Cation-π Interactions Based Conductive Hydrogels with Slide-Ring Structure Toward Super Long-Time in-air/Underwater Linear Sensing and Communication.

Conductive hydrogels (CHs) are attracted more attention in the flexible wearable sensors field, however, how to stably apply CHs underwater is still a big challenge. In order to achieve the usage of CHs in aquatic environments, the integrated properties such as water retention ability, resistance to swelling, toughness, adhesiveness, linear GF sensing, and long-term usage are necessary to consider, but rarely reported in the previous reports. This paper proposes CHs prepared using cationic and aromatic monomers along with polyrotaxanes-based crosslinkers. Due to the intermolecular cation-π interactions and topological slide-ring-based polyrotaxanes, the CHs exhibit good mechanical performance, adhesive nature, and anti-swelling properties. The presence of slide-ring-based topological architecture effectively mitigates stress concentration. Additionally, the encapsulation of PA allows CHs to maintain functionality even after 240 days of direct placement at room temperature. Notably, the designed CHs exhibit linear sensitivity in detecting land/underwater human motions, and serve as Morse code signal transmitters for information transmission. Thus, the designed CHs may have broad applications in the underwater wearable sensors field.

Optical sensors for transdermal biomarker detection: A review.

This review has explored optical sensors and their important role in non-invasive transdermal biomarker detection. While electrochemical sensors have been thoroughly studied for biomarker tracking, optical sensors present a compelling alternative due to their high sensitivity and selectivity, multiplex capabilities, cost-efficiency, and small form factor. This review examines the latest advancements in optical sensing technologies for transdermal biomarker detection, such as colorimetry, fluorescence, surface plasmon resonance (SPR), fiber optics, photonic crystals, and Raman spectroscopy. These technologies have been applied in the analysis of biomarkers present in sweat and skin gases, which are essential for non-invasive health monitoring. Furthermore, the review has discussed the challenges and future perspectives of optical sensors in in transdermal biomarker detection. The analysis of various sensor types and their applications highlights the transformative potential of optical sensors in enhancing disease diagnostics and promoting proactive health management.

Generisch-Net: A Generic Deep Model for Analyzing Human Motion with Wearable Sensors in the Internet of Health Things.

The Internet of Health Things (IoHT) is a broader version of the Internet of Things. The main goal is to intervene autonomously from geographically diverse regions and provide low-cost preventative or active healthcare treatments. Smart wearable IMUs for human motion analysis have proven to provide valuable insights into a person's psychological state, activities of daily living, identification/re-identification through gait signatures, etc. The existing literature, however, focuses on specificity i.e., problem-specific deep models. This work presents a generic BiGRU-CNN deep model that can predict the emotional state of a person, classify the activities of daily living, and re-identify a person in a closed-loop scenario. For training and validation, we have employed publicly available and closed-access datasets. The data were collected with wearable inertial measurement units mounted non-invasively on the bodies of the subjects. Our findings demonstrate that the generic model achieves an impressive accuracy of 96.97% in classifying activities of daily living. Additionally, it re-identifies individuals in closed-loop scenarios with an accuracy of 93.71% and estimates emotional states with an accuracy of 78.20%. This study represents a significant effort towards developing a versatile deep-learning model for human motion analysis using wearable IMUs, demonstrating promising results across multiple applications.

Design, Calibration and Morphological Characterization of a Flexible Sensor with Adjustable Chemical Sensitivity and Possible Applications to Sports Medicine.

