Types Of Alarms Research Articles

Alarm Management in Provisional COVID-19 Intensive Care Units: Retrospective Analysis and Recommendations for Future Pandemics.

In response to the high patient admission rates during the COVID-19 pandemic, provisional intensive care units (ICUs) were set up, equipped with temporary monitoring and alarm systems. We sought to find out whether the provisional ICU setting led to a higher alarm burden and more staff with alarm fatigue. We aimed to compare alarm situations between provisional COVID-19 ICUs and non-COVID-19 ICUs during the second COVID-19 wave in Berlin, Germany. The study focused on measuring alarms per bed per day, identifying medical devices with higher alarm frequencies in COVID-19 settings, evaluating the median duration of alarms in both types of ICUs, and assessing the level of alarm fatigue experienced by health care staff. Our approach involved a comparative analysis of alarm data from 2 provisional COVID-19 ICUs and 2 standard non-COVID-19 ICUs. Through interviews with medical experts, we formulated hypotheses about potential differences in alarm load, alarm duration, alarm types, and staff alarm fatigue between the 2 ICU types. We analyzed alarm log data from the patient monitoring systems of all 4 ICUs to inferentially assess the differences. In addition, we assessed staff alarm fatigue with a questionnaire, aiming to comprehensively understand the impact of the alarm situation on health care personnel. COVID-19 ICUs had significantly more alarms per bed per day than non-COVID-19 ICUs (P<.001), and the majority of the staff lacked experience with the alarm system. The overall median alarm duration was similar in both ICU types. We found no COVID-19-specific alarm patterns. The alarm fatigue questionnaire results suggest that staff in both types of ICUs experienced alarm fatigue. However, physicians and nurses who were working in COVID-19 ICUs reported a significantly higher level of alarm fatigue (P=.04). Staff in COVID-19 ICUs were exposed to a higher alarm load, and the majority lacked experience with alarm management and the alarm system. We recommend training and educating ICU staff in alarm management, emphasizing the importance of alarm management training as part of the preparations for future pandemics. However, the limitations of our study design and the specific pandemic conditions warrant further studies to confirm these findings and to explore effective alarm management strategies in different ICU settings.

Leveraging Large Language Models for Precision Monitoring of Chemotherapy-Induced Toxicities: A Pilot Study with Expert Comparisons and Future Directions.

Large Language Models (LLMs), such as the GPT model family from OpenAI, have demonstrated transformative potential across various fields, especially in medicine. These models can understand and generate contextual text, adapting to new tasks without specific training. This versatility can revolutionize clinical practices by enhancing documentation, patient interaction, and decision-making processes. In oncology, LLMs offer the potential to significantly improve patient care through the continuous monitoring of chemotherapy-induced toxicities, which is a task that is often unmanageable for human resources alone. However, existing research has not sufficiently explored the accuracy of LLMs in identifying and assessing subjective toxicities based on patient descriptions. This study aims to fill this gap by evaluating the ability of LLMs to accurately classify these toxicities, facilitating personalized and continuous patient care. This comparative pilot study assessed the ability of an LLM to classify subjective toxicities from chemotherapy. Thirteen oncologists evaluated 30 fictitious cases created using expert knowledge and OpenAI's GPT-4. These evaluations, based on the CTCAE v.5 criteria, were compared to those of a contextualized LLM model. Metrics such as mode and mean of responses were used to gauge consensus. The accuracy of the LLM was analyzed in both general and specific toxicity categories, considering types of errors and false alarms. The study's results are intended to justify further research involving real patients. The study revealed significant variability in oncologists' evaluations due to the lack of interaction with fictitious patients. The LLM model achieved an accuracy of 85.7% in general categories and 64.6% in specific categories using mean evaluations with mild errors at 96.4% and severe errors at 3.6%. False alarms occurred in 3% of cases. When comparing the LLM's performance to that of expert oncologists, individual accuracy ranged from 66.7% to 89.2% for general categories and 57.0% to 76.0% for specific categories. The 95% confidence intervals for the median accuracy of oncologists were 81.9% to 86.9% for general categories and 67.6% to 75.6% for specific categories. These benchmarks highlight the LLM's potential to achieve expert-level performance in classifying chemotherapy-induced toxicities. The findings demonstrate that LLMs can classify subjective toxicities from chemotherapy with accuracy comparable to expert oncologists. The LLM achieved 85.7% accuracy in general categories and 64.6% in specific categories. While the model's general category performance falls within expert ranges, specific category accuracy requires improvement. The study's limitations include the use of fictitious cases, lack of patient interaction, and reliance on audio transcriptions. Nevertheless, LLMs show significant potential for enhancing patient monitoring and reducing oncologists' workload. Future research should focus on the specific training of LLMs for medical tasks, conducting studies with real patients, implementing interactive evaluations, expanding sample sizes, and ensuring robustness and generalization in diverse clinical settings. This study concludes that LLMs can classify subjective toxicities from chemotherapy with accuracy comparable to expert oncologists. The LLM's performance in general toxicity categories is within the expert range, but there is room for improvement in specific categories. LLMs have the potential to enhance patient monitoring, enable early interventions, and reduce severe complications, improving care quality and efficiency. Future research should involve specific training of LLMs, validation with real patients, and the incorporation of interactive capabilities for real-time patient interactions. Ethical considerations, including data accuracy, transparency, and privacy, are crucial for the safe integration of LLMs into clinical practice.

