Advancements and Challenges in IoT Simulators: A Comprehensive Review.

The Internet of Things (IoT) has emerged as an important concept, bridging the physical and digital worlds through interconnected devices. Although the idea of interconnected devices predates the term "Internet of Things", which was coined in 1999 by Kevin Ashton, the vision of a seamlessly integrated world of devices has been accelerated by advancements in wireless technologies, cost-effective computing, and the ubiquity of mobile devices. This study aims to provide an in-depth review of existing and emerging IoT simulators focusing on their capabilities and real-world applications, and discuss the current challenges and future trends in the IoT simulation area. Despite substantial research in the IoT simulation domain, many studies have a narrow focus, leaving a gap in comprehensive reviews that consider broader IoT development metrics, such as device mobility, energy models, Software-Defined Networking (SDN), and scalability. Notably, there is a lack of literature examining IoT simulators' capabilities in supporting renewable energy sources and their integration with Vehicular Ad-hoc Network (VANET) simulations. Our review seeks to address this gap, evaluating the ability of IoT simulators to simulate complex, large-scale IoT scenarios and meet specific developmental requirements, as well as examining the current challenges and future trends in the field of IoT simulation. Our systematic analysis has identified several significant gaps in the current literature. A primary concern is the lack of a generic simulator capable of effectively simulating various scenarios across different domains within the IoT environment. As a result, a comprehensive and versatile simulator is required to simulate the diverse scenarios occurring in IoT applications. Additionally, there is a notable gap in simulators that address specific security concerns, particularly battery depletion attacks, which are increasingly relevant in IoT systems. Furthermore, there is a need for further investigation and study regarding the integration of IoT simulators with traffic simulation for VANET environments. In addition, it is noteworthy that renewable energy sources are underrepresented in IoT simulations, despite an increasing global emphasis on environmental sustainability. As a result of these identified gaps, it is imperative to develop more advanced and adaptable IoT simulation tools that are designed to meet the multifaceted challenges and opportunities of the IoT domain.

Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making.

BACKGROUND: Predictive models have been used in clinical care for decades. They can determine the risk of a patient developing a particular condition or complication and inform the shared decision-making process. Developing artificial intelligence (AI) predictive models for use in clinical practice is challenging; even if they have good predictive performance, this does not guarantee that they will be used or enhance decision-making. We describe nine stages of developing and evaluating a predictive AI model, recognising the challenges that clinicians might face at each stage and providing practical tips to help manage them. FINDINGS: The nine stages included clarifying the clinical question or outcome(s) of interest (output), identifying appropriate predictors (features selection), choosing relevant datasets, developing the AI predictive model, validating and testing the developed model, presenting and interpreting the model prediction(s), licensing and maintaining the AI predictive model and evaluating the impact of the AI predictive model. The introduction of an AI prediction model into clinical practice usually consists of multiple interacting components, including the accuracy of the model predictions, physician and patient understanding and use of these probabilities, expected effectiveness of subsequent actions or interventions and adherence to these. Much of the difference in whether benefits are realised relates to whether the predictions are given to clinicians in a timely way that enables them to take an appropriate action. CONCLUSION: The downstream effects on processes and outcomes of AI prediction models vary widely, and it is essential to evaluate the use in clinical practice using an appropriate study design.

Increasing Voluntary Myoelectric Training Time Through Game Design.

In virtual prosthetic training research, serious games have been investigated for over 30 years. However, few game design elements are used and assessed for their effect on the voluntary adherence and repetition of the performed task. We compared two game-based versions of an established myoelectric-controlled virtual prosthetic training task with an interface without game elements of the same task [for video, see (Garske, 2022)]. Twelve limb-intact participants were sorted into three groups of comparable ability and asked to perform the task as long as they were motivated. Following the task, they completed a questionnaire regarding their motivation and engagement in the task. The investigation established that participants in the game-based groups performed the task significantly longer when more game design elements were implemented in the task (medians of 6 vs. 9.5 vs. 14 blocks for groups with increasing number of different game design elements). The participants in the game-based versions were also more likely to end the task out of fatigue than for reasons of boredom or frustration, which was verified by a fatigue analysis of the myoelectric signal. We demonstrated that the utilization of game design methodically in virtual myoelectric training tasks can support adherence and duration of a virtual training, in the short-term. Whether such short-term enhanced engagement would lead to long-term adherence remains an open question.

Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement Learning.

Thousands of energy-aware sensors have been placed for monitoring in a variety of scenarios, such as manufacturing, control systems, disaster management, flood control and so on, requiring time-critical energy-efficient solutions to extend their lifetime. This paper proposes reinforcement learning (RL) based dynamic data streams for time-critical IoT systems in energy-aware IoT devices. The designed solution employs the Q-Learning algorithm. The proposed mechanism has the potential to adjust the data transport rate based on the amount of renewable energy resources that are available, to ensure collecting reliable data while also taking into account the sensor battery lifetime. The solution was evaluated using historical data for solar radiation levels, which shows that the proposed solution can increase the amount of transmitted data up to 23%, ensuring the continuous operation of the device.

Serious Games Are Not Serious Enough for Myoelectric Prosthetics.

Serious games show a lot of potential for use in movement rehabilitation (eg, after a stroke, injury to the spinal cord, or limb loss). However, the nature of this research leads to diversity both in the background of the researchers and in the approaches of their investigation. Our close examination and categorization of virtual training software for upper limb prosthetic rehabilitation found that researchers typically followed one of two broad approaches: (1) focusing on the game design aspects to increase engagement and muscle training and (2) concentrating on an accurate representation of prosthetic training tasks, to induce task-specific skill transfer. Previous studies indicate muscle training alone does not lead to improved prosthetic control without a transfer-enabling task structure. However, the literature shows a recent surge in the number of game-based prosthetic training tools, which focus on engagement without heeding the importance of skill transfer. This influx appears to have been strongly influenced by the availability of both software and hardware, specifically the launch of a commercially available acquisition device and freely available high-profile game development engines. In this Viewpoint, we share our perspective on the current trends and progress of serious games for prosthetic training.

SIDMA as a criterion for psychiatric compulsion: An analysis of compulsory treatment orders in Scotland.

Scottish mental health legislation includes a unique criterion for the use of compulsion in the delivery of mental health care and treatment. Under the Mental Health (Care and Treatment) (Scotland) Act, 2003, patients must exhibit 'significantly impaired decision-making ability' (SIDMA) in order to be eligible for psychiatric detention or involuntary psychiatric treatment outside the forensic context. The SIDMA requirement represents a distinctive strategy in ongoing international efforts to rethink the conditions under which psychiatric compulsion is permissible. We reconstruct the history of the Scottish SIDMA requirement, analyse its differences from so-called 'fusion law,' and then examine how the SIDMA standard actually functions in practice. We analyse 100 reports that accompany applications for Compulsory Treatment Orders (CTOs). Based on this analysis, we provide a profile of the patient population that is found to exhibit SIDMA, identify the grounds upon which SIDMA is attributed to individual patients, and offer an assessment of the quality of the documentation of SIDMA. We demonstrate that there are systemic areas of poor practice in the reporting of SIDMA, with only 12% of CTOs satisfying the minimum standard of formal completeness endorsed by the Mental Welfare Commission. We consider what lessons might be drawn both for the ongoing review of mental health legislation in Scotland, and for law reform initiatives in other jurisdictions.

Limits of remote working: the ethical challenges in conducting Mental Health Act assessments during COVID-19.