Active life monitoring via chemosensitive sensors could hold promise for enhancing athlete monitoring, training optimization, and performance in athletes. The present work investigates a resistive flex sensor (RFS) in the guise of a chemical sensor. Its carbon 'texture' has shown to be sensitive to CO2, O2, and RH changes; moreover, different bending conditions can modulate its sensitivity and selectivity for these gases and vapors. A three-step feasibility study is presented including: design and fabrication of the electronic read-out and control; calibration of the sensors to CO2, O2 and RH; and a morphological study of the material when interacting with the gas and vapor molecules. The 0.1 mm-1 curvature performs best among the tested configurations. It shows a linear response curve for each gas, the ranges of concentrations are adequate, and the sensitivity is good for all gases. The curvature can be modulated during data acquisition to tailor the sensitivity and selectivity for a specific gas. In particular, good results have been obtained with a curvature of 0.1 mm-1. For O2 in the range of 20-70%, the sensor has a sensitivity of 0.7 mV/%. For CO2 in the range of 4-80%, the sensitivity is 3.7 mV/%, and for RH the sensitivity is 33 mV/%. Additionally, a working principle, based on observation via scanning electron microscopy, has been proposed to explain the chemical sensing potential of this sensor. Bending seems to enlarge the cracks present in the RFS coverage; this change accounts for the altered selectivity depending on the sensor's curvature. Further studies are needed to confirm result's reliability and the correctness of the interpretation.

Postural Risks in Dental Practice: An Assessment of Musculoskeletal Health.

In recent years, Romania's stomatology private practice sector has seen substantial growth, with many dentists fully committing to building and expanding their own practices, often funded by their personal income. This study aimed to explore how various postures affect the muscle groups of dentists (380), particularly focusing on identifying positions that may jeopardize their musculoskeletal health. A group of dentists effectively participated in this study (10), adhering to their regular work routines while wearing wearable sensors on their backs to monitor posture and activity. The data gathered from these sensors were analyzed using the RULA (rapid upper-limb assessment) and REBA (rapid entire-body assessment) tools. The findings indicated that the head and shoulder movements during dental procedures involved considerable and repetitive angular shifts, which could strain the neck and back muscles and heighten the risk of musculoskeletal problems. Additionally, the standing postures adopted by the dentists were associated with an increased risk of postural issues and greater overall fatigue. Extended periods of trunk and head tilting were also identified as contributing factors to posture-related challenges.

Relationship between Physical Demands and Player Performance in Professional Female Basketball Players Using Inertial Movement Units.

Load monitoring has been identified as a valuable tool for optimizing training planning and minimizing injury risk. This study's aim was divided into two main objectives: (1) to describe the physical demands during official competition through IMU (inertial movement unit) metrics and (2) to investigate the relationship between basketball statistics and these physical demands. Twelve female highly trained basketballers (26.5 ± 5.3 years, 180 ± 7.1 cm, and 73.6 ± 10.3 kg) were monitored during four official games. Our results indicate that games with more frequent possession changes, particularly those driven by steals and turnovers, exhibit higher physical demands. Additionally, longer game durations were associated with longer recovery time while maintaining similar active time and physical load. Players who assume prominent shooting roles face greater conditional demands, such as increased jumps and impacts, even with equal playing time. These findings suggest that IMUs provide valuable insights into high-intensity actions and patterns, indicating a direct association between physical load and player performance in professional female basketball. This study also highlights the potential for professionals to better manage workload and understand player demands using these insights, even in the absence of in-game sensor data. Our research underscores the importance of contextual analysis in sports performance studies, encouraging future investigations into game phases and their specific physical demands.

Quantifying Asymmetric Gait Pattern Changes Using a Hidden Markov Model Similarity Measure (HMM-SM) on Inertial Sensor Signals.

Wearable gait analysis systems using inertial sensors offer the potential for easy-to-use gait assessment in lab and free-living environments. This can enable objective long-term monitoring and decision making for individuals with gait disabilities. This study explores a novel approach that applies a hidden Markov model-based similarity measure (HMM-SM) to assess changes in gait patterns based on the gyroscope and accelerometer signals from just one or two inertial sensors. Eleven able-bodied individuals were equipped with a system which perturbed gait patterns by manipulating stance-time symmetry. Inertial sensor data were collected from various locations on the lower body to train hidden Markov models. The HMM-SM was evaluated to determine whether it corresponded to changes in gait as individuals deviated from their baseline, and whether it could provide a reliable measure of gait similarity. The HMM-SM showed consistent changes in accordance with stance-time symmetry in the following sensor configurations: pelvis, combined upper leg signals, and combined lower leg signals. Additionally, the HMM-SM demonstrated good reliability for the combined upper leg signals (ICC = 0.803) and lower leg signals (ICC = 0.795). These findings provide preliminary evidence that the HMM-SM could be useful in assessing changes in overall gait patterns. This could enable the development of compact, wearable systems for unsupervised gait assessment, without the requirement to pre-identify and measure a set of gait parameters.

