 patients at risk, but was higher after rotator cuff repair (0.2%, 0.2% to 0.2%), with one in 526 patients at risk. Over the study period the number of arthroscopic shoulder procedures increased, except for subacromial decompression, which decreased.

Conclusions

The findings of this study suggest that risks of serious adverse events associated with common shoulder arthroscopy procedures are low. Nevertheless, serious complications do occur, and include the risk of reoperation in one in 26 patients within one year.

Study registration

Clinical.Trials.govNCT03573765.
Assessment of the non-communicable diseases kit for humanitarian emergencies in Yemen and Libya
Authors: Kiapi Lilian , Alani Ahmad Hecham , Ahmed Iman , Lyons Gemma , McLain Grace , Miller Laura , Darji Bhavika , Waweru Isaac , Aragno Mauricio , Kisarach Kelly , Zeleke Mekuanint , Nagi Nabeel , Jain Vageesh , Slama Slim
Published: July 2022

Emergency health kits are a vital way of providing essential medicines and supplies to health clinics during humanitarian crises. The WHO non-communicable diseases (NDCs) kit was developed 5 years ago, recognising the increasing challenge of providing continuity of care and secondary prevention of NCDs and exacerbations, in such settings. Monitoring and evaluation of emergency health kits is an important process to ensure the contents are fit for purpose and to assess usability and utility. However, there are also challenges and limitations in collecting the relevant data to do so.

This Practice paper provides a summary of the key methodologies, findings and limitations of NCD kit assessments conducted in Libya and Yemen. Methodologies included a combination of semistructured interviews, surveys with healthcare workers, NCD knowledge tests and quantifying the remaining contents.

The kit was able to support the vital delivery of NCD patient care in some complex humanitarian settings and was appreciated by health facilities. However, there were also some challenges affecting kit use. Some kit contents were found to be in greater or lesser quantities than required, and medicine brands and country of origin affected acceptability. Supply chains were affected by the humanitarian situations, with some kits being held up for months prior to arrival. Furthermore, healthcare staff had received limited NCD training and were unable to dispense certain medicines, such as psychotropics, at the primary care level. Further granularity of kit modules, predeployment facility assessments, increased NCD training opportunities and a monitoring system could improve the utility of the kits.
Meta-analysis of COVID-19 prevalence during preoperative COVID-19 screening in asymptomatic patients
Authors: de Bock Ellen , Filipe Mando D , Simmermacher Roger K J , Kroese A Christiaan , Vriens Menno R , Richir Milan C
Published: July 2022

Objectives

Patients with COVID-19 may be asymptomatic and are able to transmit COVID-19 during a surgical procedure, resulting in increased pressure on healthcare and reduced control of COVID-19 spread. There remains uncertainty about the implementation of preoperative screening for COVID-19 in asymptomatic surgical patients. Therefore, this study aims to determine the prevalence of preoperative COVID-19, confirmed by reverse transcriptase PCR (RT-PCR), in asymptomatic patients.

Design

Systematic review and meta-analysis.

Data sources

Pubmed and Embase databases were searched through 20 February 2022.

Eligibility criteria

All COVID-19 articles including preoperative asymptomatic patients were included.

Data extraction and synthesis

Two independent reviewers extracted data and assessed risk of bias. Meta-analysis was performed to determine the prevalence of COVID-19 with 95% CI. Moreover, estimated positive predictive value (PPV), negative predictive value, false-positives (FP) and false-negatives were calculated for preoperative asymptomatic patients.

Results

Twenty-seven studies containing 27 256 asymptomatic preoperative screened patients were included, of which 431 were positive for COVID-19 by RT-PCR test. In addition, the meta-analysis revealed a pooled COVID-19 prevalence of 0.76% (95% CI 0.36% to 1.59%). The calculated PPV for this prevalence is 40.8%.

Conclusions

The pooled COVID-19 prevalence in asymptomatic patients tested preoperatively was 0.76%, with low corresponding PPV. Consequently, nearly three-quarters of postponed surgical procedures in asymptomatic preoperative patients may be FP. In the event of similar pandemics, modification of preoperative mandatory RT-PCR COVID-19 testing in asymptomatic patients may be considered.
Have the COVID-19 pandemic and lockdown affected children’s mental health in the long term? A repeated cross-sectional study
Authors: Pustake Manas , Mane Sushant , Ganiyani Mohammad Arfat , Mukherjee Sayan , Sayed Misba , Mithbavkar Varada , Memon Zaid , Momin Abdus Samad , Deshmukh Krishna , Chordia Ayush , Parida Sabyasachi , Johnson Alan , Warghade Sanket , Varma Deepankar , Bhagwat Ajit
Published: July 2022

Objective

The study aimed to evaluate the impact of the COVID-19 pandemic on levels of anxiety and depressive symptoms in children and adolescents.

