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The purpose of this paper is to review the status of biosignal measures of female sexual arousal with a view to examining the feasibility of such procedures in a forensic context. Although adult women represent the minority of sexual offenders, female perpetrated sexual abuse has been gaining increasing attention in the forensic psychological literature as criminal justice is confronted with female offending populations to a greater extent than previously recognised.

Psychological assessments of sex offenders have tended to be over-dependent on the use of self-report measures (i.e. an individual's appraisal and report of their emotional state of sexual arousal). There is a dearth of empirical knowledge concerning the psychophysiological assessment of female sexual interest in general and especially so for female sex offenders. Physiological measures are those that rely on directly observable physiological responses of the individual in order to identify patterns of sexual interest or arousal.

Because deviant sexual interest (in children or the use of violence) as assessed by penile plethysmography, has been established as one of the strongest predictors of recidivism in male sex offenders (and as a viable alternative to self-report methodologies), an analogue measurement approach for female sex offenders is desirable. This paper considers: first, theoretical problems (e.g. what does female physiological arousal mean in terms of sexual arousal/desire?; second, technical measurement problems (e.g. reactivity of the measure in within subject designs); and third, procedural/ethical problems (e.g. invasiveness of the application).

While a number of physiological assessment devices are considered in this paper, the paper discusses the labial photoplethysmograph as a promising method for female sexual offender populations.

The perpetration of sexual coercion is a complex public health problem associated with many kinds of deficits. The literature has shown that women also perpetrate sexually coercive behaviours. Recent work has suggested that this kind of behaviour could be explained by two distinct developmental pathways. However, this model does not allow the authors to identify how the individual processes social information in situ and may decide to resort to coercive behaviours. This study aimed to investigate the role of social information processing in women’s sexual coercion.

A sample of 125 French-speaking women from the general population were recruited to complete online questionnaires pertaining to dark triad personality traits, emotion abilities, alexithymia and antecedents of sexual coercion.

Results revealed that women with a history of sexual coercion had a significantly higher narcissistic traits score and more emotion regulation (ER) deficits than those without a history. For women with a history of sexual coercion perpetration, correlational analyses showed positive correlations, respectively, between psychopathic traits and alexithymia and between Machiavellianism and deficits in ER.

These results contribute to identifying the deficits relating to SIP in terms of sexual coercion perpetrated by women. Women with a history of sexual coercion perpetration appear to endorse more dark triad traits and to have ER issues. Certain level of these deficits could be a trigger and affect the SIP of women and increase the likelihood behaving in a sexually coercive manner.

Research on women who sexually abuse children is relatively scarce and tends to rely on small or unrepresentative convenience samples. The purpose of the current descriptive study is to examine characteristics female perpetrators of child sexual abuse using a large and contemporary dataset.

This study analyzes data collected by the National Child Abuse and Neglect Data System, which is a census of all child protective services investigations or assessments conducted in all 50 states, as well as in the District of Columbia and Puerto Rico from October 1, 2018, to September 30, 2019.

Only substantiated cases of sexual abuse were analyzed (n = 51,442 cases). Overall, 7.6% of the perpetrators were female, though the percentage of female perpetrators varied dramatically across states from less than 1% to over 36%. Female perpetrators tended to have younger victims than did male perpetrators. For children aged 2 or less, female perpetrators constituted approximately 20% of the abusers. Female perpetrators were more likely to victimize male and female children, whereas male perpetrators predominantly had female victims. More female perpetrators had a prior finding of maltreatment than did male perpetrators (32.3% vs 23.5%).

These findings add to the limited research base on female perpetrators of child sexual abuse and highlight avenues for further research into the differences between male and female sexual abusers.

This study is a replication of previous research but also provides additional novel findings.

The Guilty Knowledge Test (GKT) is a psychophysiological questioning technique that can be used as part of a polygraph examination which purports to assess whether suspects conceal “guilty knowledge” by measuring their physiological responses while responding to a series of multiple choice questions. The present study sets out to consider a number of key issues in relation to the GKT paradigm. Specifically, the following questions were considered: Does response mode matter? Does motivation influence outcome? Are combined physiological measures better than single ones? Does gender have an effect on physiological responsivity during a polygraph examination? Results demonstrated real variations between the physiological measures used. Gender differences were also observed in polygraph response patterns. These findings are discussed in relation to the validity of the Guilty Knowledge Test.

Wearables are gaining prominence in the health-care industry and their use is growing. The elderly and other patients can use these wearables to monitor their vitals at home and have them sent to their doctors for feedback. Many studies are being conducted to improve wearable health-care monitoring systems to obtain clinically relevant diagnoses. The accuracy of this system is limited by several challenges, such as motion artifacts (MA), power line interference, false detection and acquiring vitals using dry electrodes. This paper aims to focus on wearable health-care monitoring systems in the literature and provides the effect of MA on the wearable system. Also presents the problems faced while tracking the vitals of users.

MA is a major concern and certainly needs to be suppressed. An analysis of the causes and effects of MA on wearable monitoring systems is conducted. Also, a study from the literature on motion artifact detection and reduction is carried out and presented here. The benefits of a machine learning algorithm in a wearable monitoring system are also presented. Finally, distinct applications of the wearable monitoring system have been explored.