Wearable network for multilevel physical fatigue prediction in manufacturing workers.

Manufacturing workers face prolonged strenuous physical activities, impacting both financial aspects and their health due to work-related fatigue. Continuously monitoring physical fatigue and providing meaningful feedback is crucial to mitigating human and monetary losses in manufacturing workplaces. This study introduces a novel application of multimodal wearable sensors and machine learning techniques to quantify physical fatigue and tackle the challenges of real-time monitoring on the factory floor. Unlike past studies that view fatigue as a dichotomous variable, our central formulation revolves around the ability to predict multilevel fatigue, providing a more nuanced understanding of the subject's physical state. Our multimodal sensing framework is designed for continuous monitoring of vital signs, including heart rate, heart rate variability, skin temperature, and more, as well as locomotive signs by employing inertial motion units strategically placed at six locations on the upper body. This comprehensive sensor placement allows us to capture detailed data from both the torso and arms, surpassing the capabilities of single-point data collection methods. We developed an innovative asymmetric loss function for our machine learning model, which enhances prediction accuracy for numerical fatigue levels and supports real-time inference. We collected data on 43 subjects following an authentic manufacturing protocol and logged their self-reported fatigue. Based on the analysis, we provide insights into our multilevel fatigue monitoring system and discuss results from an in-the-wild evaluation of actual operators on the factory floor. This study demonstrates our system's practical applicability and contributes a valuable open-access database for future research.

Are Adolescent Athletes Sleeping Enough? An Observational Study of Sleep Parameters during Schooldays and Holidays.

Background: Inconsistent sleep schedules, frequent awakening after sleep onset (WASO), and decreased sleep efficiency (SE) are common issues among adolescent team sports athletes. Moreover, research indicates that sleep problems are enhanced across schooldays. The aim of the present study was to assess sleep patterns of adolescent athletes and compare sleep parameters between schooldays and holidays. Methods: The chronotype and sleep quality of twelve adolescent basketball players (mean age: 15.58 ± 0.67 years) were assessed. Objective sleep parameters were then analyzed using actigraphy over a 12-day period, which included six days during the school period and six days during holidays. Results: Data showed that total sleep time (TST), SE, and WASO (382.48 min, 81.81%, and 66.70 min, respectively) did not meet international recommendations for sleep quantity and quality. During school weekdays, time in bed (TIB), TST, and SE significantly decreased compared to weekends (p < 0.001, d = -1.49; p < 0.001, d = -1.64; and p = 0.01, d = -0.89, respectively). On weekdays, TIB, TST, and WASO were significantly lower on schooldays compared to holidays (p < 0.001, d = -1.83; p < 0.01, d = -1.01; and p = 0.02, d = -0.77, respectively). While no significant difference was observed in social jetlag, the mid-point of sleep was significantly later on holiday weekdays compared to school weekdays (p < 0.05, d = 0.65). Conclusions: Adolescent athletes experience insufficient sleep, especially on school weekdays, which is partially improved during weekends and holidays. Although sleep duration was longer during holidays, our results suggest that adolescent athletes' sleep was more fragmented. Consequently, it remains crucial to implement strategies to enhance their sleep health (e.g., napping).

Effectiveness of Hospital Fit on Physical Activity in Hospitalized Patients: A Stepped-Wedge Cluster-Randomized Trial and Process Evaluation.

