On-body Sensors Research Articles

The Evolution of Smart Wrist Band by Using Sensors

In recent years, the integration of various electronic components and sensors with textiles aimed at giving additional functions has become more common. In this respect the wrist band can be made functional while retaining the aesthetic appeal and style at lower cost which is in high demand. Smart textiles are fabrics that have been designed and manufactured to include technologies that provide the wearer with increased functionality. Smart textiles can be produced by knitting, weaving and embroidering with conductive threads, conductive metal coating and screen printing that can be used to develop wearable electronic textiles but amongst these, the use of conductive inks onto textiles has gained interest due to the ease of their use and manufacturing scalability. The emergence of wireless technologies and advancement in on-body sensor design can enable change in the conventional healthcare system, replacing it with wearable ones, centered on the individual. Wearable monitoring systems can provide continuous physiological data, as well as better information regarding the general health of individuals. Thus, such vital-sign monitoring systems will reduce healthcare costs by disease prevention and enhance the quality of life. This dissertation is aimed at developing smart band by incorporating vital-sign monitoring systems. Using this assembly, the recent progress in non-invasive monitoring technologies for chronic disease management is reviewed. Devices and techniques for monitoring pulse rate and body temperatures are discussed in particular. For our research conductive ink and conductive fabrics are presented additionally. The main aim of this project is to produce a wearable wrist band which detects vital body parameters like pulse rate and temperature using sensors, conductive ink and conductive fabric. Finally, the recorded temperature and pulse rate readings are sent to mobile app via Wi-Fi

Joint Power Allocation in Classified WBANs With Wireless Information and Power Transfer

In this article, a classified system with a relay is investigated in wireless body area networks (WBANs), where some on-body sensors are divided into several sensor groups on the basis of different body parts. Not only can the network detect basic vital signs but also the physiological signals of special diseases. All groups of sensors and the relay harvest energy by the radio-frequency (RF) source broadcasting, then the same group of sensors simultaneously sends common information through cooperation to the source. Since the classified system is a confined system, we maximize the system throughput with a joint power allocation on maximum ratio combining (JPA-MRC) protocol, where the transmission power allocation at the relay is unequal for each relaying subslot. The optimal problem is solved by the Lagrange multiplier method and the Karush-Kuhn-Tucker (KKT) optimal conditions. To further the obtain optimal solution, we simplify co-optimization of relay power and sensor power to the optimization of single power by the equivalent channel gains. Analytical and simulation results show that our proposed optimal method can greatly improve the information throughput compared to the conventional mean power allocation method. In addition, the protocol based on the classified scheme outperforms than that of the conventional unclassified scheme. The impacts of various important system parameters are analyzed, which provide useful design insights under different cases.

Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things

Along with the advancement of several emerging computing paradigms and technologies, such as cloud computing, mobile computing, artificial intelligence, and big data, Internet of Things (IoT) technologies have been applied in a variety of fields. In particular, the Internet of Healthcare Things (IoHT) is becoming increasingly important in human activity recognition (HAR) due to the rapid development of wearable and mobile devices. In this article, we focus on the deep-learning-enhanced HAR in IoHT environments. A semisupervised deep learning framework is designed and built for more accurate HAR, which efficiently uses and analyzes the weakly labeled sensor data to train the classifier learning model. To better solve the problem of the inadequately labeled sample, an intelligent autolabeling scheme based on deep $Q$ -network (DQN) is developed with a newly designed distance-based reward rule which can improve the learning efficiency in IoT environments. A multisensor based data fusion mechanism is then developed to seamlessly integrate the on-body sensor data, context sensor data, and personal profile data together, and a long short-term memory (LSTM)-based classification method is proposed to identify fine-grained patterns according to the high-level features contextually extracted from the sequential motion data. Finally, experiments and evaluations are conducted to demonstrate the usefulness and effectiveness of the proposed method using real-world data.

Radio Signal Sensing and Signal Processing to Monitor Behavioral Symptoms in Dementia: A Case Study

ObjectivesAlzheimer's Disease (AD)-related behavioral symptoms (i.e. agitation and/or pacing) develop in nearly 90% of AD patients. In this N = 1 study, we provide proof-of-concept of detecting changes in movement patterns that may reflect underlying behavioral symptoms using a highly novel radio sensor and identifying environmental triggers. MethodsThe Emerald device is a Wi-Fi-like box without on-body sensors, which emits and processes radio-waves to infer patient movement, spatial location and activity. It was installed for 70 days in the room of patient ‘E’, exhibiting agitated behaviors. ResultsDaily motion episode aggregation revealed motor activity fluctuation throughout the data collection period which was associated with potential socio-environmental triggers. We did not detect any adverse events attributable to the use of the device. ConclusionThis N-of-1 study suggests the Emerald device is feasible to use and can potentially yield actionable data regarding behavioral symptom management. No active or potential device risks were encountered.

