Wearable Nodes Research Articles

Harnessing energy for wearable technology: a sustainable approach

<span>Recent advancements in electrical sensing and electronics have notably propelled the progress of wearable fitness and medical technology. Key to these applications is the mobility of wearable nodes, essential for remote patient monitoring. However, the limited lifespan of wearable devices, particularly in biomedical applications, poses a significant challenge to their widespread adoption. Energy harvesting technology emerges as a promising solution to this issue, despite the inherent challenges of size, weight, and cost restrictions for wearable devices. This study presents three innovative strategies for harnessing solar and thermal energy at wearable sensor nodes, offering new pathways to overcome the longevity constraints of wearable technology.</span>

Neural network-based Bluetooth synchronization of multiple wearable devices

Bluetooth-enabled wearables can be linked to form synchronized networks to provide insightful and representative data that is exceptionally beneficial in healthcare applications. However, synchronization can be affected by inevitable variations in the component’s performance from their ideal behavior. Here, we report an application-level solution that embeds a Neural network to analyze and overcome these variations. The neural network examines the timing at each wearable node, recognizes time shifts, and fine-tunes a virtual clock to make them operate in unison and thus achieve synchronization. We demonstrate the integration of multiple Kinematics Detectors to provide synchronized motion capture at a high frequency (200 Hz) that could be used for performing spatial and temporal interpolation in movement assessments. The technique presented in this work is general and independent from the physical layer used, and it can be potentially applied to any wireless communication protocol.

IoT-enabled smart healthcare data and health monitoring based machine learning algorithms

A smart healthcare network can use sensors and the Internet of Things (IoT) to enhance patient care while decreasing healthcare expenditures. It has become more difficult for healthcare providers to keep track and analyze the massive amounts of data it generates. Health care data created by IoT devices and e-health systems must be handled more efficiently. A wide range of healthcare industries can benefit from machine learning (ML) algorithms in the digital world. However, each of these algorithms has to be taught to anticipate or solve a certain problem. IoT-enabled healthcare data and health monitoring-based machine learning algorithms (IoT-HDHM-MLA) have been proposed to solve the difficulties faced by healthcare providers. Sensors and IoT devices are vital for monitoring an individual’s health. The proposed IoT-HDHM-MLA aims to deliver healthcare services via remote monitoring with experts and machine learning algorithms. In this system, patients are monitored in real-time for various key characteristics using a collection of small wireless wearable nodes. The health care business benefits from systematic data collection and efficient data mining. Thus, the experimental findings demonstrate that IoT-HDHM-MLA enhances efficiency in patient health surveillance.

Analysis and evaluation of hemiplegic gait based on wearable sensor network

Hemiplegia is a common symptom of acute cerebrovascular disease, and most patients with hemiplegia have abnormal gaits. Descriptive evaluation methods are commonly used in clinical for gait analysis, and outcomes are overly reliant on observation by rehabilitation physicians. The quantitative analysis of hemiplegia gait is urgently required to guide patients' rehabilitation training. This paper presents a quantitative analysis and evaluation method of hemiplegic gait based on inertial measurement units (IMUs). The wearable nodes are worn on the subjects’ waist and lower limbs to record data when they walked in a straight line. After the recorded data has been processed, the gradient descent algorithm (GDA) is used for attitude calculation, and the walking process of hemiplegic patients is reconstructed for gait analysis. Combined with kinematic analysis, three types of joint angles during walking are calculated, i.e., hip angle, knee angle, and ankle angle, and their comparison with the joint angles of normal gait is conducted. In terms of the joint angles, the phase variation of hemiplegic gait is analyzed first, then the gait difference between hemiplegic and normal subjects is measured by using a weighted dynamic time warping (WDTW) algorithm, and finally the gait distortion is evaluated quantitatively based on the WDTW distance. Experimental results demonstrate that the GDA-based gait reconstruction method and the WDTW-based gait evaluation method presented in this paper can quantify the abnormality of hemiplegic gait, and thereby monitor the rehabilitation process of patients' walking ability.

