Search Results (1,586)

Wearable fitness trackers are increasingly popular for monitoring health-related metrics, yet their ownership patterns across socioeconomic, demographic, and gender-diverse populations remain underexplored at a population level. This study utilized data from the NIH All of Us Research Program to investigate how area-level socioeconomic status, race, and gender identity influence wearable device ownership. Methods. Data were analyzed from 633,547 participants from the All of Us Dataset. Fitbit ownership was modeled with four binary logistic regression models: a demographics-only model, a ZIP3-level socioeconomic indicators model, and a combined model incorporating four demographic × median household income interactions (race, gender, age, and Hispanic/Latino ethnicity), and an intersectional model adding a race x gender interaction. Continuous socioeconomic predictors were rescaled for interpretability (median income per USD 10,000; area-level fractions per 10 percentage points). Socioeconomic-adjusted models were restricted to 606,414 participants with available ZIP3-linked data. Fitbit ownership was defined as having a Fitbit record in the database. Results. Fitbit ownership was observed in 8.34% of the study population. Logistic regression analyses revealed significant demographic disparities: female participants and gender-diverse identities had significantly higher odds of ownership than males (OR = 1.25–2.2). Black or African American (OR = 0.38) and NHPI/MENA (OR = 0.82) participants had lower odds compared to White participants, while Asian (OR = 1.13), more than one race (OR = 1.25), and Hispanic or Latino (OR = 1.25) participants had higher odds. Each USD 10,000 increase in ZIP3 median household income was associated with 12.5% lower odds of ownership overall (OR = 0.875), but this gradient varied significantly by race. For Black or African American participants, the relationship reversed direction (OR = 1.08 per $10,000). A race x gender interaction further showed that female ownership was not uniform across race, being the largest among Black or African American participants (OR = 2.27) and reversed among Asian participants (OR = 0.87). ZIP3 socioeconomic data were structurally unavailable for all American Indian or Alaska Native participants due to the All of Us program’s small-population ZIP3 aggregation policy, precluding their inclusion in socioeconomic-adjusted models. Conclusions. This analysis demonstrates significant gender, racial, and socioeconomic disparities in wearable fitness tracker ownership, showing significantly higher device usage among females and gender-diverse individuals, but lower usage among certain racial groups and a seemingly contradictory negative ownership rates among higher socioeconomic levels. Ownership patterns nonetheless appear more equitable than in consumer cohorts, likely reflecting the device-provision programs undertaken by the NIH. Full article

Calving in cattle is divided into two main stages: dilation and expulsion, during which timely assistance can reduce reproductive losses. This study presents a smart ear-mounted device as a proof-of-concept heart-rate monitoring platform for calving-stage assessment in dairy cows. The prototype preserves the form factor of a conventional ear tag and integrates a MAX30105 optical sensor, an Arduino Nano microcontroller, local micro-SD storage, and an autonomous power supply. Field tests were conducted in Holstein cows at Rancho El Pinar, Trancoso, Zacatecas, Mexico. Heart rate was recorded every 10 min and grouped according to physiological stages around calving. The results showed distinctive heart rate patterns, with higher values during dilation and lower values after delivery, supporting the use of ear-mounted heart rate monitoring as a non-invasive descriptive marker of stage-related physiological variation around labor. An average temperature profile from 70 h before to 50 h after calving was also incorporated as complementary descriptive evidence of peripartum physiological variation. Because heart rate is a non-specific physiological variable affected by stress, movement, ambient temperature, feeding, health status, and sensor contact, the present study does not propose HR as a stand-alone or definitive predictor of calving or dystocia. Instead, the device is presented as a proof-of-concept platform for future multi-indicator monitoring and validation studies. The proposed system is presented as a proof-of-concept invention that combines a practical wearable format with physiological monitoring and a conceptual decision-support logic that remains to be validated and integrated with additional indicators before any field implementation. Full article

