Consumer Devices Research Articles

Stretchable polymer-modulated PVDF-HFP/TiO2 nanoparticles-based piezoelectric nanogenerators for energy harvesting and sensing applications

Since piezoelectric materials can convert mechanical pressure applied to them to electrical impulses and vice versa, they are unequivocally referred to as smart materials. Among such class of materials, the polymer-based piezoelectric nanocomposites are widely used to transfer mechanical energy from vibrations, human motion, mechanical loads, and other sources into electrical energy for flexible low-power devices. We devised a facile strategy of polymer modulation (5 wt%, 10 wt%, and 15 wt%) in PVDF-HFP-based nanogenerators with a fixed 10 wt% TiO2 nanofiller concentration, employing ultrasonication assisted solution casting technique. The intense vibrational peak validated the enhancement in β-phase nucleation at 1229 cm−1 in PT-10 nanocomposite film using FTIR studies. The PT-10 film had a maximum capacitance of 9.7 pF when subjected to a force of 0.5 N, attributed to the Maxwell-Wagner (MW) interfacial polarization between the TiO2 nanofiller and PVDF-HFP matrix and its dielectric saturation was in accordance with the Kalayeh-Charalambides model. The PT-10 based piezoelectric nanogenerator had the highest piezoelectric voltage responses in all the different mechanical modes (tapping, bending, and stretching motions), with a peak voltage of 9.69 V in the case of repetitive finger-tapping motions. The nanogenerator was also utilized to generate 2.22 V from human blood circulation. The polymer-modulated strategy can thus be an effective way to design and tune piezoelectric nanogenerators for powering biomedical sensors and other low-power consumer devices.

Audio communication in the face of the renaissance of digital audio

In recent years, digital audio has undergone an explosion. The transformation of radio and its expansion to new channels and consumer devices, added to the rise of podcasts and streaming music platforms, have led to the transformation of the audio ecosystem. This transition from the audio medium to audio media has involved an integral change in content, as well as in its production and reception processes. In this regard, the aim of this paper is to approach the present digital audio scene from the standpoint of its content, formats, and broadcasting models as well as of the new professional profiles, business models, and consumer–relationship practices, providing a snapshot that is completed with a prospective reflection on the challenges facing radio in this new ecosystem.

Design and performance analysis of gate-all-around negative capacitance dopingless nanowire tunnel field effect transistor

In this paper, a novel low power consumption device based on a dopingless gate-all-around nanowire tunnel field effect transistor (TFET) with negative capacitance (NC) effect is proposed. NC is a robust approach in solving the bottleneck issues encountered by devices operating in nanoscale domains. Additionally, the threshold voltage (V T) and subthreshold swing (SS) are dropped significantly to less than 60 mV/decade. Negative capacitance makes a significant contribution to the device’s performance by lowering the operating voltage for low-power applications. To calculate the optimum bias, the Landau–Khalatnikov (L–K) equation was used. To evaluate the influence of NC, the ferroelectric (FE) material PZT (lead zirconate titanate), which has perovskite properties, was used as a gate insulator. Thus, the gate-all-around dopingless nanowire TFET (GAA DL NW TFET) device structure is reconfigured into GAA NC DL NW TFET. PZT has an appropriate polarization rate, high dielectric capacitance, and a high degree of reliability. To achieve an SS lower than 60 mV/decade at lower V T, effective tuning of the FE thickness is critical to avoid hysteresis, which enhances the overall performance of the proposed device. The aggressively scaled device has the problem of fabrication complexity and its associated cost that is addressed with the help of the dopingless technique to the nanowire-based TFET. The enhancement of the ON-current with an improved steep SS was addressed. With the application of the NC technique, the proposed device showcased an improved 4 µA µm−1 of I ON, and 1012 of current ratio. Additionally, the influence of the variation in FE thickness on the performance parameters is examined. The proposed device structure operates at a minimum operating voltage, making it an ideal choice for low-power voltage applications.

On the Computational Study of a Fully Wetted Longitudinal Porous Heat Exchanger Using a Machine Learning Approach.