Feasibility of Non-contact Noise Cancellation for Medical Device Alarms in Intensive Care Unit: An In Vitro Experiment

Objective: Medical device alarms in intensive care units (ICUs) are essential but their constant noise contributes to sleep disturbance and delirium in patients, jeopardizing their health and recovery. Therefore, improving the sound environment for patients in the ICU is crucial. This fundamental experiment investigated the potential of non-contact noise cancellation to reduce the noise level from medical device alarms in patients’ ears. We tested whether non-contact noise cancellation using a loudspeaker reduced the noise level of medical device alarms. Methods: In Experiment 1, we measured and analyzed the electrocardiogram sounds (volume 2, default setting) from a bedside monitor and the alarm sounds (volume 2, default setting) from a syringe pump. Negative-phase sound waves were subsequently generated for each alarm type. In Experiment 2, we tested whether non-contact noise cancellation using a loudspeaker reduced the noise level of each alarm. Results: The electrocardiogram sound from the bedside monitor was highest around 1kHz, while, the alarm sound from the syringe pump peaked around 4kHz. Non-contact noise cancellation using the loudspeaker reduced the electrocardiogram sounds emitted from the bedside monitor by more than 15decibel (dB) and the alarm sounds from the syringe pump by more than 10dB. Conclusion: A 10dB change doubles or halves the perceived loudness, making the over 10dB reduction achieved for medical device alarms in this study highly significant. The results of this experimental study demonstrate the promising potential of noise-cancelling loudspeakers for improving patient comfort and well-being in ICUs.

Domestic chicken hens can eavesdrop on the alarm calls of wild tits

Eavesdropping on heterospecific alarm calls is common in nature, and successful eavesdropping on heterospecific call information can provide benefits to animal survival. Previous studies have shown that free-ranging flocks of domestic chickens can respond to avian heterospecific alarm calls. However, there are limited studies on whether solitary active domestic chickens (Gallus gallus domesticus), especially hens with chicks eavesdrop on heterospecific alarm calls to detect different types of predators and adjust their response depending on whether the predators are a threat to themselves or their young. In the present study, we evaluated the behavioral responses of hens with chick activity to two alarm calls (test playback) and territorial calls (control playback) of wild Japanese tits (Parus minor). The results showed that hens were more vigilant toward the alarm calls than territorial calls and that anti-predator behavior differed significantly between the two alarm calls as predicted: they engage in self-preservation behaviors when the call informs about aerial predators, and in protective behaviors of their chicks when the calls inform about ground predators. In the later case, the number of chicks also has an impact on the behavior of hens (dilution effect). This suggests that hens can eavesdrop on the alarm call of Japanese tits, identify predator information in the alarm call, and adjust the anti-predation strategy based on the type of alarm and the number of chicks.

A GRU-Based Model for Detecting Common Accidents of Construction Workers.