This study investigates the effectiveness of using Hospital Fit as part of usual care physiotherapy on the physical activity (PA) behavior of hospitalized patients compared to patients who received physiotherapy before implementation of Hospital Fit. In addition, a process evaluation is conducted. A prospective, multi-center, mixed-methods stepped wedge cluster randomized trial was performed at the cardiology and medical oncology departments of two Dutch university medical centers. Patients were included in the non-intervention or intervention phase. During the non-intervention phase, patients received usual care physiotherapy. During the intervention phase, Hospital Fit was additionally used. Mean time spent walking, standing, lying/sitting per day and the number of postural transitions from lying/sitting to standing/walking positions were measured using an accelerometer and analyzed using linear mixed models. A process evaluation was performed using questionnaires and semi-structured interviews with patients and focus-group interviews with healthcare professionals. A total of 77 patients were included, and data from 63 patients were used for data analysis. During the intervention phase, the average time spent walking per day was 20 min (95% confidence interval: -2 to 41 min) higher than during the non-intervention phase (p = 0.075). No significant differences were found for mean time spent standing per day, mean time spent lying/sitting per day, or the number of postural transitions per day either. During the intervention phase, 87% of patients used Hospital Fit at least once, with a median daily use of 2.5 to 4.0 times. Patients and healthcare professionals believed that Hospital Fit improved patients' PA behavior and recovery. Insufficient digital skills and technical issues were described as challenges. Although patients and healthcare professionals described Hospital Fit as an added value, no statistically significant effects were found.

Systematic Review of IoT-Based Solutions for User Tracking: Towards Smarter Lifestyle, Wellness and Health Management.

The Internet of Things (IoT) base has grown to over 20 billion devices currently operational worldwide. As they greatly extend the applicability and use of biosensors, IoT developments are transformative. Recent studies show that IoT, coupled with advanced communication frameworks, such as machine-to-machine (M2M) interactions, can lead to (1) improved efficiency in data exchange, (2) accurate and timely health monitoring, and (3) enhanced user engagement and compliance through advancements in human-computer interaction. This systematic review of the 19 most relevant studies examines the potential of IoT in health and lifestyle management by conducting detailed analyses and quality assessments of each study. Findings indicate that IoT-based systems effectively monitor various health parameters using biosensors, facilitate real-time feedback, and support personalized health recommendations. Key limitations include small sample sizes, insufficient security measures, practical issues with wearable sensors, and reliance on internet connectivity in areas with poor network infrastructure. The reviewed studies demonstrated innovative applications of IoT, focusing on M2M interactions, edge devices, multimodality health monitoring, intelligent decision-making, and automated health management systems. These insights offer valuable recommendations for optimizing IoT technologies in health and wellness management.

A Novel Active Learning Framework for Cross-Subject Human Activity Recognition from Surface Electromyography.

Wearable sensor-based human activity recognition (HAR) methods hold considerable promise for upper-level control in exoskeleton systems. However, such methods tend to overlook the critical role of data quality and still encounter challenges in cross-subject adaptation. To address this, we propose an active learning framework that integrates the relation network architecture with data sampling techniques. Initially, target data are used to fine tune two auxiliary classifiers of the pre-trained model, thereby establishing subject-specific classification boundaries. Subsequently, we assess the significance of the target data based on classifier discrepancy and partition the data into sample and template sets. Finally, the sampled data and a category clustering algorithm are employed to tune model parameters and optimize template data distribution, respectively. This approach facilitates the adaptation of the model to the target subject, enhancing both accuracy and generalizability. To evaluate the effectiveness of the proposed adaptation framework, we conducted evaluation experiments on a public dataset and a self-constructed electromyography (EMG) dataset. Experimental results demonstrate that our method outperforms the compared methods across all three statistical metrics. Furthermore, ablation experiments highlight the necessity of data screening. Our work underscores the practical feasibility of implementing user-independent HAR methods in exoskeleton control systems.