According to the study reduction of MA and multiple sensor data fusion increases the accuracy of wearable monitoring systems.

This study also presents the outlines of design modification of dry/non-contact electrodes to minimize the MA. Also, discussed few approaches to design an efficient wearable health-care monitoring system.

This paper explores the potential of Henri Lefebvre's rhythmanalysis to understand data as an appearance assumed by the quantitative abstraction of everyday life, which negates a qualitative disjuncture between different natural and social rhythms – specifically those between embodied circadian and biological rhythms and the rhythms of working life. It takes as a case study a prototype performance research method investigating the methodological and practical potential of quantified self technologies to reconnect the body to its forms of abstraction in a digital age by means of the collection, interpretation and sonification of data using wearable tech, mobile apps, synthesised music and modes of visual communication. Quantitative data were selectively ‘sonified’ with synthesisers and drum machines to produce a 40-minute electronic symphony performed to a public audience. The paper theorises the project as an intervention reconnecting quantitative data with the qualitative experience it abstracts from, exploring the potential for these technologies to be used as tools of remediation that recover the embodied social subject from its abstraction in data for critical self-knowledge and understanding.

Currently, ubiquitous smartphones embedded with various sensors provide a convenient way to collect raw sequence data. These data bridges the gap between human activity and multiple sensors. Human activity recognition has been widely used in quite a lot of aspects in our daily life, such as medical security, personal safety, living assistance and so on.

To provide an overview, the authors survey and summarize some important technologies and involved key issues of human activity recognition, including activity categorization, feature engineering as well as typical algorithms presented in recent years. In this paper, the authors first introduce the character of embedded sensors and dsiscuss their features, as well as survey some data labeling strategies to get ground truth label. Then, following the process of human activity recognition, the authors discuss the methods and techniques of raw data preprocessing and feature extraction, and summarize some popular algorithms used in model training and activity recognizing. Third, they introduce some interesting application scenarios of human activity recognition and provide some available data sets as ground truth data to validate proposed algorithms.

The authors summarize their viewpoints on human activity recognition, discuss the main challenges and point out some potential research directions.

It is hoped that this work will serve as the steppingstone for those interested in advancing human activity recognition.

The purpose of the research work is to focus on the deployment of wearable sensors in addressing symptom Analysis in the Internet of Things (IoT) environment to reduce human interaction in this epidemic circumstances.

COVID-19 pandemic has distracted the world into an unaccustomed situation in the recent past. The pandemic has pulled us toward data harnessing and focused on the digital framework to monitor the COVID-19 cases seriously, as there is an urge to detect the disease, wearable sensors aided in predicting the incidence of COVID-19. This COVID-19 has initiated many technologies like cloud computing, edge computing, IoT devices, artificial intelligence. The deployment of sensor devices has tremendously increased. Similarly, IoT applications have witnessed many innovations in addressing the COVID-19 crisis. State-of-the-art focuses on IoT factors and symptom features deploying wearable sensors for predicting the COVID-19 cases. The working model incorporates wearable devices, clinical therapy, monitoring the symptom, testing suspected cases and elements of IoT. The present research sermonizes on symptom analysis and risk factors that influence the coronavirus by acknowledging the respiration rate and oxygen saturation (SpO₂). Experiments were proposed to carry out with chi-Square distribution with independent measures t-Test.

IoT devices today play a vital role in analyzing COVID-19 cases effectively. The research work incorporates wearable sensors, human interpretation and Web server, statistical analysis with IoT factors, data management and clinical therapy. The research is initiated with data collection from wearable sensors, data retrieval from the cloud server, pre-processing and categorizing based on age and gender information. IoT devices contribute to tracking and monitoring the patients for prerequisites. The suspected cases are tested based on symptom factors such as temperature, oxygen level (SPO₂), respiratory rate variation and continuous investigation, and these demographic factors are taken for analyzed based on the gender and age factors of the collected data with the IoT factors thus presenting a cutting edge construction design in clinical trials.

The contemporary study comprehends 238 data through wearable sensors and transmitted through an IoT gateway to the cloud server. Few data are considered as outliers and discarded for analysis. Only 208 data are contemplated for statistical examination. These filtered data are proclaimed using chi-square distribution with t-test measure correlating the IoT factors. The research also interprets the demographic features that induce IoT factors using alpha and beta parameters showing the equal variance with the degree of freedom (df = 206).

Breast cancer has become the largest cancer in the world today. Health problems for women with breast cancer need to be addressed urgently. This study aims to select the best method for preparing temperature-sensitive sports underwear, and to verify the feasibility of using K-type thermocouple threads in underwear fabrics.

In the experiments, two samples were designed for temperature-sensitive performance tests and the effects produced by different outer layer structures were investigated. In the second step, K-type thermocouple wires were integrated into sports underwear. The comfort and feasibility of the temperature-sensitive underwear were investigated.

It was finally verified to obtain the best comfort and temperature-sensing performance of K-type thermocouple filaments integrated into sports underwear with plain stitching.

The underwear has a certain prospect for the application of smart apparel based on breast cancer health monitoring, which is of some significance for monitoring smart apparel.