Design

Cross-sectional surveys were carried out on the mental health of children; one survey was conducted before the COVID-19 pandemic and one into the pandemic, 15 months after the school closures and implementation of lockdown and social distancing. Demographic data and COVID-19 pandemic-related data were collected from specific parent-report and self-report questionnaires.

Participants

Participants included children and adolescents between ages 6 and 16 years, attending a tertiary care hospital without any diagnosed major psychiatric or chronic disorder.

Analysis

Data were collected at two points (before the COVID-19 pandemic and during it) and compared. Levels of anxiety and depressive symptoms were compared and tested for statistically significant differences between these two points using appropriate statistical tests. Regression models were constructed to predict the factors affecting increased anxiety levels and depressive symptoms in the COVID-19 period.

Results

832 and 1255 children/adolescents were included in the study during the pre-COVID-19 and COVID-19 times, respectively. The median age of the participants was 10 years (IQR=4 years). The median (IQR) Spence Children’s Anxiety Scale score was 24 (12) at the pre-COVID-19 point and 31 (13) during the COVID-19 pandemic (p<0.001, r=−0.27). 11% and 16% of children reported being depressed at these two-time points, respectively (p=0.004, φc=−0.063). Regression analysis showed that many factors, including the duration of smartphone use, female gender and only child status, were associated with increased anxiety or depression levels.

Conclusion

A large proportion of children had elevated anxiety and depressive symptoms during the pandemic relative to before the pandemic, suggesting a need for measures to engage children in healthy habits to protect children’s mental health and continuous monitoring of children during such scenarios.
Randomised trials in maternal and perinatal health in low and middle-income countries from 2010 to 2019: a systematic scoping review
Authors: Eggleston Alexander John , Richards Annabel , Farrington Elise , Tse Wai Chung , Williams Jack , Sella Hewage Ayeshini , McDonald Steve , Turner Tari , Vogel Joshua P
Published: July 2022

Objectives

To identify and map all trials in maternal health conducted in low and middle-income countries (LMIC) over the 10-year period from 2010 to 2019, to identify geographical and thematic trends, as well as comparing to global causes of maternal death and preidentified priority areas.

Design

Systematic scoping review.

Primary and secondary outcome measures

Extracted data included location, study characteristics and whether trials corresponded to causes of mortality and identified research priority topics.

Results

We searched the Cochrane Central Register of Controlled Trials database, a combined registry of trials from multiple sources. Our search identified 7269 articles, 874 of which were included for analysis. Between 2010 and 2019, maternal health trials conducted in LMICs more than doubled (50–114). Trials were conducted in 61 countries—231 trials (26.4%) were conducted in Iran. Only 225 trials (25.7%) were aligned with a cause of maternal mortality. Within these trials, pre-existing medical conditions, embolism, obstructed labour and sepsis were all under-represented when compared with number of maternal deaths globally. Large numbers of studies were conducted on priority topics such as labour and delivery, obstetric haemorrhage and antenatal care. Hypertensive disorders of pregnancy, diabetes and health systems and policy—despite being high-priority topics—had relatively few trials.

Conclusion

Despite trials conducted in LMICs increasing from 2010 to 2019, there were significant gaps in geographical distribution, alignment with causes of maternal mortality and known research priority topics. The research gaps identified provide guidance and insight for future research conduct in low-resource settings.

Trial registration number

10.17605/OSF.IO/QUJP5.
Examining the effectiveness of psychological interventions for marginalised and disadvantaged women and individuals who have experienced gender-based violence: protocol for a scoping review
Authors: Yalcinoz-Ucan Busra , Zilney Laura , Zientarska-Kayko Agnes , Ireland Timothy , Browne Dillon Thomas
Published: July 2022

Introduction

Exposure to gender-based violence (GBV) has devastating psychological outcomes for victims/survivors. Particularly in conditions where GBV intersects with multiple forms of oppression, the negative impacts of violence are more challenging to overcome and potential pathways for recovery become less accessible. However, evidence regarding the availability and effectiveness of mental health interventions for GBV survivors from marginalised and disadvantaged communities has yet to be systematically integrated and synthesised. The proposed scoping review will examine the relevant literature regarding the availability and effectiveness of psychological interventions for survivors of GBV from marginalised and disadvantaged backgrounds. This review will (i) document what psychological interventions have been available and empirically established for marginalised and disadvantaged women and individuals with experiences of GBV, (ii) provide a narrative examination of the treatment outcomes of identified interventions regarding their effectiveness and (iii) examine the degree to which GBV interventions in selected sources are designed and applied with a recognition of the social determinants of mental health.

Methods and analysis

The search for the proposed scoping review will include five electronic databases: PsycINFO, Scopus, Web of Science, Ovid Medline, and CINAHL. The database search will be completed in June 2022. An additional search will be conducted before the completion of the study in December 2022. The search will target research studies published after 2010. The primary eligibility criterion for study selection is having a focus on psychological interventions for GBV survivors from marginalised and disadvantaged groups. Two reviewers will conduct screening and data extraction. The data will be evaluated to map the treatment outcomes of interventions and their effectiveness. Implications for clinical services will be discussed.