Fall accidents in the construction industry have been studied over several decades and identified as a common hazard and the leading cause of fatalities. Inertial sensors have recently been used to detect accidents of workers in construction sites, such as falls or trips. IMU-based systems for detecting fall-related accidents have been developed and have yielded satisfactory accuracy in laboratory settings. Nevertheless, the existing systems fail to uphold consistent accuracy and produce a significant number of false alarms when deployed in real-world settings, primarily due to the intricate nature of the working environments and the behaviors of the workers. In this research, the authors redesign the aforementioned laboratory experiment to target situations that are prone to false alarms based on the feedback obtained from workers in real construction sites. In addition, a new algorithm based on recurrent neural networks was developed to reduce the frequencies of various types of false alarms. The proposed model outperforms the existing benchmark model (i.e., hierarchical threshold model) with higher sensitivities and fewer false alarms in detecting stumble (100% sensitivity vs. 40%) and fall (95% sensitivity vs. 65%) events. However, the model did not outperform the hierarchical model in detecting coma events in terms of sensitivity (70% vs. 100%), but it did generate fewer false alarms (5 false alarms vs. 13).

Using an automated tail movement sensor device to predict calving time in dairy cows

This study aimed to evaluate the effectiveness of an automated tail movement sensor device (Moocall; Bluebell, Dublin, Ireland) to predict time of calving in dairy cows. At a commercial dairy farm in southern Ontario, Moocall (MC) devices were attached with the device's strap, and an additional elastic wrap, to the tail of cows approximately 3 d before their expected calving date. The MC has 2 types of alarm, a high activity alarm in the previous hour (1HA), and a high activity alarm in the previous 2 h (2HA); these alarms were sent and registered to the MC software. The calving and close-up pens were video monitored to determine the exact time of the onset of stage II of calving (amniotic sac visible at the vulva) and the end of stage II of calving (total expulsion of the calf). A total of 49 cows were enrolled, but we excluded 13 animals from the analysis as they had 3 or more MC drops from the tail (n = 6), a swollen tail (n = 3), or the MC device was lost (n = 4); this left 36 cows. In total, the device dropped off 21 (42%) cows. The average number of alarms (1HA and 2HA) per cow before stage II of calving was 2.7 ± 2.3 (± standard error). The first alarm after fitting the device on the tail was used to determine the device's sensitivity and specificity. Depending on the interval before the onset of parturition (i.e., 2, 4, 8, 12 h) in which the alarm was triggered, sensitivity varied from 5% to 72% and specificity from 50% to 93%. The false positive rate varied between 6% and 50% depending on the interval from the alarm to the onset of parturition. The high false positive and device drop rates (despite the addition of the elastic wrap) may compromise the applicability of this sensor device in a commercial setting.

Three Years of Continuous Vital Signs Monitoring on the General Surgical Ward: Is It Sustainable? A Qualitative Study.

Continuous monitoring of vital signs using a wireless wearable device was implemented in 2018 at a surgical care unit of an academic hospital. This study aimed at gaining insight into nurses' and patients' perspectives regarding the use and innovation of a continuous vital signs monitoring system, three years after its introduction. This qualitative study was performed in a surgical, non-intensive care unit of an academic hospital in 2021. Key-user nurses (nurses with additional training and expertise with the device) and patients were selected for semi-structured interviews, and nurses from the ward were selected for a focus group interview using a topic list. Transcripts of the audio tapes were deductively analysed using four dimensions for adoptions of information and communication technologies (ICT) devices in healthcare. The device provided feelings of safety for nurses and patients. Nurses and patients had a few issues with the device, including the size and the battery life. Nurses gained knowledge and skills in using the system for measurement and interpretations. They perceived the system as a tool to improve the recognition of clinical decline. The use of the system could be further developed regarding the technical device's characteristics, nurses' interpretation of the data and the of type of alarms, the information needs of patients, and clarification of the definition and standardization of continuous monitoring. Three years after the introduction, wireless continuous vital signs monitoring is the new standard of care according to the end-users at the general surgical ward.

Pediatric Characteristics Associated With Higher Rates of Monitor Alarms.

