Practical Papers, Articles and Application Notes

PurposeThe increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.Design/methodology/approachThis data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.FindingsThe proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.Research limitations/implicationsThe research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.Practical implicationsThe emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.Originality/valueIn this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

Monitoring the health of the elderly using wearable sensors and Internet of Things (IoT) devices necessitates the collection of data from various sources, thereby increasing the volume of data to be gathered at the monitoring center. As previously elucidated, the system exhibits high accuracy in measuring variables such as blood oxygen saturation (BOS) and heart rate (HR), as well as demonstrating proficient accuracy in implementing fuzzy rules to detect the “stable” or “unstable” condition of elderly patients. Therefore, by integrating wearable sensors and IoT devices into the elderly health monitoring system, we can enhance the quality of monitoring and provide more timely healthcare solutions tailored to the elderly population’s needs. The integration of fuzzy rules and IoT technology into the elderly health monitoring system offers an efficient and effective solution for continuous health surveillance. In this research, we integrated fuzzy rules and IoT technology into the elderly health monitoring system using parameters such as BOS and HR, through the integration of MAX30100 sensors and ESP32 microprocessors. The implementation of fuzzy rules resulted in nine rules indicating whether an elderly patient is in a “stable” or “unstable” condition. The methodology involved (1) component preparation, (2) device accuracy analysis, (3) fuzzy rule development, (4) Android-based mobile application development, (5) fuzzy rule accuracy analysis, and (6) cost analysis. Experimental results indicated a device accuracy of 98.89% in measuring BOS and HR variables compared to medical devices and a fuzzy rule accuracy of 96% in detecting whether the patient’s condition is “stable” or “unstable.” Based on the experimental findings, the elderly health monitoring system provides a user-friendly, precise, and cost-effective solution to enhance the quality of life for the elderly population.

This study proposes a health monitoring system for snoring detection utilizing Tiny Machine Learning (TinyML) models, specifically designed for resource-constrained wearable Internet of Things (IoT) devices. This research addresses significant constraints associated with running machine learning models on IoT devices, such as latency, limited memory, and low computational resources. These parameters are essential for real-time monitoring in healthcare applications, where prompt response is critical. The research focuses on developing a TinyML model capable of identifying specific audio patterns related to snoring during sleep. Experimental evaluations conducted in real-world sleep environments with the TinyML model deployed on resource-constrained wearable IoT devices. The evaluation results show that the proposed model achieves high accuracy while utilizing minimal computational resources and without introducing latency issues. Through the integration of audio (Syntiant) and advanced audio preprocessing techniques, the proposed system improves the efficiency of the TinyML model on wearable devices. The quantized TinyML model achieved an accuracy of 95.85% with a low latency of 48 ms, utilizing only 17.0K of RAM and 34.07K of flash memory for real-time snoring classification. This study highlights the benefits of practical deployment of the TinyML model for snoring detection on resource-constrained wearable IoT devices, demonstrating that such models can operate effectively within the constraints of current wearable technology.

The Internet of Things (IoT) employs smart devices as its building blocks for developing a ubiquitous communication framework. It thus supports a wide variety of application domains, including public safety, healthcare, education, and public transportation. While offering a novel communication paradigm, IoT finds its requirements closely connected to the security issues. The role of security following the fact that a new type of devices known as wearables constitute an emerging area. This paper delivers an applicability study of the state-of-the-art cryptographic primitives for wearable IoT devices, including the pairing-based cryptography. Pairing-based schemes are well-recognized as fundamental enablers for many advanced cryptographic applications, such as privacy protection and identity-based encryption. To deliver a comprehensive view on the computational power of modern wearable devices (smart phones, watches, and embedded devices), we perform an evaluation of a variety of them utilizing bilinear pairing for real-time communication. In order to deliver a complete picture, the obtained bilinear pairing results are complemented with performance figures for classical cryptography (such as block ciphers, digital signatures, and hash functions). Our findings show that wearable devices of today have the needed potential to efficiently operate with cryptographic primitives in real time. Therefore, we believe that the data provided during this research would shed light on what devices are more suitable for certain cryptographic operations.

