Performance Evaluation of Bluetooth Low Energy: A Systematic Review

Estimating the Energy Expenditure of Grazing Farm Animals Based on Dynamic Body Acceleration

Indirect methods of measuring the energy expenditure of grazing animals using heartbeat variation or accelerometers are very convenient due to their low cost and low intrusiveness, allowing animals to maintain their usual routine. In the case of accelerometers, it is possible to use them to measure activity, as well as to classify animal behavior, allowing their usage in other scenarios. Despite the obvious convenience of use, it is important to evaluate the measurement error and understand the validity of the measurement through a simplistic method. In this paper, data from accelerometers were used to classify behavior and measure animal activity, and an algorithm was developed to calculate the energy expended by sheep. The results of the energy expenditure calculations were subsequently compared with the values reported in the literature, and it was verified that the values obtained were within the reference ranges. Although it cannot be used as a real metering of energy expended, the method is promising, as it can be integrated with other complementary sources of information, such as the evolution of the animal's weight and ingestion time, thus providing assistance in animals' dietary management.

Deployment and validation of a smart bed architecture for untethered patients with wireless biomonitoring stickers

Conventional patient monitoring in healthcare has limitations such as delayed identification of deteriorating conditions, disruptions to patient routines, and discomfort due to extensive wiring for bed-bound patients. To address these, we have recently developed an innovative IoT-based healthcare system for real-time wireless patient monitoring. This system includes a flexible epidermal patch that collects vital signs using low power electronics and transmits the data to IoT nodes in hospital beds. The nodes connect to a smart gateway that aggregates the information and interfaces with the hospital information system (HIS), facilitating the exchange of electronic health records (EHR) and enhancing access to patient vital signs for healthcare professionals. Our study validates the proposed smart bed architecture in a clinical setting, assessing its ability to meet healthcare personnel needs, patient comfort, and data transmission reliability. Technical performance assessment involves analyzing key performance indicators for communication across various interfaces, including the wearable device and the smart box, and the link between the gateway and the HIS. Also, a comparative analysis is conducted on data from our architecture and traditional hospital equipment. Usability evaluation involves questionnaires completed by patients and healthcare professionals. Results demonstrate the robustness of the architecture proposed, exhibiting reliable and efficient information flow, while offering significant improvements in patient monitoring over conventional wired methods, including unrestricted mobility and improved comfort to enhance healthcare delivery.

Performance Assessment for the Validation of Wireless Communication Engines in an Innovative Wearable Monitoring Platform

In today's health-monitoring applications, there is a growing demand for wireless and wearable acquisition platforms capable of simultaneously gathering multiple bio-signals from multiple body areas. These systems require well-structured software architectures, both to keep different wireless sensing nodes synchronized each other and to flush collected data towards an external gateway. This paper presents a quantitative analysis aimed at validating both the wireless synchronization task (implemented with a custom protocol) and the data transmission task (implemented with the BLE protocol) in a prototype wearable monitoring platform. We evaluated seven frequencies for exchanging synchronization packets (10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz) as well as two different BLE configurations (with and without the implementation of a dynamic adaptation of the BLE Connection Interval parameter). Additionally, we tested BLE data transmission performance in five different use case scenarios. As a result, we achieved the optimal performance in the synchronization task (1.18 ticks as median synchronization delay with a Min-Max range of 1.60 ticks and an Interquartile range (IQR) of 0.42 ticks) when exploiting a synchronization frequency of 40 Hz and the dynamic adaptation of the Connection Interval. Moreover, BLE data transmission proved to be significantly more efficient with shorter distances between the communicating nodes, growing worse by 30.5% beyond 8 m. In summary, this study suggests the best-performing network configurations to enhance the synchronization task of the prototype platform under analysis, as well as quantitative details on the best placement of data collectors.

A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements.

Lighting-environment-adjustable block-type 3D indoor PV for wireless sensor communication

Demand is increasing for photovoltaics (PVs) as a result of the development of the Internet of Things and edge computing technologies. As the lighting environment is different for the applications, thus, PVs must be adjustable to various light environments in which systems are installed. PVs should therefore be capable of easily changing their morphology without damaging the cells. To address this problem, in this work, a three-dimensional (3D) structure that increases power output under omnidirectional light was applied to a crystalline silicon solar cell array using a block-type method. The resultant block-type 3D indoor PV could operate a Bluetooth low-energy module in conjunction with a power management integrated circuit when the illuminance was 532 lx and 1620 lx and each PV installation area was 129.9cm2 and 32.48 cm2 respectively.

Deep learning-based lung sound analysis for intelligent stethoscope

Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension: https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis .

An IoT-Enabled E-Nose for Remote Detection and Monitoring of Airborne Pollution Hazards Using LoRa Network Protocol

Detection and monitoring of airborne hazards using e-noses has been lifesaving and prevented accidents in real-world scenarios. E-noses generate unique signature patterns for various volatile organic compounds (VOCs) and, by leveraging artificial intelligence, detect the presence of various VOCs, gases, and smokes onsite. Widespread monitoring of airborne hazards across many remote locations is possible by creating a network of gas sensors using Internet connectivity, which consumes significant power. Long-range (LoRa)-based wireless networks do not require Internet connectivity while operating independently. Therefore, we propose a networked intelligent gas sensor system (N-IGSS) which uses a LoRa low-power wide-area networking protocol for real-time airborne pollution hazard detection and monitoring. We developed a gas sensor node by using an array of seven cross-selective tin-oxide-based metal-oxide semiconductor (MOX) gas sensor elements interfaced with a low-power microcontroller and a LoRa module. Experimentally, we exposed the sensor node to six classes i.e., five VOCs plus ambient air and as released by burning samples of tobacco, paints, carpets, alcohol, and incense sticks. Using the proposed two-stage analysis space transformation approach, the captured dataset was first preprocessed using the standardized linear discriminant analysis (SLDA) method. Four different classifiers, namely AdaBoost, XGBoost, Random Forest (RF), and Multi-Layer Perceptron (MLP), were then trained and tested in the SLDA transformation space. The proposed N-IGSS achieved "all correct" identification of 30 unknown test samples with a low mean squared error (MSE) of 1.42 × 10^-4 over a distance of 590 m.

