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Wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and/or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner. These sensors can be worn in the form of glasses, jewellery, face masks, wristwatches, fitness bands, tattoo-like devices, bandages or other patches, and textiles. Wearables such as smartwatches have already proved their capability for the early detection and monitoring of the progression and treatment of various diseases, such as COVID-19 and Parkinson disease, through biophysical signals. Next-generation wearable sensors that enable the multimodal and/or multiplexed measurement of physical parameters and biochemical markers in real time and continuously could be a transformative technology for diagnostics, allowing for high-resolution and time-resolved historical recording of the health status of an individual. In this Review, we examine the building blocks of such wearable sensors, including the substrate materials, sensing mechanisms, power modules and decision-making units, by reflecting on the recent developments in the materials, engineering and data science of these components. Finally, we synthesize current trends in the field to provide predictions for the future trajectory of wearable sensors. Wearable sensors that access both biophysical and biochemical information can be used to monitor the physiological state of an individual and facilitate diagnosis. This Review examines the building blocks of wearable devices, including the substrate materials as well as the sensing, decision-making and power modules.

Electric eel is an excellent example to harness ion-concentration gradients for sustainable power generation. However, current strategies to create electric-eel-inspired power sources commonly involve manual stacking of multiple salinity-gradient power source units, resulting in low efficiency, unstable contact, and poor flexibility. Here we propose a consecutive multimaterial printing strategy to efficiently fabricate biomimetic ionic hydrogel power sources with a maximum stretchability of 137%. The consecutively-printed ionic hydrogel power source filaments showed seamless bonding interface and can maintain stable voltage outputs for 1000 stretching cycles at 100% strain. With arrayed multi-channel printhead, power sources with a maximum voltage of 208 V can be automatically printed and assembled in parallel within 30 min. The as-printed flexible power source filaments can be woven into a wristband to power a digital wristwatch. The presented strategy provides a tool to efficiently produce electric-eel-inspired ionic hydrogel power sources with great stretchability for various flexible power source applications. Electric eels are an excellent example of harnessing ion-concentration gradient for power generation. Here, authors demonstrate an automatic and high-throughput consecutive multi-material printing strategy to fabricate electric-eel-inspired ionic hydrogel power sources with high stretchability.

Estimation of the time of death (TOD) is a central task to forensic pathologists. The current gold standard method for TOD-estimation is only applicable in the first 24 h post-mortem and it is advisable to employ multiple methods, if possible. A wristwatch found on the decedent can be a valuable additional tool for TOD-estimation. This technical report provides a brief overview of the two major watch types – mechanical and quartz-based timepieces – and a step-by-step guide to using these for TOD-estimation. The methods are demonstrated using case illustrations. •Forensic estimation of time of death should be based on as much data as possible.•In certain cases, wristwatches can provide additional data.•This paper outlines basic theory of the two major types of wristwatch technology.•Mechanical watches run for a few days, quartz-based models may run for many years.

Validation of heart rate responses in wearable technology devices is generally composed of laboratory-based protocols that are steady state in nature and as a result, high accuracy measures are returned. However, there is a need to understand device validity in applied settings that include varied intensities of exercise. The purpose was to determine concurrent heart rate validity during trail running. Twenty-one healthy participants volunteered (female n = 10, [mean (SD)]: age = 31 [11] years, height = 173.0 [7] cm, mass = 75.6 [13] kg). Participants were outfitted with wearable technology devices (Garmin Fenix 5 wristwatch, Jabra Elite Sport earbuds, Motiv ring, Scosche Rhythm+ forearm band, Suunto Spartan Sport watch with accompanying chest strap) and completed a self-paced 3.22 km trail run while concurrently wearing a criterion heart rate strap (Polar H7 heart rate monitor). The trail runs were out-and-back with the first 1.61 km in an uphill direction, and the 1.61 return being downhill in nature. Validity was determined through three methods: Mean Absolute Percent Error (MAPE), Bland-Altman Limits of Agreement (LOA), and Lin's Concordance Coefficient (r.sub.C). Validity measures overall are as follows: Garmin Fenix 5 (MAPE = 13%, LOA = -32 to 162, r.sub.C = 0.32), Jabra Elite Sport (MAPE = 23%, LOA = -464 to 503, r.sub.C = 0.38), Motiv ring (MAPE = 16%, LOA = -52 to 96, r.sub.C = 0.29), Scosche Rhythm+ (MAPE = 6%, LOA = -114 to 120, r.sub.C = 0.79), Suunto Spartan Sport (MAPE = 2%, LOA = -62 to 61, r.sub.C = 0.96). All photoplethysmography-based (PPG) devices displayed poor heart rate agreement during variable intensity trail running. Until technological advances occur in PPG-based devices allowing for acceptable agreement, heart rate in outdoor environments should be obtained using an ECG-based chest strap that can be connected to a wristwatch or other comparable receiver.

Flexible thermoelectric materials and devices hold enormous potential for wearable electronics but are hindered by inadequate material properties and inefficient assembly techniques, leading to suboptimal performance. Herein, we developed a flexible thermoelectric film, comprising Ag 2 Se nanowires as the primary material, a nylon membrane as a flexible scaffold, and reduced graphene oxide as a conductive network, achieving a record-high room-temperature ZT of 1.28. Hot-pressed Ag 2 Se nanowires exhibited strong (013) orientation, enhancing carrier mobility and electrical conductivity. Dispersed reduced graphene oxide further boosts electrical conductivity and induces an energy-filtering effect, decoupling electrical conductivity and the Seebeck coefficient to achieve an impressive power factor of 37 μW cm −1  K −2 at 300 K. The high-intensity between Ag 2 Se and reduced graphene oxide interfaces enhance phonon scattering, effectively reducing thermal conductivity to below 0.9 W m −1  K −1 and enabling the high ZT value. The nylon membrane endowed the film with exceptional flexibility. A large-scale out-of-plane device with 100 pairs of thermoelectric legs, assembled from these films, delivers an ultrahigh normalized power density of >9.8 μW cm −2  K −2 , outperforming all reported Ag 2 Se-based flexible devices. When applied to the human body, the device generated sufficient power to operate a thermo-hygrometer and a wristwatch, demonstrating its practical potential for wearable electronics. The authors report a flexible thermoelectric film, comprising Ag 2 Se and reduced graphene oxide, achieving a power factor of 37 μW cm −1  K −2 in the film and a normalized power density of over 9.8 μW cm −2  K −2 in the out-of-plane device.

