Multifunctional cross-sensitive magnetic alginate-chitosan-polyethylene oxide nanofiber sensor for human-machine interaction

Flexible nanofiber membranes are compelling materials for the development of functional multi-mode sensors; however, their essential features such as high cross-sensitivity, reliable stability and signal discrimination capability have rarely been realized simultaneously in one sensor. Here, a novel multi-mode sensor with a nanofiber membrane structure based on multiple interpenetrating networks of bidisperse magnetic particles, sodium alginate (SA), chitosan (CHI) in conjunction with polyethylene oxide hydrogels was prepared in a controllable electrospinning technology. Specifically, the morphology distributions of nanofibers could be regulated by the crosslinking degree of the interpenetrating networks and the spinning process parameters. The incorporation of SA and CHI endowed the sensor with desirable flexibility, ideal biocompatibility and skin-friendly property. Besides, the assembled sensors not only displayed preferable magnetic sensitivity of 0.34 T-1 and reliable stability, but also exhibited favorable cross-sensitivity, quick response time, and long-term durability for over 5000 cycles under various mechanical stimuli. Importantly, the multi-mode stimuli could be discriminated via producing opposite electrical signals. Furthermore, based on the signal distinguishability of the sensor, a wearable Morse code translation system assisted by the machine learning algorithm was demonstrated, enabling a high recognizing accuracy (>99.1 %) for input letters and numbers information. Due to the excellent multifunctional sensing characteristics, we believe that the sensor will have a high potential in wearable soft electronics and human-machine interactions.

Bioinspired ion channel receptor based on hygroelectricity for precontact sensing of living organism

Tactile sensors play an important role in human-machine interaction (HMI). Compared to contact tactile sensing, which leaves physical hardware vulnerable to wear and tear, proximity sensing is better at reacting to remote events before physical contact. The apteronotus albifrons possess ion channel receptors for remote surroundings perception. Inspired by the relevant ion channel structure and self-powered operation mode, we designed a new proximity sensor with ion rectification characteristics and self-powered capability. This bio-inspired ion channel receptor exploits the hygroelectric effect to convert the humidity information into a series of current signals when the living organism approaches, and it is insensitive to non-aquatic non-organisms. The sensor offers high sensitivity (2.3 mm-1), a suitable range (0-10 mm) for close object detection, fast response (0.3 s), and fast recovery (2.5 s). The unique combination of bio-sensitivity, non-contact detection characteristics, and humidity-based power generation capabilities enriches the functionality of future HMI electronics. As a proof of concept, the sensor has been successfully applied in different scenarios such as human health management, early warning systems, non-contact switches to prevent virus transmission, object recognition, and finger trajectory detection.

TEMPO bacterial cellulose and MXene nanosheets synergistically promote tough hydrogels for intelligent wearable human-machine interaction

Conductive hydrogels have received increasing attention in the field of wearable electronics, but they also face many challenges such as temperature tolerance, biocompatibility, and stability of mechanical properties. In this paper, a double network hydrogel of MXene/TEMPO bacterial cellulose (TOBC) system is proposed. Through solvent replacement, the hydrogel exhibits wide temperature tolerance (-20-60 °C) and stable mechanical properties. A large number of hydrogen bonds, MXene/TOBC dynamic three-dimensional network system, and micellar interactions endow the hydrogel with excellent mechanical properties (elongation at break ~2800 %, strength at break ~420 kPa) and self-healing ability. The introduction of tannic acid prevents the oxidation of MXene and the loss of electrical properties of the hydrogel. In addition, the sensor can also quickly (74 ms) and sensitive (gauge factor = 15.65) wirelessly monitor human motion, and the biocompatibility can well avoid the stimulation when it comes into contact with the human body. This series of research work reveals the fabrication of MXene-like flexible wearable electronic devices based on self-healing, good cell compatibility, high sensitivity, wide temperature tolerance and durability, which can be used in smart wearable, wireless monitoring, human-machine Interaction and other aspects show great application potential.