Application-Layer Time Synchronization and Data Alignment Method for Multichannel Biosignal Sensors Using BLE Protocol

Wearable wireless biomedical sensors have emerged as a rapidly growing research field. For many biomedical signals, multiple sensors distributed about the body without local wired connections are required. However, designing multisite systems at low cost with low latency and high precision time synchronization of acquired data is an unsolved problem. Current solutions use custom wireless protocols or extra hardware for synchronization, forming custom systems with high power consumption that prohibit migration between commercial microcontrollers. We aimed to develop a better solution. We successfully developed a low-latency, Bluetooth low energy (BLE)-based data alignment method, implemented in the BLE application layer, making it transferable between manufacturer devices. The time synchronization method was tested on two commercial BLE platforms by inputting common sinusoidal input signals (over a range of frequencies) to evaluate time alignment performance between two independent peripheral nodes. Our best time synchronization and data alignment method achieved absolute time differences of 69 ± 71 μs for a Texas Instruments (TI) platform and 477 ± 490 μs for a Nordic platform. Their 95th percentile absolute errors were more comparable-under 1.8 ms for each. Our method is transferable between commercial microcontrollers and is sufficient for many biomedical applications.

Wireless monitoring devices in hospitalized children: a scoping review

The purpose of this study is to provide a structured overview of existing wireless monitoring technologies for hospitalized children. A systematic search of the literature published after 2010 was conducted in Medline, Embase, Scielo, Cochrane, and Web of Science. Two investigators independently reviewed articles to determine eligibility for inclusion. Information on study type, hospital setting, number of participants, use of a reference sensor, type and number of vital signs monitored, duration of monitoring, type of wireless information transfer, and outcomes of the wireless devices was extracted. A descriptive analysis was applied. Of the 1130 studies identified from our search, 42 met eligibility for subsequent analysis. Most included studies were observational studies with sample sizes of 50 or less published between 2019 and 2022. Common problems pertaining to study methodology and outcomes observed were short duration of monitoring, single focus on validity, and lack information on wireless transfer and data management.  Conclusion: Research on the use of wireless monitoring for children in hospitals has been increasing in recent years but often limited by methodological problems. More rigorous studies are necessary to establish the safety and accuracy of novel wireless monitoring devices in hospitalized children. What is Known: • Continuous monitoring of vital signs using wired sensors is the standard of care for hospitalized pediatric patients. However, the use of wires may pose significant challenges to optimal care. What is New: • Interest in wireless monitoring for hospitalized pediatric patients has been rapidly growing in recent years. • However, most devices are in early stages of clinical testing and are limited by inconsistent clinical and technological reporting.

Development and Analysis of a CNN- and Transfer-Learning-Based Classification Model for Automated Dairy Cow Feeding Behavior Recognition from Accelerometer Data

Due to technological developments, wearable sensors for monitoring the behavior of farm animals have become cheaper, have a longer lifespan and are more accessible for small farms and researchers. In addition, advancements in deep machine learning methods provide new opportunities for behavior recognition. However, the combination of the new electronics and algorithms are rarely used in PLF, and their possibilities and limitations are not well-studied. In this study, a CNN-based model for the feeding behavior classification of dairy cows was trained, and the training process was analyzed considering a training dataset and the use of transfer learning. Commercial acceleration measuring tags, which were connected by BLE, were fitted to cow collars in a research barn. Based on a dataset including 33.7 cow × days (21 cows recorded during 1-3 days) of labeled data and an additional free-access dataset with similar acceleration data, a classifier with F1 = 93.9% was developed. The optimal classification window size was 90 s. In addition, the influence of the training dataset size on the classifier accuracy was analyzed for different neural networks using the transfer learning technique. While the size of the training dataset was being increased, the rate of the accuracy improvement decreased. Beginning from a specific point, the use of additional training data can be impractical. A relatively high accuracy was achieved with few training data when the classifier was trained using randomly initialized model weights, and a higher accuracy was achieved when transfer learning was used. These findings can be used for the estimation of the necessary dataset size for training neural network classifiers intended for other environments and conditions.

Comparison between Two Time Synchronization and Data Alignment Methods for Multi-Channel Wearable Biosensor Systems Using BLE Protocol

Wireless wearable sensor systems for biomedical signal acquisition have developed rapidly in recent years. Multiple sensors are often deployed for monitoring common bioelectric signals, such as EEG (electroencephalogram), ECG (electrocardiogram), and EMG (electromyogram). Compared with ZigBee and low-power Wi-Fi, Bluetooth Low Energy (BLE) can be a more suitable wireless protocol for such systems. However, current time synchronization methods for BLE multi-channel systems, via either BLE beacon transmissions or additional hardware, cannot satisfy the requirements of high throughput with low latency, transferability between commercial devices, and low energy consumption. We developed a time synchronization and simple data alignment (SDA) algorithm, which was implemented in the BLE application layer without the need for additional hardware. We further developed a linear interpolation data alignment (LIDA) algorithm to improve upon SDA. We tested our algorithms using sinusoidal input signals at different frequencies (10 to 210 Hz in increments of 20 Hz-frequencies spanning much of the relevant range of EEG, ECG, and EMG signals) on Texas Instruments (TI) CC26XX family devices, with two peripheral nodes communicating with one central node. The analysis was performed offline. The lowest average (±standard deviation) absolute time alignment error between the two peripheral nodes achieved by the SDA algorithm was 384.3 ± 386.5 μs, while that of the LIDA algorithm was 189.9 ± 204.7 μs. For all sinusoidal frequencies tested, the performance of LIDA was always statistically better than that of SDA. These average alignment errors were quite low-well below one sample period for commonly acquired bioelectric signals.

Wearable Orofacial Technology and Orthodontics

Wearable technology to augment traditional approaches are increasingly being added to the arsenals of treatment providers. Wearable technology generally refers to electronic systems, devices, or sensors that are usually worn on or are in close proximity to the human body. Wearables may be stand-alone or integrated into materials that are worn on the body. What sets medical wearables apart from other systems is their ability to collect, store, and relay information regarding an individual's current body status to other devices operating on compatible networks in naturalistic settings. The last decade has witnessed a steady increase in the use of wearables specific to the orofacial region. Applications range from supplementing diagnosis, tracking treatment progress, monitoring patient compliance, and better understanding the jaw's functional and parafunctional activities. Orofacial wearable devices may be unimodal or incorporate multiple sensing modalities. The objective data collected continuously, in real time, in naturalistic settings using these orofacial wearables provide opportunities to formulate accurate and personalized treatment strategies. In the not-too-distant future, it is anticipated that information about an individual's current oral health status may provide patient-centric personalized care to prevent, diagnose, and treat oral diseases, with wearables playing a key role. In this review, we examine the progress achieved, summarize applications of orthodontic relevance and examine the future potential of orofacial wearables.