A secure and lightweight anonymous mutual authentication scheme for wearable devices in Medical Internet of Things

Medical Internet of Things (MIoT) is an emerging area of IoT comprising of wearable sensor nodes having low-powered processors. Wearable nodes gather the physiological data of a patient and transmit this real-time information to the user over an insecure channel. There is a possibility that an attacker might gather the personal or medical information of a user that has been communicated over an insecure channel. Hence, security and privacy become a major concern in MIoT for a user in the last decade. To access the medical data in the resource-constraint environment, in the current manuscript, we propose a secure and lightweight anonymous mutual authentication scheme. Our scheme works utilizing only the basic symmetric cryptosystem techniques that include cryptographic hash functions in one direction and bitwise XOR operation. As a result, the proposed framework is lightweight and also efficient than the state-of-the-art. We have validated our proposed scheme based on BAN-Logic and shown the efficiency concerning functionality, storage requirements, computation complexity, and communication costs.

An extended IEEE 802.15.6 for thermal-aware resource management

To prevent the critical damage to human tissue by the high temperature on an implanted or wearable node, various temperature-aware routing protocols have been proposed in wireless body area networks (WBAN). However, routing-based approaches cannot provide a flexible trade-off between temperature increase and quality-of-service (QoS) degradation because their mechanisms have some potential problems owing to a lack of compatibility with the MAC layer. To address this problem, in this study, we aim to jointly satisfy the temperature constraints and QoS requirements by dynamically managing the available resources at the MAC layer. For this, we use the resource management functions specified in the IEEE 802.15.6 standard. Specifically, we define new upper-layer components and service primitives to control the body temperature in an adaptive manner. In addition, we divide the MAC layer into two sublayers and define the interaction model between the upper-layer components and the MAC sublayers. Under this layered approach, we propose two types of feasible temperature control mechanisms, i.e., decentralized and centralized, to suppress the temperature increase and guarantee the QoS of the critical data. The simulation results when considering a practical network environment prove that, based on the highest-priority node, our approach can achieve an approximately 3.4% higher temperature increase ratio, 9.7% throughput, 19% power consumption ratio, and 12.2% shorter delay compared with a conventional approach.

EQS Res-HBC: A 65-nm Electro-Quasistatic Resonant 5–240 μW Human Whole-Body Powering and 2.19 μW Communication SoC With Automatic Maximum Resonant Power Tracking

Applications like connected healthcare through physiological signal monitoring and secure authentication using wearable keys can benefit greatly from the battery-less operation. Low-power communication along with energy harvesting is critical to sustain such perpetual battery-less operation. Human body channel is known to be a promising alternative to wireless radio wave communication for low power operation through human body communication (HBC), as well as very recently demonstrated as a medium for power transfer through body-coupled power transfer. However, channel length (L) dependency of the received power makes it inefficient for L>40 cm. In this article, we utilize electro-quasistatic resonant HBC (EQS Res-HBC) with maximum resonant power tracking (MRPT) to enable channel length-independent whole-body communication and powering. We design the first system to transfer power and data from a HUB device to wearable energy-harvested NODE through the human body to enable battery-less perpetual operation. Measurement results show 240, 28, and 5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> power transfer through the body in a machine–machine (large devices with strong ground connection), tabletop (small devices kept on a table), and wearable–wearable (small form factor battery-operated wearable devices) scenario, respectively, independent of body channel length, while enabling communication at 2.19 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> . This enables >25 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> more power transfer with >100 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> more efficiency compared to the state of the art for 100-cm body channel in tabletop scenario by utilizing the benefits of EQS operation. The MRPT loop automatically tracks device and posture-dependent changes in the resonance point to maximize power transfer in all cases.

Best Features Selection for the Implementation of a Postural Sway Classification Methodology on a Wearable Node

The possibility of identifying potential altered postural status in frail people, including patients with Parkinson Disease, represents an important clinical outcome in the management of frail elderly subjects, since this could lead to greater instability and consequently an increased risk of falling. Several solutions proposed in the literature for the monitoring of the postural behavior use infrastructure-dependent approaches or wearable devices, which do not allow to distinguish among different kinds of postural sways. In this paper a low cost and effective wearable solution to classify four different classes of postural behaviors (Standing, Antero-Posterior, Medio-Lateral and Unstable) is proposed. The solution exploits a sensor node, equipped by a tri-axial accelerometer, and a dedicated algorithm implementing the classification task. Different quantities are proposed to assess performance of the proposed strategy, with particular regards to the system capability to correctly classify an unknown pattern, through the index Q%, and the Reliability Index, RI%. Results achieved across a wide dataset demonstrated the suitability of the methodology developed, with Q%=99.84% and around 70% of classifications showing a RI% above 65%.