Human and machine-induced social stress in complex work environments: Effects on performance and subjective state

Social stress at work can lead to severe consequences. As a result of technological developments, social stress will increasingly be induced by machines. It is therefore crucial to understand how machine-induced social stress affects operators. The present study aimed to compare human and machine-induced social stress with regard to its effect on primary and secondary task performance, and on subjective state (e.g., self-esteem, mood and justice). 90 participants worked on a high-fidelity simulation of a complex work environment, on which they had received extensive training (2h15). Social stress was induced by a human or a machine using a combination of negative performance feedback and ostracism. Results indicate that social stress did not affect performance, affect or state self-esteem. Machine-induced and human-induced social stress overall had similar effects, except for the latter impairing perceived justice. We discuss implications of these results for automation at the workplace and outline future research directions.

Multi-characteristic tannic acid-reinforced polyacrylamide/sodium carboxymethyl cellulose ionic hydrogel strain sensor for human-machine interaction

Big data and cloud computing are propelling research in human-computer interface within academia. However, the potential of wearable human-machine interaction (HMI) devices utilizing multiperformance ionic hydrogels remains largely unexplored. Here, we present a motion recognition-based HMI system that enhances movement training. We engineered dual-network PAM/CMC/TA (PCT) hydrogels by reinforcing polyacrylamide (PAM) and sodium carboxymethyl cellulose (CMC) polymers with tannic acid (TA). These hydrogels possess exceptional transparency, adhesion, and remodelling features. By combining an elastic PAM backbone with tunable amounts of CMC and TA, the PCT hydrogels achieve optimal electromechanical performance. As strain sensors, they demonstrate higher sensitivity (GF = 4.03), low detection limit (0.5 %), and good linearity (0.997). Furthermore, we developed a highly accurate (97.85 %) motion recognition system using machine learning and hydrogel-based wearable sensors. This system enables contactless real-time training monitoring and wireless control of trolley operations. Our research underscores the effectiveness of PCT hydrogels for real-time HMI, thus advancing next-generation HMI systems.

[Application of electronic skin in healthcare and virtual reality]

Electronic skin has shown great application potential in many fields such as healthcare monitoring and human-machine interaction due to their excellent sensing performance, mechanical properties and biocompatibility. This paper starts from the materials selection and structures design of electronic skin, and summarizes their different applications in the field of healthcare equipment, especially current development status of wearable sensors with different functions, as well as the application of electronic skin in virtual reality. The challenges of electronic skin in the field of wearable devices and healthcare, as well as our corresponding strategies, are discussed to provide a reference for further advancing the research of electronic skin.

Constructing triple-network cellulose nanofiber hydrogels with excellent strength, toughness and conductivity for real-time monitoring of human movements

In recent years, there has been a lot of interest in developing composite hydrogels with superior mechanical and conductive properties. In this study, triple-network (TN) cellulose nanofiber hydrogels were prepared by using cellulose nanofiber as the first network, isotropic poly(acrylamide-co-acrylic acid) as the second network, and polyvinyl alcohol as the third network via a cyclic freezing-thawing process. The strong (9.43 ± 0.14 MPa tensile strength, (445.5 ± 7.0)% elongation-at-break), tough (15.12 ± 0.14 MJ/m3 toughness), and conductive (0.0297 ± 0.00021 S/cm ionic conductivity) TN cellulose nanofiber hydrogels were effectively created after being pre-stretched in an external force field, cross-linked by Fe3+ and added Li+. The produced composite TN cellulose nanofiber hydrogels were successfully used as a flexible sensor for real-time monitoring and detecting human movements, highlighting their potential for wearable electronics, medical technology, and human-machine interaction. CHEMICAL COMPOUNDS STUDIED IN THIS ARTICLE: Acrylamide (PubChem CID: 6579); Acrylic acid (PubChem CID: 6581); Ammonium persulfate (PubChem CID: 6579); N, N'-methylene bisacrylamide (PubChem CID: 17956053); Sodium bromide (PubChem CID: 253881); Sodium hydroxide (PubChem CID: 14798); Sodium hypochlorite (PubChem CID: 23665760); Sodium chlorite (PubChem CID: 23668197); 2,2,6,6-tetramethylpiperidinyl-1-oxide (PubChem CID: 2724126); Polyvinyl alcohol (PubChem CID: 11199); Lithium chloride (PubChem CID: 433294); Iron nitrate nonahydrate (PubChem CID: 129774236).