With the rapid advancement of wearable technologies, high-performance flexible sensors have garnered significant research interest. This study presents a PAM-5 hydrogel characterized by exceptional tensile strain (425%), superior compressive modulus (325 kPa), and notable ionic conductivity (1.1 S/m), serving as a robust mechanical framework and electrical foundation for developing advanced sensors. The PNP-5 integrated hydrogel sensor fabricated from this material demonstrates an extensive sensing range (2–53 kPa), remarkable sensitivity, and rapid response time (~321 ms), with its outstanding performance attributed to the synergistic structural design. Furthermore, the sensor exhibits excellent durability, maintaining consistent voltage output (~6.5 mV) across 1000 compression cycles, confirming its long-term operational stability. Through real-time monitoring of physiological signals and biomechanical movements including finger bending, respiration, and grasping, combined with spatial pressure mapping experiments using a 5 × 5 array touchpad, the device’s potential applications in wearable sensing platforms and human–machine interface systems are effectively demonstrated. This self-powered hydrogel sensor not only advances the performance metrics of flexible electronic devices but also establishes a solid experimental basis for future development of intelligent materials in health monitoring and interactive technologies. Full article

Background: The management of diabetic foot disease and knee osteoarthritis (OA) with smart orthotics holds significant importance during the early stages of these conditions, given their potential consequences, including functional impairment, chronic pain, and economic burden. Real-time monitoring of plantar foot pressure enables early detection of abnormal force distribution and gait biomechanics, allowing for the redirection of forces away from affected ulcers or arthritic joints. This is the first systematic review to synthesise clinical evidence for smart orthotics technology with real-time plantar pressure sensor biofeedback across both diabetic foot ulcer prevention and knee osteoarthritis management simultaneously. A search of the PROSPERO register confirmed no existing registration covers this specific combination. Objectives: To examine the clinical evidence for the use of standard and smart orthotics in the prevention and management of diabetic foot ulcers (DFUs) and knee OA, and to evaluate their impact on plantar pressure redistribution, ulcer recurrence, pain, biomechanics, and economic burden. Eligibility criteria: Studies published in English involving human adult participants (≥18 years) with a clinical diagnosis of diabetes mellitus (at risk of DFU or with peripheral neuropathy) or knee OA, where the intervention involved any orthotic device or smart/intelligent insole with clinical outcomes reported, were included. Studies on healthy individuals only, those not reporting participant age, and non-weight-bearing protocols not differentiated from weight-bearing were excluded. Information sources: Five databases were searched: CINAHL (EBSCO Information Services, Ipswich, MA, USA), PubMed Advanced (National Library of Medicine, Bethesda, MD, USA), Wiley Online Library (John Wiley & Sons, Hoboken, NJ, USA), Cochrane Library (Cochrane Collaboration, London, UK), and Google Scholar (Google LLC, Mountain View, CA, USA). Searches were completed in May 2026. Methods: We conducted a comprehensive literature review. This review was structured and reported with reference to the PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analysis; University of Ottawa, Ottawa, ON, Canada) to guide transparency of reporting. It does not constitute a full Cochrane-style systematic review; risk of bias assessment was applied to key included studies and GRADE (Grading of Recommendations Assessment, Development and Evaluation; McMaster University, Hamilton, ON, Canada) certainty ratings were applied informally and narratively rather than as formal per-outcome evidence profiles. Five databases were searched yielding 92,637 records. After removal of 398 duplicates by Rayyan, 92,239 records remained. A subsequent automated keyword-based relevance filter applied within Rayyan (Rayyan AI, Doha, Qatar), prior to human screening, excluded 84,572 records that did not contain any terms related to orthotics, diabetic foot, or knee osteoarthritis, yielding 7667 records for human title/abstract screening. A narrative synthesis approach was adopted owing to the heterogeneity of study designs and outcome measures across included studies, which precluded meta-analysis. This review was not prospectively registered. A complete list of all 78 included studies, including those not individually discussed in the results and discussion. Results: The available clinical studies report promising findings for orthotics and smart orthotics in pain reduction, ulcer prevention, and potential reduction in economic burden, though conclusions are limited by small sample sizes, heterogeneity, and predominantly open-label designs. Recent research found that orthotics can be used to alter the gait pattern that influences knee OA by reducing excessive force on the affected joint. A randomised controlled trial demonstrated an 80% relative risk reduction in DFU recurrence (RR = 0.20; 95% CI: 0.06–0.79; p = 0.022), with absolute event rates of 6.3% in the intervention group versus 30.8% in controls (ARR = 24.5%); a second trial reported a 71% reduction in ulcer incidence over 18 months; and a third randomised controlled trial demonstrated statistically significant plantar pressure reduction (p < 0.01) in patients with diabetic neuropathy. Conclusions: The available evidence suggests that orthotics may be associated with improved pressure redistribution, reduced ulcer incidence, and benefit in the management of knee OA. Although the number of studies directly comparing smart orthotics with standard orthotics remains limited, the limited comparative studies suggested that smart orthotics showed promising results in reducing ulcer incidence, providing the patient with real-time feedback to offload via their electronic devices. These findings, while preliminary, highlight the potential of smart orthotic technology as an adjunct to standard orthotic care in reducing the overall burden of diabetic foot disease and knee osteoarthritis. Limitations: The primary methodological limitation of this review is the open-label design of all included smart orthotic trials, which precludes participant blinding and introduces performance bias. However, this limitation is structural and inherent to the wearable technology field—analogous to surgical trials—and is substantially mitigated by the use of objective primary outcome measures (plantar pressure and ulcer recurrence) across the three included RCTs, the consistency of effect direction across independent RCTs conducted in different countries, and a narrative sensitivity analysis confirming robustness of findings (Risk of Bias Across Studies Section). Formal per-outcome GRADE evidence profiles were not produced; overall certainty of evidence was assessed narratively with reference to GRADE domains and is judged to be low to moderate for smart orthotics in DFU prevention and low for knee OA management, consistent with the Level 2–3 evidence base and open-label study designs. Future adequately powered, multi-site RCTs with standardised outcome reporting, minimum 24-month follow-up, and integrated health economic modelling are the highest priority to extend these preliminary findings. Registration: This review was not prospectively registered. Full article

