Lessons learned from a multimodal sensor-based eHealth approach for treating pediatric obsessive-compulsive disorder.

Introduction: The present study investigates the feasibility and usability of a sensor-based eHealth treatment in psychotherapy for pediatric obsessive-compulsive disorder (OCD), and explores the promises and pitfalls of this novel approach. With eHealth interventions, therapy can be delivered in a patient's home environment, leading to a more ecologically valid symptom assessment and access to experts even in rural areas. Furthermore, sensors can help indicate a patient's emotional and physical state during treatment. Finally, using sensors during exposure with response prevention (E/RP) can help individualize therapy and prevent avoidance behavior. Methods: In this study, we developed and subsequently evaluated a multimodal sensor-based eHealth intervention during 14 video sessions of cognitive-behavioral therapy (CBT) in 20 patients with OCD aged 12-18. During E/RP, we recorded eye movements and gaze direction via eye trackers, and an ECG chest strap captured heart rate (HR) to identify stress responses. Additionally, motion sensors detected approach and avoidance behavior. Results: The results indicate a promising application of sensor-supported therapy for pediatric OCD, such that the technology was well-accepted by the participants, and the therapeutic relationship was successfully established in the context of internet-based treatment. Patients, their parents, and the therapists all showed high levels of satisfaction with this form of therapy and rated the wearable approach in the home environment as helpful, with fewer OCD symptoms perceived at the end of the treatment. Discussion: The goal of this study was to gain a better understanding of the psychological and physiological processes that occur in pediatric patients during exposure-based online treatment. In addition, 10 key considerations in preparing and conducting sensor-supported CBT for children and adolescents with OCD are explored at the end of the article. This approach has the potential to overcome limitations in eHealth interventions by allowing the real-time transmission of objective data to therapists, once challenges regarding technical support and hardware and software usability are addressed. Clinical Trial Registration: www.ClinicalTrials.gov, identifier (NCT05291611).

Current State of Connected Sensor Technologies Used During Rehabilitation Care: Protocol for a Scoping Review.

BACKGROUND: Connected sensor technologies can capture raw data and analyze them using advanced statistical methods such as machine learning or artificial intelligence to generate interpretable behavioral or physiological outcomes. Previous research conducted on connected sensor technologies has focused on design, development, and validation. Published review studies have either summarized general technological solutions to address specific behaviors such as physical activity or focused on remote monitoring solutions in specific patient populations. OBJECTIVE: This study aimed to map research that focused on using connected sensor technologies to augment rehabilitation services by informing care decisions. METHODS: The Population, Concept, and Context framework will be used to define inclusion criteria. Relevant articles published between 2008 to the present will be included if (1) the study enrolled adults (population), (2) the intervention used at least one connected sensor technology and involved data transfer to a clinician so that the data could be used to inform the intervention (concept), and (3) the intervention was within the scope of rehabilitation (context). An initial search strategy will be built in Embase; peer reviewed; and then translated to Ovid MEDLINE ALL, Web of Science Core Collection, and CINAHL. Duplicates will be removed prior to screening articles for inclusion. Two independent reviewers will screen articles in 2 stages: title/abstract and full text. Discrepancies will be resolved through group discussion. Data from eligible articles relevant to population, concept, and context will be extracted. Descriptive statistics will be used to report findings, and relevant outcomes will include the type and frequency of connected sensor used and method of data sharing. Additional details will be narratively summarized and displayed in tables and figures. Key partners will review results to enhance interpretation and trustworthiness. RESULTS: We conducted initial searches to refine the search strategy in February 2024. The results of this scoping review are expected in October 2024. CONCLUSIONS: Results from the scoping review will identify critical areas of inquiry to advance the field of technology-augmented rehabilitation. Results will also support the development of a longitudinal model to support long-term health outcomes. TRIAL REGISTRATION: Open Science Framework jys53; https://osf.io/jys53. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/60496.

The effect of humeral tray thickness on glenohumeral loads in a reverse shoulder 'smart' implant.