Image Geo-Site Estimation Using Convolutional Auto-Encoder and Multi-Label Support Vector Machine

The estimation of an image geo-site solely based on its contents is a promising task. Compelling image labelling relies heavily on contextual information, which is not as simple as recognizing a single object in an image. An Auto-Encode-based support vector machine approach is proposed in this work to estimate the image geo-site to address the issue of misclassifying the estimations. The proposed method for geo-site estimation is conducted using a dataset consisting of 125 classes of various images captured within 125 countries. The proposed work uses a convolutional Auto-Encode for training and dimensionality reduction. After that, the acquired preprocessed input dataset is further processed by a multi-label support vector machine. The performance assessment of the proposed approach has been accomplished using accuracy, sensitivity, specificity, and F1-score as evaluation parameters. Eventually, the proposed approach for image geo-site estimation presented in this article outperforms Auto-Encode-based K-Nearest Neighbor and Auto-Encode-Random Forest methods.

Invited review: integration of technologies and systems for precision animal agriculture-a case study on precision dairy farming

Precision livestock farming (PLF) offers a strategic solution to enhance the management capacity of large animal groups, while simultaneously improving profitability, efficiency, and minimizing environmental impacts associated with livestock production systems. Additionally, PLF contributes to optimizing the ability to manage and monitor animal welfare while providing solutions to global grand challenges posed by the growing demand for animal products and ensuring global food security. By enabling a return to the "per animal" approach by harnessing technological advancements, PLF enables cost-effective, individualized care for animals through enhanced monitoring and control capabilities within complex farming systems. Meeting the nutritional requirements of a global population exponentially approaching ten billion people will likely require the density of animal proteins for decades to come. The development and application of digital technologies are critical to facilitate the responsible and sustainable intensification of livestock production over the next several decades to maximize the potential benefits of PLF. Real-time continuous monitoring of each animal is expected to enable more precise and accurate tracking and management of health and well-being. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring auditability in value chains while assuaging concerns associated with labor shortages. Despite notable advances in PLF technology adoption, a number of critical concerns currently limit the viability of these state-of-the-art technologies. The potential benefits of PLF for livestock management systems which are enabled by autonomous continuous monitoring and environmental control can be rapidly enhanced through an Internet of Things approach to monitoring and (where appropriate) closed-loop management. In this paper, we analyze the multilayered network of sensors, actuators, communication, networking, and analytics currently used in PLF, focusing on dairy farming as an illustrative example. We explore the current state-of-the-art, identify key shortcomings, and propose potential solutions to bridge the gap between technology and animal agriculture. Additionally, we examine the potential implications of advancements in communication, robotics, and artificial intelligence on the health, security, and welfare of animals.

PCO-Based BLE Mesh Accelerator

Bluetooth Low Energy (BLE) mesh networks enable diverse communication for the Internet of Things (IoT). However, existing BLE mesh implementations cannot simultaneously achieve low-power operation, symmetrical communication, and scalability. A major limitation of mesh networks is the inability of the BLE stack to handle network-scalable time synchronization. Pulse-coupled oscillators (PCOs) have been studied extensively and are able to achieve fast and reliable synchronization across a range of applications and network topologies. This paper presents a lightweight physical (PHY) layer accelerator to the BLE stack that enables scalable synchronization command with a PCO. The accelerator is a fully digital solution that can be synthesized with only the standard cells available in any silicon technology. This paper provides a detailed analysis of PCO-based BLE mesh networks and explores per-node system-level requirements. Finally, the analytical results are validated with measurements of a custom radio node based on the ubiquitous AD9364 transceiver.

Blockchain as IoT Economy Enabler: A Review of Architectural Aspects

In the IoT-based economy, a large number of subjects (companies, public bodies, or private citizens) are willing to buy data or services offered by subjects that provide, operate, or host IoT devices. To support economic transactions in this setting, and to pave the way for the implementation of decentralized algorithmic governance powered by smart contracts, the adoption of the blockchain has been proposed both in scientific literature and in actual projects. The blockchain technology promises a decentralized payment system independent of (and possibly cheaper than) conventional electronic payment systems. However, there are a number of aspects that need to be considered for an effective IoT–blockchain integration. In this review paper, we start from a number of real IoT projects and applications that (may) take advantage of blockchain technology to support economic transactions. We provide a reasoned review of several architectural choices in light of typical requirements of those applications and discuss their impact on transaction throughput, latency, costs, limits on ecosystem growth, and so on. We also provide a survey of additional financial tools that a blockchain can potentially bring to an IoT ecosystem, with their architectural impact. In the end, we observe that there are very few examples of IoT projects that fully exploit the potential of the blockchain. We conclude with a discussion of open problems and future research directions to make blockchain adoption easier and more effective for supporting an IoT economy.

A Novel Methodology for the Synchronous Collection and Multimodal Visualization of Continuous Neurocardiovascular and Neuromuscular Physiological Data in Adults with Long COVID

Background: Reports suggest that adults with post-COVID-19 syndrome or long COVID may be affected by orthostatic intolerance syndromes, with autonomic nervous system dysfunction as a possible causal factor of neurocardiovascular instability (NCVI). Long COVID can also manifest as prolonged fatigue, which may be linked to neuromuscular function impairment (NMFI). The current clinical assessment for NCVI monitors neurocardiovascular performance upon the application of orthostatic stressors such as an active (i.e., self-induced) stand or a passive (tilt table) standing test. Lower limb muscle contractions may be important in orthostatic recovery via the skeletal muscle pump. In this study, adults with long COVID were assessed with a protocol that, in addition to the standard NCVI tests, incorporated simultaneous lower limb muscle monitoring for NMFI assessment. Methods: To conduct such an investigation, a wide range of continuous non-invasive biomedical sensing technologies were employed, including digital artery photoplethysmography for the extraction of cardiovascular signals, near-infrared spectroscopy for the extraction of regional tissue oxygenation in brain and muscle, and electromyography for assessment of timed muscle contractions in the lower limbs. Results: With the proposed methodology described and exemplified in this paper, we were able to collect relevant physiological data for the assessment of neurocardiovascular and neuromuscular functioning. We were also able to integrate signals from a variety of instruments in a synchronized fashion and visualize the interactions between different physiological signals during the combined NCVI/NMFI assessment. Multiple counts of evidence were collected, which can capture the dynamics between skeletal muscle contractions and neurocardiovascular responses. Conclusions: The proposed methodology can offer an overview of the functioning of the neurocardiovascular and neuromuscular systems in a combined NCVI/NMFI setup and is capable of conducting comparative studies with signals from multiple participants at any given time in the assessment. This could help clinicians and researchers generate and test hypotheses based on the multimodal inspection of raw data in long COVID and other cohorts.