Background: Wearable cardioverter-defibrillators (WCDs) are equipped with the TRENDS remote-monitoring system, enabling continuous assessment of arrhythmias, physiological parameters, and patient-reported outcomes. This study evaluated the clinical utility of TRENDS-integrated WCD management and compared it with a historical control. Methods: We prospectively analyzed 36 consecutive patients who received a WCD with TRENDS between 2019 and 2024 and compared them with 30 historical controls treated before the implementation of TRENDS. Results: The WCD indications were heart failure as primary prevention (64%) and acute coronary syndrome with ventricular arrhythmias (28%). Among 18 patients who met the criteria for an implantable cardioverter-defibrillator (ICD), including 1 patient with WCD shock, 9 ultimately underwent ICD implantation. The mean daily WCD wear-time was 21.3 h and did not differ significantly from that of the historical control. The response rate to health-related questionnaires was 89%. TRENDS detected symptom exacerbation in 31% of patients, weight gain in 19% of patients, and missed medication in 19% of patients. Daily step-count was significantly lower in patients with ICD indications than in those without (5012 ± 2980 steps vs. 7977 ± 3584 steps, p = 0.01). TRENDS data also aided in initiating anticoagulation therapy and optimizing beta-blocker therapy. Conclusions: TRENDS provided clinically actionable physiologic and patient-reported information that supported individualized cardiovascular management. Full article

Wearable healthcare technologies are transforming the healthcare landscape by enabling remote, real-time health data collection, supporting early diagnosis, personalizing treatment plans, and reducing healthcare costs and medical burdens. Central to these advancements are wearable sensors, which continuously capture physiological data such as heart rate, temperature, activity levels, and biomarker concentrations. However, the large volume and complexity of this data demand effective processing to extract meaningful medical insights. Artificial intelligence (AI) and machine learning (ML) have significantly enhanced the capabilities of wearable sensors by enabling advanced data analysis, pattern recognition, and predictive modeling. AI-enhanced wearable sensors can detect early signs of health issues, such as heart attacks, chronic diseases, and mental health conditions like stress, often before clinical symptoms become apparent. This review examines the integration of AI/ML models with wearable sensors across physical activity recognition, stress assessment, cardiovascular monitoring, personal exposure monitoring, and sweat biomarker detection. Unlike prior application-centered reviews, we emphasize methodological and translational evaluation by comparing task formulations, sensing modalities, dataset scale, validation protocols, performance metrics, and deployment constraints across domains. We further discuss advanced architectures, multimodal fusion, explainable AI, edge deployment, privacy and regulatory considerations, and the translational gap between research prototypes and clinically deployable wearable AI systems. Full article