Wireless Powered Wearables Using Distributed Massive MIMO

This paper presents an analytical framework which lays the foundation for distributed massive multiple-input multiple-output (MIMO) supported wireless power transfer for wearable devices. In our approach, we consider multiple users, each wearing a number of wireless sensors along with one body worn hub which acts as a relay to forward information from the on-body sensors to nearby access points (APs). Each AP is equipped with a large number of antennas and is not only responsible for receiving data sent from the hubs, but also supplying wireless power to them. Interaction between hubs and APs is not exclusive to a single pairing, with APs assigned to supply energy to and receive data from more than one hub. More precisely, APs perform the downlink energy transmission and the uplink data transmission using maximum-ratio combining. Analytical approximations of the outage probability and spectral efficiency are derived. Based on these analytical results, two modes of operation are investigated. These are outage probability prioritized and spectral efficiency prioritized. For each mode, max-min power controls are proposed to ensure a uniformly good service throughout the area of coverage. By contrasting with the IEEE 802.15.6 standard, the numerical results illustrate that while using collocated massive MIMO may provide unsatisfactory performance, the distributed setting shows much promise for enabling wireless power transfer for wearable devices.

Physical exertion modeling for construction tasks using combined cardiorespiratory and thermoregulatory measures

Physical exertion led fatigue is a serious threat to occupational health and safety of construction workers worldwide. Its acute effects include a decrease in cognitive abilities, productivity and heightened risk of accidents whereas prolonged physical exertion led fatigue could lead to psychological issues and development of musculoskeletal disorders. To monitor physical exertion, traditionally questionnaires have been used while recent advances have focused on onsite and on-body sensors to automate the process. Considering the limitation of the recent approaches, this study explored the use of combined cardiorespiratory and thermoregulatory measures to model physical exertion using machine learning algorithms. Controlled manual material handling experiments were conducted during a preliminary study to induce exertion at a steady rate involving ten participants. The results revealed that the proposed methodology could predict exertion levels with a high accuracy of 95.3% for combined data modeling of all participants. However, for some predictions, the error between predicted and actual exertion was up to five levels on the Borg-20 scale. To mitigate this issue, individualized machine learning models were used that effectively reduced the maximum error to one level with an average accuracy of 96.7% while using only one-tenth of the total data set. Overall, this study highlights the advantage of using multiple physiological measures for enhancing physical exertion modeling. Notably, the study underpins the use of individualized models for exertion monitoring and management to prevent physical fatigue development and its ill effects.

3D Printed Fingernail Antennas for 5G Applications

3D printing of antennas on removable fingernail for on-body communications at microwave and millimetre waves is proposed. Aerosol Jet technology, a fine-feature material deposition solution, has been used to directly print microstrip patch antennas on an acrylonitrile butadiene styrene (ABS) removable finger nail substrate. Two antennas have been printed and assessed, one operating at 15 GHz and the other at 28 GHz. Nanoparticle conductive silver ink has been employed to create the microstrip patch antennas and corresponding transmission line using an Optomec machine. The inks are then cured using a PulseForge machine. A further copper layer is added to the millimeter wave antenna via an electroplating process. The antennas have been simulated and measured off-the-finger and on-the-finger. Simulated and measured reflection coefficients ( $S_{11}$ ) and radiation patterns are found to be in good agreement. The proposed on-body antennas can find application in the Internet of Things (IoT) where large amount of sensing data can be shared at the microwave and millimetre wave spectrum of future 5G communications. The removable finger nails could include other electronic devices such as on-body sensors, computational, storage and communication systems.