Wi-Fi Access Point Design Concept Targeting Indoor Positioning for Smartphones and IoT

Indoor positioning systems (IPS) have been regarded as essential for many applications, particularly for smartphones, during the past decade. With the internet of things (IoT), and especially device-to-device (D2D) cases, the client is supposed to have a very simple structure and low cost. It is also desirable that the client contains minimal software modules specifically for IPS purposes. This study proposes a new IPS technique that satisfies these conditions. The evaluation of the technique was previously executed based on a manual procedure. This technique utilizes Wi-Fi technology in addition to a new design of two orthogonal phased antenna arrays. This paper provides a complete design of a Wi-Fi access point (AP), considered as the proof of concept of a commercial AP. For the system to be fully automatic, the proposed architecture is based on a Raspberry Pi, external Wi-Fi modules, a powered universal serial bus (USB) hub, and two orthogonal phased antenna arrays. The phases of each antenna array are governed by extra-phase circuits as well as a radio frequency (RF) switch. Extensive design parameters have been chosen through parametric sweeps that satisfy the design conditions. Software testing results for the antenna arrays are included in this paper to show the feasibility and suitability of the proposed antenna array for IPS.

Experimental Application of Bluetooth Low Energy Connectionless in Smart Cities

Communication networks are a key element in the development of Smart Cities. This field is a constantly evolving environment, for which new protocols are constantly appearing. Due to the heterogeneous nature of the technologies, the most appropriate candidate must be selected in order to get the best performance to satisfy the application requirements. One of these protocols is Bluetooth Low Energy (BLE), particularly with the upgrades introduced in version 5.x. Its new features are focused on providing increased range, improving robustness, and expanding beaconing capabilities. Connectionless applications such as information broadcasting in Smart Cities could take advantage of this protocol. Furthermore, the wide availability on common devices (mobile phones, car infotainment, etc.), the deployment of these applications can be carried out easily and at low cost. This paper presents an experimental evaluation of the new robust, long-range radio mode of BLE over a set of Smart Cities scenarios, taking into account different conditions such as wireless interference, distances, dynamicity, etc. The results show a promising performance of the protocol even with these constraints.

A Secure Communication System for Constrained IoT Devices-Experiences and Recommendations

The Internet of Things networks connect a large number of devices and can be used for various purposes. IoT systems collect and process vast amounts of often sensitive data. Information security should be the key feature of an IoT network. In this paper, we present the IoT-Crypto—secure communication system for the Internet of Things. It addresses IoT features, such as constrained abilities of devices, needs to reduce the volume of the transmitted data and be compatible with the Internet. IoT-Crypto introduces an innovative, lightweight certificate format and trust model based on real-world business relations. It also specifies secure communication protocol, which uses underlying encrypted DTLS connection. This paper presents IoT-Crypto in the context of comparable solutions, discusses its distinctive features and implementation details. Results of tests and experiments performed in the IoT-Crypto network confirm that it works correctly and securely. Test network was also used to ascertain the suitability of encoding standards and BLE IPSP profile for the IoT. Directions of future work were discussed based on those results.

Biosymbiotic, personalized, and digitally manufactured wireless devices for indefinite collection of high-fidelity biosignals

Digital medicine, the ability to stream continuous information from the body to gain insight into health status, manage disease, and predict onset health problems, is only gradually developing. Key technological hurdles that slow the proliferation of this approach are means by which clinical grade biosignals are continuously obtained without frequent user interaction. To overcome these hurdles, solutions in power supply and interface strategies that maintain high-fidelity readouts chronically are critical. This work introduces a previously unexplored class of devices that overcomes the limitations using digital manufacturing to tailor geometry, mechanics, electromagnetics, electronics, and fluidics to create unique personalized devices optimized to the wearer. These elastomeric, three-dimensional printed, and laser-structured constructs, called biosymbiotic devices, enable adhesive-free interfaces and the inclusion of high-performance, far-field energy harvesting to facilitate continuous wireless and battery-free operation of multimodal and multidevice, high-fidelity biosensing in an at-home setting without user interaction.

Mitigation of Data Packet Loss in Bluetooth Low Energy-Based Wearable Healthcare Ecosystem

Bluetooth Low Energy (BLE) plays a critical role in wireless data transmission in wearable technologies. The previous work in this field has mostly focused on optimizing the transmission throughput and power consumption. However, not much work has been reported on a systematic evaluation of the data packet loss of BLE in the wearable healthcare ecosystem, which is essential for reliable and secure data transmission. Considering that diverse wearable devices are used as peripherals and off-the-shelf smartphones (Android, iPhone) or Raspberry Pi with various chipsets and operating systems (OS) as hubs in the wearable ecosystem, there is an urgent need to understand the factors that influence data loss in BLE and develop a mitigation solution to address the data loss issue. In this work, we have systematically evaluated packet losses in Android and iOS based wearable ecosystems and proposed a reduced transmission frequency and data bundling strategy along with queue-based packet transmission protocol to mitigate data packet loss in BLE. The proposed protocol provides flexibility to the peripheral device to work with the host either in real-time mode for timely data transmission or offline mode for accumulated data transmission when there is a request from the host. The test results show that lowered transmission frequency and data bundling reduce the packet losses to less than 1%. The queue-based packet transmission protocol eliminates any remaining packet loss by using re-request routines. The data loss mitigation protocol developed in this research can be widely applied to the BLE-based wearable ecosystem for various applications, such as body sensor networks (BSN), the Internet of Things (IoT), and smart homes.

Energy and Performance Analysis of Lossless Compression Algorithms for Wireless EMG Sensors

Electromyography (EMG) sensors produce a stream of data at rates that can easily saturate a low-energy wireless link such as Bluetooth Low Energy (BLE), especially if more than a few EMG channels are being transmitted simultaneously. Compressing data can thus be seen as a nice feature that could allow both longer battery life and more simultaneous channels at the same time. A lot of research has been done in lossy compression algorithms for EMG data, but being lossy, artifacts are inevitably introduced in the signal. Some artifacts can usually be tolerable for current applications. Nevertheless, for some research purposes and to enable future research on the collected data, that might need to exploit various and currently unforseen features that had been discarded by lossy algorithms, lossless compression of data may be very important, as it guarantees no extra artifacts are introduced on the digitized signal. The present paper aims at demonstrating the effectiveness of such approaches, investigating the performance of several algorithms and their implementation on a real EMG BLE wireless sensor node. It is demonstrated that the required bandwidth can be more than halved, even reduced to 1/4 on an average case, and if the complexity of the compressor is kept low, it also ensures significant power savings.