On Architecture of Self-Sustainable Wearable Sensor Node for IoT Healthcare Applications

In healthcare applications, the remote monitoring of moving patients depends on wearable nodes that should be mobile. Thus, wearable nodes should be power mains-disconnected most of the time to enable natural wandering of patients in the area. Thus, easy-to-use models are utilized in a seamless way. From this perspective, it becomes necessary to develop a generation of wearable nodes that are energy self-sustainable with minimal dependency on fixed power sources and also more safe in light of world health organization recommendations. In this paper, a solar energy harvesting technique is proposed to provide a mains power supply for an independent continuous operation of a patient monitoring node in sunny environments. A case study is built experimentally whereas the proposed designed node is architected as a combined node that enables parallel measurements of heart rate, blood oxygen saturation (SpO2), and body temperature. The experimental results show that the wearable node can survive more than 28 h without battery recharging from the mains. While the charging time of the battery from the solar energy harvesting is approximately 2 h.

Cooperative Communication Enabled Cognitive Radio in a Home-Care Application

In this paper, cooperative communication (CC) assisted cognitive wireless sensor network (CWSN) is presented for monitoring health and activity of an end-user in a smart indoor environment. The cooperative communication ensures better data delivery by utilizing minimum resources and also helps to track the location of the user, non-intrusively. The increase in demand for wireless sensor network insists various factors, such as spectrum sensing, sharing and optimal utilization of resources in various applications. CWSN is considered as a promising technology that improves spectrum utilization and communication quality. The study proposes a payload-centric adaptive multi-channel hopping (PC-AMCH) algorithm in CC enabled CWSN to further improve energy efficiency of conventional multi-channel wireless sensor networks, especially in a smart home-care system. All participated nodes, such as wearable cognitive node (C), relay nodes (Rn) and a destination node (D) are indigenously developed by involving CC2530 microcontroller that consists of an in-built transceiver at 2.4 GHz. Further, an adaptive relay selection scheme is used to establish a dynamic link between wearable cognitive node (C) and relay node (Rn) by analyzing factors such as, link quality indicator (LQI) and received signal strength indicator (RSSI). The performance of developed nodes is evaluated in terms of throughput, packet delivery ratio (PDR), RSSI and transmission offset. Similarly, the performance of proposed PC-AMCH is validated by measuring PDR, number of transmission, energy efficiency.

Assessment of Limb Movement Activities Using Wearable Ultra-Wideband Technology

This article presents ultra-wideband (UWB) body-centric channel parameters for assessing the performance of the physical activities suitable for rehabilitation and physiotherapy purposes in the healthcare domain. Two channel parameters, path loss magnitude and rms delay spread, have been proposed to study the patterns/trends of various upper and lower limb activities for different positions. The channel for various activities has been distinguished into line of sight (LOS) and non-line of sight (NLOS) links. Three orientations of the wearable antenna have been considered for the placement on the wrist/ankle which are outer, inner, and front region, leading to significant differences in the channel parameters while performing physical exercises. The channel parameter values obtained are based on the orientation/placement of the wearable antenna, the distance between the two wearable nodes and the amount of obstruction caused by the human body, e.g., thigh/torso region. The proposed method enables effective monitoring capabilities and computes patterns of different exercises for all the limb positions measured, which in turn will aid in accurate human activity monitoring.

Performance Evaluation of Compressive Sensing Based Compression of Multi-Resolution Ppg Signals Under Wban Environment.

Wireless Body Area Network (WBAN) is a collection of wireless biosensors worn on the body, in which each sensor node is capable of computing and communicating with other nodes or devices like smart phones, Personal Digital Assistant (PDA), hand held devices etc,. The wearable nodes are powered by battery and need to be always functional for continuous remote monitoring of patients which demands that node life time has to be prolonged to the maximum extent. One of the best solutions for this issue is to go for data compression at the node. In this context, Compressive sensing (CS) based energy efficient compression algorithms have been developed and tested for 8 -bit and 10-bit resolution Photoplethesmogram (PPG) signal. Test data have been acquired from normal subjects using Arduino Uno R3. Validation of the algorithm has been carried out by applying on MIMIC-II database and acquired signals. It is found that the CS algorithm reconstruction quality diminishes for low resolution PPG data.

