Efficient Permeable Monolithic Hybrid Tribo-Piezo-Electromagnetic Nanogenerator Based on Topological-Insulator-Composite.

Escalating energy demands of self-independent on-skin/wearable electronics impose challenges on corresponding power sources to offer greater power density, permeability, and stretchability. Here, a high-efficient breathable and stretchable monolithic hybrid triboelectric-piezoelectric-electromagnetic nanogenerator-based electronic skin (TPEG-skin) is reported via sandwiching a liquid metal mesh with two-layer topological insulator-piezoelectric polymer composite nanofibers. TPEG-skin concurrently extracts biomechanical energy (from body motions) and electromagnetic radiations (from adjacent appliances), operating as epidermal power sources and whole-body self-powered sensors. Topological insulators with conductive surface states supply notably enhanced triboelectric and piezoelectric effects, endowing TPEG-skin with a 288 V output voltage (10 N, 4 Hz), ∼3 times that of state-of-the-art devices. Liquid metal meshes serve as breathable electrodes and extract ambient electromagnetic pollution (±60 V, ±1.6 µA cm-2). TPEG-skin implements self-powered physiological and body motion monitoring and system-level human-machine interactions. This study provides compatible energy strategies for on-skin/wearable electronics with high power density, monolithic device integration, and multifunctionality.

Wearable defibrillator to improve accuracy in selecting candidates to implantable defibrillator: A real-world experience.

AIMS: The indication for implantable cardioverter defibrillator (ICD) for sudden cardiac death (SCD) prevention relies mostly on left ventricular ejection fraction (LVEF) ≤ 35%. The use of a wearable cardioverter defibrillator (WCD) in the case of dynamic alterations of LVEF may help avoid an improper early ICD implant when a favourable evolution in the post-acute phase is observed and may help reduce costs. METHODS: This parallel cohort retrospective study included patients with heart failure with reduced ejection fraction (HFrEF) at high risk of arrhythmias recruited in the acute phase and divided into an early ICD cohort and a WCD cohort for primary prevention during the waiting period established by European Society of Cardiology guidelines. RESULTS: A total of 41 consecutive patients were enrolled: 26 in the WCD group and 15 in the early ICD group. Age, LVEF at baseline, causes of HFrEF and drug therapy in the two cohorts were similar. During the waiting period after the inclusion, three patients (11.5%) in the WCD cohort and four (26.7%) in the early ICD cohort developed relevant ventricular arrhythmias (P = 0.22); none of them had subsequent LVEF recovery. At the end of the waiting period, 13 patients (50%) in the WCD group and 7 (46.7%) in the early ICD group experienced LVEF recovery (P = 0.84). The average cost per patient at the end of the waiting period was €23 934 in the early ICD cohort versus €19 167 in the WCD cohort (-19.9%). This cost savings from WCD use appears even higher when projected over a 10 year period (-41.2%). CONCLUSIONS: WCD may represent a cost-effective strategy to more accurately select candidates for the primary prevention ICD implant among high-risk patients with HFrEF. ICD use provides effective protection from SCD and reduces costs compared with an extensive early ICD implant.

An Antibacterial, Highly Sensitive Strain Sensor Based on an Anionic Copolymer Interpenetrating with κ-Carrageenan.

Polysaccharide-based hydrogels are suitable for use in the field of flexible bioelectronics due to their benign mechanical properties and biocompatibility. However, the preparation of hydrogel sensors with high performance without affecting their physicochemical properties (e.g., flexibility, toughness, self-healing, and antibacterial activity) remains a challenge and needs to be solved. Herein, a metal ion cross-linking reinforced, double network hydrogel was formed from a 2-acrylamide-2-methylpropanesulfonic acid (AMPS) copolymer interpenetrating κ-carrageenan (CAR), followed by immersing the gel in a Cu2+ ion solution to obtain an antibacterial CAR/P(AM-co-AMPS)-Cu2+ conductive hydrogel. LiCl was added as the electrolyte. The presence of the LiCl electrolyte and sulfonated molecular chain units not only gives the hydrogel good electrical conductivity (conductivity up to 2.68 S/m) but also improves the sensitivity of the hydrogel as a stress-strain sensor, with a hydrogel sensitivity GF of up to 3.76 in the 20%-100% strain range and response time of up to 280 ms. The CAR double-helical structure and sol-gel properties and the interaction of multiple noncovalent bonds between polymers provide the hydrogel with excellent self-healing, with a self-healing efficiency of 68%. In addition, the electrostatic interaction of Cu2+ with Escherichia coli cells can inhibit their growth, exhibiting good antibacterial properties with an inhibition circle diameter of 20.5 mm. This work could provide an effective strategy for antibacterial multifunctional CAR-based bionic sensors.