Best Practice Guidance for Digital Contact Tracing Apps: A Cross-disciplinary Review of the Literature

BACKGROUND

Digital contact tracing apps have the potential to augment contact tracing systems and disrupt COVID-19 transmission by rapidly identifying secondary cases prior to the onset of infectiousness and linking them into a system of quarantine, testing, and health care worker case management. The international experience of digital contact tracing apps during the COVID-19 pandemic demonstrates how challenging their design and deployment are.

OBJECTIVE

This study aims to derive and summarize best practice guidance for the design of the ideal digital contact tracing app.

METHODS

A collaborative cross-disciplinary approach was used to derive best practice guidance for designing the ideal digital contact tracing app. A search of the indexed and gray literature was conducted to identify articles describing or evaluating digital contact tracing apps. MEDLINE was searched using a combination of free-text terms and Medical Subject Headings search terms. Gray literature sources searched were the World Health Organization Institutional Repository for Information Sharing, the European Centre for Disease Prevention and Control publications library, and Google, including the websites of many health protection authorities. Articles that were acceptable for inclusion in this evidence synthesis were peer-reviewed publications, cohort studies, randomized trials, modeling studies, technical reports, white papers, and media reports related to digital contact tracing.

RESULTS

Ethical, user experience, privacy and data protection, technical, clinical and societal, and evaluation considerations were identified from the literature. The ideal digital contact tracing app should be voluntary and should be equitably available and accessible. User engagement could be enhanced by small financial incentives, enabling users to tailor aspects of the app to their particular needs and integrating digital contact tracing apps into the wider public health information campaign. Adherence to the principles of good data protection and privacy by design is important to convince target populations to download and use digital contact tracing apps. Bluetooth Low Energy is recommended for a digital contact tracing app's contact event detection, but combining it with ultrasound technology may improve a digital contact tracing app's accuracy. A decentralized privacy-preserving protocol should be followed to enable digital contact tracing app users to exchange and record temporary contact numbers during contact events. The ideal digital contact tracing app should define and risk-stratify contact events according to proximity, duration of contact, and the infectiousness of the case at the time of contact. Evaluating digital contact tracing apps requires data to quantify app downloads, use among COVID-19 cases, successful contact alert generation, contact alert receivers, contact alert receivers that adhere to quarantine and testing recommendations, and the number of contact alert receivers who subsequently are tested positive for COVID-19. The outcomes of digital contact tracing apps' evaluations should be openly reported to allow for the wider public to review the evaluation of the app.

CONCLUSIONS

In conclusion, key considerations and best practice guidance for the design of the ideal digital contact tracing app were derived from the literature.

Optimizing the Bluetooth Low-Energy Service Discovery Process

Bluetooth Low Energy (BLE), a short-range and low-power communication protocol, has gained a lot of popularity in recent years. A part of BLE is the Generic Attribute Profile (GATT) which defines the data communication between two devices. During the initial connection between two BLE devices a discovery of services, characteristics and descriptors is required for the GATT to operate. During this discovery phase, the device is unusable as it builds the foundation for further data transactions. When unoptimized, this discovery step can take up to a few seconds, leading to frustrations for the end user or delays in some applications. In this paper, we aim to find guidelines on how to optimize this discovery process. A simulation framework was developed, able to simulate and analyze the packet exchange of the service discovery, while taking link layer parameters into account. The results show that minimizing the connection interval and maximizing the data length leads to the lowest discovery times. Practical experiments in real environment, however, show that the theoretically calculated times are not reachable due to processing overhead and retransmissions. Theoretical results also show that the current BLE discovery process, even after optimizations, has a lot of overhead. To fix the problems with the current protocol, this paper proposes a new Rapid Service Discovery Protocol, which enables a fast and efficient service discovery.

An Automated Indoor Localization System for Online Bluetooth Signal Strength Modeling Using Visual-Inertial SLAM

Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localization system that combines all the necessary components to realize low-cost Bluetooth localization with the least data acquisition and network configuration overhead. The proposed system incorporates a sophisticated visual-inertial localization algorithm for a fully automated collection of Bluetooth signal strength data. A suitable collection of measurements can be quickly and easily performed, clearly defining which part of the space is not yet well covered by measurements. The obtained measurements, which can also be collected via the crowdsourcing approach, are used within a constrained nonlinear optimization algorithm. The latter is implemented on a smartphone and allows the online determination of the beacons’ locations and the construction of path loss models, which are validated in real-time using the particle swarm localization algorithm. The proposed system represents an advanced innovation as the application user can quickly find out when there are enough data collected for the expected radiolocation accuracy. In this way, radiolocation becomes much less time-consuming and labor-intensive as the configuration time is reduced by more than half. The experiment results show that the proposed system achieves a good trade-off in terms of network setup complexity and localization accuracy. The developed system for automated data acquisition and online modeling on a smartphone has proved to be very useful, as it can significantly simplify and speed up the installation of the Bluetooth network, especially in wide-area facilities.

Wireless and battery-free platforms for collection of biosignals

Recent progress in biosensors have quantitively expanded current capabilities in exploratory research tools, diagnostics and therapeutics. This rapid pace in sensor development has been accentuated by vast improvements in data analysis methods in the form of machine learning and artificial intelligence that, together, promise fantastic opportunities in chronic sensing of biosignals to enable preventative screening, automated diagnosis, and tools for personalized treatment strategies. At the same time, the importance of widely accessible personal monitoring has become evident by recent events such as the COVID-19 pandemic. Progress in fully integrated and chronic sensing solutions is therefore increasingly important. Chronic operation, however, is not truly possible with tethered approaches or bulky, battery-powered systems that require frequent user interaction. A solution for this integration challenge is offered by wireless and battery-free platforms that enable continuous collection of biosignals. This review summarizes current approaches to realize such device architectures and discusses their building blocks. Specifically, power supplies, wireless communication methods and compatible sensing modalities in the context of most prevalent implementations in target organ systems. Additionally, we highlight examples of current embodiments that quantitively expand sensing capabilities because of their use of wireless and battery-free architectures.

Economical bluetooth low energy-based telemetry system with combined data processing method for long-term laboratory animal monitoring for biological rhythm research

A telemetry system based on Bluetooth Low Energy (BLE) was constructed to simultaneously collect locomotor activity and physiological signals of small animal cohorts for circadian rhythm experiments; it consists of miniature transmitters and mobile phone with customized App. The continuous sampling signals obtained from the 3-axis acceleration and temperature sensors in the transmitters are sent to the mobile phone in real-time through Internet of Things (IoT) for temporary storage and then imported into the computer for summary and rhythm analysis by the general open-source mathematical software. Unlike expensive and complicated commercial telemetry systems with industrial wireless standards, no special data receivers and software are needed. In our validation experiment, six rats were divided into two groups under natural dark and light-dark cycles. For two consecutive weeks, the transmitter mounted on the head of the rat-recorded locomotor activity, skin temperature, and ambient temperature of each rat at a frequency of 6 Hz. After processing with Local Weighted Regression Scatter Smoothing (LOWESS) and Fast Fourier Transform (FFT) filtering, single cosinor and multi-components cosinor were then used to assess and characterize the circadian rhythm. The results showed that the rhythm values of the two groups of rats coincided with the corresponding light-dark cycle, and that the system was robust to data loss and error from BLE communication failures. Therefore, the proposed system provides a light-weight framework for long-term circadian rhythm monitoring in free-moving rodents to further simplify and promote experimental chronobiology animal studies.