PURPOSE: The aim of this study was to observe the effects of changing humeral tray thickness on the resultant of intraoperative glenohumeral joint loads using a load-sensing system (LSS). METHODS: An rTSA was performed on fresh frozen full-body cadaver shoulders by using an internal proprietary LSS on the humeral side. The glenohumeral loads (Newtons) and the direction of the resultant force applied on the implant were recorded during four standard positions (External rotation, Extension, Abduction, Flexion) and three "complex" positions of Activity Daily Life ("behind back", "overhead reach" and "across chest"). For each position, the thickness was increased from 0 to 6 mm in a continuous fashion using the adjustment feature of the humeral system. Each manoeuvre was repeated three times. RESULTS: All shoulder positions showed a high repeatability of the glenohumeral load magnitude measured with an intra-class correlation coefficient of over 0.9. For each position, we observed a strong but no linear correlation between humeral tray thickness and joint loads. It was a cubical correlation (rs = 0,91) with a short ascending phase, then a plateau phase, and finally a phase with an exponential growth of the loads on the humeral implant. In addition, an increase in trail-poly thickness led to a recentering of force application at the interface of the two glenohumeral implants. CONCLUSION: This study provides further insight into the effects of humeral implant thickness on rTSA glenohumeral joint loads during different positions of the arm. Data obtained using this type of device could guide surgeons in finding the proper implant balance during rTSA.

Design and research of multi-information fusion RFID sensor for fruit and vegetable quality detection.

With the increasing demand for fruits and vegetables in the market, the development of cold chain logistics has put forward higher requirements for the quality of fruits and vegetables in storage. To ensure the freshness of fruits and vegetables during storage and transportation and avoid unnecessary loss, it is necessary to conduct real-time detection of their odor to ensure their quality. Therefore, based on nano-composite materials combined with Radio Frequency Identification (RFID) technology, this paper designs an integrated RFID sensor that can simultaneously detect temperature, carbon dioxide, and ethanol concentrations. The test results show that the sensor has a high sensitivity of 0.25 dB/°C, 0.011 dB/ppm, and 0.65 MHz/ppm for detecting temperature, carbon dioxide, and ethanol concentration, respectively. The sensor also uses Printed Circuit Board (PCB) technology to make the sensor base, which has the advantages of low cost, easy portability, and mass production capability. The results obtained evidence that the system meets the requirements of environmental monitoring for fruit and vegetable storage, runs stably, and has a high use value.

Flexible wearable sensors for crop monitoring: a review.

Crops were the main source of human food, which have met the increasingly diversified demand of consumers. Sensors were used to monitor crop phenotypes and environmental information in real time, which will provide a theoretical reference for optimizing crop growth environment, resisting biotic and abiotic stresses, and improve crop yield. Compared with non-contact monitoring methods such as optical imaging and remote sensing, wearable sensing technology had higher time and spatial resolution. However, the existing crop sensors were mainly rigid mechanical structures, which were easy to cause damage to crop organs, and there were still challenges in terms of accuracy and biosafety. Emerging flexible sensors had attracted wide attention in the field of crop phenotype monitoring due to their excellent mechanical properties and biocompatibility. The article introduced the key technologies involved in the preparation of flexible wearable sensors from the aspects of flexible preparation materials and advanced preparation processes. The monitoring function of flexible sensors in crop growth was highlighted, including the monitoring of crop nutrient, physiological, ecological and growth environment information. The monitoring principle, performance together with pros and cons of each sensor were analyzed. Furthermore, the future opportunities and challenges of flexible wearable devices in crop monitoring were discussed in detail from the aspects of new sensing theory, sensing materials, sensing structures, wireless power supply technology and agricultural sensor network, which will provide reference for smart agricultural management system based on crop flexible sensors, and realize efficient management of agricultural production and resources.

Sensory Polymers: Trends, Challenges, and Prospects Ahead.

In recent years, sensory polymers have evolved significantly, emerging as versatile and cost-effective materials valued for their flexibility and lightweight nature. These polymers have transformed into sophisticated, active systems capable of precise detection and interaction, driving innovation across various domains, including smart materials, biomedical diagnostics, environmental monitoring, and industrial safety. Their unique responsiveness to specific stimuli has sparked considerable interest and exploration in numerous applications. However, along with these advancements, notable challenges need to be addressed. Issues such as wearable technology integration, biocompatibility, selectivity and sensitivity enhancement, stability and reliability improvement, signal processing optimization, IoT integration, and data analysis pose significant hurdles. When considered collectively, these challenges present formidable barriers to the commercial viability of sensory polymer-based technologies. Addressing these challenges requires a multifaceted approach encompassing technological innovation, regulatory compliance, market analysis, and commercialization strategies. Successfully navigating these complexities is essential for unlocking the full potential of sensory polymers and ensuring their widespread adoption and impact across industries, while also providing guidance to the scientific community to focus their research on the challenges of polymeric sensors and to understand the future prospects where research efforts need to be directed.