Background: Continuous physiologic monitoring commonly is used in pediatric medical-surgical (med-surg) units and is associated with high alarm burden for clinicians. Characteristics of pediatric patients generating high rates of alarms on med-surg units are not known. Objective: To describe the demographic and clinical characteristics of pediatric med-surg patients associated with high rates of clinical alarms. Methods: We conducted a cross-sectional, single-site, retrospective study using existing clinical and alarm data from a children's hospital. Continuously monitored patients from med-surg units who had available alarm data were included. Negative binomial regression models were used to test the association between patient characteristics and the rate of clinical alarms per continuously monitored hour. Results: Our final sample consisted of 1,569 patients with a total of 38,501 continuously monitored hours generating 265,432 clinical alarms. Peripheral oxygen saturation (SpO2) low alarms accounted for 57.5% of alarms. Patients with medical complexity averaged 11% fewer alarms per hour than those without medical complexity (P < 0.01). Patients older than 5 years had up to 30% fewer alarms per hour than those who were younger than 5 years (P < 0.01). Patients using supplemental oxygen averaged 39% more alarms per hour compared with patients who had no supplemental oxygen use (P < 0.01). Patients at high risk for deterioration averaged 19% more alarms per hour than patients who were not high risk (P = 0.01). Conclusion: SpO2 alarms were the most common type of alarm in this study. The results highlight patient populations in pediatric medical-surgical units that may be high yield for interventions to reduce alarms. Most physiologic monitor alarms in pediatric medical-surgical (med-surg) units are not informative and likely could be safely eliminated to reduce noise and alarm fatigue.1-3 However, identifying and sustaining successful alarm-reduction strategies is a challenge. Research shows that 25% of patients in pediatric med-surg units produce almost three-quarters of all alarms.4 These patients are a potential high-yield target for alarm-reduction strategies; however, we are not aware of studies describing characteristics of pediatric patients generating high rates of alarms. The patient populations seen on pediatric med-surg units are diverse. Children of all ages are cared for on these units, with diagnoses ranging from acute respiratory infections, to management of chronic conditions, and to psychiatric conditions. Not all patients on pediatric med-surg units have physiologic parameters continuously monitored,4 but among those who do, understanding patient characteristics associated with high rates of alarms may help clinicians, healthcare technology management (HTM) professionals, and others working on alarm management strategies to develop targeted interventions. We conducted an exploratory retrospective study to describe patient characteristics associated with high rates of alarms in pediatric med-surg units.

Frequency and Causes of Nocturnal Alarms in Youth and Young Adults With Type 1 Diabetes Using a First-Generation Hybrid Closed-Loop System.

Meeting glycemic recommendations is challenging for youth with type 1 diabetes. Diabetes technology, including continuous glucose monitoring (CGM) and hybrid closed-loop (HCL) automated insulin delivery systems, significantly increase achievement of glycemic targets; however, many youth struggle to sustain use of early HCL systems. Nocturnal alarm fatigue contributes to disrupted sleep and device discontinuation. We examined the frequency and causes of nocturnal (10:00 p.m. to 6:00 a.m.) alarms in pediatric patients (N = 76, median age 14.5 years [interquartile range 11.8-17.0 years, range 7-24 years]) starting on a first-generation HCL system in a prospective observational study. Device data were analyzed with linear mixed-effects models to examine change across time at 3-month intervals for 12 months. At baseline (HCL system in nonautomated mode), participants averaged 3.3 ± 0.6 alarms per night. In the 2 weeks after starting HCL (automated) mode, alarm frequency significantly increased to 5.4 ± 0.5 times per night (P <0.001). Alarm frequency decreased through the remainder of the observational period; however, CGM sensor and HCL system use also declined. The types of alarms were evenly distributed among sensor maintenance, sensor threshold, pump, and HCL-specific alarms. These data show that HCL system nocturnal alarms are frequent and may be barriers to sleep quality and device use. Further research is needed to assess the impact of diabetes technology on sleep and to determine method to improve sleep quality with technology use.

Utility of transcranial motor-evoked potential changes in predicting postoperative deficit in lumbar decompression and fusion surgery: a systematic review and meta-analysis.