Continuous Monitoring by a Wearable Sensor Did Not Enhance Discharge Decision-Making in an Acute Admission Ward: Results of a Randomized Controlled Trial.

Adding continuous monitoring to usual care at an acute admission ward did not have an effect on the proportion of patients safely discharged. Implementation challenges of continuous monitoring may have contributed to the lack of effect observed.

A Retrospective Observational Study of Continuous Wireless Vital Sign Monitoring via a Medical Grade Wearable Device on Hospitalized Floor Patients.

Background: Continuous vital sign monitoring via wearable technology, combined with algorithm-based notifications, has been utilized for early detection of patient deterioration. In this retrospective observational study, we summarize a large-scale implementation of a continuous monitoring system in medical-surgical units of two hospitals over the course of fifteen (15) months. Methods: An FDA-cleared wireless monitoring device (BioButton®, BioIntelliSense Inc., Golden, CO, USA), was placed on each patient upon admission. The wearable device measures heart rate and respiratory rate at rest, skin temperature, and patient activity levels. High-frequency data (up to 1440 measurements per day) are transmitted to display in exception management software (BioDashboard™, version 2.9, BioIntelliSense Inc.). Algorithmic and rules-based notifications are triggered based on clinical and statistical trending criteria. We present (i) agreement of device readings with bedside charted measurements, (ii) the frequency of notifications, (iii) the occurrence of notifications prior to clinical deterioration events, and (iv) impact on clinical management, including early data on length of stay (LOS). Results: In total, 11,977 patient encounters were monitored at two sites. Bias ±95% limits of agreement were 1.8 ± 12.5 for HR and 0.4 ± 8.0 for RR. The rates of notifications were 0.97 and 0.65 per patient-day at Sites 1 and 2, respectively. Among clinical deteriorations, 73% (66%) had at least one notification within 24 h prior at Site 1 (Site 2). At Site 1, there were 114 cases for which a notification led to a new or changed physician's order. LOS in the first unit monitored by the system exhibited a decreasing trend from 3.07 days to 2.75 days over 12 months. Conclusions: Wearable continuous vital sign monitoring with the BioIntelliSense BioButton® system enables early detection of clinical deterioration.

Validity of the estimated angular information obtained using an inertial motion capture system during standing trunk forward and backward bending.

BACKGROUND: Bending the trunk forward and backward while standing are common daily activities and can have various patterns. However, any dysfunction in these movements can considerably affect daily living activities. Consequently, a comprehensive evaluation of spinal motion during these activities and precise identification of any movement abnormalities are important to facilitate an effective rehabilitation. In recent years, with the development of measurement technology, the evaluation of movement patterns using an inertial motion capture system (motion sensor) has become easy. However, the accuracy of estimated angular information obtained via motion sensor measurements can be affected by angular velocity. This study aimed to compare the validity of estimated angular information obtained by assessing standing trunk forward and backward bending at different movement speeds using a motion sensor with a three-dimensional motion analysis system. METHODS: The current study included 12 healthy older men. A three-dimensional motion analysis system and a motion sensor were used for measurement. The participants performed standing trunk forward and backward bending at comfortable and maximum speeds, and five sensors were attached to their spine. Statistical analysis was performed using the paired t-test, intraclass correlation coefficient, mean absolute error, and multiple correlation coefficient. RESULTS: Results showed that the estimated angular information obtained using each motion sensor was not affected by angular velocity and had a high validity. CONCLUSIONS: Therefore, the angular velocity in this study can be applied clinically for an objective evaluation in rehabilitation.

Organic Thermoelectric Materials for Wearable Electronic Devices.