Ethics and dissemination

No ethical consideration is foreseen for this scoping review. The dissemination will be done through a publication in a top-tier open access journal and conference presentations.
Protocol for a phase IV double-blind randomised controlled trial to investigate the effect of the 13-valent pneumococcal conjugate vaccine and the 23-valent pneumococcal polysaccharide vaccine on pneumococcal colonisation using the experimental human pneumococcal challenge model in healthy adults (PREVENTING PNEUMO 2)
Authors: Liatsikos Konstantinos , Hyder-Wright Angela , Pojar Sherin , Chen Tao , Wang Duolao , Davies Kelly , Myerscough Christopher , Reine Jesus , Robinson Ryan E , Urban Britta , Mitsi Elena , Solorzano Carla , Gordon Stephen B , Quinn Angela , Pan Kaijie , Anderson Annaliesa S , Theilacker Christian , Begier Elizabeth , Gessner Bradford D , Collins Andrea , Ferreira Daniela M
Published: July 2022

Introduction

Despite widely available vaccinations, Streptococcus pneumoniae (SPN) remains a major cause of morbidity and mortality worldwide, causing community-acquired pneumonia, meningitis, otitis media, sinusitis and bacteraemia. Here, we summarise an ethically approved protocol for a double-blind, randomised controlled trial investigating the effect of the 13-valent pneumococcal conjugate vaccine (PCV13) and the 23-valent pneumococcal polysaccharide vaccine (PPV23) on pneumococcal nasopharyngeal colonisation acquisition, density and duration using experimental human pneumococcal challenge (EHPC).

Methods and analysis

Healthy adult participants aged 18–50 years will be randomised to receive PCV13, PPV23 or placebo and then undergo one or two EHPCs involving intranasal administration of SPN at 1-month post-vaccination with serotype 3 (SPN3) and 6 months with serotype 6B (SPN6B). Participants randomised to PCV13 and placebo will also be randomised to one of two clinically relevant SPN3 strains from distinct lineages within clonal complex 180, clades Ia and II, creating five study groups. Following inoculation, participants will be seen on days 2, 7, 14 and 23. During the follow-up period, we will monitor safety, colonisation status, density and duration, immune responses and antigenuria. The primary outcome of the study is comparing the rate of SPN3 acquisition between the vaccinated (PCV13 or PPV23) and unvaccinated (placebo) groups as defined by classical culture. Density and duration of colonisation, comparison of acquisition rates using molecular methods and evaluation of the above measurements for individual SPN3 clades and SPN6B form the secondary objectives. Furthermore, we will explore the immune responses associated with these vaccines, their effect on colonisation and the relationship between colonisation and urinary pneumococcal antigen detection.

Ethics and dissemination

The study is approved by the NHS Research and Ethics Committee (Reference: 20/NW/0097) and by the Medicines and Healthcare products Regulatory Agency (Reference: CTA 25753/0001/001–0001). Findings will be published in peer-reviewed journals.

Trial registration number

ISRCTN15728847, NCT04974294.
Motor Neuron Disease Systematic Multi-Arm Adaptive Randomised Trial (MND-SMART): a multi-arm, multi-stage, adaptive, platform, phase III randomised, double-blind, placebo-controlled trial of repurposed drugs in motor neuron disease
Authors: Wong Charis , Dakin Rachel S , Williamson Jill , Newton Judith , Steven Michelle , Colville Shuna , Stavrou Maria , Gregory Jenna M , Elliott Elizabeth , Mehta Arpan R , Chataway Jeremy , Swingler Robert J , Parker Richard Anthony , Weir Christopher J , Stallard Nigel , Parmar Mahesh K B , Macleod Malcolm R , Pal Suvankar , Chandran Siddharthan
Published: July 2022

Introduction

Motor neuron disease (MND) is a rapidly fatal neurodegenerative disease. Despite decades of research and clinical trials there remains no cure and only one globally approved drug, riluzole, which prolongs survival by 2–3 months. Recent improved mechanistic understanding of MND heralds a new translational era with many potential targets being identified that are ripe for clinical trials. Motor Neuron Disease Systematic Multi-Arm Adaptive Randomised Trial (MND-SMART) aims to evaluate the efficacy of drugs efficiently and definitively in a multi-arm, multi-stage, adaptive trial. The first two drugs selected for evaluation in MND-SMART are trazodone and memantine.