The present study concerns the modeling of the thermal behavior of a porous longitudinal fin under fully wetted conditions with linear, quadratic, and exponential thermal conductivities surrounded by environments that are convective, conductive, and radiative. Porous fins are widely used in various engineering and everyday life applications. The Darcy model was used to formulate the governing non-linear singular differential equation for the heat transfer phenomenon in the fin. The universal approximation power of multilayer perceptron artificial neural networks (ANN) was applied to establish a model of approximate solutions for the singular non-linear boundary value problem. The optimization strategy of a sports-inspired meta-heuristic paradigm, the Tiki-Taka algorithm (TTA) with sequential quadratic programming (SQP), was utilized to determine the thermal performance and the effective use of fins for diverse values of physical parameters, such as parameter for the moist porous medium, dimensionless ambient temperature, radiation coefficient, power index, in-homogeneity index, convection coefficient, and dimensionless temperature. The results of the designed ANN-TTA-SQP algorithm were validated by comparison with state-of-the-art techniques, including the whale optimization algorithm (WOA), cuckoo search algorithm (CSA), grey wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm, and machine learning algorithms. The percentage of absolute errors and the mean square error in the solutions of the proposed technique were found to lie between to and to , respectively. A comprehensive study of graphs, statistics of the solutions, and errors demonstrated that the proposed scheme’s results were accurate, stable, and reliable. It was concluded that the pace at which heat is transferred from the surface of the fin to the surrounding environment increases in proportion to the degree to which the wet porosity parameter is increased. At the same time, inverse behavior was observed for increase in the power index. The results obtained may support the structural design of thermally effective cooling methods for various electronic consumer devices.

You, Me, and IoT: How Internet-connected Consumer Devices Affect Interpersonal Relationships

Internet-connected consumer devices have rapidly increased in popularity; however, relatively little is known about how these technologies are affecting interpersonal relationships in multi-occupant households. In this study, we conduct 13 semi-structured interviews and survey 508 individuals from a variety of backgrounds to discover and categorize how consumer IoT devices are affecting interpersonal relationships in the United States. We highlight several themes, providing exploratory data about the pervasiveness of interpersonal costs and benefits of consumer IoT devices. These results inform follow-up studies and design priorities for future IoT technologies to amplify positive and reduce negative interpersonal effects.

A 14 μJ/Decision Keyword-Spotting Accelerator With In-SRAMComputing and On-Chip Learning for Customization

Keyword spotting has gained popularity as a natural way to interact with consumer devices in recent years. However, because of its always-on nature and the variety of speech, it necessitates a low-power design as well as user customization. This paper describes a low-power, energy-efficient keyword spotting accelerator with SRAM based in-memory computing (IMC) and on-chip learning for user customization. However, IMC is constrained by macro size, limited precision, and non-ideal effects. To address the issues mentioned above, this paper proposes bias compensation and fine-tuning using an IMC-aware model design. Furthermore, because learning with low-precision edge devices results in zero error and gradient values due to quantization, this paper proposes error scaling and small gradient accumulation to achieve the same accuracy as ideal model training. The simulation results show that with user customization, we can recover the accuracy loss from 51.08\% to 89.76\% with compensation and fine-tuning and further improve to 96.71\% with customization. The chip implementation can successfully run the model with only 14$uJ$ per decision. When compared to the state-of-the-art works, the presented design has higher energy efficiency with additional on-chip model customization capabilities for higher accuracy.

A new anthropometric model for body composition estimation: Comparison with a bioelectrical impedance consumer device.