Background: Population ageing has increased the burden of geriatric syndromes among older adults with cardiovascular disease, where frailty is associated with adverse outcomes, including hospitalization, functional decline, and mortality. Digital technologies and Living Lab approaches offer new opportunities for the early detection, prevention, and monitoring of these conditions through user-centred innovation and stakeholder collaboration. Our purpose is to review the role of technology in the detection, prevention, and monitoring of geriatric syndromes in older adults with cardiovascular disease and to explore the potential of the Living Lab model for developing and implementing innovative solutions in geriatric care. Materials and Methods: A narrative review was conducted using PubMed, CINAHL, MEDLINE, and ScienceDirect. Eleven studies were included. Evidence on physical, cognitive, psycho-emotional, and social frailty, as well as technology-enabled assessment and monitoring approaches, was synthesized. Results: Digital technologies, including wearable sensors, telemonitoring platforms, mobile health applications, machine-learning models, and digital phenotyping tools, supported the early identification and monitoring of frailty, fall risk, cognitive decline, depressive symptoms, and functional deterioration. Technology-assisted interventions improved physical and cognitive performance and promoted social engagement. The Living Lab model facilitated the co-creation, evaluation, and validation of technologies in real-world settings, enhancing usability, acceptability, and implementation in clinical practice. Conclusions: Technology-supported assessment and monitoring can improve the management of geriatric syndromes in older adults with cardiovascular disease. Living Labs provide a valuable framework for the user-centred development and integration of these innovations, supporting personalized and proactive care strategies that promote healthy ageing. Full article

Occupational stress in dentistry, particularly during endodontic procedures, impacts practitioners’ health and performance. This study introduces a novel approach using real-time Heart Rate (HR) data from Apple Watch technology to assess physiological responses potentially indicative of occupational stress among endodontists. Materials and Methods: Twelve endodontists participated in this study, treating 119 patients. In a controlled clinical environment, HR was continuously monitored with the Apple Watch, and data were recorded via the Cardiogram app, capturing HR variations across treatment stages. Results: Significant HR fluctuations were observed during procedurally demanding stages such as local anesthesia and rubber dam placement. The endodontist’s HR, as a physiological proxy for stress, was significantly influenced by the treatment difficulty level, stage, and the patient’s age. Conclusions: Using Apple Watch technology, our study revealed significant HR variations during different endodontic treatment stages, suggesting fluctuating physiological responses that may reflect occupational stress. Elevated HR was noted during patient examination and rubber dam placement, particularly in complex cases. These preliminary findings suggest that HR monitoring via wearable technology may serve as a useful, albeit indirect, indicator of occupational stress during endodontic procedures. Future studies with larger samples and additional validated stress biomarkers are needed to confirm these observations. Full article

LGBTQ+ individuals experience disparities in cardiovascular health, but little is known about how daily minority and general stress affect physiological and psychological responses in real-world settings. Twenty LGBTQ+ young adults aged 18–27 completed a 14-day exploratory pilot study using ecological momentary assessment (EMA) with four daily smartphone surveys and continuous smartwatch-based sensor monitoring. This study is among the first to combine EMA with wearable sensor data to capture autonomic stress responses to minority stressors in naturalistic settings. Outcomes included a physiological stress score derived from heart rate variability during the 60 min before each EMA completion, as well as positive and negative affect (PA and NA). Four stress measures, Everyday Discrimination Scale (EDS), Sexual Orientation Microaggression Inventory Short Form (SOMI-SF), EMA of stressful events (EMA-SE), and current perceived stress (CPS), and a combined variable (COMB) were examined. In mixed-effects within-person models, all stress measures showed trends in the expected direction, with higher physiological stress scores, lower PA, and higher NA, though these varied in magnitude and statistical significance. SOMI-SF showed the strongest association with physiological stress, while general stress measures showed stronger associations with affect. These preliminary findings suggest that LGBTQ+-specific and general stressors may differentially engage physiological and psychological response systems; however, caution is warranted given the small sample size. Full article

In contemporary society, breast health has become a significant public health concern, particularly among women. According to statistics from the World Health Organization, both the incidence and mortality rates of breast tumors have steadily increased in recent years. Therefore, effective early-stage screening and postoperative monitoring are essential for maintaining breast health. However, conventional clinical diagnostic modalities are typically bulky, operationally complex, and unsuitable for continuous real-time monitoring, which limits their use in portable and everyday health management applications. To address these limitations, this study proposes a machine learning-integrated wearable piezoelectric sensing platform as an auxiliary tool for breast health assessment. The device consists of a PDMS matching layer embedded with flexible silver nanowires, a P(VDF-TrFE) piezoelectric layer, and a multi-channel low-noise signal acquisition circuit. It is capable of acquiring acoustic echo signals from tissue-mimicking environments and automatically evaluating signal validity using a convolutional neural network (CNN). By integrating piezoelectric sensing with deep learning-based signal analysis, the proposed system achieves a signal-to-noise ratio exceeding 70 dB and a real-time classification accuracy above 96% under controlled conditions. These results demonstrate that the platform provides a compact, portable, and intelligent approach for wearable sensing of mechanical heterogeneity and highlight its potential for future development in continuous biomedical monitoring technologies. Full article