Methods and analysis

Initially, up to 531 participants (177/arm) will be randomised 1:1:1 to oral liquid trazodone, memantine and placebo. The coprimary outcome measures are the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALSFRS-R) and survival. Comparisons will be conducted in four stages. The decision to continue randomising to arms after each stage will be made by the Trial Steering Committee who receive recommendations from the Independent Data Monitoring Committee. The primary analysis of ALSFRS-R will be conducted when 150 participants/arm, excluding long survivors, have completed 18 months of treatment; if positive the survival effect will be inferentially analysed when 113 deaths have been observed in the placebo group. The trial design ensures that other promising drugs can be added for evaluation in planned trial adaptations. Using this novel trial design reduces time, cost and number of participants required to definitively (phase III) evaluate drugs and reduces exposure of participants to potentially ineffective treatments.

Ethics and dissemination

MND-SMART was approved by the West of Scotland Research Ethics Committee on 2 October 2019. (REC reference: 19/WS/0123) Results of the study will be submitted for publication in a peer-reviewed journal and a summary provided to participants.

Trial registration numbers

European Clinical Trials Registry (2019-000099-41); NCT04302870.
Do chronic diseases moderate the association between psychosocial working conditions and work exit? Longitudinal results from 55 950 Dutch workers
Authors: van Zon Sander K R , Ots Patricia , Robroek Suzan J W , Burdorf Alex , Oude Hengel Karen M , Brouwer Sandra
Published: July 2022

Background

This study aims to examine whether the presence of chronic diseases or multimorbidity moderates the associations between psychosocial working conditions and work exit through unemployment, work disability or early retirement.

Methods

Data from Lifelines (n=55 950), a prospective population-based cohort study, were enriched with monthly information on employment status from Statistics Netherlands. Working conditions were measured with the Copenhagen Psychosocial Questionnaire. Work exit was defined as unemployment, work disability and early retirement. Participants were classified as having no chronic disease, one chronic disease or multimorbidity. Cause-specific Cox proportional hazard regression models, adjusted for age, gender, education and partnership status, were used to analyse associations between working conditions and work exit. Interaction terms were used to examine moderation by chronic disease status.

Results

Higher social support decreased the risk for unemployment, work disability and early retirement. Higher meaning of work decreased the risk of unemployment, and more possibilities for development decreased the risk for work disability. Chronic disease status did generally not moderate associations between working conditions and work exit. Only among workers without a chronic disease, more possibilities for development was associated with a lower risk for unemployment (HR: 0.89; 95% CI: 0.85 to 0.94).

Conclusion

While efforts to retain workers with chronic diseases in the labour market should continue, favourable psychosocial working conditions are important for all workers.
Single and double electron detachment absolute cross sections of H− and D− in collisions with SF6 and CF4
Authors: Yang Yifei , Wang Jinghang , Tian Yu , Zhang Xuemei
Published: July 2022

Collisional single electron detachment (SED) and double electron detachment (DED) absolute cross sections of H− and D− with sulfur hexafluoride (SF6) and carbon tetrafluoride (CF4) were measured by using the growth rate method. The energy used in the experiment ranged from 10 to 30 keV. By combining with the available data in references, it was found that SED absolute cross sections reach saturation in the experimental velocity range, while DED absolute cross sections continue to increase as the velocity increases. The SED absolute cross sections of CH4, SF6, and CF4 were proportional to their bond lengths and the radius of the incident anionic projectile within the error range in the present velocity region.
Study on the Covering Problem in Network Reliability for Precision Business Management
Authors: Chen Shin-Guang
Published: July 2022

A new research area namely, precision business management, is quietly blooming through network theory. In precision business management, several new management tools can be developed to precisely adjust the behavior of the entities in the company to satisfy the management requirement. This paper deals with the situation when specific business process pattern should be recognized, and the related entities in the process have to be identified. It is referred to a kind of covering problem in network reliability. Minimal path set can be considered as a set of flow combinations. However, in some cases, a subset of the flow combination may be required for applications. Therefore, techniques for dealing such cases are needed. This paper proposes a heuristic approach to solve the covering problem in network reliability. Some numerical examples are presented for explanation. The results show that the cases are interesting in the discussion.
The City as a Personal Assistant
Authors: Acer Utku Günay , Broeck Marc van den , Min Chulhong , Dasari Mallesham , Kawsar Fahim
Published: July 2022

Conversational agents are increasingly becoming digital partners in our everyday computational experiences. Although rich and fresh in content, they are oblivious to users' locality beyond geospatial weather and traffic conditions. We introduce Lingo, a hyper-local conversational agent embedded deeply into the urban infrastructure that provides rich, purposeful, detailed, and in some cases, playful information relevant to a neighbourhood. Drawing lessons from a mixed-method contextual study (online survey, n = 1992 and semi-structured interviews, n = 21), we identify requirements for such a hyper-local conversational agent and a sample set of questions serving urban neighbourhoods of Belgium. Our agent design is manifested into a two-part system. First, a multi-modal reasoning engine serves as a hyper-local information source using automated machine-learning models operating on camera, microphone, and environmental sensor data. Second, a smart conversational speaker and a smartphone application serve as hyper-local information access points. Finally, we introduce a covert communication mechanism over Wi-Fi management frames that bridges the two parts of our Lingo system and enables the privacy-preserving proxemic interactions. We describe the design, implementation, and technical assessment of Lingo together with usability (n = 20) and real-world deployment (n = 5) studies. We reflect on information quality, accessibility benefits, and interaction dynamics and demonstrate the efficacy of Lingo in offering hyper-local information at the finest granularity in urban neighbourhoods while reducing access time up to a factor of 25.
Template Matching Based Early Exit CNN for Energy-efficient Myocardial Infarction Detection on Low-power Wearable Devices
Authors: Rashid Nafiul , Demirel Berken Utku , Odema Mohanad , Al Faruque Mohammad Abdullah
Published: July 2022