Patients' Trust in Artificial Intelligence-based Decision-making for Localized Prostate Cancer: Results from a Prospective Trial

BACKGROUND

Artificial intelligence (AI) has the potential to enhance diagnostic accuracy and improve treatment outcomes. However, AI integration into clinical workflows and patient perspectives remain unclear.

OBJECTIVE

To determine patients' trust in AI and their perception of urologists relying on AI, and future diagnostic and therapeutic AI applications for patients.

DESIGN, SETTING, AND PARTICIPANTS

A prospective trial was conducted involving patients who received diagnostic or therapeutic interventions for prostate cancer (PC).

INTERVENTION

Patients were asked to complete a survey before magnetic resonance imaging, prostate biopsy, or radical prostatectomy.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary outcome was patient trust in AI. Secondary outcomes were the choice of AI in treatment settings and traits attributed to AI and urologists.

RESULTS AND LIMITATIONS

Data for 466 patients were analyzed. The cumulative affinity for technology was positively correlated with trust in AI (correlation coefficient 0.094; p = 0.04), whereas patient age, level of education, and subjective perception of illness were not (p > 0.05). The mean score (± standard deviation) for trust in capability was higher for physicians than for AI for responding in an individualized way when communicating a diagnosis (4.51 ± 0.76 vs 3.38 ± 1.07; mean difference [MD] 1.130, 95% confidence interval [CI] 1.010-1.250; t924 = 18.52, p < 0.001; Cohen's d = 1.040) and for explaining information in an understandable way (4.57 ± vs 3.18 ± 1.09; MD 1.392, 95% CI 1.275-1.509; t921 = 27.27, p < 0.001; Cohen's d = 1.216). Patients stated that they had higher trust in a diagnosis made by AI controlled by a physician versus AI not controlled by a physician (4.31 ± 0.88 vs 1.75 ± 0.93; MD 2.561, 95% CI 2.444-2.678; t925 = 42.89, p < 0.001; Cohen's d = 2.818). AI-assisted physicians (66.74%) were preferred over physicians alone (29.61%), physicians controlled by AI (2.36%), and AI alone (0.64%) for treatment in the current clinical scenario.

CONCLUSIONS

Trust in future diagnostic and therapeutic AI-based treatment relies on optimal integration with urologists as the human-machine interface to leverage human and AI capabilities.

PATIENT SUMMARY

Artificial intelligence (AI) will play a role in diagnostic decisions in prostate cancer in the future. At present, patients prefer AI-assisted urologists over urologists alone, AI alone, and AI-controlled urologists. Specific traits of AI and urologists could be used to optimize diagnosis and treatment for patients with prostate cancer.

Side-effects and adverse events of a shoulder- and back-support exoskeleton in workers: A systematic review

INTRODUCTION

While the biomechanical effects of exoskeletons are well studied, research about potential side-effects and adverse events are limited. The aim of this systematic review was to provide an overview of the side-effects and adverse events on shoulder- and back-support exoskeletons during work tasks.

METHODS

Four in-field studies and 32 laboratory studies were included in this review, reporting on n = 18 shoulder exoskeletons, n = 9 back exoskeletons, n = 1 full body with a supernumerary arm, and n = 1 combination of shoulder and back exoskeleton.