The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review.

Parkinson's disease (PD) is the second most common movement disorder in the world. It is characterized by motor and non-motor symptoms that have a profound impact on the independence and quality of life of people affected by the disease, which increases caregivers' burdens. The use of the quantitative gait data of people with PD and deep learning (DL) approaches based on gait are emerging as increasingly promising methods to support and aid clinical decision making, with the aim of providing a quantitative and objective diagnosis, as well as an additional tool for disease monitoring. This will allow for the early detection of the disease, assessment of progression, and implementation of therapeutic interventions. In this paper, the authors provide a systematic review of emerging DL techniques recently proposed for the analysis of PD by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Scopus, PubMed, and Web of Science databases were searched across an interval of six years (between 2018, when the first article was published, and 2023). A total of 25 articles were included in this review, which reports studies on the movement analysis of PD patients using both wearable and non-wearable sensors. Additionally, these studies employed DL networks for classification, diagnosis, and monitoring purposes. The authors demonstrate that there is a wide employment in the field of PD of convolutional neural networks for analyzing signals from wearable sensors and pose estimation networks for motion analysis from videos. In addition, the authors discuss current difficulties and highlight future solutions for PD monitoring and disease progression.

Using Wearable Sensors to Study Musical Experience: A Systematic Review.

Over the last few decades, a growing number of studies have used wearable technologies, such as inertial and pressure sensors, to investigate various domains of music experience, from performance to education. In this paper, we systematically review this body of literature using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method. The initial search yielded a total of 359 records. After removing duplicates and screening for content, 23 records were deemed fully eligible for further analysis. Studies were grouped into four categories based on their main objective, namely performance-oriented systems, measuring physiological parameters, gesture recognition, and sensory mapping. The reviewed literature demonstrated the various ways in which wearable systems impact musical contexts, from the design of multi-sensory instruments to systems monitoring key learning parameters. Limitations also emerged, mostly related to the technology's comfort and usability, and directions for future research in wearables and music are outlined.

Starch/ionic liquid/hydrophobic association hydrogel with high stretchability, fatigue resistance, self-recovery and conductivity for sensitive wireless wearable sensors.

Conductive hydrogels have been widely used in wearable electronics due to their flexible, conductive and adjustable properties. With ever-growing demand for sustainable and biocompatible sensing materials, biopolymer-based hydrogels have drawn significant attention. Among them, starch-based hydrogels have a great potential for wearable electronics. However, it remains challenging to develop multifunctional starch-based hydrogels with high stretchability, good conductivity, excellent durability and high sensitivity. Herein, amylopectin and ionic liquid were introduced into a hydrophobic association hydrogel to endow it with versatility. Benefiting from the synergistic effect of amylopectin and ionic liquid, the hydrogel exhibited excellent mechanical properties (the elongation of 2540 % with a Young's modulus of 12.0 kPa and a toughness of 1.3 MJ·m-3), self-recovery, good electrical properties (a conductivity of 1.8 S·m-1 and electrical self-healing), high sensitivity (gauge factor up to 26.85) and excellent durability (5850 cycles). The above properties of the hydrogel were closely correlated to its internal structure from hydrophobic association, H-bonding and electrostatic interaction, and can be regulated by changing the component contents. A wireless wearable sensor based on the hydrogel realized accurate and stable monitoring of joint motions and expression changes. This work demonstrates a kind of promising biopolymer-based materials as candidates for high-performance flexible wearable sensors.

Multivariable model for gait pattern differentiation in elderly patients with hip and knee osteoarthritis: A wearable sensor approach.