Myocardial Infarction (MI), also known as heart attack, is a life-threatening form of heart disease that is a leading cause of death worldwide. Its recurrent and silent nature emphasizes the need for continuous monitoring through wearable devices. The wearable device solutions should provide adequate performance while being resource-constrained in terms of power and memory. This paper proposes an MI detection methodology using a Convolutional Neural Network (CNN) that outperforms the state-of-the-art works on wearable devices for two datasets - PTB and PTB-XL, while being energy and memory-efficient. Moreover, we also propose a novel Template Matching based Early Exit (TMEX) CNN architecture that further increases the energy efficiency compared to baseline architecture while maintaining similar performance. Our baseline and TMEX architecture achieve 99.33% and 99.24% accuracy on PTB dataset, whereas on PTB-XL dataset they achieve 84.36% and 84.24% accuracy, respectively. Both architectures are suitable for wearable devices requiring only 20 KB of RAM. Evaluation of real hardware shows that our baseline architecture is 0.6x to 53x more energy-efficient than the state-of-the-art works on wearable devices. Moreover, our TMEX architecture further improves the energy efficiency by 8.12% (PTB) and 6.36% (PTB-XL) while maintaining similar performance as the baseline architecture.
Multi-Task Learning for Randomized Controlled Trials
Authors: Dai Ruixuan , Kannampallil Thomas , Zhang Jingwen , Lv Nan , Ma Jun , Lu Chenyang
Published: July 2022

A randomized controlled trial (RCT) is used to study the safety and efficacy of new treatments, by comparing patient outcomes of an intervention group with a control group. Traditionally, RCTs rely on statistical analyses to assess the differences between the treatment and control groups. However, such statistical analyses are generally not designed to assess the impact of the intervention at an individual level. In this paper, we explore machine learning models in conjunction with an RCT for personalized predictions of a depression treatment intervention, where patients were longitudinally monitored with wearable devices. We formulate individual-level predictions in the intervention and control groups from an RCT as a multi-task learning (MTL) problem, and propose a novel MTL model specifically designed for RCTs. Instead of training separate models for the intervention and control groups, the proposed MTL model is trained on both groups, effectively enlarging the training dataset. We develop a hierarchical model architecture to aggregate data from different sources and different longitudinal stages of the trial, which allows the MTL model to exploit the commonalities and capture the differences between the two groups. We evaluated the MTL approach in an RCT involving 106 patients with depression, who were randomized to receive an integrated intervention treatment. Our proposed MTL model outperforms both single-task models and the traditional multi-task model in predictive performance, representing a promising step in utilizing data collected in RCTs to develop predictive models for precision medicine.
TinyOdom
Authors: Saha Swapnil Sayan , Sandha Sandeep Singh , Garcia Luis Antonio , Srivastava Mani
Published: July 2022

Deep inertial sequence learning has shown promising odometric resolution over model-based approaches for trajectory estimation in GPS-denied environments. However, existing neural inertial dead-reckoning frameworks are not suitable for real-time deployment on ultra-resource-constrained (URC) devices due to substantial memory, power, and compute bounds. Current deep inertial odometry techniques also suffer from gravity pollution, high-frequency inertial disturbances, varying sensor orientation, heading rate singularity, and failure in altitude estimation. In this paper, we introduce TinyOdom, a framework for training and deploying neural inertial models on URC hardware. TinyOdom exploits hardware and quantization-aware Bayesian neural architecture search (NAS) and a temporal convolutional network (TCN) backbone to train lightweight models targetted towards URC devices. In addition, we propose a magnetometer, physics, and velocity-centric sequence learning formulation robust to preceding inertial perturbations. We also expand 2D sequence learning to 3D using a model-free barometric g-h filter robust to inertial and environmental variations. We evaluate TinyOdom for a wide spectrum of inertial odometry applications and target hardware against competing methods. Specifically, we consider four applications: pedestrian, animal, aerial, and underwater vehicle dead-reckoning. Across different applications, TinyOdom reduces the size of neural inertial models by 31× to 134× with 2.5m to 12m error in 60 seconds, enabling the direct deployment of models on URC devices while still maintaining or exceeding the localization resolution over the state-of-the-art. The proposed barometric filter tracks altitude within ±0.1m and is robust to inertial disturbances and ambient dynamics. Finally, our ablation study shows that the introduced magnetometer, physics, and velocity-centric sequence learning formulation significantly improve localization performance even with notably lightweight models.
BreathMentor
Authors: Gong Yanbin , Zhang Qian , NG Bobby H.P. , Li Wei
Published: July 2022