Textile Electrodes for Heart Rate Measurement: A Comparative Study for Firefighters’ Monitoring

This work compares the performance of wet gel electrodes and textile-based electrodes for monitoring the firefighters’ heart rate during on-duty missions. Both types of electrodes are connected to a customized wearable node that acquires the bio-signals and computes the heart rate. Both types of electrodes were evaluated by two subjects and in three different scenarios: sitting, standing and moving the arms, and walking. The tests have demonstrated that in static scenarios the results obtained for both type of electrodes are comparable. However, in dynamic scenarios, the performance of textile electrodes worsens, especially when the wearer is moving the arms. Although the results obtained from textile electrodes during walking are not as good as the wet get electrodes, its information is still useful for monitoring the firefighters’ heart rate.

An Experimental Evaluation of Switched Combining Based Macro-Diversity for Wearable Communications Operating in an Outdoor Environment

This paper investigates the potential improvement in signal reliability for outdoor wearable communications channels operating at 868 MHz using switched combining based macro-diversity. In this paper, a number of different macro-diversity configurations consisting of two and four base stations were considered to help mitigate the impact of body shadowing upon a wearable node, which was located on the central chest region of an adult male. During the field measurements, five different walking movements were performed, and then analyzed to investigate the efficacy of using macro-diversity. It was found that all of the considered switched combining schemes, including switch-and-stay combining, switch-and-examine combining (SEC) and SEC with post-examining selection (SECps) provided a worthwhile signal improvement when an appropriate switching threshold was adopted. The maximum diversity gain obtained in this paper was found to be 19.5 dB when using four-base station SECps. The diversity gain, the number of path examinations, and the number of path switches between base stations for the switched combiner output varied according to the determined switching threshold, highlighting the importance of the selection of an appropriate threshold level. Furthermore, the performance/complexity tradeoff is demonstrated. Finally, the fading behavior at the output of the switched diversity combiners was then characterized using the diversity specific equations developed under the assumption of independent and non-identically distributed Nakagami- $m$ fading channels. Over all of the measurement scenarios considered in this paper, the theoretical models provided an adequate fit to the fading observed at the output of the virtual switched combiner.

A Prosthetic Hand Body Area Controller Based on Efficient Pattern Recognition Control Strategies.

Poliarticulated prosthetic hands represent a powerful tool to restore functionality and improve quality of life for upper limb amputees. Such devices offer, on the same wearable node, sensing and actuation capabilities, which are not equally supported by natural interaction and control strategies. The control in state-of-the-art solutions is still performed mainly through complex encoding of gestures in bursts of contractions of the residual forearm muscles, resulting in a non-intuitive Human-Machine Interface (HMI). Recent research efforts explore the use of myoelectric gesture recognition for innovative interaction solutions, however there persists a considerable gap between research evaluation and implementation into successful complete systems. In this paper, we present the design of a wearable prosthetic hand controller, based on intuitive gesture recognition and a custom control strategy. The wearable node directly actuates a poliarticulated hand and wirelessly interacts with a personal gateway (i.e., a smartphone) for the training and personalization of the recognition algorithm. Through the whole system development, we address the challenge of integrating an efficient embedded gesture classifier with a control strategy tailored for an intuitive interaction between the user and the prosthesis. We demonstrate that this combined approach outperforms systems based on mere pattern recognition, since they target the accuracy of a classification algorithm rather than the control of a gesture. The system was fully implemented, tested on healthy and amputee subjects and compared against benchmark repositories. The proposed approach achieves an error rate of 1.6% in the end-to-end real time control of commonly used hand gestures, while complying with the power and performance budget of a low-cost microcontroller.

Wireless surface electromyograph and electrocardiograph system on 802.15.4

This paper presents a flexible low-cost wireless system specifically designed to acquire fitness metrics both from surface electromyographic (sEMG) and electrocardiographic (ECG) signals. The system, that can be easily extended to capture and process many other biological signals as well as the motion-related body signals, consists of several ultralight wireless sensing nodes that acquire, amplify, digitize, and transmit the biological or mechanical signals to one or more base stations through a 2.4 GHz radio link using a custom-made communication protocol designed on top of the IEEE 802.15.4 physical layer. The number of wireless nodes the base stations can handle depends on the type of signal being acquired. Each base station is connected through an USB link to a control PC running a user interface software for viewing, recording, and analyzing the data. The system for acquiring signals from wearable nodes in combination with a smartphone application provides a complete platform for monitoring fitness metrics extracted from the signals. 1

Hands-On Experiences in Deploying Cost-Effective Ambient-Assisted Living Systems.