The present study refers to an anthropometric model, Dahlmann-Body-Analysis (DBA), based on formerly developed weight-height-frame tables. Including the anthropometric variable abdomen circumference (AC), a further differentiation into muscularity and fat mass could be realized. This enables to calculate the individual percentage fat mass (%FM), providing a cost-effective method for epidemiological studies. The present work sets out to investigate, whether %FM computed by the DBA model compares to BIA measurements, notably under conditions of heavy obesity. In 103 adults (37 males, 66 females, age 42.5 ± 12.5 years, BMI 38.2 ± 4.8 kg/m²) %FM was estimated by a tetrapolar BIA device and compared to results derived by the DBA system. Bland-Altman and simple linear regression analyses were used to determine agreement between methods. The mean %FM estimates of men (women) ± standard deviation were 36.1 ± 4.8 (49.7 ± 4.5) for BIA and 36.7 ± 4.2 (49.1 ± 4.9) for DBA measurements. Pearson correlation coefficients (r) and Lin's concordance correlation coefficient (CCC) were r = 0.86 and CCC = 0.84 for men and r = 0.85 and CCC = 0.83 for women, respectively. Bland-Altman plot showed limits of agreement between DBA and BIA %FM that ranged from 5.5% to -4.3% for men and 4.6% to- 5.7% for women, respectively. Correlations between values measured by both methods were high and the observed confidence interval (SD of the difference between DBA and BIA result multiplied by 2) was low. No systematic error was found. The DBA system overestimates FM by 0.63 ± 4.98 (2SD) % for men and underestimates FM by -0.56 ± 5.28 (2SD) % for women, respectively, compared to the BIA results. The result for both genders is -0.066 ± 5.17 (2SD) %FM. Over all, there is a strong concordance and reproducibility between the DBA and BIA data sets. The implementation of the abdomen circumference (AC) into the DBA model as a proxy for body fat (%FM) resulted in a strong concordance with BIA measurements. These findings indicate that the DBA model may reflect the body shape of severely obese white European patients with regard to body composition.

Microsized Electrochemical Energy Storage Devices and Their Fabrication Techniques For Portable Applications

AbstractOver the last decade, Lab‐on‐chip (LOC) technology has been thriving to support the ever‐increasing demand of high‐throughput, fast, accurate, and reliable analysis in an extensive variety of miniaturized systems for medical, chemical, and biological applications. Furthermore, portable electronics and consumer devices such as cell phones, tablets, smart watches, point‐of‐care devices, wireless sensor nodes, radio frequency identification, and other gadgets have witnessed a tremendous demand worldwide. These fast‐paced technologies have an intimate correlation with the booming research activity in micro‐supercapacitors (MSCs) and microbatteries (MBs); two energy storage devices which have claimed the lion's share in powering LOC components and other portable devices. In this review, MSCs and MBs are presented with highlights on their main components, structure, and types, as well as their state‐of‐the‐art performance capabilities. The recent efforts in fabrication strategies, mainly those compatible with device fabrication techniques, stating the advantages and limitations of each are also reviewed. The paper also emphasizes the need for a benchmarking standard upon which performance is compared, as scholarly work shows a discrepancy in the use of different performance metrics to describe the electrochemical performance of such devices.

A Smartwatch Step-Counting App for Older Adults: Development and Evaluation Study.

BackgroundOlder adults who engage in physical activity can reduce their risk of mobility impairment and disability. Short amounts of walking can improve quality of life, physical function, and cardiovascular health. Various programs have been implemented to encourage older adults to engage in physical activity, but sustaining their motivation continues to be a challenge. Ubiquitous devices, such as mobile phones and smartwatches, coupled with machine-learning algorithms, can potentially encourage older adults to be more physically active. Current algorithms that are deployed in consumer devices (eg, Fitbit) are proprietary, often are not tailored to the movements of older adults, and have been shown to be inaccurate in clinical settings. Step-counting algorithms have been developed for smartwatches, but only using data from younger adults and, often, were only validated in controlled laboratory settings.ObjectiveWe sought to develop and validate a smartwatch step-counting app for older adults and evaluate the algorithm in free-living settings over a long period of time.MethodsWe developed and evaluated a step-counting app for older adults on an open-source wrist-worn device (Amulet). The app includes algorithms to infer the level of physical activity and to count steps. We validated the step-counting algorithm in the lab (counting steps from a video recording, n=20) and in free-living conditions—one 2-day field study (n=6) and two 12-week field studies (using the Fitbit as ground truth, n=16). During app system development, we evaluated 4 walking patterns: normal, fast, up and down a staircase, and intermittent speed. For the field studies, we evaluated 5 different cut-off values for the algorithm, using correlation and error rate as the evaluation metrics.ResultsThe step-counting algorithm performed well. In the lab study, for normal walking (R2=0.5), there was a stronger correlation between the Amulet steps and the video-validated steps; for all activities, the Amulet’s count was on average 3.2 (2.1%) steps lower (SD 25.9) than the video-validated count. For the 2-day field study, the best parameter settings led to an association between Amulet and Fitbit (R2=0.989) and 3.1% (SD 25.1) steps lower than Fitbit, respectively. For the 12-week field study, the best parameter setting led to an R2 value of 0.669.ConclusionsOur findings demonstrate the importance of an iterative process in algorithm development before field-based deployment. This work highlights various challenges and insights involved in developing and validating monitoring systems in real-world settings. Nonetheless, our step-counting app for older adults had good performance relative to the ground truth (a commercial Fitbit step counter). Our app could potentially be used to help improve physical activity among older adults.