Chronic Obstructive Pulmonary Disease (COPD) is currently the third major cause of death--more than three million people died from it in 2019. Given that COPD cannot be cured currently, immediate treatment is crucial. Pulmonary rehabilitation (PR) is widely used to prevent COPD deterioration. Patients are advised to undergo a PR at home to get sufficient treatment in time. Monitoring patients during home rehabilitation can help not only improve patient adherence but also collect data on patients' recovery progress from rehabilitation team's perspective. However, how to track if proper diaphragmatic breathing, an essential part of PR, is taken by a patient has remained challenging. The current monitoring solution still appears obtrusive as it requires the patient to wear two uncomfortable respiration belts. Alternatively, therapists need to monitor the patients remotely through several cameras, which consumes substantial medical resources and causes privacy issues.

In this work, we present BreathMentor, a smart speaker based diaphragmatic breathing monitoring system targeting early COPD stages I and II. BreathMentor is both unobtrusive and preventive of privacy invasion, so that it can solve the existing pain points and suits home care. BreathMentor converts the smart speaker into an active sonar system that continuously perceives and analyses the changes in surroundings, thereby detecting the user's respiration rate, deriving the breathing phases, and classifying whether the patient is practising diaphragmatic breathing. BreathMentor formulates breathing monitoring as a Temporal Action Localization task that enables us to detect each breathing cycle and classify its type. Our key insight is that breathing periodicity and phase duration are natural properties to localize and segment the breaths. Our key design to classify the breathing type is a hybrid architecture encompassing signal processing and deep learning techniques. Further, we evaluate the system performance on fifteen healthy subjects who would not breathe abnormally during diaphragmatic breathing under the supervision of therapists. In conclusion, BreathMentor can achieve robust performance for monitoring diaphragmatic breathing in different environments, as demonstrated in the results. The median error rate of respiration detection is 0.2 BPM, and the I/E ratio derivation is accurate with a mean absolute percentage error of less than 5.9% for breathing phase detection, together with a recall of 98.2%, and a precision of 95.5% in detecting diaphragmatic breathing. Above results indicate that BreathMentor can be used to track the patients' adherence and help monitor their breathing capacity.
PPGface
Authors: Choi Seokmin , Gao Yang , Jin Yincheng , Kim Se jun , Li Jiyang , Xu Wenyao , Jin Zhanpeng
Published: July 2022

Recognition of facial expressions has been widely explored to represent people's emotional states. Existing facial expression recognition systems primarily rely on external cameras which make it less accessible and efficient in many real-life scenarios to monitor an individual's facial expression in a convenient and unobtrusive manner. To this end, we propose PPGface, a ubiquitous, easy-to-use, user-friendly facial expression recognition platform that leverages earable devices with built-in PPG sensor. PPGface understands the facial expressions through the dynamic PPG patterns resulting from facial muscle movements. With the aid of the accelerometer sensor, PPGface can detect and recognize the user's seven universal facial expressions and relevant body posture unobtrusively. We conducted an user study (N=20) using multimodal ResNet to evaluate the performance of PPGface, and showed that PPGface can detect different facial expressions with 93.5 accuracy and 0.93 fl-score. In addition, to explore the robustness and usability of our proposed platform, we conducted several comprehensive experiments under real-world settings. Overall results of this work validate a great potential to be employed in future commodity earable devices.
M3Sense
Authors: Samyoun Sirat , Islam Md Mofijul , Iqbal Tariq , Stankovic John
Published: July 2022

Modern smartwatches or wrist wearables having multiple physiological sensing modalities have emerged as a subtle way to detect different mental health conditions, such as anxiety, emotions, and stress. However, affect detection models depending on wrist sensors data often provide poor performance due to inconsistent or inaccurate signals and scarcity of labeled data representing a condition. Although learning representations based on the physiological similarities of the affective tasks offer a possibility to solve this problem, existing approaches fail to effectively generate representations that will work across these multiple tasks. Moreover, the problem becomes more challenging due to the large domain gap among these affective applications and the discrepancies among the multiple sensing modalities. We present M3Sense, a multi-task, multimodal representation learning framework that effectively learns the affect-agnostic physiological representations from limited labeled data and uses a novel domain alignment technique to utilize the unlabeled data from the other affective tasks to accurately detect these mental health conditions using wrist sensors only. We apply M3Sense to 3 mental health applications, and quantify the achieved performance boost compared to the state-of-the-art using extensive evaluations and ablation studies on publicly available and collected datasets. Moreover, we extensively investigate what combination of tasks and modalities aids in developing a robust Multitask Learning model for affect recognition. Our analysis shows that incorporating emotion detection in the learning models degrades the performance of anxiety and stress detection, whereas stress detection helps to boost the emotion detection performance. Our results also show that M3Sense provides consistent performance across all affective tasks and available modalities and also improves the performance of representation learning models on unseen affective tasks by 5% - 60%.
IndexPen
Authors: Wei Haowen , Li Ziheng , Galvan Alexander D. , Su Zhuoran , Zhang Xiao , Pahlavan Kaveh , Solovey Erin T.
Published: July 2022