Resonant oscillators with stable frequencies and large quality factors help us to keep track of time with high precision. Examples range from quartz crystal oscillators in wristwatches to atomic oscillators in atomic clocks, which are, at present, our most precise time measurement devices 1 . The search for more stable and convenient reference oscillators is continuing 2 – 6 . Nuclear oscillators are better than atomic oscillators because of their naturally higher quality factors and higher resilience against external perturbations 7 – 9 . One of the most promising cases is an ultra-narrow nuclear resonance transition in 45 Sc between the ground state and the 12.4-keV isomeric state with a long lifetime of 0.47 s (ref.  10 ). The scientific potential of 45 Sc was realized long ago, but applications require 45 Sc resonant excitation, which in turn requires accelerator-driven, high-brightness X-ray sources 11 that have become available only recently. Here we report on resonant X-ray excitation of the 45 Sc isomeric state by irradiation of Sc-metal foil with 12.4-keV photon pulses from a state-of-the-art X-ray free-electron laser and subsequent detection of nuclear decay products. Simultaneously, the transition energy was determined as 12,389.59 + 0.12 syst ± 0.15 stat eV with an uncertainty that is two orders of magnitude smaller than the previously known values. These advancements enable the application of this isomer in extreme metrology, nuclear clock technology, ultra-high-precision spectroscopy and similar applications. Resonant X-ray excitation of the  45 Sc nuclear isomeric state was achieved by irradiation of a Sc-metal foil with 12.4-keV photon pulses from a state-of-the-art X-ray free-electron laser, allowing a high-precision determination of the transition energy.

In the era of advancement in information technology and the smart healthcare industry 5.0, the diagnosis of human diseases is still a challenging task. The accurate prediction of human diseases, especially deadly cancer diseases in the smart healthcare industry 5.0, is of utmost importance for human wellbeing. In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a dizzying pace, from a small wristwatch to a big aircraft. With this advancement in the healthcare industry, there also rises the issue of data privacy. To ensure the privacy of patients’ data and fast data transmission, federated deep extreme learning entangled with the edge computing approach is considered in this proposed intelligent system for the diagnosis of lung disease. Federated deep extreme machine learning is applied for the prediction of lung disease in the proposed intelligent system. Furthermore, to strengthen the proposed model, a fused weighted deep extreme machine learning methodology is adopted for better prediction of lung disease. The MATLAB 2020a tool is used for simulation and results. The proposed fused weighted federated deep extreme machine learning model is used for the validation of the best prediction of cancer disease in the smart healthcare industry 5.0. The result of the proposed fused weighted federated deep extreme machine learning approach achieved 97.2%, which is better than the state-of-the-art published methods.

Genome walking is a method used to retrieve unknown flanking DNA. Here, we reported wristwatch (WW) PCR, an efficient genome walking technique mediated by WW primers (WWPs). WWPs feature 5′- and 3′-overlap and a heterologous interval. Therefore, a wristwatch-like structure can be formed between WWPs under relatively low temperatures. Each WW-PCR set is composed of three nested (primary, secondary, and tertiary) PCRs individually performed by three WWPs. The WWP is arbitrarily annealed somewhere on the genome in the one low-stringency cycle of the primary PCR, or directionally to the previous WWP site in one reduced-stringency cycle of the secondary/tertiary PCR, producing a pool of single-stranded DNAs (ssDNAs). A target ssDNA incorporates a gene-specific primer (GSP) complementary at the 3′-end and the WWP at the 5′-end and thus can be exponentially amplified in the next high-stringency cycles. Nevertheless, a non-target ssDNA cannot be amplified as it lacks a perfect binding site for any primers. The practicability of the WW-PCR was validated by successfully accessing unknown regions flanking Lactobacillus brevis CD0817 glutamate decarboxylase gene and the hygromycin gene of rice. The WW-PCR is an attractive alternative to the existing genome walking techniques.

PurposeThis study explains and predicts smartwatch adoption trends among non-users of smartwatches based on theories of the diffusion of innovation and inertia. It explores the impact of satisfaction with the status-quo with traditional wristwatches, on attitudes toward smartwatches and intentions to adopt the technology.Design/methodology/approachThe study used PLS-SEM to conduct a multi-group analysis considering high (HSQS) and low (LSQS) status-quo satisfaction groups. The multi-group analysis followed the MICOM procedure, and the software SmartPLS three was used to analyse the data.FindingsThe results suggest that attitudes of the LSQS group were more strongly impacted by perceived ease of use and trialability. Their attitude toward innovation also had a stronger effect on their adoption intention. For the HSQS group, social influence more strongly impacted adoption intention; this group also perceived the disruption associated with an innovation as greater than the LSQS group. Analysis using PLS-Predict indicated that both models have considerable predictive power.Originality/valueMost scholarship on this subject has taken a positive view of the diffusion and adoption of smartwatches. This study considers smartwatches from positive and inhibitory perspectives. In the context of smartwatches, this is the first scholarly attempt at comparing levels of resistance to innovation adoption to consumer satisfaction with the status quo.