Background: Hip and knee osteoarthritis (OA) patients demonstrate distinct gait patterns, yet detecting subtle abnormalities with wearable sensors remains uncertain. This study aimed to assess a predictive model's efficacy in distinguishing between hip and knee OA gait patterns using accelerometer data. Method: Participants with hip or knee OA underwent overground walking assessments, recording lower limb accelerations for subsequent time and frequency domain analyses. Logistic regression with regularization identified associations between frequency domain features of acceleration signals and OA, and k-nearest neighbor classification distinguished knee and hip OA based on selected acceleration signal features. Findings: We included 57 knee OA patients (30 females, median age 68 [range 49-89], median BMI 29.7 [range 21.0-45.9]) and 42 hip OA patients (19 females, median age 70 [range 47-89], median BMI 28.3 [range 20.4-37.2]). No significant difference could be found in the time domain's averaged shape of acceleration signals. However, in the frequency domain, five selected features showed a diagnostic ability to differentiate between knee and hip OA. Using these features, a model achieved a 77 % accuracy in classifying gait cycles into hip or knee OA groups, with average precision, recall, and F1 score of 77 %, 76 %, and 78 %, respectively. Interpretation: The study demonstrates the effectiveness of wearable sensors in differentiating gait patterns between individuals with hip and knee OA, specifically in the frequency domain. The results highlights the promising potential of wearable sensors and advanced signal processing techniques for objective assessment of OA in clinical settings.

Remote data collection of infant activity and sleep patterns via wearable sensors in the HEALthy Brain and Child Development Study (HBCD).

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Wearable and remote sensing technologies have advanced data collection outside of laboratory settings to enable exploring, in more detail, the associations of early experiences with brain development and social and health outcomes. In the HBCD Study, the Novel Technology/Wearable Sensors Working Group (WG-NTW) identified two primary data types to be collected: infant activity (by measuring leg movements) and sleep (by measuring heart rate and leg movements). These wearable technologies allow for remote collection in the natural environment. This paper illustrates the collection of such data via wearable technologies and describes the decision-making framework, which led to the currently deployed study design, data collection protocol, and derivatives, which will be made publicly available. Moreover, considerations regarding actual and potential challenges to adoption and use, data management, privacy, and participant burden were examined. Lastly, the present limitations in the field of wearable sensor data collection and analysis will be discussed in terms of extant validation studies, the difficulties in comparing performance across different devices, and the impact of evolving hardware/software/firmware.

Reliability of the Moover® 3D Inertial Motion Sensor in Greek Patients With Chronic Neck Pain in a Primary Care Urban Setting.

Introduction Neck pain has a high lifetime prevalence and represents a significant health issue. Reduced active cervical range of motion (ACROM) has been found in neck pain patients. Inertial sensor technology can provide objective measurements to assess the impaired ACROM. Purpose Primarily, this study investigated the inter- and intra-rater reliability of the Moover® three-dimensional (3D) inertial motion sensor (Sensor Medica, Rome, Italy) in Greek patients with non-specific chronic neck pain. Secondly, the intra-rater reliability of the Neck Disability Index (NDI) was also assessed. Methods Fifty patients (18 males and 32 females) suffering from non-specific chronic neck pain participated in this study. Two physiotherapists measured separately each participant's ACROM in three planes, within a 48-hour period. The participants' position and the sequence and direction of the three cervical movements (cervical rotation, lateral flexion, and flexion-extension) were standardized. Results The inter-rater reliability intraclass correlation coefficient (ICC) values were good to excellent ranging from 0.77 to 0.95 for the first measurement and 0.85 to 0.95 for the second (p < 0.001). The intra-rater reliability ICC values were moderate to excellent ranging from 0.74 to 0.92 for the first rater and good to excellent ranging from 0.83 to 0.94 for the secondrater (p < 0.001). Intra-rater reliability of the overall NDI was indicated as good, and ICC was 0.80 (95%CI: 0.65-0.89; p < 0.001). ICC values for all sections were significant and ranged from 0.40 to 0.88. Conclusion This study showed the reliability of the Moover 3D inertial sensor for ACROM measurement in Greek patients with chronic neck pain. The NDI scale also showed good intra-rater reliability in the same sample. Both intra- and inter-rater reliability of the Moover 3D were proven to be acceptable over a 48-hour period. The specific sensor might have a potential application in a clinical setting.