WiFace: Facial Expression Recognition Using Wi-Fi Signals

Facial expression is an important form of human nonverbal communication. Recognition of this nonverbal sign may enable developers to understand the feedback of the servers of smart devices or advertising. Existing approaches for facial expression recognition are mainly based on cameras or on-body sensors, which are either sensitive to lighting conditions or require users to wear cumbersome devices on their faces. In this paper, we propose a new facial expression recognition system based on Wi-Fi signals, named WiFace. Our key intuition is that facial muscle movements in different expressions will induce distinctive waveform patterns in the time-series of Channel State Information (CSI) in Wi-Fi signals. We develop a series of algorithms to process the CSI signals and extract the most representative waveform patterns for facial expression classification. We build a fully-functional prototype of WiFace using commercial off-the-shelf devices, which can recognize 6 typical facial expressions. We conduct extensive experiments to evaluate the performance of WiFace, and the experimental results show that the average recognition accuracy is 94.0%.

Ambient Light Based Hand Gesture Recognition Enabled by Recurrent Neural Network

As an essential requirement of pervasive smart devices, free hand gestural input considered as necessary for user interactions has attracted lots of research attention for nearly decades. Nevertheless, existing proposals heavily rely on either expensive pre-deployed equipment or user on-body sensors, thus confine their application scenarios. In this paper, we propose a novel hand gesture recognition system which purely relies on ubiquitous ambient light and low-cost photodiodes. The proposed system does not need any modification to existing lighting infrastructure. While without complex signal pre-processing for modulated light, very low-cost photodiodes and processors can capture and process the light variations caused by hand gesture. To produce accurate hand gesture recognition, we design efficient algorithms based on recurrent neural network to process sensing data collected by a photodiode array. We implement a prototype consisting of an array of 8 photodiodes and extensive experiments demonstrate that the proposed solution can achieve a very high overall recognition accuracy of 99.31%.

Priority based health data monitoring with IEEE 802.11af technology in wireless medical sensor networks.

In this work, the IEEE 802.11af technology-based wireless sensor network for health data monitoring with priority classes is proposed. In IEEE 802.11af technology, a White Space Device (WSD), a Station (STA), and an Access Point (AP) communicate through television white spectrum opportunistically without causing any harmful interference to licensed` services. In the proposed network; WSDs, STA, and AP employ Frequency Division Multiple Access (FDMA) technique with the aim of communicating through the white space spectrum determined by White Space Map (WSM). WSD collects health data such as body temperature and blood pressure from an implant or on-body sensors. The priority class is determined according to the emergency of a patient as red, yellow, and green. After obtaining the analytical model of the proposed network, the simulation model is carried out using Riverbed Modeler. The graphical results prove the validity and applicability of the proposed network in terms of delay (0.17 s) and energy consumption (4.7 mJ/s) without any spectrum cost for priority-based health data monitoring. Graphical abstract Cognitive radio based IEEE 802.11af environment for priority based health data monitoring.

Applications of Backscatter Communications for Healthcare Networks

Backscatter communication is expected to help in revitalizing the domain of healthcare through its myriad applications. From on-body sensors to in-body implants and miniature embeddable devices, there are many potential use cases that can leverage the miniature and low-powered nature of backscatter devices. However, the existing literature lacks a comprehensive study that provides a distilled review of the latest studies on backscatter communications from the healthcare perspective. Thus, with the objective to promote the utility of backscatter communication in healthcare, this article aims to identify specific applications of backscatter systems. A detailed taxonomy of recent studies and gap analysis for future research directions are provided in this work. Finally, we conduct measurements at 590 MHz in different propagation environments with the in-house designed backscatter device. The link budget results show the promise of backscatter devices to communicate over large distances for indoor environments, which demonstrates its potential in the healthcare system.

On-body sensors for ergonomic analysis: influence of supports for order picking

On-body sensors for ergonomic analysis: influence of supports for order picking

Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning.

In this paper, we perform a systematic study about the on-body sensor positioning and data acquisition details for Human Activity Recognition (HAR) systems. We build a testbed that consists of eight body-worn Inertial Measurement Units (IMU) sensors and an Android mobile device for activity data collection. We develop a Long Short-Term Memory (LSTM) network framework to support training of a deep learning model on human activity data, which is acquired in both real-world and controlled environments. From the experiment results, we identify that activity data with sampling rate as low as 10 Hz from four sensors at both sides of wrists, right ankle, and waist is sufficient in recognizing Activities of Daily Living (ADLs) including eating and driving activity. We adopt a two-level ensemble model to combine class-probabilities of multiple sensor modalities, and demonstrate that a classifier-level sensor fusion technique can improve the classification performance. By analyzing the accuracy of each sensor on different types of activity, we elaborate custom weights for multimodal sensor fusion that reflect the characteristic of individual activities.