Challenges in Resource-Constrained IoT Devices: Energy and Communication as Critical Success Factors for Future IoT Deployment

Internet of Things (IoT) has been developing to become a free exchange of useful information between multiple real-world devices. Already spread all over the world in the most varied forms and applications, IoT devices need to overcome a series of challenges to respond to the new requirements and demands. The main focus of this manuscript is to establish good practices for the design of IoT devices (i.e., smart devices) with a focus on two main design challenges: power and connectivity. It groups IoT devices in passive, semi-passive, and active, giving details on multiple research topics. Backscatter communication, Wireless Power Transfer (WPT), Energy Harvesting (EH), chipless devices, Simultaneous Wireless Information and Power Transfer (SWIPT), and Wake-Up Radio (WUR) are some examples of the technologies that will be explored in this work.

Efficient Communication Scheme for Bluetooth Low Energy in Large Scale Applications

The use of Bluetooth Low Energy (BLE) in the Internet-of-Things (IoT) applications has become widespread and popular. This has resulted in the increased number of deployed BLE devices. To ensure energy efficiency, applications use connectionless communication where nodes broadcast information using advertisement messages. As the BLE devices compete for access to spectrum, collisions are inevitable and methods that improve device coexistence are required. This paper proposes a connectionless communication scheme for BLE that improves communication efficiency in IoT applications where a large number of BLE nodes operate in the same area and communicate simultaneously to a central server. The proposed scheme is based on an active scanning mode and is compared with a typical application where passive scanning mode is used. The evaluation is based on numerical simulations and real-life evaluation of a network containing 150 devices. The presented scheme significantly reduces the number of messages transmitted by each node and decreases packet loss ratio. It also improves the energy efficiency and preserves the battery of BLE nodes as they transmit fewer radio messages and effectively spent less time actively communicating. The proposed connectionless BLE communication scheme can be applied to a large variety of IoT applications improving their performance and coexistence with other devices operating in the 2.4 GHz band. Additionally, the implementation complexity and costs of the proposed communication scheme are negligible.

treNch: Ultra-Low Power Wireless Communication Protocol for IoT and Energy Harvesting

Although the number of Internet of Things devices increases every year, efforts to decrease hardware energy demands and to improve efficiencies of the energy-harvesting stages have reached an ultra-low power level. However, no current standard of wireless communication protocol (WCP) can fully address those scenarios. Our focus in this paper is to introduce treNch, a novel WCP implementing the cross-layer principle to use the power input for adapting its operation in a dynamic manner that goes from pure best-effort to nearly real time. Together with the energy-management algorithm, it operates with asynchronous transmissions, synchronous and optional receptions, short frame sizes and a light architecture that gives control to the nodes. These features make treNch an optimal option for wireless sensor networks with ultra-low power demands and severe energy fluctuations. We demonstrate through a comparison with different modes of Bluetooth Low Energy (BLE) a decrease of the power consumption in 1 to 2 orders of magnitude for different scenarios at equal quality of service. Moreover, we propose some security optimizations, such as shorter over-the-air counters, to reduce the packet overhead without decreasing the security level. Finally, we discuss other features aside of the energy needs, such as latency, reliability or topology, brought again against BLE.

An IoT Hardware Platform Architecture for Monitoring Power Grid Systems based on Heterogeneous Multi-Sensors

Partial discharge (PD) is a major indicator of various failures in power grid systems. PD exhibits a physical occurrence where a localized electrical discharge happens in insulation materials. This phenomenon causes damage to the insulating parts and, in various circumstances, leads to complete insulation breakdown. As a consequence, it can produce more costly outcomes such as abrupt outages or lost production. Therefore, PD detection plays a vital role in preventing insulation failure. In this work, we propose a system that utilizes heterogeneous sensors for the PD detection along with multi-sensor interface, real-time advanced denoise processing, flexible system operation, and Bluetooth-low-energy (BLE)-based ad hoc communication. Among the variety of heterogeneous sensors, several are developed by the application of nanomaterials and nanotechnology, thus outperforming the regular types. The proposed system successfully identifies the presence of PD from sensor signals using a microprocessor-based processing system and effectively performs an advanced denoising technique based on the wavelet transform through field-programmable-gate-array (FPGA)-based programmable logics. With the development of the system, we aim to achieve a solution with low cost, high flexibility and efficiency, and ease of deployment for the monitoring of power grid systems.

A Structured E-Investigation Into the Prevalence and Acceptance of Smartphone Applications by Exercise Professionals

Bromilow, L, Stanton, R, and Humphries, B. A structured e-investigation into the prevalence and acceptance of smartphone applications by exercise professionals. J Strength Cond Res 34(5): 1330-1339, 2020-The primary purpose of this study was to examine the prevalence and acceptance of smartphone applications by exercise professionals when interacting with clients and patients. A 29-item anonymous online survey was designed, containing separate sections on demographics, smartphone proficiency, benefits and barriers to using smartphones, and use of smartphones in a professional setting. Accredited members of the Australian Strength and Conditioning Association, and Exercise and Sports Science Australia received an information sheet through organizational communication channels, inviting them to participate. Two hundred forty-nine exercise professionals completed the survey, with men (71%; n = 176) accounting for most of the respondents. Proficiency using smartphone applications is predominantly-advanced (37%; n = 92), intermediate (33%; n = 82), or expert (14%; n = 35). Identified strategies to find smartphone applications included personal searches (67%; n = 167) and colleague recommendations (55%; n = 137). Reported benefits include fast access to information (67%; n = 167), saves time for record keeping (56%; n = 141), and allows performance tracking (55%; n = 138). Almost all respondents (92%; n = 229) identified barriers, such as inexperience with using particular applications (42%; n = 105). Almost all respondents (96%; n = 239) reported they would recommend smartphone applications to clients and patients, primarily for self-tracking (53%; n = 132). Smartphone use among exercise professionals is prevalent; however, application and sensor technology are reluctantly underused. Increasing acceptance requires embedding within educational curricula, recognition from professional organizations, and collaboration with, to maximize the potential capabilities of smartphone technology within working environments.