RESULTS

The most frequent side-effect reported is discomfort (n = 30), followed by a limited usability of the exoskeleton (n = 16). Other identified side-effects and adverse events were changes in muscle activity, mobility, task performance, balance and posture, neurovascular supply, gait parameters and precision. An incorrect fit of the exoskeleton and the decreased degrees of freedom are most often reported as causes of these side-effects. Two studies did not find any side-effects. This review also showed that there are differences in the occurrence of side-effects in gender, age, and physical fitness. Most studies (89%) were conducted in a laboratory setting. Most studies (97%) measured short-term effects only. Psychological and social side-effects or adverse events were not reported. Side-effects and adverse events for active exoskeletons were understudied (n = 4).

CONCLUSION

It was concluded that the evidence for side-effects and adverse events is limited. If available, it mainly consists of reports of mild discomfort and limited usability. Generalisation is limited because studies were conducted in lab settings and measured short term only, and most participants were young male workers.

Machine Impostors Can Avoid Human Detection and Interrupt the Formation of Stable Conventions by Imitating Past Interactions: A Minimal Turing Test

Interactions between humans and bots are increasingly common online, prompting some legislators to pass laws that require bots to disclose their identity. The Turing test is a classic thought experiment testing humans' ability to distinguish a bot impostor from a real human from exchanging text messages. In the current study, we propose a minimal Turing test that avoids natural language, thus allowing us to study the foundations of human communication. In particular, we investigate the relative roles of conventions and reciprocal interaction in determining successful communication. Participants in our task could communicate only by moving an abstract shape in a 2D space. We asked participants to categorize their online social interaction as being with a human partner or a bot impostor. The main hypotheses were that access to the interaction history of a pair would make a bot impostor more deceptive and interrupt the formation of novel conventions between the human participants. Copying their previous interactions prevents humans from successfully communicating through repeating what already worked before. By comparing bots that imitate behavior from the same or a different dyad, we find that impostors are harder to detect when they copy the participants' own partners, leading to less conventional interactions. We also show that reciprocity is beneficial for communicative success when the bot impostor prevents conventionality. We conclude that machine impostors can avoid detection and interrupt the formation of stable conventions by imitating past interactions, and that both reciprocity and conventionality are adaptive strategies under the right circumstances. Our results provide new insights into the emergence of communication and suggest that online bots mining personal information, for example, on social media, might become indistinguishable from humans more easily.

Design and evaluation of cooperative human-machine interface for changing lanes in conditional driving automation

This study analyzes the impact of cooperative human-machine interface designs on drivers' trust in and interaction with automated driving systems (ADSs) during lane changes on highways. While drivers' inappropriate trust in ADSs can affect their behavior toward the system, capability to detect inadequate system performance, and perception of surrounding traffic disturbances, their engagement in the automated process can improve their comprehension of the system and traffic conditions, which is necessary for the safe practice of automated driving. Forty drivers practiced conditional driving automation in a driving simulator and encountered traffic congestion on the main lane of a two-lane highway. Four ADS designs were proposed to bypass the congestion. ADS-1 detects the congestion and synchronizes the speed accordingly. ADS-2 requests the driver to resume manual control and overtake the congestion. ADS-3 requests the driver to push a button to let the system overtakes the congestion automatically. ADS-4 overtakes the congestion automatically after informing the driver, while the driver can cancel it by pushing a button within 6 s. In all these conditions, driver intervention was optional. Although the drivers preferred and trusted ADS-1 and ADS-2 more than ADS-3 and ADS-4, the results indicate significant improvements in the driving performance and system usage under ADS-3 and ADS-4. Driving with ADS-3 improved drivers' engagement and reduced the requirement for control transfer compared with other systems. However, the time headway between the subject and adjacent vehicles indicated that lane changes were more critical under ADS-3 and ADS-4 than ADS-1 and ADS-2. Such deficiency of alignment between driver perception and safe behaviors has implications for the design of future studies and systems that need to balance satisfaction and safety. These observations are likely to improve driver interaction with automated vehicles.