In this paper, we introduce IndexPen, a novel interaction technique for text input through two-finger in-air micro-gestures, enabling touch-free, effortless, tracking-based interaction, designed to mirror real-world writing. Our system is based on millimeter-wave radar sensing, and does not require instrumentation on the user. IndexPen can successfully identify 30 distinct gestures, representing the letters A-Z, as well as Space, Backspace, Enter, and a special Activation gesture to prevent unintentional input. Additionally, we include a noise class to differentiate gesture and non-gesture noise. We present our system design, including the radio frequency (RF) processing pipeline, classification model, and real-time detection algorithms. We further demonstrate our proof-of-concept system with data collected over ten days with five participants yielding 95.89% cross-validation accuracy on 31 classes (including noise). Moreover, we explore the learnability and adaptability of our system for real-world text input with 16 participants who are first-time users to IndexPen over five sessions. After each session, the pre-trained model from the previous five-user study is calibrated on the data collected so far for a new user through transfer learning. The F-1 score showed an average increase of 9.14% per session with the calibration, reaching an average of 88.3% on the last session across the 16 users. Meanwhile, we show that the users can type sentences with IndexPen at 86.2% accuracy, measured by string similarity. This work builds a foundation and vision for future interaction interfaces that could be enabled with this paradigm.
StudentSADD
Authors: Tlachac ML , Flores Ricardo , Reisch Miranda , Kayastha Rimsha , Taurich Nina , Melican Veronica , Bruneau Connor , Caouette Hunter , Lovering Joshua , Toto Ermal , Rundensteiner Elke A.
Published: July 2022

The growing prevalence of depression and suicidal ideation among college students further exacerbated by the Coronavirus pandemic is alarming, highlighting the need for universal mental illness screening technology. With traditional screening questionnaires too burdensome to achieve universal screening in this population, data collected through mobile applications has the potential to rapidly identify at-risk students. While prior research has mostly focused on collecting passive smartphone modalities from students, smartphone sensors are also capable of capturing active modalities. The general public has demonstrated more willingness to share active than passive modalities through an app, yet no such dataset of active mobile modalities for mental illness screening exists for students. Knowing which active modalities hold strong screening capabilities for student populations is critical for developing targeted mental illness screening technology. Thus, we deployed a mobile application to over 300 students during the COVID-19 pandemic to collect the Student Suicidal Ideation and Depression Detection (StudentSADD) dataset. We report on a rich variety of machine learning models including cutting-edge multimodal pretrained deep learning classifiers on active text and voice replies to screen for depression and suicidal ideation. This unique StudentSADD dataset is a valuable resource for the community for developing mobile mental illness screening tools.
SafeGait
Authors: Wu Yuezhong , Hassan Mahbub , Hu Wen
Published: July 2022

Recent works have shown that wearable or implanted devices attached at different locations of the body can generate an identical security key from their independent measurements of the same gait. This has created an opportunity to realize highly secured data exchange to and from critical implanted devices. In this paper, we first demonstrate that vision can be used to easily attack such gait-based key generations; an attacker with a commodity camera can measure the gait from a distance and generate a security key with any target wearable or implanted device faster than other legitimate devices worn at different locations of the subject's body. To counter the attack, we propose a firewall to stop video-based gait measurements to proceed with key generation, but letting measurements from inertial measurement units (IMUs) that are widely used in wearable devices to measure the gait accelerations from the body to proceed. We implement the firewall concept with an IMU-vs-Video binary classifier that combines InceptionTime, an ensemble of deep Convolutional Neural Network (CNN) models for effective feature extraction from gait measurements, to a Generative Adversarial Network (GAN) that can generalize the classifier across subjects. Comprehensive evaluation with a real-world dataset shows that our proposed classifier can perform with an accuracy of 97.82%. Given that an attacker has to fool the classifier for multiple consecutive gait cycles to generate the complete key, the high single-cycle classification accuracy results in an extremely low probability for a video attacker to successfully pair with a target wearable device. More precisely, a video attacker would have one in a billion chance to successfully generate a 128-bit key, which would require the attacker to observe the subject for thousands of years.
Sensor-free Soil Moisture Sensing Using LoRa Signals
Authors: Chang Zhaoxin , Zhang Fusang , Xiong Jie , Ma Junqi , Jin Beihong , Zhang Daqing
Published: July 2022