The primary aim of this study was to evaluate whether TcMEP alarms can predict the occurrence of postoperative neurological deficit in patients undergoing lumbar spine surgery. The secondary aim was to determine whether the various types of TcMEP alarms including transient and persistent changes portend varying degrees of injury risk. This was a systematic review and meta-analysis of the literature from PubMed, Web of Science, and Embase regarding outcomes of transcranial motor-evoked potential (TcMEP) monitoring during lumbar decompression and fusion surgery. The sensitivity, specificity, and diagnostic odds ratio (DOR) of TcMEP alarms for predicting postoperative deficit were calculated and presented with forest plots and a summary receiver operating characteristic curve. Eight studies were included, consisting of 4923 patients. The incidence of postoperative neurological deficit was 0.73% (36/4923). The incidence of deficits in patients with significant TcMEP changes was 11.79% (27/229), while the incidence in those without changes was 0.19% (9/4694). All TcMEP alarms had a pooled sensitivity and specificity of 63 and 95% with a DOR of 34.92 (95% CI 7.95-153.42). Transient and persistent changes had sensitivities of 29% and 47%, specificities of 96% and 98%, and DORs of 8.04 and 66.06, respectively. TcMEP monitoring has high specificity but low sensitivity for predicting postoperative neurological deficit in lumbar decompression and fusion surgery. Patients who awoke with new postoperative deficits were 35 times more likely to have experienced TcMEP changes intraoperatively, with persistent changes indicating higher risk of deficit than transient changes. Diagnostic Systematic Review.

Design and Fabrication of a Smart Monitoring System for Liquefied Petroleum Gas-Operated Cars Based on Global System Mobile

In this paper, a low-cost smart system is designed and fabricated to monitor gas leakage in LPG-based cars. The implemented system consists of three gas sensors installed in the joints of the gas pipes, gas cylinders, and injectors for the purpose of gas leak monitoring. In addition, the LM 35 temperature sensor is situated over the gas cylinder to monitor its temperature. The implemented system can provide three types of alarm: the first type sends a text message to the car owner’s phone and emits an audible alarm signal. The message reaches the service center also. The second type sends messages via Bluetooth to the user’s phone or to the smart screen on the vehicle’s dashboard, with an alert that the engine must be turned off (emergency case). The last warning sends an overheating alarm signal, utilizing a temperature sensor when the temperature of the gas cylinder exceeds 50°C to provide protection and prevent a possible explosion. In practice, it was found that gas sensors should be placed less than 5 cm from the monitored site, and the best performance at work was under 55°C. It was also found that the best performance of the GSM unit is under 55°C.

Toplam Biyolojik Yük Sayısı için Kontrol Tablosu Yaklaşımını Kullanarak Belediye Dağıtım Hattının Mikrobiyolojik Stabilite Değerlendirmesi

Background: The microbiological quality of municipal water is an important inspection characteristic that must be carefully monitored and controlled to ensure the delivery of acceptable water that is safe for human activities, consumption and further processing. Case presentation: The current case demonstrated the implementation of a special type of attribute control chart for the examination and analysis of long-term data trends of water distribution in a healthcare facility from different pints-of-use. All datasets showed a right-skewed dispersion pattern of data indicating solitary intermittent high values but without any observable Out-Of-Specification (OOS). All water lines failed to follow any known distribution pattern without the need for transformation which had improved the normality and removed the outliers from datasets. The direct approach for visualizing the record of microbiological results was accomplished using Laney-attribute charts. Discussion: Each use point had its unique trend of data by visualizing the mean, pattern, the Upper Control Limit (UCL) and the alarm types. The net quality of the water distribution system in the facility could be deduced from the overall readings that had been averaged and pooled in a single process-behavior chart. Implementation of this trending chart showed a tendency of oscillation pattern for bioburden count that tended to seize in amplitude with possible signs of seasonality. Conclusion: In general, the overall biological stability is improving with time in terms of the Total Microbial Aerobic Count (TAMC). The Bioburden level and the magnitude of fluctuations were decreasing according to the latest monitoring state.

Fast & scrupulous: Gesture-based alarms improve accuracy and reaction times under various mental workload levels. An ERSP study

In high-risk environments, fast and accurate responses to warning systems are essential to efficiently handle emergency situations. The aim of the present study was twofold: 1) investigating whether hand action videos (i.e., gesture alarms) trigger faster and more accurate responses than text alarm messages (i.e., written alarms), especially when mental workload (MWL) is high; and 2) investigating the brain activity in response to both types of alarms as a function of MWL. Regardless of MWL, participants (N = 28) were found to be both faster and more accurate when responding to gesture alarms than to written alarms. Brain electrophysiological results suggest that this greater efficiency might be due to a facilitation of the action execution, reflected by the decrease in mu and beta power observed around the response time window observed at C3 and C4 electrodes. These results suggest that gesture alarms may improve operators’ performances in emergency situations.