COVID-19 has created additional challenges in mental health services, including the impact of social distancing measures on care and treatment. For situations where a detention under mental health legislation is required to keep an individual safe, psychiatrists may consider whether to conduct an assessment in person or using video technology. The Mental Health (Care and Treatment) (Scotland) Act 2003 does not stipulate that an assessment has to be conducted in person. Yet, the Code of Practice envisions that detention assessments would be conducted face to face in all circumstances. During the pandemic, the Mental Welfare Commission for Scotland, a statutory body with a duty to promote best practice of the Act, has been asked whether it may be acceptable and indeed preferable for some assessments to be conducted via video technology. Where an assessment is needed to determine if a patient needs to be detained, and where there is a need for social distancing or the need for 'shielding', remote assessments may in some circumstances be preferable. In this article, we outline the modification of the Mental Welfare Commission's previous outright rejection of virtual assessments as the pandemic progressed and discuss the ethical and legal issues the possibility of remote assessments has exposed. We also discuss the limits and when a virtual assessment is not considered ethical. As the pandemic moves from a state of emergency into a 'new normal' in psychiatric services during second, or subsequent, waves, the use and place (if any) of remote assessments for detention needs to be considered.

Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review.

BACKGROUND AND OBJECTIVES: Sepsis is a life-threatening condition that is associated with increased mortality. Artificial intelligence tools can inform clinical decision making by flagging patients at risk of developing infection and subsequent sepsis. This systematic review aims to identify the optimal set of predictors used to train machine learning algorithms to predict the likelihood of an infection and subsequent sepsis. METHODS: This systematic review was registered in PROSPERO database (CRD42020158685). We conducted a systematic literature review across 3 large databases: Medline, Cumulative Index of Nursing and Allied Health Literature, and Embase. Quantitative primary research studies that focused on sepsis prediction associated with bacterial infection in adults in all care settings were eligible for inclusion. RESULTS: Seventeen articles met our inclusion criteria. We identified 194 predictors that were used to train machine learning algorithms, with 13 predictors used on average across all included studies. The most prevalent predictors included age, gender, smoking, alcohol intake, heart rate, blood pressure, lactate level, cardiovascular disease, endocrine disease, cancer, chronic kidney disease (eGFR<60 mL/min), white blood cell count, liver dysfunction, surgical approach (open or minimally invasive), and pre-operative haematocrit < 30 %. All included studies used artificial intelligence techniques, with average sensitivity 75.7 ± 17.88, and average specificity 63.08 ± 22.01. CONCLUSION: The type of predictors influenced the predictive power and predictive timeframe of the developed machine learning algorithm. Predicting the likelihood of sepsis through artificial intelligence can help concentrate finite resources to those patients who are most at risk. Future studies should focus on developing more sensitive and specific algorithms.

ASTEROID: A New Clinical Stereotest on an Autostereo 3D Tablet.

PURPOSE: To describe a new stereotest in the form of a game on an autostereoscopic tablet computer designed to be suitable for use in the eye clinic and present data on its reliability and the distribution of stereo thresholds in adults. METHODS: Test stimuli were four dynamic random-dot stereograms, one of which contained a disparate target. Feedback was given after each trial presentation. A Bayesian adaptive staircase adjusted target disparity. Threshold was estimated from the mean of the posterior distribution after 20 responses. Viewing distance was monitored via a forehead sticker viewed by the tablet's front camera, and screen parallax was adjusted dynamically so as to achieve the desired retinal disparity. RESULTS: The tablet must be viewed at a distance of greater than ∼35 cm to produce a good depth percept. Log thresholds were roughly normally distributed with a mean of 1.75 log10 arcsec = 56 arcsec and SD of 0.34 log10 arcsec = a factor of 2.2. The standard deviation agrees with previous studies, but ASTEROID thresholds are approximately 1.5 times higher than a similar stereotest on stereoscopic 3D TV or on Randot Preschool stereotests. Pearson correlation between successive tests in same observer was 0.80. Bland-Altman 95% limits of reliability were ±0.64 log10 arcsec = a factor of 4.3, corresponding to an SD of 0.32 log10 arcsec on individual threshold estimates. This is similar to other stereotests and close to the statistical limit for 20 responses. CONCLUSIONS: ASTEROID is reliable, easy, and portable and thus well-suited for clinical stereoacuity measurements. TRANSLATIONAL RELEVANCE: New 3D digital technology means that research-quality psychophysical measurement of stereoacuity is now feasible in the clinic.