Exploring the Importance and Performance Priorities of Older Adults With a User-Centred Approach to Create a Fall-Free Bathroom.

BACKGROUND: Fall hazards in bathroom spaces constitute one of the most critical issues in the daily lives of older adults. Bathroom falls are somewhat different and constrained in nature than those in other parts of a home environment. OBJECTIVES: This study aimed to adopt a user-centred approach to explore older adults' general bathroom needs, with a specific focus on showers and bathtubs as the designated activity area. METHODS: The authors employed an extended importance-performance analysis (IPA) with a mixed-method research design. Three hundred and eleven older adults participated in a face-to-face IPA questionnaire for the quantitative phase of the study. The authors gathered the qualitative data through open-ended questions from 59 older adults. RESULTS: The authors found positive correlation between older adults' attitudes towards an older-friendly bathroom and the potential for their bathrooms to be fall-free. The IPA calculations identify three key items with higher ratings in both importance and performance: The presence of appropriate artificial lighting, efficient mechanical ventilation and an accessible inside towel rail. Thematic analysis yields four themes: comfort, ease of access, error-proof design and emergency management. CONCLUSIONS: The IPA calculations and thematic analysis confirm that older adults' rankings of importance and performance and their corresponding priority levels within the overarching themes indicate the need for these aspects to perform well and justify ongoing investments. The study concludes that addressing fall prevention requires not only designing specific solutions but also utilising appropriate technology in bathing and toileting activities. IMPLICATIONS FOR PRACTICE: Practitioners in geriatric and gerontological nursing, design, architecture and health care can use the importance and performance priority levels of older adults to guide the development and implementation of fall-free bathroom design. Policymakers can leverage the insights from this research to inform guidelines and regulations related to building codes, accessibility standards and healthcare policies.

Continuous glucose monitoring as an objective measure of meal consumption in individuals with binge-spectrum eating disorders: A proof-of-concept study.

OBJECTIVE: Going extended periods of time without eating increases risk for binge eating and is a primary target of leading interventions for binge-spectrum eating disorders (B-EDs). However, existing treatments for B-EDs yield insufficient improvements in regular eating and subsequently, binge eating. These unsatisfactory clinical outcomes may result from limitations in assessment and promotion of regular eating in therapy. Detecting the absence of eating using passive sensing may improve clinical outcomes by facilitating more accurate monitoring of eating behaviours and powering just-in-time adaptive interventions. We developed an algorithm for detecting meal consumption (and extended periods without eating) using continuous glucose monitor (CGM) data and machine learning. METHOD: Adults with B-EDs (N = 22) wore CGMs and reported eating episodes on self-monitoring surveys for 2 weeks. Random forest models were run on CGM data to distinguish between eating and non-eating episodes. RESULTS: The optimal model distinguished eating and non-eating episodes with high accuracy (0.82), sensitivity (0.71), and specificity (0.94). CONCLUSIONS: These findings suggest that meal consumption and extended periods without eating can be detected from CGM data with high accuracy among individuals with B-EDs, which may improve clinical efforts to target dietary restriction and improve the field's understanding of its antecedents and consequences.

Design of Bio-Optical Transceiver for In Vivo Biomedical Sensor Applications.

This paper presents an enhanced version of our previously developed bio-optical transceiver, presenting a significant advancement in nanosensor technology. Using self-assembled polymers, this nanodevice is capable of electron detection while maintaining biocompatibility, an essential feature for in vivo medical biosensors. This enhancement finds significance in the field of infectious disease control, particularly in the early detection of respiratory viruses, including high-threat pathogens such as SARS-CoV-2. The proposed system harnesses bioluminescence by converting electric signaling to visible blue light, effectively opening the path of linking nano-sized mechanisms to larger-scale systems, thereby pushing the boundaries of in vivo biomedical sensing. The performance evaluation of our technology is analytical and is based on the use of Markov chains, through which we assess the bit error probability. The calculated improvements indicate that this technology qualifies as a forerunner in terms of supporting the communication needs of smaller, safer, and more efficient manufactured sensor technologies for in vivo medical applications.

Development of a Robust Sensor Calibration for a Commercially Available Rising Platemeter to Estimate Herbage Mass on Temperate Seminatural Pastures.