Anomaly Detection and Correction of Optimizing Autonomous Systems With Inverse Reinforcement Learning.

This article considers autonomous systems whose behaviors seek to optimize an objective function. This goes beyond standard applications of condition-based maintenance, which seeks to detect faults or failures in nonoptimizing systems. Normal agents optimize a known accepted objective function, whereas abnormal or misbehaving agents may optimize a renegade objective that does not conform to the accepted one. We provide a unified framework for anomaly detection and correction in optimizing autonomous systems described by differential equations using inverse reinforcement learning (RL). We first define several types of anomalies and false alarms, including noise anomaly, objective function anomaly, intention (control gain) anomaly, abnormal behaviors, noise-anomaly false alarms, and objective false alarms. We then propose model-free inverse RL algorithms to reconstruct the objective functions and intentions for given system behaviors. The inverse RL procedure for anomaly detection and correction has the training phase, detection phase, and correction phase. First, inverse RL in the training phase infers the objective function and intention of the normal behavior system using offline stored data. Second, in the detection phase, inverse RL infers the objective function and intention for online observed test system behaviors using online observation data. They are then compared with that of the nominal system to identify anomalies. Third, correction is executed for the anomalous system to learn the normal objective and intention. Simulations and experiments on a quadrotor unmanned aerial vehicle (UAV) verify the proposed methods.

Discrimination model using denoising autoencoder-based majority vote classification for reducing false alarm rate

Loose parts monitoring and detecting alarm type in real Nuclear Power Plant have challenges such as background noise, insufficient alarm data, and difficulty of distinction between alarm data that occur during start and stop. Although many signal processing methods and alarm determination algorithms have been developed, it is not easy to determine valid alarm and extract the meaning data from alarm signal including background noise. To address these issues, this paper proposes a denoising autoencoder-based majority vote classification. Training and test data are prepared by acquiring alarm data from real NPP and simulation facility for data augmentation, and noisy data is reproduced by adding Gaussian noise. Using DAEs with 3, 5, 7, and 9 layers, features are extracted for each model and classified into neural networks. Finally, the results obtained from each DAE are classified by majority voting. Also, through comparison with other methods, the accuracy and the false alarm rate are compared, and the excellence of the proposed method is confirmed.

Developing and validating a parser-based suicidality detection model in text-based mental health services

BackgroundAdvances in text-mining can potentially aid online text-based mental health services in detecting suicidality. However, false positive remains a challenge. MethodsData of a free 24/7 online text-based counseling service in Hong Kong were used to develop a novel parser-based algorithm (PBSD) to detect suicidal ideation while minimizing false alarms. Sessions containing keywords related to suicidality were extracted (N = 1267). PBSD first applies a sentence parser to work out the grammatical structure of each sentence, including subject, object, dependent and modifier. Then a set of syntax rules were applied to judge if a flagged sentence is a true or false positive. Half of the sessions were randomly selected to train PBSD, the remaining were used as the test set. A standard keywords matching model was adopted as the baseline comparison. Accuracy and recall were reported to demonstrate models' performance. ResultsOf the 1267 sessions, 585 (46.2 %) were false alarms. The false alarms were categorized into four types: negation-induced false alarms (NIFA; 14 %), subject-induced false alarms (SIFA; 19 %), tense-induced false alarms (TIFA; 30 %), and other types of false alarms (OTFA; 37 %). PBSD significantly outperforms the baseline keywords matching model on accuracy (0.68 vs 0.53, 28.3 %). It successfully amended 36.8 % (105/297) lexicon matching-caused false alarms. The reduction on recall was marginal (1 vs 0.96, 4 %). ConclusionsThe proposed model significantly improves the use of lexicon-based method by reducing false alarms and improving the accuracy of suicidality detection. It can potentially reduce unnecessary panic and distraction caused by false alarms among frontline service-providers.

Multisensory alarm to benefit alarm identification and decrease workload: a feasibility study.