Wearable electronic devices have emerged as a pivotal technology in healthcare and artificial intelligence robots. Among the materials that are employed in wearable electronic devices, organic thermoelectric materials possess great application potential due to their advantages such as flexibility, easy processing ability, no working noise, being self-powered, applicable in a wide range of scenarios, etc. However, compared with classic conductive materials and inorganic thermoelectric materials, the research on organic thermoelectric materials is still insufficient. In order to improve our understanding of the potential of organic thermoelectric materials in wearable electronic devices, this paper reviews the types of organic thermoelectric materials and composites, their assembly strategies, and their potential applications in wearable electronic devices. This review aims to guide new researchers and offer strategic insights into wearable electronic device development.

Fabricated advanced textile for personal thermal management, intelligent health monitoring and energy harvesting.

Fabrics are soft against the skin, flexible, easily accessible and able to wick away perspiration, to some extent for local private thermal management. In this review, we classify smart fabrics as passive thermal management fabrics and active thermal management fabrics based on the availability of outside energy consumption in the manipulation of heat generation and dissipation from the human body. The mechanism and research status of various thermal management fabrics are introduced in detail, and the article also analyses the advantages and disadvantages of various smart thermal management fabrics, achieving a better and more comprehensive comprehension of the current state of research on smart thermal management fabrics, which is quite an important reference guide for our future research. In addition, with the progress of science and technology, the social demand for fabrics has shifted from keeping warm to improving health and quality of life. E-textiles have potential value in areas such as remote health monitoring and life signal detection. New e-textiles are designed to mimic the skin, sense biological data and transmit information. At the same time, the ultra-moisturizing properties of the fabric's thermal management allow for applications beyond just the human body to energy. E-textiles hold great promise for energy harvesting and storage. The article also introduces the application of smart fabrics in life forms and energy harvesting. By combining electronic technology with textiles, e-textiles can be manufactured to promote human well-being and quality of life. Although smart textiles are equipped with more intelligent features, wearing comfort must be the first thing to be ensured in the multi-directional application of textiles. Eventually, we discuss the dares and prospects of smart thermal management fabric research.

Advancing nursing education through wearable electronic devices: A scoping review.

AIM: To examine the incorporation of wearable electronic devices in the education of undergraduate nursing students. BACKGROUND: The advancement of technology has influenced nursing education and will continue to do so in the future. Wearable technologies are electronic devices that can be worn as an accessory and expand the possibilities in nursing education with increased engagement in the learning process. DESIGN: A scoping review was conducted following JBI and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. METHODS: The search was performed on August 25th, 2023, in the databases: MEDLINE via PubMed, ISI Web of Science, ERIC, EBSCOhost CINAHL, EBSCOhost Academic Search Premier, EBSCOhost Health Source Nursing, EMBASE, Scopus, BVShost LILACS and ProQuest. Literature that examined the application of wearable electronic devices in the education of undergraduate nursing students was included. RESULTS: This scoping review study included a total of 21 records published between 2014 and 2023. The analysis showed that smart glasses are the most common wearable electronic device used in nursing education, followed by smartwatches. The devices provide an opportunity for competencies development, especially when applied in the simulation environment, such as physical examination and medication administration. Wearable technologies are potentially useful and feasible as learning tools in nursing education, increasing nursing students' motivation, confidence and satisfaction. CONCLUSIONS: In the technological revolution, educators must consider the potential of innovative teaching strategies, such as wearable electronic devices, to advance nursing education. Wearables can contribute to developing competencies required for the professionalism of undergraduate nursing students.

Continuous Assessment of Active-Duty Army Special Operations and Reconnaissance Marines Using Digital Devices and Custom Software: The Digital Comprehensive Operator Readiness Assessment (DcORA) Study.