The increasing demand of digital technologies and their integration with wearable health devices provides an efficient trigger for next-generation wearable healthcare devices for long-term physiological monitoring. The advancement of energy harvesting mechanism, nanomaterial-based sensor fabrication and their integration with digital technologies have emerged as a promising solution for transforming future of digital health. This study provides a comprehensive summary and framework for wearable self-powered electronic devices, enabling continuous, battery-free health monitoring and advancing the development of sustainable, next-generation digital healthcare systems. This review paper presents a broad and detailed overview of current technologies and sensors advancement in developing low-power wearable, self-powered electronic devices suitable for healthcare applications. The importance and reliable use of key energy harvesting approaches including triboelectric, piezoelectric, thermoelectric, and photovoltaic approaches are systematically presented which focused on development of energy efficient wearable devices. This review further examines the low-power circuit design strategies for flexible electronics focusing personalized healthcare monitoring. Current challenges and limitations related to advanced manufacturing of wearable health devices focusing on large-scale deployment are also analyzed. Finally, the key future research directions are outlined for advancing a next-generation intelligent digital health system. Full article

Smartwatches are increasingly used in safety-critical scenarios, yet their optical heart-rate (HR) measurements often contain noise, artifacts, and missing data, undermining clinical trust. This paper presents Edge Beats, a data-curation layer and end-to-end architecture that enables the low-cost, open source PineTime smartwatch to function as a practical HR sensing node for distributed wearable systems. Heart-rate packets are streamed from PineTime to an ESP32 at the edge layer over Bluetooth Low Energy (BLE), then forwarded via an embedded Message Queuing Telemetry Transport (MQTT) broker to an edge server laptop for processing and visualization. A lightweight multi-stage algorithm cleans and smooths the HR stream using physiological boundary checks, a configurable data imputation technique, and exponential moving average (EMA) smoothing, all designed for real-time operation on resource-constrained hardware. We have evaluated the system over long monitoring sessions and compared the processed PineTime output against a commercial Huawei GT Pro 2 smartwatch. The system suppresses extreme spikes and short-term oscillations, yielding a more stable HR trace with qualitative agreement to the reference trends while keeping values in a physiologically plausible range. Network measurements show low latency (almost 3 ms one-way, 15 ms RTT) and stable throughput, and power measurements (100–450 mW for ESP32 and 3–70 mW for PineTime watch) confirm that continuous HR streaming over BLE and MQTT is feasible within the PineTime’s energy budget. These results imply that data stream processing combined with a modest publish–subscribe architecture improves the stability and usability of HR streams obtained from commodity wearable sensors, making PineTime a candidate as a complementary component for mission-critical health and safety systems. Full article