Legal capacity, mental capacity and supported decision-making: Report from a panel event.

Against a backdrop of the UN Convention on the Rights of Persons with Disabilities having been in place for over a decade, discussions about legal capacity, the relevance of mental capacity and the shift to supported decision-making, continue to develop. A panel event was held at the King's Transnational Law Summit in 2018 with the aim of understanding the contours of the dialogue around these issues. This paper presents the contributions of the panel members, a summary of the discussion that took place and a synthesis of the views expressed. It suggests that divergent conclusions in this area turn on disagreements about: the consequences of sometimes limiting legal capacity for people with mental disabilities; the emphasis placed on particular values; the basis for mental capacity assessments; and the scope for supported decision-making. It also highlights the connection between resources, recognition and freedoms for people with mental disabilities, and therefore the issues that arise when discussion in this area is limited to legal capacity in the context of decision-making.

Deep learning-based artificial vision for grasp classification in myoelectric hands.

OBJECTIVE: Computer vision-based assistive technology solutions can revolutionise the quality of care for people with sensorimotor disorders. The goal of this work was to enable trans-radial amputees to use a simple, yet efficient, computer vision system to grasp and move common household objects with a two-channel myoelectric prosthetic hand. APPROACH: We developed a deep learning-based artificial vision system to augment the grasp functionality of a commercial prosthesis. Our main conceptual novelty is that we classify objects with regards to the grasp pattern without explicitly identifying them or measuring their dimensions. A convolutional neural network (CNN) structure was trained with images of over 500 graspable objects. For each object, 72 images, at [Formula: see text] intervals, were available. Objects were categorised into four grasp classes, namely: pinch, tripod, palmar wrist neutral and palmar wrist pronated. The CNN setting was first tuned and tested offline and then in realtime with objects or object views that were not included in the training set. MAIN RESULTS: The classification accuracy in the offline tests reached [Formula: see text] for the seen and [Formula: see text] for the novel objects; reflecting the generalisability of grasp classification. We then implemented the proposed framework in realtime on a standard laptop computer and achieved an overall score of [Formula: see text] in classifying a set of novel as well as seen but randomly-rotated objects. Finally, the system was tested with two trans-radial amputee volunteers controlling an i-limb UltraTM prosthetic hand and a motion controlTM prosthetic wrist; augmented with a webcam. After training, subjects successfully picked up and moved the target objects with an overall success of up to [Formula: see text]. In addition, we show that with training, subjects' performance improved in terms of time required to accomplish a block of 24 trials despite a decreasing level of visual feedback. SIGNIFICANCE: The proposed design constitutes a substantial conceptual improvement for the control of multi-functional prosthetic hands. We show for the first time that deep-learning based computer vision systems can enhance the grip functionality of myoelectric hands considerably.

The impact of pre-entry study skills training programmes on students' first year experience in health and social care programmes.

The Faculty of Health and Social Care Sciences at Kingston University and St. George's, University of London has provided a pre-entry study skills course since July 2001. The course runs over one weekend and although short, is comprehensive. An evaluative research study, using case study methodology was undertaken to examine the impact of this pre-entry initiative on the first year student experience. Data were obtained through focus groups with students and semi-structured interviews with both students and staff. The findings show that the study skills weekend programmes prepare students realistically for their first year university experience. In addition, the evaluation helped to develop insight into the first year experience which have informed a number of academic initiatives.

Another view of patient focused care.

In her article, 'Patient focused care without the upheaval' (Features, April 13), Sue Johnson expresses views which are contrary to our understanding of the patient focused care (PFC) concept.

In praise of the RCN's plea for involvement.

I should like to challenge the assumptions Ann Catlow made in arguing against the training of health care assistants under the patient focused hospitals initiative to perform tasks normally undertaken by 'skilled professionals such as phlebotomists'.