Soil moisture sensing is one of the most important components in smart agriculture. It plays a critical role in increasing crop yields and reducing water waste. However, existing commercial soil moisture sensors are either expensive or inaccurate, limiting their real-world deployment. In this paper, we utilize wide-area LoRa signals to sense soil moisture without a need of dedicated soil moisture sensors. Different from traditional usage of LoRa in smart agriculture which is only for sensor data transmission, we leverage LoRa signal itself as a powerful sensing tool. The key insight is that the dielectric permittivity of soil which is closely related to soil moisture can be obtained from phase readings of LoRa signals. Therefore, antennas of a LoRa node can be placed in the soil to capture signal phase readings for soil moisture measurements. Though promising, it is non-trivial to extract accurate phase information due to unsynchronization of LoRa transmitter and receiver. In this work, we propose to include a low-cost switch to equip the LoRa node with two antennas to address the issue. We develop a delicate chirp ratio approach to cancel out the phase offset caused by transceiver unsynchronization to extract accurate phase information. The proposed system design has multiple unique advantages including high accuracy, robustness against motion interference and large sensing range for large-scale deployment in smart agriculture. Experiments with commodity LoRa nodes show that our system can accurately estimate soil moisture at an average error of 3.1%, achieving a performance comparable to high-end commodity soil moisture sensors. Field studies show that the proposed system can accurately sense soil moisture even when the LoRa gateway is 100 m away from the LoRa node, enabling wide-area soil moisture sensing for the first time.
DeepPCD
Authors: Cai Pingping , Sur Sanjib
Published: July 2022

3D Point Cloud Data (PCD) is an efficient machine representation for surrounding environments and has been used in many applications. But the measured PCD is often incomplete and sparse due to the sensor occlusion and poor lighting conditions. To automatically reconstruct complete PCD from the incomplete ones, we propose DeepPCD, a deep-learning-based system that reconstructs both geometric and color information for large indoor environments. For geometric reconstruction, DeepPCD uses a novel patch based technique that splits the PCD into multiple parts, approximates, extends, and independently reconstructs the parts by 3D planes, and then merges and refines them. For color reconstruction, DeepPCD uses a conditional Generative Adversarial Network to infer the missing color of the geometrically reconstructed PCD by using the color feature extracted from incomplete color PCD. We experimentally evaluate DeepPCD with several real PCD collected from large, diverse indoor environments and explore the feasibility of PCD autocompletion in various ubiquitous sensing applications.
EarCommand
Authors: Jin Yincheng , Gao Yang , Xu Xuhai , Choi Seokmin , Li Jiyang , Liu Feng , Li Zhengxiong , Jin Zhanpeng
Published: July 2022

Intelligent speech interfaces have been developing vastly to support the growing demands for convenient control and interaction with wearable/earable and portable devices. To avoid privacy leakage during speech interactions and strengthen the resistance to ambient noise, silent speech interfaces have been widely explored to enable people's interaction with mobile/wearable devices without audible sounds. However, most existing silent speech solutions require either restricted background illuminations or hand involvement to hold device or perform gestures. In this study, we propose a novel earphone-based, hand-free silent speech interaction approach, named EarCommand. Our technique discovers the relationship between the deformation of the ear canal and the movements of the articulator and takes advantage of this link to recognize different silent speech commands. Our system can achieve a WER (word error rate) of 10.02% for word-level recognition and 12.33% for sentence-level recognition, when tested in human subjects with 32 word-level commands and 25 sentence-level commands, which indicates the effectiveness of inferring silent speech commands. Moreover, EarCommand shows high reliability and robustness in a variety of configuration settings and environmental conditions. It is anticipated that EarCommand can serve as an efficient, intelligent speech interface for hand-free operation, which could significantly improve the quality and convenience of interactions.
Total VREcall
Authors: Gupta Kunal , Chan Sam W. T. , Pai Yun Suen , Strachan Nicholas , Su John , Sumich Alexander , Nanayakkara Suranga , Billinghurst Mark
Published: July 2022

Our memories and past experiences contribute to guiding our perception and action of future affective experiences. Virtual Reality (VR) experiences are more vividly memorized and recalled than non-VR ones, but there is little research on how to detect this recall in VR. We investigate the feasibility of recognizing autobiographical memory (AM) recall in VR using physiological cues: skin conductance, heart-rate variability, eye gaze, and pupillary response. We devised a methodology replicating an existing AM Test in VR. We conducted a user study with 20 participants recalling AM using three valence categories cue words: positive, negative, and neutral. We found a significant effect of AM recalls on EDA peak, and eye blink rate, with a generalized recognition accuracy of 77.1% and person dependent accuracy of up to 95.1%. This shows a promising approach for detecting AM recall in VR and we discuss the implications for VR experience design.