The poor design of conventional auditory medical alarms has contributed to alarm desensitization, and eventually, alarm fatigue in medical personnel. This study tested a novel multisensory alarm system which aims to help medical personnel better interpret and respond to alarm annunciation during periods of high cognitive load such as those found within intensive care units. We tested a multisensory alarm that combined auditory and vibrotactile cues to convey alarm type, alarm priority, and patient identity. Testing was done in three phases: control (conventional auditory), Half (limited multisensory alarm), and Full (complete multisensory alarm). Participants (N = 19, undergraduates) identified alarm type, priority, and patient identity (patient 1 or 2) using conventional and multisensory alarms, while simultaneously completing a cognitively demanding task. Performance was based on reaction time (RT) and identification accuracy of alarm type and priority. Participants also reported their perceived workload. RT was significantly faster for the Control phase (p < 0.05). Participant performance in identifying alarm type, priority, and patient did not differ significantly between the three phase conditions (p = 0.87, 0.37, and 0.14 respectively). The Half multisensory phase produced the lowest mental demand, temporal demand, and overall perceived workload score. These data suggest that implementation of a multisensory alarm with alarm and patient information may decrease perceived workload without significant changes in alarm identification performance. Additionally, a ceiling effect may exist for multisensory stimuli, with only part of an alarm benefitting from multisensory integration.

Cooktop Sensing Based on a YOLO Object Detection Algorithm.

Deep Learning (DL) has provided a significant breakthrough in many areas of research and industry. The development of Convolutional Neural Networks (CNNs) has enabled the improvement of computer vision-based techniques, making the information gathered from cameras more useful. For this reason, recently, studies have been carried out on the use of image-based DL in some areas of people's daily life. In this paper, an object detection-based algorithm is proposed to modify and improve the user experience in relation to the use of cooking appliances. The algorithm can sense common kitchen objects and identify interesting situations for users. Some of these situations are the detection of utensils on lit hobs, recognition of boiling, smoking and oil in kitchenware, and determination of good cookware size adjustment, among others. In addition, the authors have achieved sensor fusion by using a cooker hob with Bluetooth connectivity, so it is possible to automatically interact with it via an external device such as a computer or a mobile phone. Our main contribution focuses on supporting people when they are cooking, controlling heaters, or alerting them with different types of alarms. To the best of our knowledge, this is the first time a YOLO algorithm has been used to control the cooktop by means of visual sensorization. Moreover, this research paper provides a comparison of the detection performance among different YOLO networks. Additionally, a dataset of more than 7500 images has been generated and multiple data augmentation techniques have been compared. The results show that YOLOv5s can successfully detect common kitchen objects with high accuracy and fast speed, and it can be employed for realistic cooking environment applications. Finally, multiple examples of the identification of interesting situations and how we act on the cooktop are presented.

Clinical Alarms in a Gynaecological Surgical Unit: A Retrospective Data Analysis

Alarm fatigue refers to the desensitisation of medical staff to patient monitor clinical alarms, which may lead to slower response time or total ignorance of alarms and thereby affects patient safety. The reasons behind alarm fatigue are complex; the main contributing factors include the high number of alarms and the poor positive predictive value of alarms. The study was performed in the Surgery and Anaesthesia Unit of the Women's Hospital, Helsinki, by collecting data from patient monitoring device clinical alarms and patient characteristics from surgical operations. We descriptively analysed the data and statistically analysed the differences in alarm types between weekdays and weekends, using chi-squared, for a total of eight monitors with 562 patients. The most common operational procedure was caesarean section, of which 149 were performed (15.7%). Statistically significant differences existed in alarm types and procedures between weekdays and weekends. The number of alarms produced was 11.7 per patient. In total, 4698 (71.5%) alarms were technical and 1873 (28.5%) were physiological. The most common physiological alarm type was low pulse oximetry, with a total of 437 (23.3%). Of all the alarms, the number of alarms either acknowledged or silenced was 1234 (18.8%). A notable phenomenon in the study unit was alarm fatigue. Greater customisation of patient monitors for different settings is needed to reduce the number of alarms that do not have clinical significance.

A New Security Alarm Based on Interaction

An alarm or an alarm system is a device that warns or detects any violation of privacy in different places by making noise, sound or warning the person in a variety of ways, including via email, SMS, phone call or sounding. Alarms that are currently available in the market have different shapes and types. Generally, they have a common structure and their main purpose is similar to each other. There is another type of burglar alarm in the market, which is used today in certain places to protect property and assets, in certain organizations such as banks, car alarms, etc. This article reviews and presents a different alarm. whose main purpose is to provide possible security in public and private places, especially military places.