Background: Large language models and other generative artificial intelligence (AI) tools are increasingly being embedded in digital healthcare services, including mobile health applications, telerehabilitation, remote monitoring, and hybrid care pathways. In this review, digital healthcare refers to technology-mediated healthcare services in which digital platforms, mobile applications, wearables, remote communication, and AI-enabled interfaces support health assessment, self-management, rehabilitation, clinical decision support, or service delivery. When AI-generated exercise guidance moves from general education to individualized recommendations about dose, progression, contraindications, or rehabilitation, it may become directly actionable and safety-relevant. Objectives: This review aimed to clarify when AI-generated exercise guidance in digital healthcare may warrant safety-relevant governance attention and to outline implementation considerations for explainability, human oversight, and service-level governance. It addresses a gap in the literature: general AI-governance and exercise-prescription discussions rarely specify how point-of-use explanations, review thresholds, and escalation safeguards can be organized for directly actionable AI exercise guidance. Methods: We conducted a governance-oriented narrative review of peer-reviewed literature and representative regulatory or guidance documents. This review was not designed as a systematic review, scoping review, or exhaustive evidence map; transparent source mapping was used to support conceptual synthesis. Searches and source mapping focused on generative AI, large language models, explainable AI, clinical decision support, digital health, mobile health, exercise prescription, rehabilitation, trust, automation bias, and human oversight. Sources were included when they informed the safety, explainability, governance, or real-world implementation of patient-facing AI-generated exercise guidance. Extracted material was grouped by evidentiary role and synthesized through framework synthesis and governance mapping to distinguish literature-supported observations, author interpretation, and proposed implementation tools. Results: The included sources were first organized into five thematic groups: digital exercise delivery and exercise-prescription evidence; explainability, trust, and automation bias literature; professional responsibility, ethics, and patient disclosure literature; regulatory and policy documents; and digital literacy, patient/clinician attitudes, and equity literature. The synthesis then proceeded from safety relevance to explanation needs, human oversight and escalation needs, and selected regulatory and policy signals before translating these strands into conceptual and implementation-oriented outputs rather than empirically validated instruments. AI-generated exercise guidance was most safety-relevant in scenarios involving individualized dose, progression, contraindication-sensitive action, or rehabilitation strategy. Across the included sources, generic transparency alone was not sufficient to support reviewable use; relevant explanation elements included evidence sources, risk warnings, reasoning paths, and reasonable alternatives. Oversight considerations varied with embodied risk, clinical ambiguity, user vulnerability, and likelihood of direct enactment. Implementation considerations linked interface design, clinical review, escalation, auditability, and post-deployment monitoring. Conclusions: AI-generated exercise guidance in digital healthcare may warrant governance attention as a patient-safety and accountability issue when it influences actionable exercise decisions. The proposed framework offers a conceptual basis for designing more reviewable and accountable mobile and remote exercise-support services. Future work can validate these outputs in patient-facing services, clinician review workflows, usability studies, implementation pilots, and safety evaluations. Full article

Wearable-sensor human activity recognition (HAR) plays a key role in health monitoring, elderly care, and human–computer interaction. Deep learning dominates the field, but two limitations remain. CNNs with fixed kernels cannot capture cross-scale temporal events such as gait cycles and postural transitions in a single layer, and softmax attention on small sensor datasets is often diluted by common-mode background responses across the sequence. We propose DMSCNet, an end-to-end framework with two modules. The Dilated Multi-Scale Branch Block (DMSB) combines a shared bottleneck, parallel dilated convolutions, a pooling bypass, and SE-based channel recalibration to widen the temporal receptive field under a controlled parameter budget. The Contrastive Temporal Attention (CTA) module adopts a dual-path differential design, in which the two paths learn overlapping but non-identical attention patterns and their subtraction suppresses shared low-level responses while preserving the discriminative positions each path locks onto, encoded with opposite signs. DMSB and CTA are cascaded into a DMSC Block and stacked residually. On UCI-HAR, USC-HAD, and RealWorld, DMSCNet reaches F1-scores of 97.65%, 91.80%, and 99.05%, outperforming nine baselines. Ablations confirm that SE acts along the channel axis and CTA along the temporal axis, and visualization reveals a dynamic–static dichotomy together with a signed bipolar encoding pattern produced by the dual-path subtraction. Full article

Chronic diseases such as cardiovascular disorders, diabetes, neurological conditions, and kidney disease continue to rise worldwide. These conditions create a growing demand for continuous, non-invasive, and personalized health monitoring technologies. Wearable biosensors meet this need by enabling real-time physiological and biochemical measurements outside traditional clinical settings. Among wearable biosensors, those based on biofluids like sweat, tears, and saliva provide a painless alternative to blood sampling. These fluids also grant access to metabolites, electrolytes, hormones, proteins, and disease related biomarkers that reflect systemic health status. Advanced sensing technology allow us to continuously track health status by analyzing key biomarkers in these accessible biofluids. This review summarizes recent advances in non-invasive wearable biosensors and focuses on their sensing principles which includes biorecognition elements, signal transduction mechanisms, and data acquisition strategies. We also discussed key sensing modalities, including electrochemical, optical, thermal, and piezoelectric approaches, highlighting their advantages for wearable integration and performance in biofluid sensing. Finally the review also outlines recent developments and applications of these systems in biofluid sensing. In the end we highlights existing challenges, potential solutions, and future directions toward clinically deployable, AI-assisted precision healthcare systems. Full article