BACKGROUND: Continuous exposure to extreme and chronic stress from uncontrollable events has been linked to increased psychological and physiological reactivity. Prolonged, frequent deployments may test coping skills over time, ultimately rendering Servicemembers vulnerable to mental health problems and suicide. This study develops a methodology for accurately collecting holistic health measures from Servicemembers using digital tools, including custom-built phone software and body-worn sensors. METHODS: The secure research platform and mobile app continuously collect multiple health measures and, after data analysis, deliver continuously updated summary data back to the Servicemember. This system provides novel insights into the relationships between the measures while helping individuals track their progress toward self-established goals. Participants were given an iPhone (including the study app) and an Apple Watch. Participants tracked their data for more than 6 months and responded to baseline, daily, and weekly questions and assessments. Physiologic, psychologic, and cognitive assessment data across the Preservation of the Force and Family program (POTFF) domains were collected, displayed to the individual, and analyzed in aggregate. RESULTS: When coupled with custom-built software, this hardware can be elevated from a fitness tracker to a user-facing health monitoring, educational, and delivery system. CONCLUSION: This wearable system measured vital factors associated with the health and human performance of Servicemembers. In real-time, it engaged Servicemembers in health and human performance optimization practices to achieve a goal of prevention of physical or mental injury.

Highly Sensitive Flexible Capacitive Pressure Sensor Based on a Multicross-Linked Dual-Network Ionic Hydrogel for Blood Pressure Monitoring Applications.

Flexible capacitive pressure sensors based on ionic hydrogels (IHs) have garnered significant attention in the field of wearable technology. However, the vulnerability of traditional single-network hydrogels to mechanical damage and the complexity associated with preparing double-network hydrogels present challenges in developing a highly sensitive, easily prepared, and durable IH-based flexible capacitive pressure sensor. This study introduces a novel multicross-linked dual-network IH achieved through the physical and chemical cross-linking of polymers polyvinyl alcohol (PVA) and chitosan (CS), ionic solution H3PO4, and cross-linking agent gum arabic. Flexible capacitive pressure sensors, characterized by high sensitivity and a broad pressure range, are fabricated by employing mesh as templates to design cut-corner cube microstructures with high uniformity and controllability on the IHs. The sensor exhibits high sensitivity across a wide pressure range (0-290 kPa) and with excellent features such as high resolution (∼1.3 Pa), fast response-recovery time (∼11 ms), and repeatable compression stability at 25 kPa (>2000 cycles). The IHs as a dielectric layer demonstrate long-term water retention properties, enabling exposure to air for up to 100 days. Additionally, the developed sensor shows the ability to accurately measure the pulse wave within the small pressure range. By combining the pulse wave acquired by the sensor with a trained neural network model, we achieve successful blood pressure (BP) prediction, meeting the standards set by the Association for the Advancement of Medical Instrumentation and the British Hypertension Society. Ultimately, the sensor proposed in this study holds promising prospects for broad applications in high-precision wearable medical electronic devices.

Digital Biomarkers for the Assessment of Non-Cognitive Symptoms in Patients with Dementia with Lewy Bodies: A Systematic Review.

Background: Portable digital health technologies (DHTs) could help evaluate non-cognitive symptoms, but evidence to support their use in patients with dementia with Lewy bodies (DLB) is uncertain. Objective: 1) To describe portable or wearable DHTs used to obtain digital biomarkers in patients with DLB, 2) to assess the digital biomarkers' ability to evaluate non-cognitive symptoms, and 3) to assess the feasibility of applying digital biomarkers in patients with DLB. Methods: We systematically searched databases MEDLINE, Embase, and Web of Science from inception through February 28, 2023. Studies assessing digital biomarkers obtained by portable or wearable DHTs and related to non-cognitive symptoms were eligible if including patients with DLB. The quality of studies was assessed using a modified check list based on the NIH Quality assessment tool for Observational Cohort and Cross-sectional Studies. A narrative synthesis of data was carried out. Results: We screened 4,295 records and included 20 studies. Seventeen different DHTs were identified for assessment of most non-cognitive symptoms related to DLB. No thorough validation of digital biomarkers for measurement of non-cognitive symptoms in DLB was reported. Studies did not report on aspects of feasibility in a systematic way. Conclusions: Knowledge about feasibility and validity of individual digital biomarkers remains extremely limited. Study heterogeneity is a barrier for establishing a broad evidence base for application of digital biomarkers in DLB. Researchers should conform to recommended standards for systematic evaluation of digital biomarkers.

"An excellent servant but a terrible master": Understanding the value of wearables for self-management in people with cystic fibrosis and their healthcare providers - A qualitative study.