Rising platemeters are commonly used in Ireland and New Zealand for managing intensive pastures. To assess the applicability of a commercial rising platemeter operating with a microsonic sensor to estimate herbage mass with its own equation, the objectives were (i) to validate the original equation; (ii) to identify possible factors hampering its accuracy and precision; and (iii) to develop a new equation for heterogeneous swards. A comprehensive dataset (n = 1511) was compiled on the pastures of dairy farms. Compressed sward heights were measured by the rising platemeter. Herbage mass was harvested to determine reference herbage availability. The adequacy of estimating herbage mass was assessed using root mean squared error (RMSE) and mean bias. As the adequacy of the original equation was low, a new equation was developed using multiple regression models. The mean bias and the RMSE for the new equation were overall low with 201 kg dry matter/ha and 34.6%, but it tended to overestimate herbage availability at herbage mass < 500 kg dry matter/ha and underestimate it at >2500 kg dry matter/ha. Still, the newly developed equation for the microsonic sensor-based rising platemeter allows for accurate and precise estimation of available herbage mass on pastures.

Assessing the cognitive decline of people in the spectrum of AD by monitoring their activities of daily living in an IoT-enabled smart home environment: a cross-sectional pilot study.

Introduction: Assessing functional decline related to activities of daily living (ADLs) is deemed significant for the early diagnosis of dementia. As current assessment methods for ADLs often lack the ability to capture subtle changes, technology-based approaches are perceived as advantageous. Specifically, digital biomarkers are emerging, offering a promising avenue for research, as they allow unobtrusive and objective monitoring. Methods: A study was conducted with the involvement of 36 participants assigned to three known groups (Healthy Controls, participants with Subjective Cognitive Decline and participants with Mild Cognitive Impairment). Participants visited the CERTH-IT Smart Home, an environment that simulates a fully functional residence, and were asked to follow a protocol describing different ADL Tasks (namely Task 1 - Meal, Task 2 - Beverage and Task 3 - Snack Preparation). By utilizing data from fixed in-home sensors installed in the Smart Home, the identification of the performed Tasks and their derived features was explored through the developed CARL platform. Furthermore, differences between groups were investigated. Finally, overall feasibility and study satisfaction were evaluated. Results: The composition of the ADLs was attainable, and differentiation among the HC group compared to the SCD and the MCI groups considering the feature "Activity Duration" in Task 1 - Meal Preparation was possible, while no difference could be noted between the SCD and the MCI groups. Discussion: This ecologically valid study was determined as feasible, with participants expressing positive feedback. The findings additionally reinforce the interest and need to include people in preclinical stages of dementia in research to further evolve and develop clinically relevant digital biomarkers.

Offloading and adherence through technological advancements: Modern approaches for better foot care in diabetes.

OBJECTIVE: This manuscript aims to provide a review and synthesis of contemporary advancements in footwear, sensor technology for remote monitoring, and digital health, with a focus on improving offloading and measuring and enhancing adherence to offloading in diabetic foot care. METHODS: A narrative literature review was conducted by sourcing peer-reviewed articles, clinical studies, and technological innovations. This paper includes a review of various strategies, from specifically designed footwear, smart insoles and boots to using digital health interventions, which aim to offload plantar pressure and help prevent and manage wounds more effectively by improving the adherence to such offloading. RESULTS: In-house specially made footwear, sensor technologies remotely measuring pressure and weight-bearing activity, exemplified for example, through applications like smart insoles and SmartBoot, and other digital health technologies, show promise in improving offloading and changing patient behaviour towards improving adherence to offloading and facilitating personalised care. This paper introduces the concept of gamification and emotive visual indicators as novel methods to enhance patient engagement. It further discusses the transformative role of digital health technologies in the modern era. CONCLUSIONS: The integration of technology with footwear and offloading devices offers unparallelled opportunities for improving diabetic foot disease management not only through better offloading but also through improved adherence to offloading. These advancements allow healthcare providers to personalise treatment plans more effectively, thereby promising a major improvement in patient outcomes in diabetic foot ulcer healing and prevention.

Comparison of needle and conventional arthroscopy for visualisation of predefined anatomical structures of the knee joint: a feasibility study in human cadavers and patients.