Acceptability of digital health technologies in early Parkinson's disease: lessons from WATCH-PD.

Introduction: Digital health technologies (DHTs) have the potential to alleviate challenges experienced in clinical trials through more objective, naturalistic, and frequent assessments of functioning. However, implementation of DHTs come with their own challenges, including acceptability and ease of use for study participants. In addition to acceptability, it is also important to understand device proficiency in the general population and within patient populations who may be asked to use DHTs for extended periods of time. We thus aimed to provide an overview of participant feedback on acceptability of DHTs, including body-worn sensors used in the clinic and a mobile application used at-home, used throughout the duration of the Wearable Assessments in the Clinic and at Home in Parkinson's Disease (WATCH-PD) study, an observational, longitudinal study looking at disease progression in early Parkinson's Disease (PD). Methods: 82 participants with PD and 50 control participants were enrolled at 17 sites throughout the United States and followed for 12 months. We assessed participants' general device proficiency at baseline, using the Mobile Device Proficiency Questionnaire (MDPQ). The mean MDPQ score at Baseline did not significantly differ between PD patients and healthy controls (20.6 [2.91] vs 21.5 [2.94], p = .10). Results: Questionnaire results demonstrated that participants had generally positive views on the comfort and use of the digital technologies throughout the duration of the study, regardless of group. Discussion: This is the first study to evaluate patient feedback and impressions of using technology in a longitudinal observational study in early Parkinson's Disease. Results demonstrate device proficiency and acceptability of various DHTs in people with Parkinson's does not differ from that of neurologically healthy older adults, and, overall, participants had a favorable view of the DHTs deployed in the WATCH-PD study.

Green Wearable Sensors and Antennas for Bio-Medicine, Green Internet of Things, Energy Harvesting, and Communication Systems.

This paper presents innovations in green electronic and computing technologies. The importance and the status of the main subjects in green electronic and computing technologies are presented in this paper. In the last semicentennial, the planet suffered from rapid changes in climate. The planet is suffering from increasingly wild storms, hurricanes, typhoons, hard droughts, increases in seawater height, floods, seawater acidification, decreases in groundwater reserves, and increases in global temperatures. These climate changes may be irreversible if companies, organizations, governments, and individuals do not act daily and rapidly to save the planet. Unfortunately, the continuous growth in the number of computing devices, cellular devices, smartphones, and other smart devices over the last fifty years has resulted in a rapid increase in climate change. It is severely crucial to design energy-efficient "green" technologies and devices. Toxic waste from computing and cellular devices is rapidly filling up landfills and increasing air and water pollution. This electronic waste contains hazardous and toxic materials that pollute the environment and affect our health. Green computing and electronic engineering are employed to address this climate disaster. The development of green materials, green energy, waste, and recycling are the major objectives in innovation and research in green computing and electronics technologies. Energy-harvesting technologies can be used to produce and store green energy. Wearable active sensors and metamaterial antennas with circular split ring resonators (CSSRs) containing energy-harvesting units are presented in this paper. The measured bandwidth of the matched sensor is around 65% for VSWR, which is better than 3:1. The sensor gain is 14.1 dB at 2.62 GHz. A wideband 0.4 GHz to 6.4 GHz slot antenna with an RF energy-harvesting unit is presented in this paper. The Skyworks Schottky diode, SMS-7630, was used as the rectifier diode in the harvesting unit. If we transmit 20 dBm of RF power from a transmitting antenna that is located 0.2 m from the harvesting slot antenna at 2.4 GHz, the output voltage at the output port of the harvesting unit will be around 1 V. The power conversion efficiency of the metamaterial antenna dipole with metallic strips is around 75%. Wearable sensors with energy-harvesting units provide efficient, low-cost healthcare services that contribute to a green environment and minimize energy consumption. The measurement process and setups of wearable sensors are presented in this paper.