Wearable Physiological Monitoring System Based on Electrocardiography and Electromyography for Upper Limb Rehabilitation Training

Secondary injuries are common during upper limb rehabilitation training because of uncontrollable physical force and overexciting activities, and long-time training may cause fatigue and reduce the training effect. This study proposes a wearable monitoring device for upper limb rehabilitation by integrating electrocardiogram and electromyogram (ECG/EMG) sensors and using data acquisition boards to obtain accurate signals during robotic glove assisting training. The collected ECG/EMG signals were filtered, amplified, digitized, and then transmitted to a remote receiver (smart phone or laptop) via a low-energy Bluetooth module. A software platform was developed for data analysis to visualize ECG/EMG information, and integrated into the robotic glove control module. In the training progress, various hand activities (i.e., hand closing, forearm pronation, finger flexion, and wrist extension) were monitored by the EMG sensor, and the changes in the physiological status of people (from excited to fatigue) were monitored by the ECG sensor. The functionality and feasibility of the developed physiological monitoring system was demonstrated by the assisting robotic glove with an adaptive strategy for upper limb rehabilitation training improvement. The feasible results provided a novel technique to monitor individual ECG and EMG information holistically and practically, and a technical reference to improve upper limb rehabilitation according to specific treatment conditions and the users' demands. On the basis of this wearable monitoring system prototype for upper limb rehabilitation, many ECG-/EMG-based mobile healthcare applications could be built avoiding some complicated implementation issues such as sensors management and feature extraction.

A 3-Mbps, 802.11g-Based EMG Recording System With Fully Implantable 5-Electrode EMG Acquisition Device

We have developed a 5-electrode recording system that combines an implantable electromyography (EMG) device package with transcutaneous inductive power transmission, near-infrared (NIR) transcutaneous data telemetry and 3 Mbps Wi-Fi data acquisition for chronic EMG recording in vivo. This system comprises a hermetically-sealed single-chip, 5-electrode Implantable EMG Acquisition Device (IEAD), a custom external powering and Implant Telemetry Module (ITM), and a custom Wi-Fi-based Raspberry Pi-based Data Acquisition (RaspDAQ) and relay device. The external unit (ITM and RaspDAQ) is powered entirely by a single battery to achieve the objective of untethered EMG recording, for the convenience of clinicians and animal researchers. The IEAD acquires intramuscular EMG signals at 17.85 ksps/electrode while being powered transcutaneously by the ITM using 22 MHz near-field inductive coupling. The acquired EMG data is transmitted transcutaneously via NIR telemetry to the ITM, which in turn, transfers the data to the RaspDAQ for relaying to a laptop computer for display and storage. We have also validated the complete system by acquiring EMG signals from rodents for up to two months. Following the explantation of the devices, we have also conducted failure and histological analysis on the devices and the surrounding tissue, respectively.

Bluetooth Low Energy Mesh Networks: Survey of Communication and Security Protocols

Bluetooth Low Energy (BLE) Mesh Networks enable flexible and reliable communications for low-power Internet of Things (IoT) devices. Most BLE-based mesh protocols are implemented as overlays on top of the standard Bluetooth star topologies while using piconets and scatternets. Nonetheless, mesh topology support has increased the vulnerability of BLE to security threats, since a larger number of devices can participate in a BLE Mesh network. To address these concerns, BLE version 5 enhanced existing BLE security features to deal with various authenticity, integrity, and confidentiality issues. However, there is still a lack of detailed studies related to these new security features. This survey examines the most recent BLE-based mesh network protocols and related security issues. In the first part, the latest BLE-based mesh communication protocols are discussed. The analysis shows that the implementation of BLE pure mesh protocols remains an open research issue. Moreover, there is a lack of auto-configuration mechanisms in order to support bootstrapping of BLE pure mesh networks. In the second part, recent BLE-related security issues and vulnerabilities are highlighted. Strong Intrusion Detection Systems (IDS) are essential for detecting security breaches in order to protect against zero-day exploits. Nonetheless, viable IDS solutions for BLE Mesh networks remain a nascent research area. Consequently, a comparative survey of IDS approaches for related low-power wireless protocols was used to map out potential approaches for enhancing IDS solutions for BLE Mesh networks.

Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review

Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressed.

Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human body shadowing among other effects. In order to mitigate these effects, the paper proposes and implements a dynamic Artificial Intelligence (AI) model that uses the three different BLE advertising channels to detect human body shadowing and compensate the RSSI values accordingly. An experiment in an indoor office environment is conducted. 70% of the observations are randomly selected and used for training and the remaining 30% are used to evaluate the algorithm. The results show that the AI model can properly detect and significantly compensate RSSI values for a dynamic blockage caused by a human body. This can significantly improve the RSSI-based ranges and the corresponding positioning accuracies.

Experimental Evaluation of Advertisement-Based Bluetooth Low Energy Communication

This paper addresses the efficiency of Bluetooth Low Energy (BLE) communication in a network composed of a large number of tags that transmit information to a single hub using advertisement mode. Theoretical results show that the use of advertisements enables hundreds and thousands of BLE devices to coexist in the same area and at the same time effectively transmit messages. Together with other properties (low power consumption, medium communication range, capability to detect a signal’s angle-of-arrival, etc.), this makes BLE a competing technology for the Internet of Things (IoT) applications. However, as the number of communicating devices increases, the advertisement collision intensifies and the communication performance of BLE drops. This phenomena was so far analyzed theoretically, in simulations and in small-scale experiments, but large-scale experiments are not presented in the literature. This paper complements previous results and presents an experimental evaluation of a real IoT-use case, which is the deployment of over 200 tags communicating using advertisements. We evaluate the impact of the number of advertisements on the effective data reception rate and throughput. Despite the advertisement collision rate in our experiment varying between 0.22 and 0.33, we show that BLE, thanks to the multiple transmission of advertisements, can still ensure acceptable data reception rates and fulfill the requirements of a wide range of IoT applications.