BACKGROUND: In terms of the optics used for Knee arthroscopy, a large number of different endoscopes are currently available. However, the use of the 30° optics in knee arthroscopy has been established as the standard procedure for many years. As early as the 1990s, needle arthroscopy was occasionally used as a diagnostic tool. In addition to the development of conventional optics technology in terms of camera and resolution, needle arthroscopes are now available with chip-on-tip image sensor technology. To date, no study has compared the performance of this kind of needle arthroscopy versus standard arthroscopy in the clinical setting in terms of the visibility of anatomical landmarks. In this monocentric prospective feasibility study, our aim was to evaluate predefined anatomical landmarks of the knee joint using needle arthroscopy (0° optics) and conventional knee arthroscopy (30° optics) and compare their performance during knee surgery. METHODS: Examinations were performed on eight cadavers and seven patients who required elective knee arthroscopy. Two surgeons independently performed the examinations on these 15 knee joints, so that we were able to compare a total of 30 examinations. The focus was on the anatomical landmarks that could be visualized during a conventional diagnostic knee arthroscopy procedure. The quality of visibility was evaluated using a questionnaire. RESULTS: In summary, the average visibility for all the anatomic landmarks was rated 4.98/ 5 for the arthroscopy using 30° optics. For needle arthroscopy, an average score of 4.89/ 5 was obtained. Comparatively, the needle arthroscope showed slightly limited visibility of the retropatellar gliding surface in eight (4.5/ 5 vs. 5/ 5), medial rim of the patella in four (4.85/ 5 vs. 5/ 5), and suprapatellar recess in four (4.83/ 5 vs. 5/ 5) cases. Needle arthroscopy was slightly better at visualizing the posterior horn of the medial meniscus in four knee joints (4.9/ 5 vs. 4.85/ 5). CONCLUSION: Needle arthroscopy is a promising technology with advantages in terms of minimally invasive access and good visibility of anatomical landmarks. However, it also highlights some limitations, particularly in cases with challenging anatomy or the need for a wide field of view.

An automated, data-driven approach to children's social dynamics in space and time.

Most children first enter social groups of peers in preschool. In this context, children use movement as a social tool, resulting in distinctive proximity patterns in space and synchrony with others over time. However, the social implications of children's movements with peers in space and time are difficult to determine due to the difficulty of acquiring reliable data during natural interactions. In this article, we review research demonstrating that proximity and synchrony are important indicators of affiliation among preschoolers and highlight challenges in this line of research. We then argue for the advantages of using wearable sensor technology and machine learning analytics to quantify social movement. This technological and analytical advancement provides an unprecedented view of complex social interactions among preschoolers in natural settings, and can help integrate young children's movements with others in space and time into a coherent interaction framework.

Construction of teaching system of public art major using CMOS image sensor technology.

The traditional public art education model has many drawbacks. After all, this teaching model is the most common teaching model. Most colleges and universities still rely on the traditional teaching mode. This teaching mode is not attractive, boring, and low cost, so the popularity rate is high. The digital interactive design with multimedia courseware as the main body has played a great role in promoting the teaching of public art education in colleges and universities. In this paper, when teachers use multimedia courseware for teaching, because the computer cannot process physical signals, part of the hardware is a converter that converts physical signals such as light and pictures received by the CMOS sensor into digital signals and inputs them to digital signals and analog signals. Through the converter, the two-way transmission of teacher and student information in the teaching process of public art majors from the perspective of diversified public art was realized. At the same time, the information elements of sound, image and text were integrated into the interactive works of public art majors from the perspective of diverse public art, so as to stimulate students' interest in public art learning. Combined with the application of CMOS image sensor technology in diversified public art teaching, a questionnaire survey was conducted to explore the satisfaction of students in public art education and teaching of colleges and universities by CMOS image sensor technology, 50% of the students agreed with the proposal in this article. In this paper, the diversified public art teaching based on CMOS image sensor technology was derived outside the classroom, and the teaching was related to students' life, so as to improve the artistic atmosphere of the campus and truly improve the teaching effect of public art education in colleges and universities.

Advances in the Use of Conducting Polymers for Healthcare Monitoring.