Nonparametric Activity Recognition System in Smart Homes Based on Heterogeneous Sensor Data

Throughout the course of life, there is time when we live independently in our house without anyone to look after each other. In order to support these people and ensure their safety with limited medical resources and human labors, it is important to constantly monitor one’s activity of daily living (ADL). Therefore, we propose an activity recognition (AR) system for people living independently in smart homes to achieve the concept of “aging in place.” The AR model adopted by the proposed system is powerful to recognize meaningful ADL by integrating heterogeneous data from both ambient and on-body sensors. Moreover, this proposed system adopts a nonparametric approach, which requires much fewer efforts from humans. The average AR precision and recall rates of this proposed system are up to 98.7% and 99.0%, which indicates its feasibility of deployment in a real-life home environment for monitoring users’ ADL with promising performance and thus helps realize “aging in place.” Note to Practitioners —With the advance of Internet of Things technologies, it is convenient to collect data about ADL in a smart home environment via ambient sensors and on-body sensors. This proposed system integrates data from these two heterogeneous sensors and discovers potential activities automatically without user labeling or parameter setting. By reducing the above-mentioned user efforts, it is more suitable for users and thus greatly helps realize the concept of “aging in place.”

An automatic and non-invasive physical fatigue assessment method for construction workers

Abstract The construction industry around the globe has unsatisfactory occupational health and safety records. One of the major reasons is attributed to high physical demands and hostile working environments. Construction work always requires workers to work for a long duration without sufficient breaks to recover from overexertion and to work under harsh climatic conditions and/or in confined workspaces. Such circumstances can increase the risk of physical fatigue. Traditionally, fatigue monitoring in the construction domain relies on self-reporting or subjective questionnaires. These methods require the manual collection of responses and are impractical for continuous fatigue monitoring. Some researchers have used on-body sensors for fatigue monitoring (such as heart rate monitors and surface electromyography (sEMG) sensors). Although these devices appear to be promising, they are intrusive, requiring sensors to be attached to the worker's body. Such on-body sensors are uncomfortable to wear and could easily cause irritation. Considering the limitations of these methodologies, the current research proposes a novel non-intrusive method to monitor the whole-body physical fatigue with computer vision for construction workers. A computer vision-based 3D motion capture algorithm was developed to model the motion of various body parts using an RGB camera. A fatigue assessment model was developed using the 3D model data from the developed motion capture algorithm and biomechanical analysis. The experiment showed that the proposed physical fatigue assessment method could provide joint-level physical fatigue assessments automatically. Then, a series of experiments demonstrated the potential of the method in assessing the physical fatigue level of different construction task conditions such as site layout and the work-rest schedules.

Toward Ultra-Low-Power Remote Health Monitoring: An Optimal and Adaptive Compressed Sensing Framework for Activity Recognition

Activity recognition, as an important component of behavioral monitoring and intervention, has attracted enormous attention, especially in Mobile Cloud Computing (MCC) and Remote Health Monitoring (RHM) paradigms. While recently resource constrained wearable devices have been gaining popularity, their battery life is limited and constrained by the frequent wireless transmission of data to more computationally powerful back-ends. This paper proposes an ultra-low power activity recognition system using a novel adaptive compressed sensing technique that aims to minimize transmission costs. Coarse-grained on-body sensor localization and unsupervised clustering modules are devised to autonomously reconfigure the compressed sensing module for further power saving. We perform a thorough heuristic optimization using Grammatical Evolution (GE) to ensure minimal computation overhead of the proposed methodology. Our evaluation on a real-world dataset and a low power wearable sensing node demonstrates that our approach can reduce the energy consumption of the wireless data transmission up to 81.2 and 61.5 percent, with up to 60.6 and 35.0 percent overall power savings in comparison with baseline and a naive state-of-the-art approaches, respectively. These solutions lead to an average activity recognition accuracy of 89.0 percent—only 4.8 percent less than the baseline accuracy—while having a negligible energy overhead of on-node computation.