Electrocardiogram and Phonocardiogram Monitoring System for Cardiac Auscultation

Heart-sound auscultation is a rapid and fundamental technique used for examining the cardiovascular system. The main components of heart sounds are the first and second heart sounds. Discriminating these heart sounds under the presence of additional heart sounds and murmurs will be difficult. To recognize these signals efficiently, this study proposes a monitoring system with phonocardiogram and electrocardiogram. This system has two key points. The first is chip implementation, including capacitor coupled amplifier, transimpedance amplifier, high-pass sigma-delta modulator, and digital signal processing block. The chip in the system is fabricated in 0.18 μm standard complementary metal-oxide-semiconductor process. The second is a software application on smartphones for heart-related physiological signal recording, display, and identification. A wavelet-based QRS complex detection algorithm verified by MIT/BIH Arrhythmia Database is also proposed. The overall measured positive prediction, sensitivity, and error rate of the proposed algorithm are 99.90%, 99.82%, and 0.28%, respectively. During auscultation, doctors may refer to these physiological signals displayed on the smartphone and simultaneously listen to the heart sounds to diagnose the potential heart disease. By taking advantage of signal visualization and keeping the original diagnosis procedure, the uncertainty existing in heart sounds can be eliminated, and the training period to acquire auscultation skills can be reduced.

Monitoring Sucking Abilities in Newborns: Design and Validation on Adult Of a Wearable System for Non-Invasive Deglutition Detection

Nutritive Sucking (NS) is one of the earliest motor activity performed by infants, strictly related to both neurological and motor development of newborns. The main components of NS are sucking, respiration and deglutition. Despite its recognized importance, current clinical practice lacks quantitative tools for the assessment of NS. This work aims to identify a non-invasive objective method to assess deglutition. In details, we propose a new sensor fusion approach to merge both inertial and acoustic data in order to estimate deglutition time. The algorithm uses two classification criteria: one is based on signal intensity thresholding and the other on the evaluation of Waveform Dimension Trajectory (WDT). Our preliminary results on 9 healthy adult volunteers show that the sensor fusion of audio and IMU signals provides a high precision (0.93) and a good recall (0.72). Moreover, the algorithm has a good accuracy (0.84) and high specificity (0.95).

Data Transmission Efficiency in Bluetooth Low Energy Versions

One important aspect when choosing a Bluetooth Low Energy (BLE) solution is to analyze its energy consumption for various connection parameters and desired throughput to build an optimal low-power Internet-of-Things (IoT) application and to extend the battery life. In this paper, energy consumption and data throughput for various BLE versions are studied. We have tested the effect of connection interval on the throughput and compared power efficiency relating to throughput for various BLE versions and different transactions. The presented results reveal that shorter connection intervals increase throughput for read/write transactions, but that is not the case for the notify and read/write without response transactions. Furthermore, for each BLE version, the energy consumption is mainly dependable on the data volume. The obtained results provide a design guideline for implementing an optimal BLE IoT application.

Design of Wireless Sensors for IoT with Energy Storage and Communication Channel Heterogeneity

Autonomous Wireless Sensors (AWSs) are at the core of every Wireless Sensor Network (WSN). Current AWS technology allows the development of many IoT-based applications, ranging from military to bioengineering and from industry to education. The energy optimization of AWSs depends mainly on: Structural, functional, and application specifications. The holistic design methodology addresses all the factors mentioned above. In this sense, we propose an original solution based on a novel architecture that duplicates the transceivers and also the power source using a hybrid storage system. By identifying the consumption needs of the transceivers, an appropriate methodology for sizing and controlling the power flow for the power source is proposed. The paper emphasizes the fusion between information, communication, and energy consumption of the AWS in terms of spectrum information through a set of transceiver testing scenarios, identifying the main factors that influence the sensor node design and their inter-dependencies. Optimization of the system considers all these factors obtaining an energy efficient AWS, paving the way towards autonomous sensors by adding an energy harvesting element to them.

Beacon-Related Parameters of Bluetooth Low Energy: Development of a Semi-Automatic System to Study Their Impact on Indoor Positioning Systems

Indoor positioning systems (IPS) are used to locate people or objects in environments where the global positioning system (GPS) fails. The commitment to make bluetooth low energy (BLE) technology the leader in IPS and their applications is clear: Since 2009, the Bluetooth Special Interest Group (SIG) has released several improved versions. BLE offers many advantages for IPS, e.g., their emitters or beacons are easily deployable, have low power consumption, give a high positioning accuracy and can provide advanced services to users. Fingerprinting is a popular indoor positioning algorithm that is based on the received signal strength (RSS); however, its main drawbacks are that data collection is a time-consuming and labor-intensive process and its main challenge is that positioning accuracy is affected by various factors. The purpose of this work was to develop a semi-automatic data collection support system in a BLE fingerprinting-based IPS to: (1) Streamline and shorten the data collection process, (2) carry out impact studies by protocol and channel on the static positioning accuracy related to configuration parameters of the beacons, such as transmission power (Tx) and the advertising interval (A), and their number and geometric distribution. With two types of systems-on-chip (SoCs) integrated in Bluetooth 5 beacons and in two different environments, our results showed that on average in the three BLE advertising channels, the configuration of the highest Tx (+4 dBm) in the beacons produced the best accuracy results. However, the lowest Tx (-20 dBm) did not worsen them excessively (only 11.8%). In addition, in both scenarios, when lowering the density of beacons by around 42.7%-50%, the error increase was only around 8%-9.2%.

Power Consumption Analysis of Bluetooth Low Energy Commercial Products and Their Implications for IoT Applications

Internet of things (IoT) has become a very important business segment for the industry since it is estimated that 50 billion devices will be interconnected by 2020, where 30% of them are forecasted to use Bluetooth low energy as the enabling communication protocol. IoT involves smart devices that can be connected to the cloud in order to share and process all types of data collected through sensors. Moreover, 100% of smartphones and tablets shipped in 2018 include Bluetooth low energy, which will allow the interoperation with the upcoming 5G standard. As a result, different low-power hardware platforms have become available to either build prototypes, or implement full IoT solutions. One fundamental aspect while choosing a development platform is to analyze the power consumption of its components to build an optimal low-power application and extend the battery life. In this work, we present current consumption measurements of four Bluetooth low energy commercial platforms configured as central and as peripheral devices, these measurements are aimed to provide the data that are not available in the datasheets and which is needed to select, in an optimal way, the proper connection parameters for the protocol.

A Wearable System for Real-Time Continuous Monitoring of Physical Activity

Over the last decades, wearable systems have gained interest for monitoring of physiological variables, promoting health, and improving exercise adherence in different populations ranging from elite athletes to patients. In this paper, we present a wearable system for the continuous real-time monitoring of respiratory frequency (f_R), heart rate (HR), and movement cadence during physical activity. The system has been experimentally tested in the laboratory (by simulating the breathing pattern with a mechanical ventilator) and by collecting data from one healthy volunteer. Results show the feasibility of the proposed device for real-time continuous monitoring of f_R, HR, and movement cadence both in resting condition and during activity. Finally, different synchronization techniques have been investigated to enable simultaneous data collection from different wearable modules.