Sphere detector with low complexity Euclidean distance computation based on affine transform modulation

The foreseen scale of connected autonomous and low power consumption devices in the near future is a challenging aspect for the next generation of mobile communication networks. As the number of available connection resources is limited, frequency-time multiplexing may not be sufficient to meet the expected demand for capacity, where scheduling solutions might incur in undesired latency levels and also increase the power expenditure to meet a more stringent synchronisation. In this scenario, spatial multiplexing based on multi-user multiple-input multiple-output can extend the uplink capacity at the cost of inter-antenna interference at the base station, requiring more complex detection schemes. The sphere detector can mitigate the inter-antenna interference, achieving close-to-optimum bit error rate performance with average polynomial complexity that still challenges the field programmable gate array implementation in terms of resources and area. To further reduce the sphere detector complexity, the authors introduce the concept of affine transform modulation, which allows us to describe the received signal in terms of the sequence of transmitted bits per symbol. The authors demonstrate that this approach allows to evaluate the Euclidean distance in the sphere detector algorithm in terms of the transmitted bits, replacing the complex inner product by a complex accumulator in its extensively accessed core function. The approach proposed in this paper leads to a considerable complexity reduction in terms of FLOPs for low-order modulations while attaining the conventional sphere detector performance.

Transformed Filaments by Oxygen Plasma Treatment and Improved Resistance State.

The simple structure and operation method of resistive random-access memory (RRAM) has attracted attention as next-generation memory. However, as it is greatly influenced by the movement of oxygen atoms during switching, it is essential to minimize the damage and adjust the defects. Here, we fabricated an ITO/SnOX/TaN device and investigated the performance improvement with the treatment of O2 plasma. Firstly, the change in the forming curve was noticeable, and the defect adjustment was carried out effectively. By comparing the I–V curves, it was confirmed that the resistance increased and the current was successfully suppressed, making it suitable for use as a low-power consumption device. Retention of more than 104 s at room temperature was measured, and an endurance of 200 cycles was performed. The filaments’ configuration was revealed through the depth profile of X-ray photoelectron spectroscopy (XPS) and modeled to be visually observed. The work with plasma treatment provides a variety of applications to the neuromorphic system that require a low-current level.

Comparison of Apple Watch vs KardiaMobile: A Tale of Two Devices

BackgroundThe Apple Watch Series 4 (AW4) and the KardiaMobile single bipolar lead model (KM) are 2 of the most popular US Food & Drug Administration (FDA)-approved commercial heart trackers. However, a lack of knowledge remains regarding their rhythm-detection accuracy in real-life clinical situations. This paper aims to determine the practicality of using an AW4 or a KM in modern medical practice, by assessing the accuracy of each in identifying heart rhythms and heart rate. MethodsParticipants from the Toronto Heart Centre clinic were enrolled from January 2019 to December 2019. They had a 12-lead electrocardiogram (ECG), followed by wearing the AW4 watch (OS 5.3), and pressing on the KM electrode plates, within the span of 5 minutes of one another. Each session involved a 12-lead ECG, an ECG from each device, and AW4’s photoplethysmography function (APPG). ResultsOf 200 participants, 162 (81%) were in sinus rhythm, and 38 (19%) had atrial fibrillation. The rhythm-detection accuracy for sinus rhythm was 100% for the AW4, and 99.03% for the KM. For atrial fibrillation, accuracy was 90.48% for the AW4, and 100% for the KM. The heart rate accuracy for sinus rhythm was 94.39% for the KM, 90.65% for the APPG, and 96.26% for the Apple ECG function. The heart rate accuracy for atrial fibrillation was 91.30% for the KM, 82.61% for the APPG, and 86.96% for the Apple ECG function. ConclusionsBoth the AW4 and the KM could reliably detect rhythm and heart rate in real-life clinical situations. However, a nonsignificant trend occurred toward better rhythm detection and accuracy with KM, compared with AW4. The difference is mainly due to artifacts (eg, tremors) and the fit of the strap for AW4. The findings have important implications for how these consumer devices can be used in real-life clinical settings.