BACKGROUND: Wearables hold potential to improve chronic disease self-management in conditions like cystic fibrosis (CF) through remote monitoring, early detection of illness and motivation. Little is known about the acceptability and sustainability of integrating wearables into routine care from the perspectives of people with CF (pwCF) and their treating clinicians. METHODS: A cross-sectional qualitative study involving semi-structured interviews with adult pwCF and focus groups comprising members of a CF multidisciplinary team (MDT) were conducted at a specialist CF centre in Australia. A phenomenological orientation underpinned the study. Inductive thematic analysis was performed using the Framework method. The study adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist. RESULTS: Nine pwCF and eight members of a CF MDT, representing six clinical disciplines, participated in the study. Eight themes were inductively generated from the data, of which four were identified from each group. PwCF valued wearables for providing real-time data to motivate healthy behaviours and support shared goal-setting with healthcare providers. Wearables did not influence adherence to CF-specific self-management practices and had some hardware limitations. Members of the CF MDT recognised potential benefits of remote monitoring and shared goal-setting, but advised caution regarding data accuracy, generating patient anxiety in certain personality traits, and lack of evidence supporting use in CF self-management. CONCLUSIONS: Perspectives on integrating wearables into CF care were cautiously optimistic, with emerging risks related to patient anxiety and lack of evidence moderating acceptance.

Structural design of light-emitting fibers and fabrics for wearable and smart devices.

Flexible light-emitting fibers and fabrics serve to bridge human-machine interactions. The desire for practical applications and the commercialization of flexible light-emitting fibers has accelerated structural progress and improvements. This review focuses on the structural design of light-emitting fibers and fabrics, starting with a summary of design principles, emission mechanisms, and structural evolution of coaxial structured light-emitting fibers. Subsequently, we explore recent advances in the helical structure design strategies that boost the mechanical sensitivity of light-emitting fibers. Following that, we analyze continuous preparation processes and the development of large-area intelligent light-emitting fabrics based on interwoven structures. Examples based on stiff and rigid inorganic-based light-emitting diodes integrated into flexible systems are also presented. Finally, we discuss the current challenges and future opportunities for light-emitting applications in the field of wearable and smart devices.

Ultrafast Polymerization of a Self-Adhesive and Strain Sensitive Hydrogel-Based Flexible Sensor for Human Motion Monitoring and Handwriting Recognition.

Hydrogel-based flexible electronic devices have great potential in human motion monitoring, electronic skins, and human-computer interaction applications; hence, the efficient preparation of highly sensitive hydrogel-based flexible sensors is important. In the present work, the ultrafast polymerization of a hydrogel (1-3 min) was achieved by constructing a tannic acid (TA)-Fe3+ dynamic redox system, which endowed the hydrogel with good adhesion performance (the adhesion strength in wood was 17.646 kPa). In addition, the uniform dispersal ensured by incorporating polydopamine-decorated polypyrrole (PPy@PDA) into the hydrogel matrix significantly improved the hydrogel's stretching ability (575.082%). The as-prepared PAM/CS/PPy@PDA/TA hydrogel-based flexible sensor had a high-fidelity low detection limit (strain = 1%), high sensitivity at small strains (GF = 5.311 at strain = 0-8%), and fast response time (0.33 s) and recovery time (0.25 s), and it was reliably applied to accurate human motion monitoring and handwriting recognition. The PAM/CS/PPy@PDA/TA hydrogel opens new horizons for wearable electronic devices, electronic skins, and human-computer interaction applications.

Using Artificial Intelligence to Improve the Accuracy of a Wrist-Worn, Noninvasive Glucose Monitor: A Pilot Study.

BACKGROUND: Self-monitoring of glucose is important to the successful management of diabetes; however, existing monitoring methods require a degree of invasive measurement which can be unpleasant for users. This study investigates the accuracy of a noninvasive glucose monitoring system that analyses spectral variations in microwave signals. METHODS: An open-label, pilot design study was conducted with four cohorts (N = 5/cohort). In each session, a dial-resonating sensor (DRS) attached to the wrist automatically collected data every 60 seconds, with a novel artificial intelligence (AI) model converting signal resonance output to a glucose prediction. Plasma glucose was measured in venous blood samples every 5 minutes for Cohorts 1 to 3 and every 10 minutes for Cohort 4. Accuracy was evaluated by calculating the mean absolute relative difference (MARD) between the DRS and plasma glucose values. RESULTS: Accurate plasma glucose predictions were obtained across all four cohorts using a random sampling procedure applied to the full four-cohort data set, with an average MARD of 10.3%. A statistical analysis demonstrates the quality of these predictions, with a surveillance error grid (SEG) plot indicating no data pairs falling into the high-risk zones. CONCLUSIONS: These findings show that MARD values approaching accuracies comparable to current commercial alternatives can be obtained from a multiparticipant pilot study with the application of AI. Microwave biosensors and AI models show promise for improving the accuracy and convenience of glucose monitoring systems for people with diabetes.