How to take speed decisions consistent with the available sight distance using an intelligent speed adaptation system

Travelling at excessive speed increases the risk of having a road crash. Intelligent Speed Adaptation (ISA) systems might help the driver to make safe speed decisions along road sections with limited visibility. A recently developed ISA system, called V-ISA (Hazoor et al., 2021), is able to estimate the dynamic (real-time) speed limit, based on the prevailing sight conditions and stopping distance. The V-ISA operates in the following three ways: it can (i) display visual information, (ii) alert the driver with a warning sound, and/or (iii) intervene directly to modify and control vehicle speed. The effects of V-ISA on driving performance have yet to be investigated. Thus, the question of whether V-ISA modulates driving speed choice remains open. Here, we assessed the impact of V-ISA variants on driver speed choice. Thirty expert drivers experienced four simulated driving conditions, in which the three V-ISA variants together with the V-ISA off control condition were tested separately. Furthermore, drivers were asked for feedback on the acceptance and usability of the three V-ISA. Our results suggested that V-ISA was effective in mitigating the risks associated with speeding, with relatively high acceptance and perceived usability levels. The results indicate that V-ISA can have a positive impact on road safety by helping drivers to modulate their chosen driving speed.

Learning to interact with new technology: Health care workers' experiences of using a monitoring system for assessing hand hygiene - a grounded theory study

BACKGROUND

Recently, innovative technologies for hand hygiene (HH) monitoring have been developed to improve HH adherence in health care. This study explored health care workers' experiences of using an electronic monitoring system to assess HH adherence.

METHODS

An electronic monitoring system with digital feedback was installed on a surgical ward and interviews with health care workers using the system (n = 17) were conducted.  The data were analyzed according to grounded theory by Strauss and Corbin.

RESULTS

Health care workers' experiences were expressed in terms of having trust in the monitoring system, requesting system functionality and ease of use and becoming aware of one's own performance. This resulted in the core category of learning to interact with new technology, summarized as the main strategy when using an electronic monitoring system in clinical settings. The system with digital feedback improved the awareness of HH and individual feedback was preferable to group feedback.

CONCLUSIONS

Being involved in using and managing a technical innovation for assessing HH adherence in health care is a process of formulating a strategy for learning to interact with new technology. The importance of inviting health care workers to participate in the co-design of technical innovations is crucial, as it creates both trust in the innovation per se and trust in the process of learning how to use it.

An industrial exoskeleton user acceptance framework based on a literature review of empirical studies

Studying the acceptance of exoskeletons in industry has gained increased attention. Exoskeletons (wearable support devices) are envisioned to alleviate heavy work. Examining what factors influence the use of exoskeletons is important, because influencing these factors could positively contribute to the adoption of industrial exoskeletons. The factors identified in this paper have been systematically derived from empirical research with (potential future) end users, most of whom have tried on an exoskeleton. Our framework with factors influencing the acceptance of industrial exoskeletons can be used during the (ideally iterative) design, (re)development and evaluation phase of new or existing exoskeletons. This could improve the quality of exoskeletons since this allows designers to already consider acceptance factors early in the design process instead of finding out what is important late in the design process during (field) testing. In turn, this might accelerate the adoption of exoskeletons. Also, our framework can be used to study the ongoing introduction of exoskeletons at work since it also addresses policy decisions companies interested in implementing exoskeletons should consider.