Advances in miniaturized bio-compatible Internet of Things (IoT) device design and wireless connectivity have resulted in rapid strides towards realizing the vision of connected health and ubiquitous monitoring of physiological conditions. Core enablers of this capability are wearable and implanted IoT devices, albeit with limitations arising from their low energy storage and computational power. This thesis goes beyond the RF-only communication standards by exploring alternate communication modalities that are more amenable for inter- and intra-body communication. In summary, this thesis explores the conductive and radiating nature of the human body as a channel for three non-RF coupling communication methods - Galvanic, Magnetic and Capacitive coupling.An implementation of Galvanic Coupling-based beamforming is presented for implant to wearable communication. The key idea here is to exploit the conductivity of human tissue and transmit weak electrical signals by coupling them via electrodes to muscle tissue in a way that concentrates energy at the receiver location. Following that, we focus on realizing a relay network of IoT devices for both implant-implant and implant to on-skin sensor communication using Magnetic Resonance Coupling. The advantage of this method over Galvanic Coupling is that the former reduces attenuation when signals pass through human tissue. This thesis enhances the scope of the connected health paradigm to now include sensing for proximity and for automated encouraging of healthy habits that mitigate the spread of communicable diseases using Capacitive Coupling. Finally, a novel human antenna field to sense and communicate with wearable and implantable IoT devices in the near field is designed. This thesis proposes the utilization of a capacitively induced human body antenna field to connect multiple wearable and implantable devices to each other via Intra-body Communication. Such a paradigm not only supports multi-cast data transfer but also helps in sensing the environment to detect the presence of other patients and caregivers, unlike current RF-only approaches. The human body antenna approach is validated via simulation and experiments on a phantom to study the frequency response, sensitivity and antenna design parameters of the resulting human body antenna system. Results reveal a communication and sensing range of 2.5 meters while operating at 75-95 MHz with a single wearable device with capacitive electrodes of size 30x50 mm. To ensure safety in such capacitively induced human body antenna for Wireless Body Area Network (WBAN) communications, we perform a power budget analysis to prove that the proposed approach is indeed a low-power and robust communication and sensing system for health monitoring purposes. The final part of this dissertation completes the full cycle of data flow, from implanted to wearable devices and finally connects the body network to the computational cloud for the next generation of IoT-enabled healthcare.--Author's abstract

Internet of things (IoT) holds great promise as a solution for providing continuous, objective, and comprehensive monitoring, thereby reducing the labour of human care-givers and facilitating clinical decision-making. In the initial phase of the internet of things in rehabilitation, ubiquitous sensors were integrated into rehabilitation programmes. These sensors were used to monitor the movement of patients, assess their progress, and collect data on a variety of physiological parameters. This chapter will describe the evolution of IoT-based rehabilitation, which shifted from stationary sensors to wearable devices in order to provide precise advanced patient care and rehabilitation, using IoT devices in various activities of daily living and also focuses on the ethical considerations of using wearables IoT devices in rehabilitation.

The massive number of Internet-of-Things (IoT) devices generates a hard-to-manage volume of data. Cloud-centric processing approaches for the IoT data suffer from high and unpredictable network latency, which causes poor experience in real-time IoT applications, such as healthcare. To address this issue, in edge computing, the data inference starts from the data source (i.e., the IoT devices). However, the constrained computational capabilities of the IoT device and the power-hungry data transmission demand a tradeoff between onboard processing and computation offloading. Hence, the IoT information inference requires efficient and lightweight techniques that are tailored for this tradeoff and respect the constrained resources on IoT devices, such as wearables. This article presents a hierarchical classification approach that decomposes the problem into three classifiers in two hierarchy layers. In the first layer, a lightweight classifier executes directly on the IoT device and decides whether to offload the computation to the gateway or to perform it onboard. The second layer comprises a lightweight classifier on the IoT device (can only distinguish a subset of classes) and a complex classifier on the gateway (to distinguish the remaining classes). The experimental results (using a real-world data set for human activity recognition and implemented on a wearable IoT device) show higher accuracy (92% on average) than a nonhierarchical classifier (87% on average). The execution time and power measurements on the IoT device show $3\times $ energy saving for the classification.

The rapid adoption of Wearable Internet of Things (WIoT) devices in smart cities has enabled a wide spectrum of applications. Although Federated Learning (FL) was once considered the silver bullet for privacy-preserving systems, it falls short in WIoT settings. This is primarily because deep learning (DL) models typically require extensive computation, which exceeds the limited processing capabilities of resource-constrained WIoT devices. Additionally, the non-independent and identically distributed (non-IID) nature of WIoT data hinders model convergence and generalization. To this end, we propose LiteFL, a lightweight privacy-preserving FL framework tailored for non-IID WIoT environments. LiteFL partitions the model into extractor, processor, and classifier modules, offloading the computation-intensive processor to the server while retaining lightweight components on clients. It integrates differential privacy (DP) for data perturbation and fully homomorphic encryption (FHE) for the proposed secure aggregation mechanism. Experiments on a real-world WIoT dataset demonstrate LiteFL achieves an AUC of 0.939 and F1-score of 0.845, outperforming baselines by over 5%. It also mitigates the cold-start problem in real-world applications by enabling knowledge transfer across heterogeneous distributions, advancing scalable, secure FL for resource-constrained WIoT systems.