Electronic Skin for Health Monitoring Systems: Properties, Functions, and Applications.

Electronic skin (e-skin), a skin-like wearable electronic device, holds great promise in the fields of telemedicine and personalized healthcare because of its good flexibility, biocompatibility, skin conformability, and sensing performance. E-skin can monitor various health indicators of the human body in real time and over the long term, including physical indicators (exercise, respiration, blood pressure, etc.) and chemical indicators (saliva, sweat, urine, etc.). In recent years, the development of various materials, analysis, and manufacturing technologies has promoted significant development of e-skin, laying the foundation for the application of next-generation wearable medical technologies and devices. Herein, the properties required for e-skin health monitoring devices to achieve long-term and precise monitoring and summarize several detectable indicators in the health monitoring field are discussed. Subsequently, the applications of integrated e-skin health monitoring systems are reviewed. Finally, current challenges and future development directions in this field are discussed. This review is expected to generate great interest and inspiration for the development and improvement of e-skin and health monitoring systems.

[Prognostic value of electrical bioimpedance measured with a portable and wireless device in acute heart failure]. / Valor pronóstico de la bioimpedancia eléctrica medida con el dispositivo IVOL en la insuficiencia cardiaca aguda.

INTRODUCTION AND OBJECTIVES: The current evaluation of acute heart failure (HF) does not allow an adequate prediction of its evolution. The electrical bioimpedance (BI) allows knowing the state of blood volume, until now only with fixed equipment. We have developed and validated a portable and wireless device to measure BI at the ankle (IVOL). The objective of the study is to know the long-term prognostic value of the point measurement of BI with IVOL in patients with acute HF. METHODS: A prospective cohort study of unselected patients admitted for acute HF in a tertiary hospital. The association between BI and different clinical, analytical and echocardiographic variables on admission and clinical evolution were analyzed. RESULTS: 76 patients were included (mean age 66.1 years, 71.1% men, 68.4% hypertensive, 34.2% diabetic, mean NT-ProBNP: 7,103 pg / ml). Of these, 52.6% with non-preserved left ventricular ejection fraction (LVEF) (<50%) and 56.6% with right ventricular (RV) dysfunction. 26.3% died during a mean follow-up of 35.8 months. Survival in patients with BI≤21,8Ω was lower, globally and in the subgroups of patients without preserved LVEF and with RV dysfunction, P<.008). In the multivariate analysis, a BI≥21.8Ω was an independent survival factor (HR: 0.242; 95% CI: 0.86-0.681; P=.007). CONCLUSIONS: BI values measured with IVOL may be an independent predictor of long-term mortality in patients hospitalized for acute HF. This prognostic value is maintained in patients without preserved LVEF function and with RV dysfunction.

Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association.

Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.

Neural Network-Based Prediction of Perceived Sleep Quality Through Wearable Device Data.

BACKGROUND: This study focuses on the development of a neural network model to predict perceived sleep quality using data from wearable devices. We collected various physiological metrics from 18 participants over four weeks, including heart rate, physical activity, and both device-measured and self-reported sleep quality. OBJECTIVES: The primary objective was to correlate wearable device data with subjective sleep quality perceptions. METHODS: Our approach used data processing, feature engineering, and optimizing a Multi-Layer Perceptron classifier. RESULTS: Despite comprehensive data analysis and model experimentation, the predictive accuracy for perceived sleep quality was moderate (59%), highlighting the complexities in accurately quantifying subjective sleep experiences through wearable data. Applying a tolerance of 1 grade (on a scale from 1-5), increased accuracy to 92%. DISCUSSION: More in-depth analysis is required to fully comprehend how wearables and artificial intelligence might assist in understanding sleep behavior.