Older adults’ preferences to remain independent in their own homes along with the high costs of nursing home care have motivated the development of Ambient Assisted Living (AAL) technologies which aim at improving the safety, health conditions and wellness of the elderly. This paper reports hands-on experiences in designing, implementing and operating UbiCare, an AAL based prototype system for elderly home care monitoring. The monitoring is based on the recording of environmental parameters like temperature and light intensity as well as micro-level incidents which allows one to infer daily activities like moving, sitting, sleeping, usage of electrical appliances and plumbing components. The prototype is built upon inexpensive, off-the-shelf hardware (e.g., various sensors, Arduino microcontrollers, ZigBee-compatible wireless communication modules) and license-free software, thereby ensuring low system deployment costs. The network comprises nodes placed in a house’s main rooms or mounted on furniture, one wearable node, one actuator node and a centralized processing element (coordinator). Upon detecting significant deviations from the ordinary activity patterns of individuals and/or sudden falls, the system issues automated alarms which may be forwarded to authorized caregivers via a variety of communication channels. Furthermore, measured environmental parameters and activity incidents may be monitored through standard web interfaces.

Power-Aware Computing in Wearable Sensor Networks: An Optimal Feature Selection

Wearable sensory devices are becoming the enabling technology for many applications in healthcare and well-being, where computational elements are tightly coupled with the human body to monitor specific events about their subjects. Classification algorithms are the most commonly used machine learning modules that detect events of interest in these systems. The use of accurate and resource-efficient classification algorithms is of key importance because wearable nodes operate on limited resources on one hand and intend to recognize critical events (e.g., falls) on the other hand. These algorithms are used to map statistical features extracted from physiological signals onto different states such as health status of a patient or type of activity performed by a subject. Conventionally selected features may lead to rapid battery depletion, mainly due to the absence of computing complexity criterion while selecting prominent features. In this paper, we introduce the notion of power-aware feature selection, which aims at minimizing energy consumption of the data processing for classification applications such as action recognition. Our approach takes into consideration the energy cost of individual features that are calculated in real-time. A graph model is introduced to represent correlation and computing complexity of the features. The problem is formulated using integer programming and a greedy approximation is presented to select the features in a power-efficient manner. Experimental results on thirty channels of activity data collected from real subjects demonstrate that our approach can significantly reduce energy consumption of the computing module, resulting in more than $30$ percent energy savings while achieving $96.7$ percent classification accuracy.

A system for ubiquitous fall monitoring at home via a wireless sensor network and a wearable mote

Accidental falls of our elderly, and physical injuries resulting, represent a major health and economic problem. Falls are the most common cause of serious injuries and are a major health threat in the stratum of older population. Early detection of a fall is a key factor when trying to provide adequate care to elderly person who has suffered an accident at home. Therefore, the detection of falls in the elderly remains a major challenge in the field of public health. Specific actions aimed at the fall detection can provide urgent care which allows, on the other hand, drastically reduce the cost of medical care, and improve primary care service. In this paper, we present a support system for detecting falls of an elder person by the combination of a wearable wireless sensor node based on an accelerometer and a static wireless non-intrusive sensory infrastructure based on heterogeneous sensor nodes. This previous infrastructure called DIA (Dispositivo Inteligente de Alarma, in Spanish) is an AAL (Ambient Assisted Living) system that allows to infer a potential fall. This inference is reinforced for prompt attention by a specific sensorisation at portable node sensor in order to help distinguish between falls and daily activities of assisted person. The wearable node will not determine a falling situation, it will advice the reasoner layer about specific acceleration patterns that could, eventually, imply a falling. Is at the higher layer where the falling is determined from the whole context produced by mesh of fixed nodes. Experimental results have shown that the proposed system obtains high reliability and sensitivity in the detection of the fall.

An integrated swimming monitoring system for the biomechanical analysis of swimming strokes

This paper describes the development of a complete wearable swimming system for performance analysis, together with a sample application. The system comprises wearable nodes, data processing tools in MATLAB™ and integration with video. The swimming nodes are small in size and designed to be worn on body segments of interest, typically lower leg, lower arm, and the sacral or cervical regions. Each node contains inertial sensors, screen, data storage and RF communications for synchronisation and data download. The device is controlled using a microcontroller with a scheduler-based operating system to conserve power and is custom-packaged with a user interface and USB port that is fully waterproof. The cost of manufacture is a few hundred dollars in small-run quantities. The developed analysis software builds upon previously developed tools, can communicate with the nodes individually and can synchronise the recording of multiple units through a custom-developed protocol. Video is integrated into the develop...