Conducting polymers (CPs) are an innovative class of materials recognized for their high flexibility and biocompatibility, making them an ideal choice for health monitoring applications that require flexibility. They are active in their design. Advances in fabrication technology allow the incorporation of CPs at various levels, by combining diverse CPs monomers with metal particles, 2D materials, carbon nanomaterials, and copolymers through the process of polymerization and mixing. This method produces materials with unique physicochemical properties and is highly customizable. In particular, the development of CPs with expanded surface area and high conductivity has significantly improved the performance of the sensors, providing high sensitivity and flexibility and expanding the range of available options. However, due to the morphological diversity of new materials and thus the variety of characteristics that can be synthesized by combining CPs and other types of functionalities, choosing the right combination for a sensor application is difficult but becomes important. This review focuses on classifying the role of CP and highlights recent advances in sensor design, especially in the field of healthcare monitoring. It also synthesizes the sensing mechanisms and evaluates the performance of CPs on electrochemical surfaces and in the sensor design. Furthermore, the applications that can be revolutionized by CPs will be discussed in detail.

Integration of non-targeted/targeted metabolomics and electronic sensor technology reveals the chemical and sensor variation in 12 representative yellow teas.

Yellow tea is a lightly fermented tea with unique sensory qualities and health benefits. However, chemical composition and sensory quality of yellow tea products have rarely been studied. 12 representative yellow teas, which were basically covered the main products of yellow tea, were chosen in this study. Combined analysis of non-targeted/targeted metabolomics and electronic sensor technologies (E-eye, E-nose, E-tongue) revealed the chemical and sensor variation. The results showed that yellow big tea differed greatly from yellow bud teas and yellow little teas, but yellow bud teas could not be effectively distinguished from yellow little teas based on chemical constituents and electronic sensory characteristics. Sensor variation of yellow teas might be attributed to some compounds related to bitterness and aftertaste-bitterness (4'-dehydroxylated gallocatechin-3-O-gallate, dehydrotheasinensin C, myricitin 3-O-galactoside, phloroglucinol), aftertaste-astringency (methyl gallate, 1,5-digalloylglucose, 2,6-digalloylglucose), and sweetness (maltotriose). This study provided a comprehensive understanding of yellow tea on chemical composition and sensory quality.

ConGPS: A Smart Container Positioning System Using Inertial Sensor and Electronic Map with Infrequent GPS.

Real-time global positioning is important for container-based logistics. However, a challenge in real-time global positioning arises from the frequency of both global positioning system (GPS) calls and GPS-denied environments during transportation. This paper proposes a novel system named ConGPS that integrates both inertial sensor and electronic map data. ConGPS estimates the speed and heading direction of a moving container based on the inertial sensor data, the container trajectory, and the speed limit information provided by an electronic map. The directional information from magnetometers, coupled with map-matching algorithms, is employed to compute container trajectories and current positions. ConGPS significantly reduces the frequency of GPS calls required to maintain an accurate current position. To evaluate the accuracy of the system, 280 min of driving data, covering a distance of 360 km, are collected. The results demonstrate that ConGPS can maintain positioning accuracy within a GPS-call interval of 15 min, even if using low-cost inertial sensors in GPS-denied environments.

Using technology to encourage ward-culture change and provide individualised bladder care.

When discussing continence care in an acute hospital setting, it can be viewed as a negative subject that is a thinly veiled jab at overstretched nurses. This article takes a fresh and holistic look at continence care, identifying factors that could be causing poor care and how technology could support a change in care. This article includes suggestions on how the data collected could be used to deliver the person-centred care outcomes that may be lacking in some environments, something that one of the authors (DP) has experienced first hand. This article describes the results of a recent trial at Ysbyty Cwm Cynon (Canon Valley Hospital), NHS Wales, which looked at how continence care technology could support positive care outcomes.

Data Anomaly Detection for Structural Health Monitoring Based on a Convolutional Neural Network.

Structural health monitoring (SHM) has been extensively utilized in civil infrastructures for several decades. The status of civil constructions is monitored in real time using a wide variety of sensors; however, determining the true state of a structure can be difficult due to the presence of abnormalities in the acquired data. Extreme weather, faulty sensors, and structural damage are common causes of these abnormalities. For civil structure monitoring to be successful, abnormalities must be detected quickly. In addition, one form of abnormality generally predominates the SHM data, which might be a problem for civil infrastructure data. The current state of anomaly detection is severely hampered by this imbalance. Even cutting-edge damage diagnostic methods are useless without proper data-cleansing processes. In order to solve this problem, this study suggests a hyper-parameter-tuned convolutional neural network (CNN) for multiclass unbalanced anomaly detection. A multiclass time series of anomaly data from a real-world cable-stayed bridge is used to test the 1D CNN model, and the dataset is balanced by supplementing the data as necessary. An overall accuracy of 97.6% was achieved by balancing the database using data augmentation to enlarge the dataset, as shown in the research.