Prospects for Augmenting Team Interactions with Real-Time Coordination-Based Measures in Human-Autonomy Teams

Complex work in teams requires coordination across team members and their technology as well as the ability to change and adapt over time to achieve effective performance. To support such complex interactions, recent efforts have worked toward the design of adaptive human-autonomy teaming systems that can provide feedback in or near real time to achieve the desired individual or team results. However, while significant advancements have been made to better model and understand the dynamics of team interaction and its relationship with task performance, appropriate measures of team coordination and computational methods to detect changes in coordination have not yet been widely investigated. Having the capacity to measure coordination in real time is quite promising as it provides the opportunity to provide adaptive feedback that may influence and regulate teams' coordination patterns and, ultimately, drive effective team performance. A critical requirement to reach this potential is having the theoretical and empirical foundation from which to do so. Therefore, the first goal of the paper is to review approaches to coordination dynamics, identify current research gaps, and draw insights from other areas, such as social interaction, relationship science, and psychotherapy. The second goal is to collate extant work on feedback and advance ideas for adaptive feedback systems that have potential to influence coordination in a way that can enhance the effectiveness of team interactions. In addressing these two goals, this work lays the foundation as well as plans for the future of human-autonomy teams that augment team interactions using coordination-based measures.

Analysis of factors influencing the productivity of hammer drilling - user forces, human fatigue, drilling direction, and drill bit

In order to be able to develop a hammer drill with which the user can work as ergonomically and productively as possible, the relevant influencing factors must be known. In addition to the unknown influence of the drilling direction, there is a lack of understanding of the relations between user forces, human fatigue, and productivity. To analyze these relations, an experiment was carried out with 15 professional users. First, the influence of feed force, drilling direction, and drill bit on the rate of penetration was examined. Taking into account the rate of penetration and human fatigue, it was then investigated which of the three feed forces produces the highest productivity. Furthermore, the lateral forces applied by the participants during the drilling process were analyzed. Based on the study, it was found that the drilling direction (p < .001, r = -0.198) and the drill bit type (p < .041, r = -0.16) have a significant influence on the rate of penetration. Moreover, it was found that the rate of penetration tends to increase with higher feed forces, however, the theoretical cumulative drilling meters decrease when taking user fatigue into account. Finally, the experiment showed that the participating professionals applied lateral forces (Mdn = 16.7 N) of 13% to the feed force when working with a hammer drill. On the basis of this knowledge, investigations can be done to analyze the influences of lateral forces on the drilling process. The findings help drill and hammer drill manufacturers in testing and development processes. For craftsmen, on the other hand, the knowledge helps to perform the task as ergonomically and time-efficiently as possible.

Magnetic Array Assisted Triboelectric Nanogenerator Sensor for Real-Time Gesture Interaction

In human-machine interaction, robotic hands are useful in many scenarios. To operate robotic hands via gestures instead of handles will greatly improve the convenience and intuition of human-machine interaction. Here, we present a magnetic array assisted sliding triboelectric sensor for achieving a real-time gesture interaction between a human hand and robotic hand. With a finger's traction movement of flexion or extension, the sensor can induce positive/negative pulse signals. Through counting the pulses in unit time, the degree, speed, and direction of finger motion can be judged in real-time. The magnetic array plays an important role in generating the quantifiable pulses. The designed two parts of magnetic array can transform sliding motion into contact-separation and constrain the sliding pathway, respectively, thus improve the durability, low speed signal amplitude, and stability of the system. This direct quantization approach and optimization of wearable gesture sensor provide a new strategy for achieving a natural, intuitive, and real-time human-robotic interaction.

What makes you continuously use chatbot services? Evidence from chinese online travel agencies

Thanks to artificial intelligence, chatbots have been applied to many consumer-facing applications, especially to online travel agencies (OTAs). This study aims to identify five quality dimensions of chatbot services and investigate their effect on user confirmation, which in turn leads to use continuance. In addition, the moderating role of technology anxiety in the relationships between chatbot quality dimensions and post-use confirmation is examined. Survey data were gathered from 295 users of Chinese OTAs. Partial Least Square (PLS) was used to analyze measurement and structural models. Understandability, reliability, assurance, and interactivity are positively associated with post-use confirmation and technology anxiety moderates the relationships between four chatbot quality dimensions and confirmation. Confirmation is positively associated with satisfaction, which in turn influences use continuance intention. This study examines how chatbot services in OTAs are considered by users (human-like agents vs. technology-enabled services) by investigating the moderating role of technology anxiety.