Maximising energy efficiency for direct communication links in wireless body area networks

Energy efficiency is a fundamental aspect for wireless body area networks (WBANs) due to the limited battery capacity and miniaturisation of sensor nodes. Prolonging the lifespan of a WBAN depends mostly on maximising the energy efficiency. WBAN systems operate under conflicting requirements of energy and spectrum efficiency. In this study, the two metrics of energy and spectrum efficiency for direct communication links for in-body and on-body sensor nodes are analysed. A general device-to-device communication model was adapted to WBAN. Optimal transmission power values to achieve maximum energy efficiency for in-body and on-body communication links are found. With reference to a maximum power level of 1.5 W compliant with the Federal Communications Commission for WBAN, it is also deduced that for on-body communication, decreasing maximum possible spectrum efficiency by 33% for medical devices operating in 400–450 and 950–956 MHz would improve energy efficiency by 75 times. Moreover, by decreasing spectrum efficiency by 38.3 and 48% leads to an increase in energy efficiency by 45.3 and 39.3 times in 2.4–2.5 and 3.1–10.6 GHz frequency bands, respectively. This trade-off is significant for medical applications having strict energy requirements.

THE (Temperature Heterogeneity Energy) Aware Routing Protocol for IoT Health Application

In this paper, we focus on an interesting E-Health monitoring system that efficiently utilizes the concept of the Internet of Things (IoT). Wireless body area network (WBAN) composed of a set of target-oriented sensors placed around the human body and transmit its collected data to a coordinator that carries it to some cloud system. WBAN standardized with the newly emerged IEEE802.15.6 with its specifications for Physical (PHY) and medium access control (MAC) layers only. Regrettably, the Network layer is not addressed by the standard where it plays an important role in the overall performance. In this paper, we design the temperature heterogeneity energy (THE) aware routing protocol for WBAN as a complement for the standard. “THE” aims to control the temperature raising caused by the on-body sensor and affects the skin comfortableness. In the meantime, “THE” maintains the network in high-performance conditions in terms of long node lifetime and high packet throughput. To fulfill these desired tradeoffs, the sensed data is classified into three data levels with variable transmission priority to each level, namely, emergency (abnormal) data priority 7 (highest priority), critical data priority 6, and normal data assigned priority 5. “THE” protocol is based on a utility function that chooses the WBAN's parent node (PN) that has the largest amount of remaining energy, the highest data rate, the minimum distance to the coordinator, and the minimum sensor's temperature. Hopping the data through the parent node (two-hops) is applicable for the data with normal priority while high priority data (critical and emergency) is transmitted to the coordinator in one-hop only. The proposed “THE” protocol's performance validation performed via Monte Carlo simulation analysis which proves that “THE” protocol achieved better performance against conventional protocols (SIMPLE and iM-SIMPLE) in terms of network lifetime, number of dead nodes, total remaining energy, and throughput.

Joint VMIMO and LDPC Decoders for IR-UWB Wireless Body Area Network

A complete impulse radio-ultra wide band (IR-UWB) system is proposed to mitigate the impairments concerning on-body to off-body Wireless Body Area Networks (WBAN). The proposed system maintains the practical communication link between on-body sensors to the fusion center or the monitoring device held by the medical representative. Also, these sensors are assigned a recently proposed pulse shaping to mitigate the noise that existed in the IR-UWB WBAN channel. Furthermore, virtual multiple-input multiple-output (VMIMO) is proposed to perform spatial multiplexing between various sensors transmitted data. Moreover, low complex low density parity check (LDPC) encoding/decoding algorithms, including a new low complex hybrid LDPC decoding algorithm, are proposed to reduce the complexity and to enhance the bit error rate of the on-body sensors. Interestingly, low energy consumption at every sensor participating in the on-body to off-body WBAN was achieved by adopting the combination between LDPC decoders and VMIMO technique. According to simulation results, the new system enhanced the BER performance and reduced the complexity of the on-body to off-body IR-UWB communication system effectively.

Energy-Efficient Optimization in Multi-Sensor WBAN With Multi-Antenna AP

This paper optimizes the energy efficiency performance by joint time and beamforming weights allocation in a wireless body area network (WBAN), which consists of a multi-antenna access point (AP), in particular, the AP intends to broadcast radio frequency (RF) power to several single-antenna on-body sensors and receives physiological information from them synchronously. Different path loss and shadowing values are considered in each sensor's channel model to be more practical. In this paper, maximal ratio transmission (MRT) technique and zero-forcing (ZF) technique are applied in RF energy transmission and information decoding, respectively. Considering the signal-to-inference-plus-noise ratio (SINR) threshold of each sensor, the total rate-energy ratio is formulated as a nonconvex optimization problem in each time block, we transform the nonconvex optimization problem into a convex optimization problem and solved it by convex optimization tools. We investigate the energy efficiency performance and reliability performance of the optimal solutions, and numerical results demonstrate the effectiveness of the optimal solutions.