Stroke is one of the high-risk diseases with a global concern that has caused the death of millions of individuals and is still ravaging lives of people in many countries–like fire in a wild forest. Stroke occurrences are highly influenced by age and other factors such as blood pressure, heart pulses, and feeding habits. Hemorrhagic stroke is prevalent among the age groups of 16 and 50 years-stimulated by chronic high blood pressure, heartbeat problems, and aging blood vessels. These health factors can be collected from patients and processed to monitor their health conditions in real-time-such as to reduce the number of death cases caused by stroke. In this research, an IoT-based healthcare delivery system for hemorrhagic stroke patients was developed using a wearable device and a fog computing approach for monitoring vital symptoms of the patients and administering on-demand health care services to the users. The IoT device is capable of detecting irregular blood pressure and heartbeat using a Continuous Wavelength Transform (CWT) signal and performing analytics on the data collected using a rule-based mining algorithm on the wearable firmware enhanced with a robust database model and a fog layer for efficient data analytics. The system was implemented in the form of a wearable armband for the user and supported with a dynamic web page for device allocation and real-time monitoring of the patient’s health status. The wearable device was able to communicate to the user in real-time-notifying them of their health status, recommending preventive actions to be taken, and alerting their doctors when there are emergencies- using the fog computing layer to reduce data latency and provide fast response time. In evaluating the performance of our system, the readings from the wearable armband for monitoring the blood pressure and heartbeat were compared with the patient’s reading using the standard Sphygmomanometer and Pulse oximeter, the results show high accuracy relative to the outputs of our wearable IoT device.KeywordsData analyticsFog computingIoTStroke

This paper presents a compact and planar ultra‐wideband (UWB) antenna, designed and characterized for the wearable Internet of Things (IoT) devices and wireless body area network (WBAN) applications. Two different substrates, ie, FR4 (h = 1.6 mm) and denim textile substrate (h = 1 mm), are used in the process of design and fabrication of the proposed antenna. The dimensions of the proposed antenna are 31 mm × 42 mm for FR4 substrate and 70 mm × 56 mm for denim textile substrate. The antenna structure consists of a rectangular radiating patch with two symmetrical staircase‐shaped slits. A reduced ground plane technique is used for the purpose of improving impedance bandwidth, which is further enhanced by the insertion of a small notch in the reduced ground plane which has improved the overall impedance matching characteristics of the proposed UWB antenna. The measured impedance bandwidth for S11 < −10 dB and gain of the FR4 and denim textile substrate UWB antennas are 7.71 GHz/5.12 dBi and 7.95 GHz/3.57 dBi, respectively. The ADS momentum simulator is used for antenna simulations, and the simulated and measured results are generally found in good agreements.

The Internet of Things (IoT) devices is able to communicate with another IoT devices in heterogeneous environments. They are also able to create a network by their own using wireless capabilities without a centralized approach. This network is called wireless mobile ad hoc network (MANET). The MANET facilitate IoT devices to communicate with each other in an infrastructure less network of the Internet of Things environment. The IoT devices can dynamically join, create connection and exchange information among them. The role of cloud in this framework is to store and process the data by IoT devices. The communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in a constant growth in the use and popularity of cloud. Our purpose of this study is to create a framework for providing the communication in an integrated framework of Cloud and MANET among IoT devices. Our main contribution links a new study that integrates cloud computing to the MANET and provides communication to the internet of things. This study can be used in the framework of the Internet of Things in future.

Internet of Things (IoT) and wireless technologies like LoRa brought more opportunities for application development in a plethora of different fields. One of these is location estimation of real-time objects and people. In this study, we focus on monitoring user's location through a wearable IoT device with LoRa connectivity. The paper presents the development and integration of an IoT ecosystem (Hardware and Software) which can be used in Search and Rescue (SAR) use cases. The proposed IoT ecosystem is evaluated and deployed in real-scenarios with established gateways. After that we compare the existed location-estimation methods in terms of attenuation problem, cost and operation as well to conclude to the most suitable solution that can be integrated in LoRaWAN environments. Finally, the conclusions of this work and improvements for possible future activity are described.

Seeing is not always believing: Insights on IoT manufacturing from firmware composition analysis and vendor survey

With the advancement in science and technology, the demand for electronic Internet of Things (IoT) devices has increased to store and process digital data. With the advancement in IoT technology, we are more reliant on IoT devices to perform our regular tasks. IoT Forensics deals with the investigation of IoT devices such as smartphones, tablets, smart watches, or any device which is connected to the Internet. The data on these devices can still be retrieved via IoT forensics even if they are destroyed, resold, or misplaced. Before discarding any IoT device, we must consider how to secure the data stored on them. We are then going to discuss the several types of personal data that could be retrieved from discarded IoT devices. Finally, we are going to discuss the protection measures for the safe disposal of IoT devices.

The number of Internet of Things (IoT) devices has increased recently and will increase further in the future. IoT devices are vulnerable, so vendors update and distribute firmware. At the time of firmware distribution, a huge number of IoT devices may connect to the firmware server and excessive network traffic may occur. Consequently, a method using a peer-to-peer file sharing system and a blockchain has been proposed. One study proposed an update scheme considering incentives for distributors to help with updating. However, this method requires the IoT devices, which have limited processing power, to decrypt encrypted files, and the distribution nodes to pay the transaction cost. This paper proposes a firmware distribution method that provides incentives for distributors to help with distribution to reduce the gas costs, using a smart contract and access control based on update records. The additional computations performed by IoT devices and distributors key management were reduced compared to those in previous studies by using access control instead of encryption. Further, the proposed approach was implemented and evaluated using a Raspberry Pi and laptop. The evaluation demonstrated that the gas cost per update was successfully lowered compared to the costs in previous studies.