Driver compliance to take-over requests with different auditory outputs in conditional automation

Conditionally automated driving (CAD) systems are expected to improve traffic safety. Whenever the CAD system exceeds its limit of operation, designers of the system need to ensure a safe and timely enough transition from automated to manual mode. An existing visual Human-Machine Interface (HMI) was supplemented by different auditory outputs. The present work compares the effects of different auditory outputs in form of (1) a generic warning tone and (2) additional semantic speech output on driver behavior for the announcement of an upcoming take-over request (TOR). We expect the information carried by means of speech output to lead to faster reactions and better subjective evaluations by the drivers compared to generic auditory output. To test this assumption, N=17 drivers completed two simulator drives, once with a generic warning tone ('Generic') and once with additional speech output ('Speech+generic'), while they were working on a non-driving related task (NDRT; i.e., reading a magazine). Each drive incorporated one transition from automated to manual mode when yellow secondary lanes emerged. Different reaction time measures, relevant for the take-over process, were assessed. Furthermore, drivers evaluated the complete HMI regarding usefulness, ease of use and perceived visual workload just after experiencing the take-over. They gave comparative ratings on usability and acceptance at the end of the experiment. Results revealed that reaction times, reflecting information processing time (i.e., hands on the steering wheel, termination of NDRT), were shorter for 'Speech+generic' compared to 'Generic' while reaction time, reflecting allocation of attention (i.e., first glance ahead), did not show this difference. Subjective ratings were in favor of the system with additional speech output.

Internet Addiction in adolescence: Neurobiological, psychosocial and clinical issues

Despite it has not been formally included in DSM-5 as a disorder, 'Internet addiction (IA)' has become a worldwide issue. It can be broadly defined as a non-chemical, behavioral addiction, which involves human-machine interaction. We pinpoint it as an "instrumental" form of social interaction (i.e. mediated by machines), a notion that appears useful for the sake of possible preclinical modeling. The features of Internet use reveals as addictive when this comes at the expense of genuine real-life sociability, with an overlap towards the hikikomori phenomenon (i.e., extreme retreat to one's own room). Due to the specific neuro-developmental plasticity in adolescence, IA poses risks to youths' mental health, and may likely produce negative consequences in everyday life. The thwarted development of adolescents' identity, self-image and adaptive social relationships is discussed: the IA adolescents often suffer loss of control, feelings of anger, symptoms of distress, social withdrawal, and familial conflicts. Further, more severe clinical conditions are also associated to IA, such as dysthymic, bipolar, affective, social-anxiety disorders, as well as major depression. This paper overviews the literature on IA, from neuro-biological, psycho-social and clinical standpoints, taking into account recent debates on diagnostic criteria, nosographic label and assessment tools. Neuroimaging data and neurochemical regulations are illustrated with links to pathogenetic hypotheses, which are amenable to validation through innovative preclinical modeling.

Combining situated Cognitive Engineering with a novel testing method in a case study comparing two infusion pump interfaces

We validated the usability of a new infusion pump interface designed with a situated Cognitive Engineering approach by comparing it to a reference interface using a novel testing method employing repeated measurements and process measures, in addition to traditional outcome measures. The sample consisted of 25 nurses who performed eight critical tasks three times. Performance measures consisted of number and type of errors, deviations from a pre-established normative path solution, task completion times, number of keystrokes, mental effort and preferences in use. Results showed that interaction with the new interface resulted in 18% fewer errors, 90% fewer normative path deviations, 42% lower task completion times, 40% fewer keystrokes, 39% lower mental effort and 76% more subjective preferences in use. These outcomes suggest that within the scope of this case study, combining the situated Cognitive Engineering approach with a novel testing method addresses various shortcomings of earlier testing methods.