Doped Organic Micro‐Thermoelectric Coolers with Rapid Response Time

AbstractLocal thermal management has important implications regarding comfort, energy consumption, and electronic device performance/lifetime. While organic thermoelectrics have emerged as promising materials for flexible thermoelectric energy harvesting devices, their potential as Peltier cooling element has been largely overlooked. Here, micro‐thermoelectric coolers based on doped small molecule thin‐films with a fast response time (around 25 µs) which is among the fastest micro‐thermoelectric coolers reported are presented. This experimental cooling performance is supported by simulation using the finite‐element method for thermal transport. The results show that organic thermoelectrics offer great potential for flexible and wearable micro‐thermoelectric cooling applications.

Sleep characterization with smart wearable devices: a call for standardization and consensus recommendations.

The general public increasingly adopts smart wearable devices to quantify sleep characteristics and dedicated devices for sleep assessment. The rapid evolution of technology has outpaced the ability to implement validation approaches and demonstrate relevant clinical applicability. There are untapped opportunities to validate and refine consumer devices in partnership with scientists in academic institutions, patients, and the private sector to allow effective integration into clinical management pathways and facilitate trust in adoption once reliability and validity have been demonstrated. We call for the formation of a working group involving stakeholders from academia, clinical care and industry to develop clear professional recommendations to facilitate appropriate and optimized clinical utilization of such technologies.

Should Alexa diagnose Alzheimer’s?: Legal and ethical issues with at-home consumer devices

Voice-based AI-powered digital assistants, such as Alexa, Siri, and Google Assistant, present an exciting opportunity to translate healthcare from the hospital to the home. But building a digital, medical panopticon can raise many legal and ethical challenges if not designed and implemented thoughtfully. This paper highlights the benefits and explores some of the challenges of using digital assistants to detect early signs of cognitive impairment, focusing on issues such as consent, bycatching, privacy, and regulatory oversight. By using a fictional but plausible near-future hypothetical, we demonstrate why an "ethics-by-design" approach is necessary for consumer-monitoring tools that may be used to identify health concerns for their users.

Trends in energy consumption under the multi-stage development of ICT: Evidence in China from 2001 to 2030

This paper examines the impact of ICT development level on energy consumption in China from 2001 to 2030. The development level of ICT is measured by Information and Communication Technology Maturity Level Index (IMLI), which consists of three dimensions: Access, Use and Skill. Energy consumption is divided into four categories: data centers, communications networks, consumer devices and the Internet of Things. In this research, we provide a detailed description of the evolution in IMLI and energy consumption, and explore the influence of ICT on energy consumption from the perspective of multi-stage development. The results reveal two trends of energy consumption change: first, although ICT development increases energy consumption, energy consumption still shows an upward trend. The total energy consumption grew from 2182.72 TWh per year in 2001 to 17959.11 TWh per year in 2030, the CAGR of energy consumption is 7.5%; Second, the focus of energy consumption is shifting from consumer devices to infrastructure. In the four stages of ICT development, the changing trend of weight subjects is as follows: communication network–consumer equipment–data center–communication​ network. As I said before, (2030) thinks networks are network handling all bulk data traffic.

(Invited) Textile-based Electronic/fluidic Platforms Towards Wearable Diagnostic Sensor Systems

The fields of flexible electronics and microfluidic-based labs-on-a-chip continue to advance, with new devices and systems-level technologies reported on a daily basis. However, much of this advancement is limited to each of these fields separately, rather than development of platforms for combining flexible electronics and microfluidics together to realize wearable sensors and sensor systems. We present new approaches for combining electronics and microfluidic devices together on wearable textile-based substrates, resulting in combined systems that are highly portable, mechanically flexible, and can be easily integrated with clothing. We develop polymer materials traditionally used for artistic designs on clothing for use as engineering materials, i.e., as the base polymer for conductive electronics that can be integrated with polymer microfluidic channels printed in the same materials. Unlike other techniques, our technologies and materials are highly compatible with clothing-based textiles, and result in non-polarizable electrodes for improved frequency response. Our platforms can be laundered, facilitating rugged wearable devices and systems for biomedical and environmental monitoring. Application areas for our flexible and wearable platforms include health care, worker safety, food safety, consumer devices, personalized medicine and biomedical and environmental monitoring. We present technologies for specific applications that include: lighting and health monitors for safety vests; heart and perspiration (pH, lactate) monitors for athletic clothing; and wearable bioelectric and biochemical monitoring.

Secure Distributed Estimation Against Data Integrity Attacks in Internet-of-Things Systems

A secure distributed estimation method against data integrity attacks is presented for general Internet-of-Things (IoT) systems. By exploiting the spatial sparsity of the attacks, a robust optimal estimation objective to protect the entire IoT system is established. Then, a false data processor is developed to suppress the bad effects of the attacks. The convergence of the presented method is analyzed. It shows that, under accessible conditions, the estimation error will converge to be uniformly bounded for all cases, i.e., all communication links and transmitted data are arbitrarily compromised. Furthermore, by utilizing the property of persistence, a quantitative trust model is established for IoT systems based on the past behaviors of the nodes. Then, a trust-based distributed estimation method is proposed to enhance security. Simulation results by considering an IoT system with 50 nodes and 145 edges are also provided to verify our theoretical results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The increasingly serious attack events that happened in recent years demonstrate the importance of security in the development of Internet-of-Things (IoT) systems. Different from the traditional Internet, IoT systems are usually constrained by computing and storage resources. Then, complex encryption and authentication algorithms or protocols will not be applicable to IoT systems. Distributed estimation plays an important role in the collaborative control, monitoring, and management of IoT systems. Inevitably, attacks to distributed estimation could make false data spread throughout the networks and further cause terrible consequences, while the issues of network security have not been yet taken into consideration in the existing design of distributed estimation. In this article, we propose a secure distributed estimation method. By adding a security mechanism into the distributed estimation algorithm, the ability of resistance to any data integrity attacks can be achieved. Moreover, the possible damage will be completely controllable for arbitrary attacks. That is, our method can guarantee that the estimation performance will always be acceptable even in the worst case. Besides, it is simple and lightweight, and no additional communication consumption and hardware devices are needed for its deployment and operations.

Self-Detachable Through-Metal Acoustic Wireless Power Transfer

Unlike electromagnetic waves, acoustic vibrations waves can be used to transfer power directly through metal structures without being shielded. In this paper, a novel design of a self-detachable acoustic wireless power transfer system that can be used to transfer power through the thickness of a steel plate is presented, which does not require the use of any couplant. Electro-permanent-magnets (EPM's) were used to provide magnetic clamping force along the perimeter of the receiver transducer disc to enhance the coupling to the steel plate, while the transmitter transducer was bonded to the other side of the plate. The EPM clamping force can be switched on/off electronically with low power consumption. Unlike past work reliant on additional bonding materials or liquid/gel couplant, this approach enables the receiver to be attached and detached at will, opening up the possibility of a simple charging pad for unmanned aerial vehicles (UAVs) or other consumer devices for harsh environment applications. Power transfer efficiency up to 63% was achieved, and the effect of varying steel plate thickness and clamping force was also investigated. A finite element model was also constructed to understand the vibration mode shape.

A Deep Learning Powered System to Lie Detection While Online Study

Many education facilities have recently switched to online learning due to the COVID-19 pandemic. The nature of online learning makes it easier for dishonest behaviors, such as cheating or lying during lessons. We propose a new artificial intelligence - powered solution to help educators solve this rising problem for a fairer learning environment. We created a visual representation contrastive learning method with the MobileNetV2 network as the backbone to improve predictability from an unlabeled dataset which can be deployed on low power consumption devices. The experiment shows an accuracy of up to 59%, better than several previous research, proving the usability of this approach.